Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Multiparametric MRI for characterization of the tumour microenvironment

Nature Reviews Clinical Oncology (2024)Cite this article

Subjects

Abstract

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Key points

  • Imaging in clinical oncology currently relies predominantly on morphological assessments without exploiting the complexity of the tumour microenvironment (TME), which is increasingly becoming a therapeutic target.

  • Multiparametric MRI (mpMRI) combines multiple MRI sequences, each providing different but complementary information. Therefore, mpMRI is well-suited to the examination of the TME.

  • MpMRI enables assessment of cellular features of the TME, including vascularization, immune cell infiltration, metabolic alterations, cellularity, oedema and haemorrhage, and biomechanical properties.

  • TME characterization using mpMRI provides deep insight into primary tumours and their metastatic sites, going beyond size and number, and adequately reflecting intratumoural and intertumoural heterogeneity.

  • MpMRI enables early assessment of response to anticancer therapies, in particular, molecularly targeted therapies and immunotherapies, highlighting the important role of radiology in delivering treatment regimens that are tailored to each individual patient.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Multiparametric MRI enables non-invasive assessment of different characteristics of the tumour microenvironment.
Fig. 2: Characterization of the cellular tumour microenvironment with diffusion-weighted MRI.
Fig. 3: Role of multiparametric MRI in tumour grading in prostate cancer.
Fig. 4: Assessment of malignancy in renal masses.
Fig. 5: Characterization of hepatocellular carcinoma with multiparametric MRI.
Fig. 6: Monitoring of response to immune-checkpoint inhibitors using multiparametric MRI.
Fig. 7: Monitoring of response to vascular-targeting agents using multiparametric MRI.
Fig. 8: Monitoring of response to chemotherapy using multiparametric MRI.

Similar content being viewed by others

References

  1. Baghban, R. et al. Tumor microenvironment complexity and therapeutic implications at a glance. Cell Commun. Signal. 18, 59 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  2. de Visser, K. E. & Joyce, J. A. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell 41, 374–403 (2023).

    Article  PubMed  Google Scholar 

  3. Brassart-Pasco, S. et al. Tumor microenvironment: extracellular matrix alterations influence tumor progression. Front. Oncol. 10, 397 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Gerwing, M. et al. The beginning of the end for conventional RECIST – novel therapies require novel imaging approaches. Nat. Rev. Clin. Oncol. 16, 442–458 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. Hoffman, A. & Amiel, G. E. The impact of PSMA PET/CT on modern prostate cancer management and decision making – the urological perspective. Cancers 15, 3402 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Harmon, S. A. et al. Prognostic features of biochemical recurrence of prostate cancer following radical prostatectomy based on multiparametric MRI and immunohistochemistry analysis of MRI-guided biopsy specimens. Radiology 299, 613–623 (2021).

    Article  PubMed  Google Scholar 

  7. Sawlani, V. et al. Multiparametric MRI: practical approach and pictorial review of a useful tool in the evaluation of brain tumours and tumour-like lesions. Insights Imaging 11, 84 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kumar, V. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging 30, 1234–1248 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Schaaf, M. B., Garg, A. D. & Agostinis, P. Defining the role of the tumor vasculature in antitumor immunity and immunotherapy. Cell Death Dis. 9, 115 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).

    Article  CAS  PubMed  Google Scholar 

  12. Baluk, P., Hashizume, H. & McDonald, D. M. Cellular abnormalities of blood vessels as targets in cancer. Curr. Opin. Genet. Dev. 15, 102–111 (2005).

    Article  CAS  PubMed  Google Scholar 

  13. Türkbey, B., Thomasson, D., Pang, Y., Bernardo, M. & Choyke, P. L. The role of dynamic contrast-enhanced MRI in cancer diagnosis and treatment. Diagn. Interv. Radiol. 16, 186–192 (2010).

    PubMed  Google Scholar 

  14. Barrett, T., Kobayashi, H., Brechbiel, M. & Choyke, P. L. Macromolecular MRI contrast agents for imaging tumor angiogenesis. Eur. J. Radiol. 60, 353–366 (2006).

    Article  PubMed  Google Scholar 

  15. Azzi, S., Hebda, J. K. & Gavard, J. Vascular permeability and drug delivery in cancers. Front. Oncol. 3, 211 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Troprès, I. et al. Vessel size imaging. Magn. Reson. Med. 45, 397–408 (2001).

    Article  PubMed  Google Scholar 

  17. Hoskin, P. J. et al. Hypoxia in prostate cancer: correlation of BOLD-MRI with pimonidazole immunohistochemistry – initial observations. Int. J. Radiat. Oncol. Biol. Phys. 68, 1065–1071 (2007).

    Article  CAS  PubMed  Google Scholar 

  18. Lanzman, R. S. et al. Arterial spin-labeling MR imaging of renal masses: correlation with histopathologic findings. Radiology 265, 799–808 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Li, W. et al. Diffusion-weighted MRI for predicting pathologic complete response in neoadjuvant immunotherapy. Cancers 14, 4436 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Jackson, A. et al. MRI apparent diffusion coefficient (ADC) as a biomarker of tumour response: imaging-pathology correlation in patients with hepatic metastases from colorectal cancer (EORTC 1423). Cancers 15, 3580 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Taleb B, A. Tumour flare reaction in cancer treatments: a comprehensive literature review. Anticancer. Drugs 30, 953–958 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yang, S., Liu, T., Cheng, Y., Bai, Y. & Liang, G. Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer. Cancer Sci. 110, 3639–3649 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fliedner, F. P., Engel, T. B., El-Ali, H. H., Hansen, A. E. & Kjaer, A. Diffusion weighted magnetic resonance imaging (DW-MRI) as a non-invasive, tissue cellularity marker to monitor cancer treatment response. BMC Cancer 20, 134 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Karaman, A. et al. Is it better to include necrosis in apparent diffusion coefficient (ADC) measurements? The necrosis/wall ADC ratio to differentiate malignant and benign necrotic lung lesions: preliminary results. J. Magn. Reson. Imaging JMRI 46, 1001–1006 (2017).

    Article  PubMed  Google Scholar 

  25. Wu, D. et al. Time-dependent diffusion MRI for quantitative microstructural mapping of prostate cancer. Radiology 303, 578–587 (2022).

    Article  PubMed  Google Scholar 

  26. Xu, J. et al. Magnetic resonance imaging of mean cell size in human breast tumors. Magn. Reson. Med. 83, 2002–2014 (2020).

    Article  PubMed  Google Scholar 

  27. Devan, S. P. et al. Selective cell size MRI differentiates brain tumors from radiation necrosis. Cancer Res. 82, 3603–3613 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jiang, X. et al. MRI of tumor T cell infiltration in response to checkpoint inhibitor therapy. J. Immunother. Cancer 8, e000328 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hoffmann, E. et al. Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI. J. Immunother. Cancer 11, e006092 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Martínez-Reyes, I. & Chandel, N. S. Cancer metabolism: looking forward. Nat. Rev. Cancer 21, 669–680 (2021).

    Article  PubMed  Google Scholar 

  31. Bergers, G. & Fendt, S.-M. The metabolism of cancer cells during metastasis. Nat. Rev. Cancer 21, 162–180 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Philip, P. A. et al. Phase 3, multicenter, randomized study of CPI-613 with modified FOLFIRINOX (mFFX) versus FOLFIRINOX (FFX) as first-line therapy for patients with metastatic adenocarcinoma of the pancreas (AVENGER500) [abstract]. J. Clin. Oncol. 40 (Suppl. 16), 4023 (2022).

    Article  Google Scholar 

  33. Wicker, C. A. et al. Glutaminase inhibition with telaglenastat (CB-839) improves treatment response in combination with ionizing radiation in head and neck squamous cell carcinoma models. Cancer Lett. 502, 180–188 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Janku, F. et al. First-in-human study of IM156, a novel potent biguanide oxidative phosphorylation (OXPHOS) inhibitor, in patients with advanced solid tumors. Invest. New Drugs 40, 1001–1010 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhang, S. et al. Assessment of early response to neoadjuvant systemic therapy in triple-negative breast cancer using amide proton transfer-weighted chemical exchange saturation transfer MRI: a pilot study. Radiol. Imaging Cancer 3, e200155 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ward, K. M., Aletras, A. H. & Balaban, R. S. A new class of contrast agents for MRI based on proton chemical exchange dependent saturation transfer (CEST). J. Magn. Reson. 143, 79–87 (2000).

    Article  CAS  PubMed  Google Scholar 

  37. Rivlin, M. et al. Breast cancer imaging with glucosamine CEST (chemical exchange saturation transfer) MRI: first human experience. Eur. Radiol. 32, 7365–7373 (2022).

    Article  CAS  PubMed  Google Scholar 

  38. Reesink, D. J. et al. Feasibility of clinical studies of chemical exchange saturation transfer magnetic resonance imaging of prostate cancer at 7 T. NMR Biomed. 36, e4958 (2023).

    Article  CAS  PubMed  Google Scholar 

  39. Jia, G. et al. Amide proton transfer MR imaging of prostate cancer: a preliminary study. J. Magn. Reson. Imaging 33, 647–654 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Dula, A. N. et al. Amide proton transfer imaging of the breast at 3 T: establishing reproducibility and possible feasibility assessing chemotherapy response. Magn. Reson. Med. 70, 216–224 (2013).

    Article  CAS  PubMed  Google Scholar 

  41. Lindeman, L. R. et al. A comparison of exogenous and endogenous CEST MRI methods for evaluating in vivo pH. Magn. Reson. Med. 79, 2766–2772 (2018).

    Article  PubMed  Google Scholar 

  42. Tang, Y. et al. Noninvasive detection of extracellular pH in human benign and malignant liver tumors using CEST MRI. Front. Oncol. 10, 578985 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Boyd, P. S. et al. Mapping intracellular pH in tumors using amide and guanidyl CEST-MRI at 9.4 T. Magn. Reson. Med. 87, 2436–2452 (2022).

    Article  CAS  PubMed  Google Scholar 

  44. Wu, B. et al. An overview of CEST MRI for non-MR physicists. EJNMMI Phys. 3, 19 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kobus, T., Wright, A. J., Weiland, E., Heerschap, A. & Scheenen, T. W. J. Metabolite ratios in 1H MR spectroscopic imaging of the prostate. Magn. Reson. Med. 73, 1–12 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Rata, M., Giles, S. L., deSouza, N. M., Leach, M. O. & Payne, G. S. Comparison of three reference methods for the measurement of intracellular pH using 31P MRS in healthy volunteers and patients with lymphoma. NMR Biomed. 27, 158–162 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Deen, S. S. et al. Hyperpolarized carbon 13 MRI: clinical applications and future directions in oncology. Radiol. Imaging Cancer 5, e230005 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Wang, Z. J. et al. Hyperpolarized 13C MRI: state of the art and future directions. Radiology 291, 273–284 (2019).

    Article  PubMed  Google Scholar 

  49. Nelson, S. J. et al. Metabolic imaging of patients with prostate cancer using hyperpolarized [1-13C]pyruvate. Sci. Transl. Med. 5, 198ra108 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  50. De Feyter, H. M. et al. Deuterium metabolic imaging (DMI) for MRI-based 3D mapping of metabolism in vivo. Sci. Adv. 4, eaat7314 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Chen Ming Low, J., Wright, A. J., Hesse, F., Cao, J. & Brindle, K. M. Metabolic imaging with deuterium labeled substrates. Prog. Nucl. Magn. Reson. Spectrosc. 134–135, 39–51 (2023).

    Article  PubMed  Google Scholar 

  52. Ruhm, L. et al. Deuterium metabolic imaging in the human brain at 9.4 Tesla with high spatial and temporal resolution. NeuroImage 244, 118639 (2021).

    Article  CAS  PubMed  Google Scholar 

  53. Hesse, F. et al. Imaging glioblastoma response to radiotherapy using 2H magnetic resonance spectroscopy measurements of fumarate metabolism. Cancer Res. 82, 3622–3633 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lei, X. et al. Immune cells within the tumor microenvironment: biological functions and roles in cancer immunotherapy. Cancer Lett. 470, 126–133 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Labani-Motlagh, A., Ashja-Mahdavi, M. & Loskog, A. The tumor microenvironment: a milieu hindering and obstructing antitumor immune responses. Front. Immunol. 11, 940 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhao, Y. et al. The tumour vasculature as a target to modulate leucocyte trafficking. Cancers 13, 1724 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Lau, D. et al. Multiparametric MRI of early tumor response to immune checkpoint blockade in metastatic melanoma. J. Immunother. Cancer 9, e003125 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Saida, Y. et al. Multimodal molecular imaging detects early responses to immune checkpoint blockade. Cancer Res. 81, 3693–3705 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hoffmann, E. et al. Vascular response patterns to targeted therapies in murine breast cancer models with divergent degrees of malignancy. Breast Cancer Res. 25, 56 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lau, D., Corrie, P. G. & Gallagher, F. A. MRI techniques for immunotherapy monitoring. J. Immunother. Cancer 10, e004708 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Wang, Y.-X. J. Superparamagnetic iron oxide based MRI contrast agents: current status of clinical application. Quant. Imaging Med. Surg. 1, 35–40 (2011).

    PubMed  PubMed Central  Google Scholar 

  62. de Vries, I. J. M. et al. Magnetic resonance tracking of dendritic cells in melanoma patients for monitoring of cellular therapy. Nat. Biotechnol. 23, 1407–1413 (2005).

    Article  PubMed  Google Scholar 

  63. Mori, Y. et al. From cartoon to real time MRI: in vivo monitoring of phagocyte migration in mouse brain. Sci. Rep. 4, 6997 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Masthoff, M. et al. Resolving immune cells with patrolling behaviour by magnetic resonance time-lapse single cell tracking. EBioMedicine 73, 103670 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Masthoff, M. et al. Temporal window for detection of inflammatory disease using dynamic cell tracking with time-lapse MRI. Sci. Rep. 8, 9563 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Ahrens, E. T. & Bulte, J. W. M. Tracking immune cells in vivo using magnetic resonance imaging. Nat. Rev. Immunol. 13, 755–763 (2013).

    Article  CAS  PubMed  Google Scholar 

  67. Ahrens, E. T., Helfer, B. M., O’Hanlon, C. F. & Schirda, C. Clinical cell therapy imaging using a perfluorocarbon tracer and fluorine-19 MRI. Magn. Reson. Med. 72, 1696–1701 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kleijn, A. et al. Distinguishing inflammation from tumor and peritumoral edema by myeloperoxidase magnetic resonance imaging. Clin. Cancer Res. 17, 4484–4493 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Zhang, X. et al. Advanced renal-cell carcinoma pseudoprogression after combined immunotherapy: case report and literature review. Front. Oncol. 11, 640447 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Adams, L. C. et al. Use of quantitative T2 mapping for the assessment of renal cell carcinomas: first results. Cancer Imaging 19, 35 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Jiang, X., Devan, S. P., Xie, J., Gore, J. C. & Xu, J. Improving MR cell size imaging by inclusion of transcytolemmal water exchange. NMR Biomed. 35, e4799 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Jiang, X. et al. Quantification of cell size using temporal diffusion spectroscopy. Magn. Reson. Med. 75, 1076–1085 (2016).

    Article  PubMed  Google Scholar 

  73. Jiang, X. et al. In vivo imaging of cancer cell size and cellularity using temporal diffusion spectroscopy: cancer cell size and cellularity using IMPULSED. Magn. Reson. Med. 78, 156–164 (2017).

    Article  CAS  PubMed  Google Scholar 

  74. Jiang, X., Xu, J. & Gore, J. C. Mapping hepatocyte size in vivo using temporal diffusion spectroscopy MRI. Magn. Reson. Med. 84, 2671–2683 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Xing, W. et al. Evaluating hemorrhage in renal cell carcinoma using susceptibility weighted imaging. PLoS ONE 8, e57691 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Petrovic, A., Krauskopf, A., Hassler, E., Stollberger, R. & Scheurer, E. Time related changes of T1, T2, and T2* of human blood in vitro. Forensic Sci. Int. 262, 11–17 (2016).

    Article  CAS  PubMed  Google Scholar 

  77. Pagé, G., Garteiser, P. & Van Beers, B. E. Magnetic resonance elastography of malignant tumors. Front. Phys. 10, https://doi.org/10.3389/fphy.2022.910036 (2022).

  78. Heldin, C.-H., Rubin, K., Pietras, K. & Ostman, A. High interstitial fluid pressure – an obstacle in cancer therapy. Nat. Rev. Cancer 4, 806–813 (2004).

    Article  CAS  PubMed  Google Scholar 

  79. Walker-Samuel, S. et al. Investigating low-velocity fluid flow in tumors with convection-MRI. Cancer Res. 78, 1859–1872 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Muthupillai, R. et al. Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science 269, 1854–1857 (1995).

    Article  CAS  PubMed  Google Scholar 

  81. Li, J. et al. Investigating the contribution of collagen to the tumor biomechanical phenotype with noninvasive magnetic resonance elastography. Cancer Res. 79, 5874–5883 (2019).

    Article  CAS  PubMed  Google Scholar 

  82. Drost, F.-J. H. et al. Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst. Rev. 4, CD012663 (2019).

    PubMed  Google Scholar 

  83. Boschheidgen, M. et al. MRI grading for the prediction of prostate cancer aggressiveness. Eur. Radiol. 32, 2351–2359 (2022).

    Article  CAS  PubMed  Google Scholar 

  84. Wallis, C. J. D., Haider, M. A. & Nam, R. K. Role of mpMRI of the prostate in screening for prostate cancer. Transl. Androl. Urol. 6, 464–471 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Padhani, A. R., Godtman, R. A. & Schoots, I. G. Key learning on the promise and limitations of MRI in prostate cancer screening. Eur. Radiol. https://doi.org/10.1007/s00330-024-10626-6 (2024).

    Article  PubMed  Google Scholar 

  86. Znaor, A., Lortet-Tieulent, J., Laversanne, M., Jemal, A. & Bray, F. International variations and trends in renal cell carcinoma incidence and mortality. Eur. Urol. 67, 519–530 (2015).

    Article  PubMed  Google Scholar 

  87. Crestani, A. et al. Introduction to small renal tumours and prognostic indicators. Int. J. Surg. 36, 495–503 (2016).

    Article  PubMed  Google Scholar 

  88. Trevisani, F., Floris, M., Minnei, R. & Cinque, A. Renal oncocytoma: the diagnostic challenge to unmask the double of renal cancer. Int. J. Mol. Sci. 23, 2603 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Vargas, H. A. et al. Renal cortical tumors: use of multiphasic contrast-enhanced MR imaging to differentiate benign and malignant histologic subtypes. Radiology 264, 779–788 (2012).

    Article  PubMed  Google Scholar 

  90. Taouli, B. et al. Renal lesions: characterization with diffusion-weighted imaging versus contrast-enhanced MR imaging. Radiology 251, 398–407 (2009).

    Article  PubMed  Google Scholar 

  91. Heller, M. T., Furlan, A. & Kawashima, A. Multiparametric MR for solid renal mass characterization. Magn. Reson. Imaging Clin. N. Am. 28, 457–469 (2020).

    Article  PubMed  Google Scholar 

  92. Galmiche, C. et al. Is multiparametric MRI useful for differentiating oncocytomas from chromophobe renal cell carcinomas? AJR Am. J. Roentgenol. 208, 343–350 (2017).

    Article  PubMed  Google Scholar 

  93. Steinberg, R. L. et al. Prospective performance of clear cell likelihood scores (ccLS) in renal masses evaluated with multiparametric magnetic resonance imaging. Eur. Radiol. 31, 314–324 (2021).

    Article  PubMed  Google Scholar 

  94. Schieda, N. et al. Multicenter evaluation of multiparametric MRI clear cell likelihood scores in solid indeterminate small renal masses. Radiology 303, 590–599 (2022).

    Article  PubMed  Google Scholar 

  95. Ursprung, S. et al. Hyperpolarized 13C-pyruvate metabolism as a surrogate for tumor grade and poor outcome in renal cell carcinoma – a proof of principle study. Cancers 14, 335 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Laothamatas, I. et al. Multiparametric MRI of solid renal masses: principles and applications of advanced quantitative and functional methods for tumor diagnosis and characterization. J. Magn. Reson. Imaging 58, 342–359 (2023).

    Article  PubMed  Google Scholar 

  97. Elsorougy, A., Farg, H., Bayoumi, D., El-Ghar, M. A. & Shady, M. Quantitative 3-tesla multiparametric MRI in differentiation between renal cell carcinoma subtypes. Egypt. J. Radiol. Nucl. Med. 52, 49 (2021).

    Article  Google Scholar 

  98. Notohamiprodjo, M. et al. Combined diffusion-weighted, blood oxygen level-dependent, and dynamic contrast-enhanced MRI for characterization and differentiation of renal cell carcinoma. Acad. Radiol. 20, 685–693 (2013).

    Article  PubMed  Google Scholar 

  99. Adams, L. C. et al. Native T1 mapping as an in vivo biomarker for the identification of higher-grade renal cell carcinoma: correlation with histopathological findings. Invest. Radiol. 54, 118–128 (2019).

    Article  PubMed  Google Scholar 

  100. Wang, H. et al. Multiparametric MRI for bladder cancer: validation of VI-RADS for the detection of detrusor muscle invasion. Radiology 291, 668–674 (2019).

    Article  PubMed  Google Scholar 

  101. Bandini, M. et al. The value of multiparametric magnetic resonance imaging sequences to assist in the decision making of muscle-invasive bladder cancer. Eur. Urol. Oncol. 4, 829–833 (2021).

    Article  PubMed  Google Scholar 

  102. Takeuchi, M. et al. Urinary bladder cancer: diffusion-weighted MR imaging – accuracy for diagnosing T stage and estimating histologic grade. Radiology 251, 112–121 (2009).

    Article  PubMed  Google Scholar 

  103. Wang, H.-J. et al. Comparison of early submucosal enhancement and tumor stalk in staging bladder urothelial carcinoma. AJR Am. J. Roentgenol. 207, 797–803 (2016).

    Article  PubMed  Google Scholar 

  104. Panebianco, V. et al. An evaluation of morphological and functional multi-parametric MRI sequences in classifying non-muscle and muscle invasive bladder cancer. Eur. Radiol. 27, 3759–3766 (2017).

    Article  PubMed  Google Scholar 

  105. Del Giudice, F. et al. Prospective assessment of vesical imaging reporting and data system (VI-RADS) and its clinical impact on the management of high-risk non-muscle-invasive bladder cancer patients candidate for repeated transurethral resection. Eur. Urol. 77, 101–109 (2020).

    Article  PubMed  Google Scholar 

  106. Holzbeierlein, J. M. et al. Diagnosis and treatment of non-muscle invasive bladder cancer: AUA/SUO guideline: 2024 amendment. J. Urol. 211, 533–538 (2024).

    Article  PubMed  Google Scholar 

  107. Gr, V., Sakalecha, A. K. & Baig, A. Multiparametric magnetic resonance imaging in evaluation of benign and malignant breast masses with pathological correlation. Cureus 14, e22348 (2022).

    PubMed  PubMed Central  Google Scholar 

  108. Pinker, K. et al. Multiparametric MR imaging with high-resolution dynamic contrast-enhanced and diffusion-weighted imaging at 7 T improves the assessment of breast tumors: a feasibility study. Radiology 276, 360–370 (2015).

    Article  PubMed  Google Scholar 

  109. Pinker, K. et al. Improved diagnostic accuracy with multiparametric magnetic resonance imaging of the breast using dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, and 3-dimensional proton magnetic resonance spectroscopic imaging. Invest. Radiol. 49, 421–430 (2014).

    Article  CAS  PubMed  Google Scholar 

  110. Pinker, K. et al. Combined contrast-enhanced magnetic resonance and diffusion-weighted imaging reading adapted to the ‘Breast Imaging Reporting and Data System’ for multiparametric 3-T imaging of breast lesions. Eur. Radiol. 23, 1791–1802 (2013).

    Article  CAS  PubMed  Google Scholar 

  111. Kul, S. et al. Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of breast tumors. AJR Am. J. Roentgenol. 196, 210–217 (2011).

    Article  PubMed  Google Scholar 

  112. Zimmermann, F. et al. A novel normalization for amide proton transfer CEST MRI to correct for fat signal-induced artifacts: application to human breast cancer imaging. Magn. Reson. Med. 83, 920–934 (2020).

    Article  CAS  PubMed  Google Scholar 

  113. Siegmann, K. C. et al. Diagnostic value of MR elastography in addition to contrast-enhanced MR imaging of the breast – initial clinical results. Eur. Radiol. 20, 318–325 (2010).

    Article  PubMed  Google Scholar 

  114. Yang, Z. et al. Quantitative multiparametric MRI as an imaging biomarker for the prediction of breast cancer receptor status and molecular subtypes. Front. Oncol. 11, 628824 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Maheshwari, E. et al. Update on MRI in evaluation and treatment of endometrial cancer. Radiographics 42, 2112–2130 (2022).

    Article  PubMed  Google Scholar 

  116. Lefebvre, T. L. et al. Development and validation of multiparametric MRI-based radiomics models for preoperative risk stratification of endometrial cancer. Radiology 305, 375–386 (2022).

    Article  PubMed  Google Scholar 

  117. Jia, Y. et al. Radiomics analysis of multiparametric MRI for preoperative prediction of microsatellite instability status in endometrial cancer: a dual-center study. Front. Oncol. 14, 1333020 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  118. Himoto, Y. et al. Multiparametric magnetic resonance imaging facilitates the selection of patients prior to fertility-sparing management of endometrial cancer. Abdom. Radiol. 46, 4410–4419 (2021).

    Article  Google Scholar 

  119. Expert Panel on GYN and OB Imaging. et al. ACR Appropriateness Criteria® Pretreatment Evaluation and Follow-Up of Endometrial Cancer. J. Am. Coll. Radiol. 17, S472–S486 (2020).

    Article  Google Scholar 

  120. Nougaret, S. et al. Endometrial cancer MRI staging: updated guidelines of the European Society of Urogenital Radiology. Eur. Radiol. 29, 792–805 (2019).

    Article  PubMed  Google Scholar 

  121. Ramón y Cajal, S. et al. Clinical implications of intratumor heterogeneity: challenges and opportunities. J. Mol. Med. 98, 161–177 (2020).

    Article  PubMed  Google Scholar 

  122. Choi, J.-Y., Lee, J.-M. & Sirlin, C. B. CT and MR imaging diagnosis and staging of hepatocellular carcinoma: part I. Development, growth, and spread: key pathologic and imaging aspects. Radiology 272, 635–654 (2014).

    Article  PubMed  Google Scholar 

  123. Hectors, S. J. et al. Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging. Sci. Rep. 7, 2452 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Galle, P. R. et al. EASL clinical practice guidelines: management of hepatocellular carcinoma. J. Hepatol. 69, 182–236 (2018).

    Article  Google Scholar 

  125. Clarke, C. G. D. et al. Comparison of LI-RADS with other non-invasive liver MRI criteria and radiological opinion for diagnosing hepatocellular carcinoma in cirrhotic livers using gadoxetic acid with histopathological explant correlation. Clin. Radiol. 76, 333–341 (2021).

    Article  CAS  PubMed  Google Scholar 

  126. Jeon, S. K., Lee, J. M., Joo, I., Yoo, J. & Park, J.-Y. Comparison of guidelines for diagnosis of hepatocellular carcinoma using gadoxetic acid-enhanced MRI in transplantation candidates. Eur. Radiol. 30, 4762–4771 (2020).

    Article  PubMed  Google Scholar 

  127. Hwang, S. H. et al. Comparison of the current guidelines for diagnosing hepatocellular carcinoma using gadoxetic acid-enhanced magnetic resonance imaging. Eur. Radiol. 31, 4492–4503 (2021).

    Article  CAS  PubMed  Google Scholar 

  128. Moctezuma-Velázquez, C. et al. Non-invasive imaging criteria for the diagnosis of hepatocellular carcinoma in non-cirrhotic patients with chronic hepatitis B. JHEP Rep. 3, 100364 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  129. Singal, A. G. et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology 78, 1922–1965 (2023).

    Article  PubMed  Google Scholar 

  130. Ronot, M. et al. Comparison of the accuracy of AASLD and LI-RADS criteria for the non-invasive diagnosis of HCC smaller than 3 cm. J. Hepatol. 68, 715–723 (2018).

    Article  PubMed  Google Scholar 

  131. Fowler, K. J. et al. Pathologic, molecular, and prognostic radiologic features of hepatocellular carcinoma. Radiographics 41, 1611–1631 (2021).

    Article  PubMed  Google Scholar 

  132. Renzulli, M. et al. New hallmark of hepatocellular carcinoma, early hepatocellular carcinoma and high-grade dysplastic nodules on Gd-EOB-DTPA MRI in patients with cirrhosis: a new diagnostic algorithm. Gut 67, 1674–1682 (2018).

    Article  CAS  PubMed  Google Scholar 

  133. Joo, I. et al. Radiologic-pathologic correlation of hepatobiliary phase hypointense nodules without arterial phase hyperenhancement at gadoxetic acid-enhanced MRI: a multicenter study. Radiology 296, 335–345 (2020).

    Article  PubMed  Google Scholar 

  134. Ricke, J. et al. Gadoxetic acid-based hepatobiliary MRI in hepatocellular carcinoma. JHEP Rep. 2, 100173 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Lee, D. H. et al. Non-hypervascular hepatobiliary phase hypointense nodules on gadoxetic acid-enhanced MRI: risk of HCC recurrence after radiofrequency ablation. J. Hepatol. 62, 1122–1130 (2015).

    Article  CAS  PubMed  Google Scholar 

  136. Fares, J., Fares, M. Y., Khachfe, H. H., Salhab, H. A. & Fares, Y. Molecular principles of metastasis: a hallmark of cancer revisited. Signal. Transduct. Target. Ther. 5, 28 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Shirasawa, M. et al. Prognostic differences between oligometastatic and polymetastatic extensive disease – small cell lung cancer. PLoS ONE 14, e0214599 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Bauml, J. Oligometastatic disease in cancer: broadening the path to cure? J. Targeted Ther. Cancer 7, https://www.targetedonc.com/view/oligometastatic-disease-in-cancer-broadening-the-path-to-cure (2018).

  139. Lahrsow, M., Albrecht, M. H., Bickford, M. W. & Vogl, T. J. Predicting treatment response of colorectal cancer liver metastases to conventional lipiodol-based transarterial chemoembolization using diffusion-weighted MR imaging: value of pretreatment apparent diffusion coefficients (ADC) and ADC changes under therapy. Cardiovasc. Interv. Radiol. 40, 852–859 (2017).

    Article  Google Scholar 

  140. Zhao, Y. et al. Radiomics analysis based on contrast-enhanced MRI for prediction of therapeutic response to transarterial chemoembolization in hepatocellular carcinoma. Front. Oncol. 11, 582788 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. O’Connor, J. P. B. et al. DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6. Br. J. Cancer 105, 139–145 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Zhang, H. et al. MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases. Abdom. Radiol. 44, 65–71 (2019).

    Article  CAS  Google Scholar 

  143. De Bruyne, S. et al. Value of DCE-MRI and FDG-PET/CT in the prediction of response to preoperative chemotherapy with bevacizumab for colorectal liver metastases. Br. J. Cancer 106, 1926–1933 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Daye, D. et al. Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer. Eur. Radiol. 31, 5759–5767 (2021).

    Article  PubMed  Google Scholar 

  145. Cervantes, A. et al. Metastatic colorectal cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann. Oncol. 34, 10–32 (2023).

    Article  CAS  PubMed  Google Scholar 

  146. Surov, A. et al. Apparent diffusion coefficient correlates with different histopathological features in several intrahepatic tumors. Eur. Radiol. 33, 5955–5964 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Caruso, M. et al. Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases. World J. Gastroenterol. 29, 521–535 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Jain, R. K. et al. Change in tumor size by RECIST correlates linearly with overall survival in phase I oncology studies. J. Clin. Oncol. 30, 2684–2690 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  149. Kuhl, C. K. RECIST needs revision: a wake-up call for radiologists. Radiology 292, 110–111 (2019).

    Article  PubMed  Google Scholar 

  150. Maeda, H. & Khatami, M. Analyses of repeated failures in cancer therapy for solid tumors: poor tumor-selective drug delivery, low therapeutic efficacy and unsustainable costs. Clin. Transl. Med. 7, 11 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  151. Lu, N. et al. Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI. BMC Med. Imaging 21, 155 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  152. Melero, I., Rouzaut, A., Motz, G. T. & Coukos, G. T-cell and NK-cell infiltration into solid tumors: a key limiting factor for efficacious cancer immunotherapy. Cancer Discov. 4, 522–526 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Wang, J., Li, D., Cang, H. & Guo, B. Crosstalk between cancer and immune cells: role of tumor‐associated macrophages in the tumor microenvironment. Cancer Med. 8, 4709–4721 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  154. Park, H. J. et al. Incidence of pseudoprogression during immune checkpoint inhibitor therapy for solid tumors: a systematic review and meta-analysis. Radiology 297, 87–96 (2020).

    Article  PubMed  Google Scholar 

  155. Schliep, S. et al. Concealed complete response in melanoma patients under therapy with immune checkpoint inhibitors: two case reports. J. Immunother. Cancer 6, 2 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  156. Martins, F. et al. Adverse effects of immune-checkpoint inhibitors: epidemiology, management and surveillance. Nat. Rev. Clin. Oncol. 16, 563–580 (2019).

    Article  CAS  PubMed  Google Scholar 

  157. de Kouchkovsky, I. et al. Hyperpolarized 1-[13C]-pyruvate magnetic resonance imaging detects an early metabolic response to immune checkpoint inhibitor therapy in prostate cancer. Eur. Urol. 81, 219–221 (2022).

    Article  PubMed  Google Scholar 

  158. Hoffmann, E. et al. Multiparametric chemical exchange saturation transfer MRI detects metabolic changes in breast cancer following immunotherapy. J. Transl. Med. 21, 577 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Lo Gullo, R. et al. Assessing PD-L1 expression status using radiomic features from contrast-enhanced breast MRI in breast cancer patients: initial results. Cancers 13, 6273 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Tian, Y. et al. Assessing PD-L1 expression level via preoperative MRI in HCC based on integrating deep learning and radiomics features. Diagnostics 11, 1875 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Hellwig, K. et al. Predictive value of multiparametric MRI for response to single-cycle induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma. Front. Oncol. 11, 734872 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Yopp, A. C. et al. Antiangiogenic therapy for primary liver cancer: correlation of changes in dynamic contrast-enhanced magnetic resonance imaging with tissue hypoxia markers and clinical response. Ann. Surg. Oncol. 18, 2192–2199 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  163. O’Donnell, A. et al. A phase I study of the angiogenesis inhibitor SU5416 (semaxanib) in solid tumours, incorporating dynamic contrast MR pharmacodynamic end points. Br. J. Cancer 93, 876–883 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  164. Wedam, S. B. et al. Antiangiogenic and antitumor effects of bevacizumab in patients with inflammatory and locally advanced breast cancer. J. Clin. Oncol. 24, 769–777 (2006).

    Article  CAS  PubMed  Google Scholar 

  165. Morgan, B. et al. Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for the pharmacological response of PTK787/ZK 222584, an inhibitor of the vascular endothelial growth factor receptor tyrosine kinases, in patients with advanced colorectal cancer and liver metastases: results from two phase I studies. J. Clin. Oncol. 21, 3955–3964 (2003).

    Article  CAS  PubMed  Google Scholar 

  166. Magnussen, A. L. & Mills, I. G. Vascular normalisation as the stepping stone into tumour microenvironment transformation. Br. J. Cancer 125, 324–336 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Rata, M. et al. DCE-MRI is more sensitive than IVIM-DWI for assessing anti-angiogenic treatment-induced changes in colorectal liver metastases. Cancer Imaging 21, 67 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  168. Kakite, S. et al. Hepatocellular carcinoma: IVIM diffusion quantification for prediction of tumor necrosis compared to enhancement ratios. Eur. J. Radiol. Open. 3, 1–7 (2016).

    Article  PubMed  Google Scholar 

  169. Schliemann, C. et al. First-in-class CD13-targeted tissue factor tTF-NGR in patients with recurrent or refractory malignant tumors: results of a phase I dose-escalation study. Cancers 12, 1488 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  170. Gerwing, M. et al. Multiparametric magnetic resonance imaging for immediate target hit assessment of CD13-targeted tissue factor tTF-NGR in advanced malignant disease. Cancers 13, 5880 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Luo, J., Huang, Z., Wang, M., Li, T. & Huang, J. Prognostic role of multiparameter MRI and radiomics in progression of advanced unresectable hepatocellular carcinoma following combined transcatheter arterial chemoembolization and lenvatinib therapy. BMC Gastroenterol. 22, 108 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Gild, M. L., Tsang, V. H. M., Clifton-Bligh, R. J. & Robinson, B. G. Multikinase inhibitors in thyroid cancer: timing of targeted therapy. Nat. Rev. Endocrinol. 17, 225–234 (2021).

    Article  CAS  PubMed  Google Scholar 

  173. Xu, J. et al. Influence of cell cycle phase on apparent diffusion coefficient in synchronized cells detected using temporal diffusion spectroscopy. Magn. Reson. Med. 65, 920–926 (2011).

    Article  PubMed  Google Scholar 

  174. Li, W. et al. Vascular and metabolic implications of novel targeted cancer therapies: focus on kinase inhibitors. J. Am. Coll. Cardiol. 66, 1160–1178 (2015).

    Article  PubMed  Google Scholar 

  175. Mercogliano, M. F., Bruni, S., Mauro, F., Elizalde, P. V. & Schillaci, R. Harnessing tumor necrosis factor alpha to achieve effective cancer immunotherapy. Cancers 13, 564 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Duckett, D. R. & Cameron, M. D. Metabolism considerations for kinase inhibitors in cancer treatment. Expert. Opin. Drug. Metab. Toxicol. 6, 1175–1193 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Ursprung, S. et al. Multiparametric MRI for assessment of early response to neoadjuvant sunitinib in renal cell carcinoma. PLoS ONE 16, e0258988 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Tang, L. et al. MRI in predicting the response of gastrointestinal stromal tumor to targeted therapy: a patient-based multi-parameter study. BMC Cancer 18, 811 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  179. Kwee, T. C. & Kwee, R. M. Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence. Insights Imaging 12, 88 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  180. Schmeel, F. C. Variability in quantitative diffusion-weighted MR imaging (DWI) across different scanners and imaging sites: is there a potential consensus that can help reducing the limits of expected bias? Eur. Radiol. 29, 2243–2245 (2019).

    Article  PubMed  Google Scholar 

  181. Keenan, K. E. et al. Variability and bias assessment in breast ADC measurement across multiple systems. J. Magn. Reson. Imaging 44, 846–855 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  182. Hainline, A. E. et al. Evaluation of inter-site bias and variance in diffusion-weighted MRI. Proc. SPIE Int. Soc. Opt. Eng. 10574, 1057413 (2018).

    PubMed  PubMed Central  Google Scholar 

  183. Winfield, J. M. et al. Modelling DW-MRI data from primary and metastatic ovarian tumours. Eur. Radiol. 25, 2033–2040 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  184. Malyarenko, D. et al. Multi-system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice-water phantom. J. Magn. Reson. Imaging 37, 1238–1246 (2013).

    Article  PubMed  Google Scholar 

  185. Baltzer, P. et al. Diffusion-weighted imaging of the breast – a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur. Radiol. 30, 1436–1450 (2020).

    Article  PubMed  Google Scholar 

  186. Padhani, A. R. et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11, 102–125 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. McDonald, R. J., Weinreb, J. C. & Davenport, M. S. Symptoms associated with gadolinium exposure (SAGE): a suggested term. Radiology 302, 270–273 (2022).

    Article  PubMed  Google Scholar 

  188. van Leeuwen, K. G., de Rooij, M., Schalekamp, S., van Ginneken, B. & Rutten, M. J. C. M. How does artificial intelligence in radiology improve efficiency and health outcomes? Pediatr. Radiol. 52, 2087–2093 (2022).

    Article  PubMed  Google Scholar 

  189. Dercle, L. et al. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J. Immunother. Cancer 10, e005292 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  190. Daimiel Naranjo, I. et al. Radiomics and machine learning with multiparametric breast MRI for improved diagnostic accuracy in breast cancer diagnosis. Diagnostics 11, 919 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  191. Fusco, R. et al. Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn. J. Radiol. 40, 919–929 (2022).

    Article  PubMed  Google Scholar 

  192. Ibrahim, A. et al. Radiomics for precision medicine: current challenges, future prospects, and the proposal of a new framework. Methods 188, 20–29 (2021).

    Article  CAS  PubMed  Google Scholar 

  193. Shur, J. D. et al. Radiomics in oncology: a practical guide. Radiographics 41, 1717–1732 (2021).

    Article  PubMed  Google Scholar 

  194. Moskowitz, C. S., Welch, M. L., Jacobs, M. A., Kurland, B. F. & Simpson, A. L. Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies. Radiology 304, 265–273 (2022).

    Article  PubMed  Google Scholar 

  195. van der Gijp, A. et al. Interpretation of radiological images: towards a framework of knowledge and skills. Adv. Health Sci. Educ. Theory Pract. 19, 565–580 (2014).

    Article  PubMed  Google Scholar 

  196. Masthoff, M. et al. Introducing specificity to iron oxide nanoparticle imaging by combining 57Fe-based MRI and mass spectrometry. Nano Lett. 19, 7908–7917 (2019).

    Article  CAS  PubMed  Google Scholar 

  197. Keenan, K. E. et al. Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs. J. Magn. Reson. Imaging 49, e26–e39 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  198. Jadvar, H. Prostate-specific membrane antigen PET: standard imaging in prostate cancer. Radiology 304, 609–610 (2022).

    Article  PubMed  Google Scholar 

  199. Zhu, Y. & Zhu, X. MRI-driven PET image optimization for neurological applications. Front. Neurosci. 13, 782 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  200. Cox, C. P. W., Segbers, M., Graven, L. H., Brabander, T. & van Assema, D. M. E. Standardized image quality for 68Ga-DOTA-TATE PET/CT. EJNMMI Res. 10, 27 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Liu, C. et al. Evaluation of tumour heterogeneity by 18F-fluoroestradiol PET as a predictive measure in breast cancer patients receiving palbociclib combined with endocrine treatment. Breast Cancer Res. 24, 57 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  202. Hou, Y., Nitta, H. & Li, Z. HER2 intratumoral heterogeneity in breast cancer, an evolving concept. Cancers 15, 2664 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  203. Ignatov, T., Gorbunow, F., Eggemann, H., Ortmann, O. & Ignatov, A. Loss of HER2 after HER2-targeted treatment. Breast Cancer Res. Treat. 175, 401–408 (2019).

    Article  CAS  PubMed  Google Scholar 

  204. Beauchamp, N. J. et al. Integrative diagnostics: the time is now – a report from the International Society for Strategic Studies in Radiology. J. Am. Coll. Radiol. 20, 455–466 (2023).

    Article  PubMed  Google Scholar 

  205. Froelich, M. F. et al. The value proposition of integrative diagnostics for (early) detection of cancer. On behalf of the EFLM interdisciplinary Task and Finish Group “CNAPS/CTC for early detection of cancer”. Clin. Chem. Lab. Med. 60, 821–829 (2022).

    Article  CAS  PubMed  Google Scholar 

  206. Lippi, G. & Plebani, M. Integrated diagnostics: the future of laboratory medicine? Biochem. Med. 30, 010501 (2020).

    Article  Google Scholar 

  207. Haselmann, V., Schoenberg, S. O., Neumaier, M. & Froelich, M. F. Integrated diagnostics. Radiologie 62, 11–16 (2022).

    Article  PubMed  Google Scholar 

  208. Zhang, H.-M. et al. Arterial spin labeling MRI for predicting microvascular invasion of T1 staging renal clear cell carcinoma preoperatively. Front. Oncol. 11, 644975 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  209. Ji, Y. et al. Differences in molecular subtype reference standards impact AI-based breast cancer classification with dynamic contrast-enhanced MRI. Radiology 307, e220984 (2023).

    Article  PubMed  Google Scholar 

  210. Joint Head and Neck Radiotherapy-MRI Development Cooperative. Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers. Sci. Data 5, 180008 (2018).

    Article  Google Scholar 

  211. Yuan, J. et al. Quantitative dynamic contrast-enhance MRI parameters for rectal carcinoma characterization: correlation with tumor tissue composition. World J. Surg. Oncol. 21, 306 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  212. Li, Z. et al. The diagnostic performance of diffusion kurtosis imaging in the characterization of breast tumors: a meta-analysis. Front. Oncol. 10, 575272 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  213. Desar, I. M. E. et al. Functional MRI techniques demonstrate early vascular changes in renal cell cancer patients treated with sunitinib: a pilot study. Cancer Imaging 11, 259–265 (2012).

    Article  CAS  PubMed  Google Scholar 

  214. Shenhar, C. et al. Diffusion is directional: innovative diffusion tensor imaging to improve prostate cancer detection. Diagnostics 11, 563 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  215. Hottat, N. A. et al. Assessment of diffusion-weighted MRI in predicting response to neoadjuvant chemotherapy in breast cancer patients. Sci. Rep. 13, 614 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  216. Hectors, S. J. et al. Advanced diffusion-weighted imaging modeling for prostate cancer characterization: correlation with quantitative histopathologic tumor tissue composition-a hypothesis-generating study. Radiology 286, 918–928 (2018).

    Article  PubMed  Google Scholar 

  217. Sushentsev, N. et al. Imaging tumor lactate is feasible for identifying intermediate-risk prostate cancer patients with postsurgical biochemical recurrence. Proc. Natl Acad. Sci. USA 120, e2312261120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  218. Sushentsev, N. et al. Hyperpolarised 13C-MRI identifies the emergence of a glycolytic cell population within intermediate-risk human prostate cancer. Nat. Commun. 13, 466 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  219. Solomon, E. et al. Time-dependent diffusivity and kurtosis in phantoms and patients with head and neck cancer. Magn. Reson. Med. 89, 522–535 (2023).

    Article  PubMed  Google Scholar 

  220. Persigehl, T. et al. Vessel size imaging (VSI) by robust magnetic resonance (MR) relaxometry: MR-VSI of solid tumors in correlation with immunohistology and intravital microscopy. Mol. Imaging 12, 1–11 (2013).

    Article  PubMed  Google Scholar 

  221. Kiselev, V. G., Strecker, R., Ziyeh, S., Speck, O. & Hennig, J. Vessel size imaging in humans. Magn. Reson. Med. 53, 553–563 (2005).

    Article  CAS  PubMed  Google Scholar 

  222. Turkbey, B. et al. Prostate Imaging Reporting and Data System version 2.1: 2019 update of Prostate Imaging Reporting and Sata System version 2. Eur. Urol. 76, 340–351 (2019).

    Article  PubMed  Google Scholar 

  223. Maxouri, O. et al. How to 19F MRI: applications, technique, and getting started. BJR Open. 5, 20230019 (2023).

    PubMed  PubMed Central  Google Scholar 

  224. Hernandez-Garcia, L., Lahiri, A. & Schollenberger, J. Recent progress in ASL. NeuroImage 187, 3–16 (2019).

    Article  PubMed  Google Scholar 

  225. Taso, M. et al. Update on state-of-the-art for arterial spin labeling (ASL) human perfusion imaging outside of the brain. Magn. Reson. Med. 89, 1754–1776 (2023).

    Article  PubMed  Google Scholar 

  226. O’Connor, J. P. B., Robinson, S. P. & Waterton, J. C. Imaging tumour hypoxia with oxygen-enhanced MRI and BOLD MRI. Br. J. Radiol. 92, 20180642 (2019).

    Article  PubMed  Google Scholar 

  227. Goldenberg, J. M. & Pagel, M. D. Assessments of tumor metabolism with CEST MRI. NMR Biomed. 32, e3943 (2019).

    Article  CAS  PubMed  Google Scholar 

  228. De Feyter, H. M. & de Graaf, R. A. Deuterium metabolic imaging – back to the future. J. Magn. Reson. 326, 106932 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  229. Baliyan, V., Das, C. J., Sharma, R. & Gupta, A. K. Diffusion weighted imaging: technique and applications. World J. Radiol. 8, 785–798 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  230. Afzali, M., Mueller, L., Szczepankiewicz, F., Jones, D. K. & Schneider, J. E. Quantification of tissue microstructure using tensor-valued diffusion encoding: brain and body. Front. Phys. 10, https://doi.org/10.3389/fphy.2022.809133 (2022).

  231. Chikui, T. et al. The principal of dynamic contrast enhanced MRI, the method of pharmacokinetic analysis, and its application in the head and neck region. Int. J. Dent. 2012, 480659 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  232. Fu, R. et al. Diagnostic performance of DSC perfusion MRI to distinguish tumor progression and treatment-related changes: a systematic review and meta-analysis. Neurooncol. Adv. 4, vdac027 (2022).

    PubMed  PubMed Central  Google Scholar 

  233. Jørgensen, S. H. et al. Hyperpolarized MRI – an update and future perspectives. Semin. Nucl. Med. 52, 374–381 (2022).

    Article  PubMed  Google Scholar 

  234. Sack, I. Magnetic resonance elastography from fundamental soft-tissue mechanics to diagnostic imaging. Nat. Rev. Phys. 5, 25–42 (2023).

    Article  Google Scholar 

  235. Buonocore, M. H. & Maddock, R. J. Magnetic resonance spectroscopy of the brain: a review of physical principles and technical methods. Rev. Neurosci. 26, 609–632 (2015).

    Article  PubMed  Google Scholar 

  236. Galli, F. et al. Immune cell labelling and tracking: implications for adoptive cell transfer therapies. EJNMMI Radiopharm. Chem. 6, 7 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  237. Halefoglu, A. M. & Yousem, D. M. Susceptibility weighted imaging: clinical applications and future directions. World J. Radiol. 10, 30–45 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  238. Topgaard, D. Multidimensional diffusion MRI. J. Magn. Reson. 275, 98–113 (2017).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank P. Kazmierczak (Department of Radiology, University Hospital, LMU Munich) for providing the clinical case shown in Fig. 3 and B. Maus (Clinic of Radiology, University of Münster) for his advice on MRI techniques. This work was supported by the German Research Foundation (DFG, CRC1450-B01, -B02, grant no. 431460824) and the Interdisciplinary Center for Clinical Research Münster (PIX).

Author information

Author notes

  1. These authors contributed equally: Emily Hoffmann, Max Masthoff.

Authors and Affiliations

  1. Clinic of Radiology, University of Münster, Münster, Germany

    Emily Hoffmann, Max Masthoff, Mirjam Gerwing & Cornelius Faber

  2. Department of Radiology, University Hospital, LMU Munich, Munich, Germany

    Wolfgang G. Kunz, Max Seidensticker & Moritz Wildgruber

  3. Gerhard Domagk Institute of Pathology, University Hospital Münster, Münster, Germany

    Stefanie Bobe

  4. Department of Medicine A, University of Münster, Münster, Germany

    Wolfgang E. Berdel & Christoph Schliemann

Authors
  1. Emily Hoffmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Max Masthoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wolfgang G. Kunz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Max Seidensticker
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stefanie Bobe
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mirjam Gerwing
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wolfgang E. Berdel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christoph Schliemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Cornelius Faber
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Moritz Wildgruber
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors researched data for the article and substantially contributed to discussion of contents; E.H., M.M., C.F. and M.W. wrote the manuscript; and all the authors reviewed the manuscript before submission.

Corresponding author

Correspondence to Moritz Wildgruber.

Ethics declarations

Competing interests

W.E.B. is CEO of ANTUREC Pharmaceuticals.com and holds patents for vascular targeting of tissue factor. All the other authors declare no conflicts of interest.

Peer review

Peer review information

Nature Reviews Clinical Oncology thanks K. Brindle and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

19F-Perfluorocarbons

19F-MRI selectively excites 19F nuclei instead of 1H, providing high specificity owing to negligible endogenous 19F signals223. Perfluorocarbons can be used as 19F-MRI agents: for example, for labelling immune cells66.

Arterial spin labelling

This modality uses arterial blood water as an endogenous tracer for perfusion measurements, without the need for exogenous contrast agents224,225. By modifying the longitudinal magnetization of arterial blood with radiofrequency pulses and magnetic field gradients, arterial spin labelling creates a ‘labelled’ blood tracer that reduces the available magnetization, dependent on perfusion of the tissue.

Blood oxygenation level-dependent MRI

BOLD-MRI. This technique exploits the paramagnetic properties of deoxygenated haemoglobin, which induce magnetic field gradients around blood vessels and result in a phenomenon known as the BOLD effect. This effect is based on the dependence of the magnetic properties of haemoglobin on its oxygenation state. Deoxygenated haemoglobin decreases the transverse relaxation time (T2*) of the surrounding tissue. Consequently, changes in blood oxygen levels are reflected in the BOLD-MRI signal intensity226.

Chemical exchange saturation transfer MRI

CEST-MRI. This is a contrast method that enables detection of low levels of endogenous metabolites with high spatial resolution. This method is based on the selective saturation of exchangeable solute protons, such as those bound to amine, amide, carboxyl or hydroxyl groups, by the application of radiofrequency pulses at different frequency offsets. This saturation is then transferred to water protons by chemical exchange, resulting in a detectable decrease in the water signal227.

Deuterium metabolic imaging

DMI. DMI selectively excites 2H instead of protons (1H), providing insight into metabolic pathways with high specificity due to the low natural abundance of deuterium. The technique is based on the oral or intravenous administration of deuterium-enriched substrates, which can be directly observed during metabolic processes51,228.

Diffusion-weighted imaging

DWI. This technique is a tool to investigate tissue microstructure by assessing the diffusion properties of water molecules. Within tissues, water molecules encounter diffusion barriers, such as cell membranes and tissue structures, which influence the diffusion patterns observed on DWI229. Pulsed diffusion gradient spin echo (PGSE) is the DWI technique most commonly used in routine clinical settings229. Diffusion tensor imaging provides information about tissue microstructure and organization by analysing the direction and magnitude of water diffusion within tissues230. Its most important application is fibre tracking in the central neural system or muscle. Finally, diffusion kurtosis imaging is an extension of diffusion tensor imaging that further characterizes the non-Gaussian behaviour of water diffusion in tissues, providing additional insight into tissue complexity230.

Dynamic contrast-enhanced MRI

DCE-MRI. This technique evaluates the temporal enhancement pattern of tissue following intravenous injection of a contrast agent231. By acquiring MR images before, during and after injection of the contrast agent and applying pharmacokinetic models, DCE-MRI enables extraction of quantitative parameters such as tissue perfusion and permeability13.

Dynamic susceptibility contrast MRI

This technique evaluates the microvascular environment by tracking the passage of a bolus of a gadolinium-based contrast agent. The bolus causes a susceptibility-induced signal loss on T2*-weighted images, dependent on microvascular parameters232.

Hyperpolarized 13C-MRI

HP 13C-MRI. This metabolic imaging technique enhances the magnetic resonance signal of 13C-labelled molecules by more than 10,000-fold. This enhancement enables real-time in vivo imaging of injected 13C-labelled substrates and their downstream metabolites, providing insight into intracellular metabolic pathways such as glycolysis or the tricarboxylic acid cycle47,233.

Magnetic resonance elastography

MRE. This technique enables assessment of the viscoelastic properties of soft biological tissues. MRE involves the encoding of externally induced harmonic shear waves using vibration-synchronized phase-contrast MRI, followed by the use of inversion algorithms to reconstruct mechanical parameters that provide insight into tissue stiffness and elasticity234.

Magnetic resonance spectroscopy

MRS. This technique enables quantification of the levels of specific metabolites in a tissue of interest. The primary output of MRS is not an image, but a magnetic resonance spectrum of tissue metabolites. However, spectroscopic imaging techniques can generate quantitative metabolic maps235.

Relaxation time mapping

Relaxometry includes quantitative mapping techniques that are used to analyse the distribution of the T1 (longitudinal relaxation time), T2 (transverse relaxation time) and T2* (effective transverse relaxation time) values of the (tumour) tissue per voxel. Either the relaxation times or the relaxation rates (which are the inverse value of the relaxation time) are mapped70.

Superparamagnetic iron oxide

SPIO. SPIO labelling agents for MRI are nanosized iron oxide crystals surrounded by a coating, such as dextran or carboxydextran. These agents primarily affect T2* relaxation times, causing signal loss in T2-weighted and T2*-weighted sequences due to the susceptibility effects of the iron oxide core61. SPIO agents can be used for cell labelling236.

Susceptibility-weighted imaging

SWI. This technique uses magnetic susceptibility-induced phase changes in magnetic resonance images resulting from various compounds, including deoxygenated blood, blood products, iron and calcium. By detecting these compounds, SWI enables visualization of vascular structures and haemorrhagic lesions237.

Time-dependent diffusion

TDD. TDD, such as the use of oscillating diffusion gradients, is a DWI technique in which the gradients applied during imaging are modulated over time. Compared with PGSE-DWI, shorter diffusion distances of water molecules can be assessed and a microstructural analysis of the tissue can be performed, including the determination of mean cell size238.

Tissue oxygenation level-dependent MRI

This technique uses T1-weighted imaging to assess tissue oxygenation levels226.

Vessel size imaging

This technique uses the relationship between contrast-enhanced transverse relaxation times T2* and T2 to map the average calibre of vessels16. By analysing T2-weighted and T2*-weighted images during the passage of a contrast bolus through the vasculature, vessel size imaging can help distinguish between microvasculature and larger vessels.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hoffmann, E., Masthoff, M., Kunz, W.G. et al. Multiparametric MRI for characterization of the tumour microenvironment. Nat Rev Clin Oncol (2024). https://doi.org/10.1038/s41571-024-00891-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41571-024-00891-1

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer