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Harnessing multimodal data integration to advance precision oncology

Abstract

Advances in quantitative biomarker development have accelerated new forms of data-driven insights for patients with cancer. However, most approaches are limited to a single mode of data, leaving integrated approaches across modalities relatively underdeveloped. Multimodal integration of advanced molecular diagnostics, radiological and histological imaging, and codified clinical data presents opportunities to advance precision oncology beyond genomics and standard molecular techniques. However, most medical datasets are still too sparse to be useful for the training of modern machine learning techniques, and significant challenges remain before this is remedied. Combined efforts of data engineering, computational methods for analysis of heterogeneous data and instantiation of synergistic data models in biomedical research are required for success. In this Perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods. Advancing along this direction will result in a reimagined class of multimodal biomarkers to propel the field of precision oncology in the coming decade.

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Fig. 1: Example data modalities for integration include radiology, histopathology and genomic information.
Fig. 2: Multimodal models integrate features across modalities.
Fig. 3: Design choices for multimodal models with genomic, radiological and histopathological data.
Fig. 4: Active learning reduces the burden of annotation.
Fig. 5: Recommender systems could learn from retrospective data to assist in clinical decision-making.
Fig. 6: Class activation maps highlight the image areas most important for the model to make a decision.

References

  1. 1.

    AACR Project GENIE Consortium. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).

    Article  Google Scholar 

  2. 2.

    Vasan, N. et al. Double PIK3CA mutations in cis increase oncogenicity and sensitivity to PI3Kα inhibitors. Science 366, 714–723 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Razavi, P. et al. The genomic landscape of endocrine-resistant advanced breast cancers. Cancer Cell 34, 427–438.e6 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Jonsson, P. et al. Genomic correlates of disease progression and treatment response in prospectively characterized gliomas. Clin. Cancer Res. 25, 5537–5547 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Soumerai, T. E. et al. Clinical utility of prospective molecular characterization in advanced endometrial cancer. Clin. Cancer Res. 24, 5939–5947 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Cui, M. & Zhang, D. Y. Artificial intelligence and computational pathology. Lab. Invest. 101, 412–422 (2021).

    PubMed  Article  Google Scholar 

  7. 7.

    Shen, S. Y. et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 563, 579–583 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Cristiano, S. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 570, 385–389 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Klupczynska, A. et al. Study of early stage non-small-cell lung cancer using Orbitrap-based global serum metabolomics. J. Cancer Res. Clin. Oncol. 143, 649–659 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Helland, T. et al. Serum concentrations of active tamoxifen metabolites predict long-term survival in adjuvantly treated breast cancer patients. Breast Cancer Res. 19, 125 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  11. 11.

    Luo, P. et al. A large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma. Hepatology 67, 662–675 (2018).

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Medina-Martínez, J. S. et al. Isabl Platform, a digital biobank for processing multimodal patient data. BMC Bioinforma. 21, 549 (2020).

    Article  Google Scholar 

  13. 13.

    Bhinder, B., Gilvary, C., Madhukar, N. S. & Elemento, O. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 11, 900–915 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. & Aerts, H. J. W. L. Artificial intelligence in radiology. Nat. Rev. Cancer 18, 500–510 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16, 703–715 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Gutman, D. A. et al. MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 267, 560–569 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Zwanenburg, A. et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295, 328–338 (2020).

    Article  Google Scholar 

  18. 18.

    Sun, R. et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol. 19, 1180–1191 (2018).

    CAS  Article  Google Scholar 

  19. 19.

    Rizzo, S. et al. Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months. Eur. Radiol. 28, 4849–4859 (2018).

    PubMed  Article  Google Scholar 

  20. 20.

    Pisapia, J. M. et al. Predicting pediatric optic pathway glioma progression using advanced magnetic resonance image analysis and machine learning. Neurooncol. Adv. 2, vdaa090 (2020).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Chang, K. et al. Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging. Clin. Cancer Res. 24, 1073–1081 (2018).

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Li, Z., Wang, Y., Yu, J., Guo, Y. & Cao, W. Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci. Reports 7, 5467 (2017).

    Google Scholar 

  23. 23.

    Lu, C.-F. et al. Machine learning-based radiomics for molecular subtyping of gliomas. Clin. Cancer Res. 24, 4429–4436 (2018).

    PubMed  Article  Google Scholar 

  24. 24.

    Wang, S. et al. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur. Respir. J. 53, 1800986 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Khosravi, P., Lysandrou, M., Eljalby, M. & Li, Q. A deep learning approach to diagnostic classification of prostate cancer using pathology–radiology fusion. J. Magn. Reson. 54, 462–471 (2021).

    Google Scholar 

  26. 26.

    Hosny, A. et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 15, e1002711 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Rajpurkar, P. et al. AppendiXNet: deep learning for diagnosis of appendicitis from a small dataset of CT exams using video pretraining. Sci. Rep. 10, 3958 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O. & Hajirasouliha, I. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine 27, 317–328 (2018).

    PubMed  Article  Google Scholar 

  29. 29.

    Coudray, N. et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020).

    Article  Google Scholar 

  31. 31.

    Kather, J. N. et al. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat. Cancer 1, 789–799 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Ding, K. et al. Feature-enhanced graph networks for genetic mutational prediction using histopathological images in colon cancer. in Medical Image Computing and Computer Assisted Intervention 294–304 (Springer, 2020).

  33. 33.

    Rutledge, W. C. et al. Tumor-infiltrating lymphocytes in glioblastoma are associated with specific genomic alterations and related to transcriptional class. Clin. Cancer Res. 19, 4951–4960 (2013).

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Echle, A. et al. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology 159, 1406–1416.e11 (2020).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Saltz, J. et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. 23, 181–193.e7 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Diao, J. A. et al. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat. Commun. 12, 1613 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Corredor, G. et al. Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. Clin. Cancer Res. 25, 1526–1534 (2019).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    AbdulJabbar, K. et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat. Med. 26, 1054–1062 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Kong, J. et al. Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates. PLoS ONE 8, e81049 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41.

    Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Lewis, S. M. et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat. Methods https://doi.org/10.1038/s41592-021-01203-6 (2021).

    Article  PubMed  Google Scholar 

  43. 43.

    Flaherty, K. T. et al. Improved survival with MEK inhibition in BRAF-mutated melanoma. N. Engl. J. Med. 367, 107–114 (2012).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Maemondo, M. et al. Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. N. Engl. J. Med. 362, 2380–2388 (2010).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    DiNardo, C. D. et al. Durable remissions with ivosidenib in IDH1-mutated relapsed or refractory AML. N. Engl. J. Med. 378, 2386–2398 (2018).

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Mirza, M. R. et al. Niraparib maintenance therapy in platinum-sensitive, recurrent ovarian cancer. N. Engl. J. Med. 375, 2154–2164 (2016).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    de Bono, J. et al. Olaparib for metastatic castration-resistant prostate cancer. N. Engl. J. Med. 382, 2091–2102 (2020).

    PubMed  Article  Google Scholar 

  49. 49.

    Drilon, A. et al. Efficacy of larotrectinib in TRK fusion-positive cancers in adults and children. N. Engl. J. Med. 378, 731–739 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Canon, J. et al. The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575, 217–223 (2019).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Hallin, J. et al. The KRASG12C inhibitor MRTX849 provides insight toward therapeutic susceptibility of KRAS-mutant cancers in mouse models and patients. Cancer Discov. 10, 54–71 (2020).

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    André, F. et al. Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced breast cancer. N. Engl. J. Med. 380, 1929–1940 (2019).

    PubMed  Article  Google Scholar 

  53. 53.

    Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Vöhringer, H., Van Hoeck, A., Cuppen, E. & Gerstung, M. Learning mutational signatures and their multidimensional genomic properties with TensorSignatures. Nat. Commun. 12, 3628 (2021).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  57. 57.

    Macintyre, G. et al. Copy number signatures and mutational processes in ovarian carcinoma. Nat. Genet. 50, 1262–1270 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Funnell, T. et al. Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models. PLoS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1006799 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Maniatis, S. et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364, 89–93 (2019).

    CAS  PubMed  Article  Google Scholar 

  61. 61.

    Payne, A. C. et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371, eaay3446 (2021).

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Wang, W., Tran, D. & Feiszli, M. What makes training multi-modal classification networks hard? in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 12695–12705 (IEEE, 2020).

  63. 63.

    Raghu, M., Zhang, C., Kleinberg, J. & Bengio, S. Transfusion: understanding transfer learning for medical imaging. Preprint at arXiv https://arxiv.org/abs/1902.07208 (2019).

  64. 64.

    Zhang, L. et al. Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma. Front. Genet. 9, 477 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. 65.

    Chaudhary, K., Poirion, O. B., Lu, L. & Garmire, L. X. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 24, 1248–1259 (2018).

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Ramazzotti, D., Lal, A., Wang, B., Batzoglou, S. & Sidow, A. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat. Commun. 9, 4453 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Poirion, O. B., Chaudhary, K. & Garmire, L. X. Deep Learning data integration for better risk stratification models of bladder cancer. AMIA Jt. Summits Transl. Sci. Proc. 2017, 197–206 (2018).

    PubMed  Google Scholar 

  68. 68.

    Žitnik, M. & Zupan, B. Survival regression by data fusion. Syst. Biomed. 2, 47–53 (2014).

    Article  Google Scholar 

  69. 69.

    The Cancer Genome Atlas Research Network. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372, 2481–2498 (2015).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  70. 70.

    Cantini, L. et al. Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer. Nat. Commun. 12, 124 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  73. 73.

    Sun, D., Wang, M. & Li, A. A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE/ACM Trans. Comput. Biol. Bioinform. 3, 841–850 (2018).

    Google Scholar 

  74. 74.

    Huang, Z. et al. SALMON: survival analysis learning with multi-omics neural networks on breast cancer. Front. Genet. 10, 166 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Lee, B. et al. DeepBTS: prediction of recurrence-free survival of non-small cell lung cancer using a time-binned deep neural network. Sci. Rep. 10, 1952 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F. & Sun, J. Doctor AI: predicting clinical events via recurrent neural networks. in Proceedings of the 1st Machine Learning for Healthcare Conference. Proceedings of Machine Learning Research 56, 301–318 (PLMR, 2016).

  77. 77.

    Tomašev, N. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  78. 78.

    Yang, J. et al. MIA-prognosis: a deep learning framework to predict therapy response. in Medical Image Computing and Computer Assisted Intervention 211–220 (Springer, 2020).

  79. 79.

    Srivastava, R. K., Greff, K. & Schmidhuber, J. Highway networks. in Advances in Neural Information Processing Systems 28 https://arxiv.org/abs/1505.00387 (NIPS, 2015).

  80. 80.

    Cheerla, A. & Gevaert, O. Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35, i446–i454 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Gevaert, O. et al. Imaging-AMARETTO: An imaging genomics software tool to interrogate multiomics networks for relevance to radiography and histopathology imaging biomarkers of clinical outcomes. JCO Clin. Cancer Inf. 4, 421–435 (2020).

    Google Scholar 

  82. 82.

    Zhu, X. et al. Imaging-genetic data mapping for clinical outcome prediction via supervised conditional Gaussian graphical model. in 2016 IEEE International Conference on Bioinformatics and Biomedicine https://doi.org/10.1109/BIBM.2016.7822559 (IEEE, 2016).

  83. 83.

    Popovici, V. et al. Joint analysis of histopathology image features and gene expression in breast cancer. BMC Bioinformatics 17, 209 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  84. 84.

    Mobadersany, P. et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl Acad. Sci. USA 115, E2970–E2979 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Chen, R. J. et al. Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis. IEEE Trans. Med. Imaging https://doi.org/10.1109/TMI.2020.3021387 (2020).

  86. 86.

    Zadeh, A., Chen, M., Poria, S., Cambria, E. & Morency, L.-P. Tensor fusion network for multimodal sentiment analysis. in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing 1103–1114 (Biblio, 2017).

  87. 87.

    Yuan, Y., Giger, M. L., Li, H., Bhooshan, N. & Sennett, C. A. Multimodality computer-aided breast cancer diagnosis with FFDM and DCE-MRI. Acad. Radiol. 17, 1158–1167 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Chan, H.-W., Weng, Y.-T. & Huang, T.-Y. Automatic classification of brain tumor types with the MRI scans and histopathology images. in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries 353–359 (Springer, 2017).

  89. 89.

    Rathore, S. et al. Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1. Sci. Rep. 8, 5087 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  90. 90.

    Donini, M. et al. Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. Neuroimage 195, 215–231 (2019).

    PubMed  Article  Google Scholar 

  91. 91.

    Gonen, M. & Alpaydin, E. Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011).

    Google Scholar 

  92. 92.

    Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Duanmu, H. et al. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using deep learning with integrative imaging, molecular and demographic data. in Medical Image Computing and Computer Assisted Intervention 242–252 (Springer, 2020).

  94. 94.

    Bhattacharya, I. et al. CorrSigNet: learning correlated prostate cancer SIGnatures from radiology and pathology images for improved computer aided diagnosis. in Medical Image Computing and Computer Assisted InterventionMICCAI 2020 315–325 (Springer, 2020).

  95. 95.

    Deng, J. et al. ImageNet: a large-scale hierarchical image database. in IEEE Conference on Computer Vision and Pattern Recognition https://doi.org/10.1109/CVPR.2009.5206848 (IEEE, 2009).

  96. 96.

    Kay, W. et al. The kinetics human action video dataset. Preprint at arXiv http://arxiv.org/abs/1705.06950 (2017).

  97. 97.

    Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25, 954–961 (2019).

    CAS  Article  Google Scholar 

  98. 98.

    Zhou, Z.-H. A brief introduction to weakly supervised learning. Natl Sci. Rev. 5, 44–53 (2018).

    Article  Google Scholar 

  99. 99.

    Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  100. 100.

    Suphavilai, C., Bertrand, D. & Nagarajan, N. Predicting cancer drug response using a recommender system. Bioinformatics 34, 3907–3914 (2018).

    CAS  PubMed  Article  Google Scholar 

  101. 101.

    Joachims, T., Swaminathan, A. & de Rijke, M. Deep learning with logged bandit feedback. in Proceedings of the International Conference on Learning Representations (ICLR) (ICLR, 2018).

  102. 102.

    Lee, J. S. et al. Synthetic lethality-mediated precision oncology via the tumor transcriptome. Cell 184, 2487–2502.e13 (2021).

    CAS  PubMed  Article  Google Scholar 

  103. 103.

    Gundersen, G., Dumitrascu, B., Ash, J. T. & Engelhardt, B. E. End-to-end training of deep probabilistic CCA for joint modeling of paired biomedical observations. in Proceedings of the 35th Uncertainty in Artificial Intelligence Conference 945–955 (PMLR, 2020).

  104. 104.

    Li, Y. et al. Inferring multimodal latent topics from electronic health records. Nat. Commun. 11, 2536 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. 105.

    Choplin, R. H., Boehme, J. M. 2nd & Maynard, C. D. Picture archiving and communication systems: an overview. Radiographics 12, 127–129 (1992).

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Puchalski, R. B. et al. An anatomic transcriptional atlas of human glioblastoma. Science 360, 660–663 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

    Weigelt, B. et al. Radiogenomics analysis of intratumor heterogeneity in a patient with high-grade serous ovarian cancer. JCO Precis. Oncol. 3, 1–9 (2019).

    Google Scholar 

  108. 108.

    Jiménez-Sánchez, A. et al. Unraveling tumor-immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy. Nat. Genet. 52, 582–593 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  109. 109.

    Hersh, W. R. et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Med. Care 51, S30–S37 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  110. 110.

    Allison, J. J. et al. The art and science of chart review. Jt. Comm. J. Qual. Improv. 26, 115–136 (2000).

    CAS  PubMed  Google Scholar 

  111. 111.

    Vassar, M. & Holzmann, M. The retrospective chart review: important methodological considerations. J. Educ. Eval. Health Prof. 10, 12 (2013).

    PubMed  Article  Google Scholar 

  112. 112.

    Hripcsak, G. et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud. Health Technol. Inform. 216, 574–578 (2015).

    PubMed  PubMed Central  Google Scholar 

  113. 113.

    Stein, B. & Morrison, A. The enterprise data lake: better integration and deeper analytics. PwC Technol. Forecast Rethinking Integr. 1, 18 (2014).

    Google Scholar 

  114. 114.

    Armbrust, M. et al. Delta lake: high-performance ACID table storage over cloud object stores. in Proceedings of the VLDB Endowment Vol. 13 3411–3424 (ACM, 2020).

  115. 115.

    Zagan, E. & Danubianu, M. Cloud DATA LAKE: the new trend of data storage. in 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) 1–4 (IEEE, 2021).

  116. 116.

    Rieke, N. et al. The future of digital health with federated learning. NPJ Digital Med. 3, 119 (2020).

    Article  Google Scholar 

  117. 117.

    Andreux, M., Manoel, A., Menuet, R., Saillard, C. & Simpson, C. Federated survival analysis with discrete-time Cox models. in FL-ICML 2020: International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020 https://arxiv.org/abs/2006.08997 (ICML 2020).

  118. 118.

    Lin, J.-H. & Haug, P. J. Exploiting missing clinical data in Bayesian network modeling for predicting medical problems. J. Biomed. Inform. 41, 1–14 (2008).

    PubMed  Article  Google Scholar 

  119. 119.

    Khan, A., Atzori, M., Otálora, S., Andrearczyk, V. & Müller, H. Generalizing convolution neural networks on stain color heterogeneous data for computational pathology. in Medical Imaging 2020: Digital Pathology https://doi.org/10.1117/12.2549718 (International Society for Optics and Photonics, 2020).

  120. 120.

    Glatz-Krieger, K., Spornitz, U., Spatz, A., Mihatsch, M. J. & Glatz, D. Factors to keep in mind when introducing virtual microscopy. Virchows Arch. 448, 248–255 (2006).

    PubMed  Article  Google Scholar 

  121. 121.

    Janowczyk, A., Basavanhally, A. & Madabhushi, A. Stain Normalization using Sparse AutoEncoders (StaNoSA): application to digital pathology. Comput. Med. Imaging Graph. 57, 50–61 (2017).

    PubMed  Article  Google Scholar 

  122. 122.

    Lacroix, M. et al. Correction for magnetic field inhomogeneities and normalization of voxel values are needed to better reveal the potential of MR radiomic features in lung cancer. Front. Oncol. 10, 43 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  123. 123.

    Macenko, M. et al. A method for normalizing histology slides for quantitative analysis. in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro https://doi.org/10.1109/ISBI.2009.5193250 (IEEE, 2009).

  124. 124.

    Srinidhi, C. L., Ciga, O. & Martel, A. L. Deep neural network models for computational histopathology: a survey. Med. Image Anal. 67, 101813 (2021).

    PubMed  Article  Google Scholar 

  125. 125.

    Hu, Z., Tang, A., Singh, J., Bhattacharya, S. & Butte, A. J. A robust and interpretable end-to-end deep learning model for cytometry data. Proc. Natl Acad. Sci. USA 117, 21373–21380 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  126. 126.

    Kleppe, A. et al. Designing deep learning studies in cancer diagnostics. Nat. Rev. Cancer 21, 199–211 (2021).

    CAS  PubMed  Article  Google Scholar 

  127. 127.

    Lopez, K., Fodeh, S. J., Allam, A., Brandt, C. A. & Krauthammer, M. Reducing annotation burden through multimodal learning. Front. Big Data 3, 19 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  128. 128.

    Gundersen, O. E. & Kjensmo, S. State of the art: reproducibility in artificial intelligence. in Thirty-Second AAAI Conference on Artificial Intelligence (AAAI, 2018).

  129. 129.

    Courtiol, P. et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25, 1519–1525 (2019).

    CAS  PubMed  Article  Google Scholar 

  130. 130.

    McKinney, S. M. et al. Addendum: international evaluation of an AI system for breast cancer screening. Nature 586, E19 (2020).

    CAS  PubMed  Article  Google Scholar 

  131. 131.

    Haibe-Kains, B. et al. Transparency and reproducibility in artificial intelligence. Nature 586, E14–E16 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  132. 132.

    Hosny, A. et al. ModelHub.AI: dissemination platform for deep learning models. Preprint at arXiv http://arxiv.org/abs/1911.13218 (2019).

  133. 133.

    Beede, E. et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–12 (ACM, 2020).

  134. 134.

    Cruz Rivera, S. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat. Med. 26, 1351–1363 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  135. 135.

    Moher, D. et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. BMJ 340, c869 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  136. 136.

    Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat. Med. 26, 1364–1374 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. 137.

    Lauritsen, S. M. et al. Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nat. Commun. 11, 3852 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  138. 138.

    Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article  Google Scholar 

  139. 139.

    Pavel, M. A., Petersen, E. N., Wang, H., Lerner, R. A. & Hansen, S. B. Studies on the mechanism of general anesthesia. Proc. Natl Acad. Sci. USA 117, 13757–13766 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  140. 140.

    Wang, F., Kaushal, R. & Khullar, D. Should health care demand interpretable artificial intelligence or accept “black box” medicine? Ann. Intern. Med. 172, 59–60 (2020).

    PubMed  Article  Google Scholar 

  141. 141.

    Castro, D. C., Walker, I. & Glocker, B. Causality matters in medical imaging. Nat. Commun. 11, 3673 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  142. 142.

    Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. & Torralba, A. Learning deep features for discriminative localization. in Proceedings of the IEEE conference on computer vision and pattern recognition 2921–2929 (IEEE, 2016).

  143. 143.

    Olah, C. et al. The building blocks of interpretability. Distill https://distill.pub/2018/building-blocks/ (2018).

  144. 144.

    Graziani, M., Andrearczyk, V. & Müller, H. Visualizing and interpreting feature reuse of pretrained CNNs for histopathology. in Irish Machine Vision and Image Processing Conference (IMVIP, 2019).

  145. 145.

    Burns, C., Thomason, J. & Tansey, W. Interpreting black box models via hypothesis testing. in FODS ’20: Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference 47–57 (ACM, 2019).

  146. 146.

    Donoghue, M. T. A., Schram, A. M., Hyman, D. M. & Taylor, B. S. Discovery through clinical sequencing in oncology. Nat. Cancer 1, 774–783 (2020).

    Article  Google Scholar 

  147. 147.

    Kehl, K. L. et al. Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports. JAMA Oncol. 5, 1421–1429 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  148. 148.

    Cosgriff, C. V., Stone, D. J., Weissman, G., Pirracchio, R. & Celi, L. A. The clinical artificial intelligence department: a prerequisite for success. BMJ Health Care Inf. 27, e100183 (2020).

    Article  Google Scholar 

  149. 149.

    Zadeh, A. et al. Memory fusion network for multi-view sequential learning. in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI, 2018).

  150. 150.

    Zadeh, A., Liang, P. P., Poria, S., Cambria, E. & Morency, L.-P. Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph. in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2236–2246 (ACL Anthology, 2018).

  151. 151.

    Zadeh, A. et al. Multi-attention recurrent network for human communication comprehension. in Thirty-Second AAAI Conference on Artificial Intelligence Vol. 2018 5642–5649 (AAAI, 2018).

  152. 152.

    Kumar, A., Srinivasan, K., Cheng, W.-H. & Zomaya, A. Y. Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf. Process. Manag. 57, 102141 (2020).

    Article  Google Scholar 

  153. 153.

    Liang, P. P., Zadeh, A. & Morency, L.-P. Multimodal local-global ranking fusion for emotion recognition. in Proceedings of the 20th ACM International Conference on Multimodal Interaction 472–476 (ACM, 2018).

  154. 154.

    Liu, Z. et al. Efficient low-rank multimodal fusion with modality-specific factors. in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Long Papers) 2247–2256 (ACL Anthology, 2018).

  155. 155.

    Marinelli, R. J. et al. The Stanford Tissue Microarray Database. Nucleic Acids Res. 36, D871–D877 (2008).

    CAS  PubMed  Article  Google Scholar 

  156. 156.

    Clark, K. et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26, 1045–1057 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  157. 157.

    Newitt, D. & Hylton, N. Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy. The Cancer Imaging Archive https://doi.org/10.7937/K9/TCIA.2016.QHsyhJKy (2016).

  158. 158.

    Wolpert, D. H. The lack of a priori distinctions between learning algorithms. Neural Comput. 8, 1341–1390 (1996).

    Article  Google Scholar 

  159. 159.

    Wolpert, D. H. & Macready, W. G. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997).

    Article  Google Scholar 

  160. 160.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  161. 161.

    He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2016.90 (IEEE, 2016).

  162. 162.

    Iandola, F. N. et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. Preprint at arXiv http://arxiv.org/abs/1602.07360 (2016).

  163. 163.

    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2016).

  164. 164.

    Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://arxiv.org/abs/1608.06993 (IEEE, 2016).

  165. 165.

    Cho, K. et al. Learning phrase representations using RNN encoder–decoder for statistical machine translation. in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Association for Computational Linguistics, 2014).

  166. 166.

    Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    CAS  PubMed  Article  Google Scholar 

  167. 167.

    Vaswani, A. et al. Attention is all you need. in 31st Conference on Neural Information Processing Systems (NIPS, 2017).

  168. 168.

    Lee, G., Kang, B., Nho, K., Sohn, K.-A. & Kim, D. MildInt: deep learning-based multimodal longitudinal data integration framework. Front. Genet. 10, 617 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

The authors thank N. Rusk and W. Tansey for helpful comments on the manuscript. S.P.S. is supported by the Nicholls-Biondi Endowed Chair in Computational Oncology and the Susan G. Komen Scholars programme. K.M.B. is supported by the National Cancer Institute (NCI) of the US National Institutes of Health (NIH) under award number F30CA257414, the Jonathan Grayer Fellowship of Gerstner Sloan Kettering Graduate School of Biomedical Sciences and a Medical Scientist Training Program Grant from the National Institute of General Medical Sciences of the NIH under award number T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program. MSK MIND is generously supported by Cycle for Survival. All authors are supported by NIH NCI Cancer Center Support Grant P30 CA008748.

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The authors contributed equally to all aspects of the article.

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Correspondence to Sohrab P. Shah.

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S.P.S is a shareholder in and consultant for Canexia Health Inc. K.M.B., P.K., R.V. and J.J.G. declare no competing interests.

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Nature Reviews Cancer thanks Anant Madabhushi, who co-reviewed with Nathaniel Braman; Benjamin Haibe-Kains; and Aristotelis Tsirigos for their contribution to the peer review of this work.

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Glossary

Area under the receiver operating characteristic curve

(AUROC). A measurement of the ability of a binary classifier to separate the populations of interest. It describes the increase in the true positive rate relative to the increase in the false positive rate over the range of score thresholds chosen to separate the two classes. The highest value obtainable is 1, and random performance is associated with a value of 0.5.

Artificial intelligence

(AI). A broad field of computer science concerned with developing computational tools to perform tasks historically requiring human-level intelligence.

Autoencoders

Unsupervised neural network architectures trained to represent data in a lower-dimensional space. They are a form of lossy compression (reducing the size of data representations, but with some loss of information) that can be used to uncover latent structure in the data or reduce computational needs before further analysis.

Bayesian inference

A statistical method that refers to the application of Bayes’s theorem in determining the updated probability of a hypothesis given new information. Bayesian inference allows the posterior probability to be calculated given the prior probability of a hypothesis and a likelihood function.

Biomarkers

Measurements that indicate a biological state. Cancer biomarkers can be categorized into diagnostic (disease progression), predictive (treatment response) and prognostic (survival).

Concordance index

(c-index). An index that generalizes the area under the receiver operating characteristic curve (AUROC) to measure the ability of a model to separate censored data. As with the AUROC, the baseline value for a model with arbitrary predictions is 0.5, and the ceiling value for a perfect prediction model is 1.0.

Convolutional neural networks

(CNNs). A form of deep neural network typically used to analyse images. CNNs are named for their use of convolutions, a mathematical operation involving the input data and a smaller matrix known as a kernel. This parameter sharing reduces the number of parameters to be learned and encourages the learning of features which are invariant to image shifts.

Counterfactual ML

A set of techniques for machine learning (ML) based on the paradigm of modelling situations that did not factually occur. These techniques are often deployed for interpretable models or to learn from biased logged data. For example, a counterfactual analysis could involve using a model developed to predict a disease outcome using a set of measurements to predict scenarios where the input measurements are perturbed to study their causal relationship. This paradigm has also been harnessed to learn unbiased recommenders from logged data, such as user purchases on online marketplaces, despite changes in how products are recommended over time and the lack of a controlled experimental setup.

Cox proportional hazards (CPH) model

A regression model used to associate censored temporal outcomes, such as time to survival, and potential predictor variables, such as age or cancer stage. It is the most common method to evaluate prognostic variables in survival analyses of patients with cancer.

Data lakes

Places to store relational and non-relational data from a vast pool of raw data. The structure of the data or schema is not defined when data are captured. Different types of analytics on data such as structured query language (SQL) queries, big data analytics, full text search, real-time analytics and machine learning can be used to uncover insights.

Data parallelism

The approach of performing a computing task in parallel using multiple processors. It focuses on distributing data across various cores and enabling simultaneous subcomputations.

Deep learning

(DL). Comprises a class of machine learning methods based on artificial neural networks, which use multiple non-linear layers to derive progressively higher-order features from data.

Deep neural network

(DNN). A form of deep learning, namely artificial neural networks with more than one hidden layer between the input and output layers.

Federated learning

A training strategy wherein the model to be trained is passed around among institutions instead of data being centrally amalgamated. Each institution then updates the model parameters on the basis of the local dataset. This strategy enables multi-institutional model training without data sharing among institutions.

Kernel

A similarity function often used to transform input data implicitly into a form more suitable for machine learning tasks. For example, a two-dimensional pattern-based kernel could be used to identify the presence of specific shapes in an image, and a one-dimensional Gaussian kernel could be used to impute a smoothed trendline on the basis of noisy data points.

Layer-wise relevance propagation

(LRP). One of the most prominent techniques in explainable machine learning. LRP decomposes the network’s output score into the individual contributions of the input neurons using model parameters (that is, weights) and neuron activations.

Machine learning

(ML). A type of artificial intelligence that aims to discover patterns in data that are not explicitly programmed. ML models typically use a dataset for pattern discovery, known as ‘training’, to make predictions on unseen data, known as ‘inference’.

Recommender systems

Systems that aim to predict items relevant to users by building a model from past behaviour. In precision medicine, recommender systems can be used to predict the preferred treatment for a disease on the basis of multiple patient measurements.

Recurrent neural networks

(RNNs). A form of deep neural network optimized for time series data. An RNN analyses each element of the input sequence in succession and updates its representation of the data on the basis of previous elements.

Sentiment analysis

A field seeking to characterize human emotional states from text, images and sounds by the use of machine learning models.

Supervised learning

A machine learning paradigm that aims to elucidate the relationship between input data variables and predefined classes (‘classification’) or continuous labels (‘regression’) of interest. By contrast, unsupervised learning aims to identify patterns in a dataset without the use of such labels or classes.

Voxel

The volume element defined by the x, y and z coordinates in three-dimensional space used in medical imaging modalities. Its dimensions are given by the pixel, together with the thickness of the slice.

Weight decay

A regularization strategy to improve the generalizability of models whereby high estimated values of model parameters are penalized despite marginal increases in accuracy on the training set.

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Boehm, K.M., Khosravi, P., Vanguri, R. et al. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer (2021). https://doi.org/10.1038/s41568-021-00408-3

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