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  • Review Article
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Optical imaging for screening and early cancer diagnosis in low-resource settings

Nature Reviews Bioengineering volume 2, pages 25–43 (2024)Cite this article

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Abstract

Low-cost optical imaging technologies have the potential to reduce inequalities in health care by improving the detection of precancer or early cancer and enabling more effective and less invasive treatment. In this Review, we summarize technologies for in vivo widefield, multi-spectral, endoscopic and high-resolution optical imaging that could offer affordable approaches to improving cancer screening and early detection at the point of care. Additionally, we discuss approaches to slide-free microscopy, including confocal imaging, light-sheet microscopy and phase modulation techniques, that can reduce the infrastructure and expertise needed for definitive cancer diagnosis. We also evaluate how machine learning-based algorithms can improve the accuracy and accessibility of optical imaging systems and provide real-time image analysis. To achieve the potential of optical technologies, developers must ensure that devices are easy to use, the optical technologies must be evaluated in multi-institutional, prospective clinical tests in the intended setting, and the barriers to commercial scale-up in under-resourced markets must be overcome. Therefore, test developers should view the production of simple and effective diagnostic tools that are accessible and affordable for all countries and settings as a central goal of their profession.

Key points

  • Global equity gaps for cancer are growing. Early diagnosis improves patient outcomes, but screening and diagnosis programmes are scarce in low-resource settings, where limitations in infrastructure, human resources, financial resources and/or social resources limit the ability to deliver health care.

  • Advances in consumer-grade imaging tools (such as light-emitting diodes, digital cameras and plastic lenses) have enabled the use of high-performance, low-cost, portable optical imaging systems to visualize cellular, vascular and architectural hallmarks of precancers and early cancers.

  • In vivo optical imaging can improve early detection of cervical, oral, oesophageal, anal and other epithelial cancers, but large studies with commercially available, low-cost devices are needed.

  • To facilitate the adoption of optical imaging techniques in understaffed low-resource settings, technologies to improve cancer screening and early diagnosis must be simple to operate and easy to maintain; therefore, technology developers should emphasize usability throughout the design process.

  • High-quality, slide-free histology can be achieved with low-cost microscopes to improve diagnosis and guide treatments, but large-scale validation of these techniques with standardized staining protocols and commercially available systems is needed.

  • Machine learning can improve imaging performance and reduce the need for human resources by automating image interpretation; however, large, curated image databases from relevant populations are needed for the development and validation of portable algorithms.

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Fig. 1: Widefield and high-resolution imaging systems to improve early detection of precancerous epithelial lesions.
Fig. 2: Microscopy approaches to enable rapid, low-cost, slide-free histology.

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References

  1. Wild, C. P., Weiderpass, E. & Stewart, B. W. (eds) World Cancer Report: Cancer Research for Cancer Prevention (International Agency for Research on Cancer, 2020).

  2. American Association for Cancer Research. Cancer Disparities Progress Report (AACR, 2022).

  3. Pramesh, C. S. et al. Priorities for cancer research in low- and middle-income countries: a global perspective. Nat. Med. 28, 649–657 (2022).

    Article  Google Scholar 

  4. Mitchell, E. et al. Cancer healthcare disparities among African Americans in the United States. J. Natl Med. Assoc. 114, 236–250 (2022).

    Google Scholar 

  5. van Zyl, C., Badenhorst, M., Hanekom, S. & Heine, M. Unravelling ‘low-resource settings’: a systematic scoping review with qualitative content analysis. BMJ Glob. Health 6, e005190 (2021).

    Article  Google Scholar 

  6. World Health Organization. Saving Lives, Spending Less: A Strategic Response to Noncommunicable Diseases (WHO, 2018).

  7. World Health Organization. Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2030 https://iris.who.int/bitstream/handle/10665/94384/9789241506236_eng.pdf;jsessionid=499437100E28C25D028AD5B112AFBF92?sequence=1 (2013).

  8. Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 72, 7–33 (2022).

    Article  Google Scholar 

  9. Ryan, B. M. & Faupel-Badger, J. M. The hallmarks of premalignant conditions: a molecular basis for cancer prevention. Semin. Oncol. 43, 22–35 (2016).

    Article  Google Scholar 

  10. Arens, C., Betz, C., Kraft, M. & Voigt-Zimmermann, S. Narrow band imaging for early diagnosis of epithelial dysplasia and microinvasive tumors in the upper aerodigestive tract. HNO 65, 5–12 (2017).

    Article  Google Scholar 

  11. Rogalla, S. & Contag, C. H. Early cancer detection at the epithelial surface. Cancer J. 21, 179–187 (2015).

    Article  Google Scholar 

  12. Kundrod, K. A. et al. Advances in technologies for cervical cancer detection in low-resource settings. Expert Rev. Mol. Diagn. 19, 695–714 (2019).

    Article  Google Scholar 

  13. Tian, F., Hu, J. & Yang, W. GEOMScope: large field‐of‐view 3D lensless microscopy with low computational complexity. Laser Photon Rev. 15, 2100072 (2021).

    Article  Google Scholar 

  14. Lim, S. et al. Transnasal endoscopy: moving from endoscopy to the clinical outpatient–blue sky thinking in oesophageal testing. Frontline Gastroenterol. 13, e65–e71 (2022).

    Article  Google Scholar 

  15. Birur, N. P. et al. Field validation of deep learning based point-of-care device for early detection of oral malignant and potentially malignant disorders. Sci. Rep. 12, 14283 (2022).

    Article  Google Scholar 

  16. Bhowmik, A. et al. Portable, handheld, and affordable blood perfusion imager for screening of subsurface cancer in resource-limited settings. Proc. Natl Acad. Sci. USA 119, e2026201119 (2022).

    Article  Google Scholar 

  17. Liu, Y., Rollins, A. M., Levenson, R. M., Fereidouni, F. & Jenkins, M. W. Pocket MUSE: an affordable, versatile and high-performance fluorescence microscope using a smartphone. Commun. Biol. 4, 334 (2021).

    Article  Google Scholar 

  18. Glaser, A. K. et al. Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens. Nat. Biomed. Eng. 1, 0084 (2017).

    Article  Google Scholar 

  19. Perrin, L., Bayarmagnai, B. & Gligorijevic, B. Frontiers in intravital multiphoton microscopy of cancer. Cancer Rep. 3, e1192 (2020).

    Article  Google Scholar 

  20. Kim, D. H., Kim, S. W. & Hwang, S. H. Autofluorescence imaging to identify oral malignant or premalignant lesions: systematic review and meta‐analysis. Head Neck 42, 3735–3743 (2020).

    Article  Google Scholar 

  21. Jin, L. et al. Deep learning extended depth-of-field microscope for fast and slide-free histology. Proc. Natl Acad. Sci. USA 117, 33051–33060 (2020).

    Article  Google Scholar 

  22. Brenes, D. et al. Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer. Comput. Med. Imaging Graph. 97, 102052 (2022).

    Article  Google Scholar 

  23. Mueller, J. et al. Portable Pocket colposcopy performs comparably to standard-of-care clinical colposcopy using acetic acid and Lugol’s iodine as contrast mediators: an investigational study in Peru. BJOG 125, 1321–1329 (2018).

    Article  Google Scholar 

  24. Kelly, H. et al. Diagnostic accuracy of cervical cancer screening strategies for high-grade cervical intraepithelial neoplasia (CIN2+/CIN3+) among women living with HIV: a systematic review and meta-analysis. EClinicalMedicine 53, 101645 (2022).

    Article  Google Scholar 

  25. Habinshuti, P. et al. Factors associated with loss to follow-up among cervical cancer patients in Rwanda. Ann. Glob. Health 86, 117 (2020).

    Article  Google Scholar 

  26. Mumba, J. M. et al. Cervical cancer diagnosis and treatment delays in the developing world: evidence from a hospital-based study in Zambia. Gynecol. Oncol. Rep. 37, 100784 (2021).

    Article  Google Scholar 

  27. Warnakulasuriya, S. & Kerr, A. R. Oral cancer screening: past, present, and future. J. Dent. Res. 100, 1313–1320 (2021).

    Article  Google Scholar 

  28. Reich, O. & Pickel, H. 200 years of diagnosis and treatment of cervical precancer. Eur. J. Obstet. Gynecol. Reprod. Biol. 255, 165–171 (2020).

    Article  Google Scholar 

  29. Wagner, A. et al. Systematic review on optical diagnosis of early gastrointestinal neoplasia. J. Clin. Med. 10, 2794 (2021).

    Article  Google Scholar 

  30. Akarsu, M. & Akarsu, C. Evaluation of new technologies in gastrointestinal endoscopy. JSLS 22, e2017 (2018).

    Article  Google Scholar 

  31. Bhat, Y. M. et al. High-definition and high-magnification endoscopes. Gastrointest. Endosc. 80, 919–927 (2014).

    Article  Google Scholar 

  32. Prendiville, W. & Sankaranarayanan, R. Colposcopy and Treatment of Cervical Precancer (International Agency for Research on Cancer, 2017).

  33. Cherry, K. D. et al. Autofluorescence imaging to monitor the progression of oral potentially malignant disorders. Cancer Prev. Res. 12, 791–800 (2019).

    Article  Google Scholar 

  34. Yang, E. C. et al. Noninvasive diagnostic adjuncts for the evaluation of potentially premalignant oral epithelial lesions: current limitations and future directions. Oral. Surg. Oral Med. Oral Pathol. Oral Radiol. 125, 670–681 (2018).

    Article  Google Scholar 

  35. Mazur, M. et al. In vivo imaging-based techniques for early diagnosis of oral potentially malignant disorders — systematic review and meta-analysis. Int. J. Env. Res. Public Health 18, 11775 (2021).

    Article  Google Scholar 

  36. Mendonca, P. et al. Non-invasive imaging of oral potentially malignant and malignant lesions: a systematic review and meta-analysis. Oral. Oncol. 130, 105877 (2022).

    Article  Google Scholar 

  37. Parra, S. G. et al. Low-cost, high-resolution imaging for detecting cervical precancer in medically-underserved areas of Texas. Gynecol. Oncol. 154, 558–564 (2019).

    Article  Google Scholar 

  38. Lin, L. & Wang, L. V. The emerging role of photoacoustic imaging in clinical oncology. Nat. Rev. Clin. Oncol. 19, 365–384 (2022).

    Article  Google Scholar 

  39. Yang, L. et al. Research progress on the application of optical coherence tomography in the field of oncology. Front. Oncol. 12, 953934 (2022).

    Article  Google Scholar 

  40. Ilie, M. et al. Current and future applications of confocal laser scanning microscopy imaging in skin oncology (Review). Oncol. Lett. 17, 4102–4111 (2019).

    Google Scholar 

  41. Glover, B., Teare, J. & Patel, N. The status of advanced imaging techniques for optical biopsy of colonic polyps. Clin. Transl. Gastroenterol. 11, e00130 (2020).

    Article  Google Scholar 

  42. Villard, A. et al. Confocal laser endomicroscopy and confocal microscopy for head and neck cancer imaging: recent updates and future perspectives. Oral. Oncol. 127, 105826 (2022).

    Article  Google Scholar 

  43. Ramani, R. S. et al. Confocal microscopy in oral cancer and oral potentially malignant disorders: a systematic review. Oral. Dis. https://doi.org/10.1111/odi.14291 (2022).

    Article  Google Scholar 

  44. Ring, H. C., Israelsen, N. M., Bang, O., Haedersdal, M. & Mogensen, M. Potential of contrast agents to enhance in vivo confocal microscopy and optical coherence tomography in dermatology: a review. J. Biophotonics 12, e201800462 (2019).

    Article  Google Scholar 

  45. Belykh, E. et al. Molecular imaging of glucose metabolism for intraoperative fluorescence guidance during glioma surgery. Mol. Imaging Biol. 23, 586–596 (2021).

    Article  Google Scholar 

  46. Obeidy, P., Tong, P. L. & Weninger, W. Research techniques made simple: two-photon intravital imaging of the skin. J. Invest. Dermatol. 138, 720–725 (2018).

    Article  Google Scholar 

  47. Steinberg, I. et al. Photoacoustic clinical imaging. Photoacoustics 14, 77–98 (2019).

    Article  Google Scholar 

  48. Wilson, M. L. et al. Access to pathology and laboratory medicine services: a crucial gap. Lancet 391, 1927–1938 (2018).

    Article  Google Scholar 

  49. Hu, L. et al. An observational study of deep learning and automated evaluation of cervical images for cancer screening. J. Natl Cancer Inst. 111, 923–932 (2019).

    Article  Google Scholar 

  50. World Health Organization. WHO Cervical Cancer Elimination Initiative: From Call to Action to Global Movement (WHO, 2023).

  51. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  Google Scholar 

  52. Herrick, T. et al. Acting on the call for cervical cancer elimination: planning tools for low- and middle- income countries to increase the coverage and effectiveness of screening and treatment. BMC Health Serv. Res. 22, 1246 (2022).

    Article  Google Scholar 

  53. Effah, K. et al. A revolution in cervical cancer prevention in Ghana. Ecancermedicalscience 16, ed123 (2022).

    Article  Google Scholar 

  54. Ribeiro, A. et al. Rethinking cervical cancer screening in Brazil post COVID-19: a global opportunity to adopt higher impact strategies. Cancer Prev. Res. 14, 919–926 (2021).

    Article  Google Scholar 

  55. Olubodun, T. et al. Barriers and recommendations for a cervical cancer screening program among women in low-resource settings in Lagos Nigeria: a qualitative study. BMC Public Health 22, 1906 (2022).

    Article  Google Scholar 

  56. Perkins, R. B. et al. 2019 ASCCP risk-based management consensus guidelines for abnormal cervical cancer screening tests and cancer precursors. J. Low. Genit. Tract. Dis. 24, 102–131 (2020).

    Article  Google Scholar 

  57. Vidhubala, E. et al. Loss to follow-up after initial screening for cervical cancer: a qualitative exploration of barriers in Southern India. Cancer Res. Stats. Treat. 3, 700 (2020).

    Article  Google Scholar 

  58. Khozaim, K. et al. Successes and challenges of establishing a cervical cancer screening and treatment program in western Kenya. Int. J. Gynecol. Obstet. 124, 12–18 (2014).

    Article  Google Scholar 

  59. World Health Organization. WHO Guideline for Screening and Treatment of Cervical Pre-Cancer Lesions for Cervical Cancer Prevention 2nd Edition https://www.who.int/publications/i/item/9789240030824 (2021).

  60. Bogdanova, A., Andrawos, C. & Constantinou, C. Cervical cancer, geographical inequalities, prevention and barriers in resource depleted countries (Review). Oncol. Lett. 23, 113 (2022).

    Article  Google Scholar 

  61. Søfteland, S. et al. A systematic review of handheld tools in lieu of colposcopy for cervical neoplasia and female genital schistosomiasis. Int. J. Gynecol. Obstet. 153, 190–199 (2021).

    Article  Google Scholar 

  62. Peterson, C., Rose, D., Mink, J. & Levitz, D. Real-time monitoring and evaluation of a visual-based cervical cancer screening program using a decision support job aid. Diagnostics 6, 20 (2016).

    Article  Google Scholar 

  63. Goldstein, A. et al. Assessing the feasibility of a rapid, high-volume cervical cancer screening programme using HPV self-sampling and digital colposcopy in rural regions of Yunnan, China. BMJ Open 10, e035153 (2020).

    Article  Google Scholar 

  64. Gallay, C. et al. Cervical cancer screening in low-resource settings: a smartphone image application as an alternative to colposcopy. Int. J. Womens Health 9, 455–461 (2017).

    Article  Google Scholar 

  65. Kudva, V., Prasad, K. & Guruvare, S. Andriod device-based cervical cancer screening for resource-poor settings. J. Digit. Imaging 31, 646–654 (2018).

    Article  Google Scholar 

  66. Mueller, J. L. et al. International image concordance study to compare a point-of-care tampon colposcope with a standard-of-care colposcope. J. Low. Genit. Tract. Dis. 21, 112–119 (2017).

    Article  Google Scholar 

  67. Dayal, U. et al. Comparison of the AV Magnivisualizer device with colposcopy to detect cervical intraepithelial neoplasia using the Swede scoring system. Int. J. Gynecol. Obstet. 147, 219–224 (2019).

    Article  Google Scholar 

  68. Kallner, H. K. et al. Diagnostic colposcopic accuracy by the gynocular and a stationary colposcope. Int. J. Technol. Assess. Health Care 31, 181–187 (2015).

    Article  Google Scholar 

  69. Nessa, A. et al. Evaluation of the accuracy in detecting cervical lesions by nurses versus doctors using a stationary colposcope and Gynocular in a low-resource setting. BMJ Open. 4, e005313 (2014).

    Article  Google Scholar 

  70. Tanaka, Y. et al. Histologic correlation between smartphone and coloposcopic findings in patients with abnormal cervical cytology: experiences in a tertiary referral hospital. Am. J. Obstet. Gynecol. 221, 241.e1–241.e6 (2019).

    Article  Google Scholar 

  71. Tran, P. L. et al. Performance of smartphone-based digital images for cervical cancer screening in a low-resource context. Int. J. Technol. Assess. Health Care 34, 337–342 (2018).

    Article  Google Scholar 

  72. Asgary, R. et al. Evaluating smartphone strategies for reliability, reproducibility, and quality of VIA for cervical cancer screening in the Shiselweni region of Eswatini: a cohort study. PLoS Med. 17, e1003378 (2020).

    Article  Google Scholar 

  73. Mink, J. & Peterson, C. MobileODT: a case study of a novel approach to an mHealth-based model of sustainable impact. Mhealth 2, 12 (2016).

    Article  Google Scholar 

  74. Lam, C. T. et al. Design of a novel low cost point of care tampon (POCkeT) colposcope for use in resource limited settings. PLoS One 10, e0135869 (2015).

    Article  Google Scholar 

  75. Hariprasad, R. & Mehrotra, R. Pocket colposcope: could it improve attendance and increase access to cervical cancer screening programmes? Expert Rev. Anticancer. Ther. 18, 603–605 (2018).

    Article  Google Scholar 

  76. Habtemariam, L. W., Zewde, E. T. & Simegn, G. L. Cervix type and cervical cancer classification system using deep learning techniques. Med. Devices 15, 163–176 (2022).

    Article  Google Scholar 

  77. Guo, P. et al. Ensemble deep learning for cervix image selection toward improving reliability in automated cervical precancer screening. Diagnostics 10, 451 (2020).

    Article  Google Scholar 

  78. Desai, K. T. et al. The development of “automated visual evaluation” for cervical cancer screening: the promise and challenges in adapting deep‐learning for clinical testing. Int. J. Cancer 150, 741–752 (2022).

    Article  Google Scholar 

  79. Pal, A. et al. Deep metric learning for cervical image classification. IEEE Access. 9, 53266–53275 (2021).

    Article  Google Scholar 

  80. Xue, Z. et al. A demonstration of automated visual evaluation of cervical images taken with a smartphone camera. Int. J. Cancer 147, 2416–2423 (2020).

    Article  Google Scholar 

  81. Ahmed, S. R. et al. Reproducible and clinically translatable deep neural networks for cervical screening. Preprint at medRxiv https://doi.org/10.1101/2022.12.17.22282984 (2022).

  82. Xue, Z. et al. A deep clustering method for analyzing uterine cervix images across imaging devices. In Proc. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) 527–532 (IEEE, 2021).

  83. Grant, B. D. et al. A mobile-phone based high-resolution microendoscope to image cervical precancer. PLoS One 14, e0211045 (2019).

    Article  Google Scholar 

  84. Parra, S. et al. Development of a single-board computer high-resolution microendoscope (PiHRME) to detect cervical cancer in low-resource settings. J. Glob. Oncol. 2, 7s–7s (2016).

    Article  Google Scholar 

  85. Quang, T. et al. A tablet-interfaced high-resolution microendoscope with automated image interpretation for real-time evaluation of esophageal squamous cell neoplasia. Gastrointest. Endosc. 84, 834–841 (2016).

    Article  Google Scholar 

  86. Hunt, B. et al. Diagnosing cervical neoplasia in rural Brazil using a mobile van equipped with in vivo microscopy: a cluster-randomized community trial. Cancer Prev. Res. 11, 359–370 (2018).

    Article  Google Scholar 

  87. Pantano, N. et al. Is proflavine exposure associated with disease progression in women with cervical dysplasia? A brief report. Photochem. Photobiol. 94, 1308–1313 (2018).

    Article  Google Scholar 

  88. Hunt, B. et al. Cervical lesion assessment using real‐time microendoscopy image analysis in Brazil: the CLARA study. Int. J. Cancer 149, 431–441 (2021).

    Article  Google Scholar 

  89. Sheikhzadeh, F. et al. Quantification of confocal fluorescence microscopy for the detection of cervical intraepithelial neoplasia. Biomed. Eng. Online 14, 96 (2015).

    Article  Google Scholar 

  90. Tang, Y. et al. In vivo imaging of cervical precancer using a low-cost and easy-to-use confocal microendoscope. Biomed. Opt. Express. 11, 269–280 (2020).

    Article  Google Scholar 

  91. Zeng, X. et al. Ultrahigh-resolution optical coherence microscopy accurately classifies precancerous and cancerous human cervix free of labeling. Theranostics 8, 3099–3110 (2018).

    Article  Google Scholar 

  92. Ma, Y. et al. Computer-aided diagnosis of label-free 3-D optical coherence microscopy images of human cervical tissue. IEEE Trans. Biomed. Eng. 66, 2447–2456 (2019).

    Article  Google Scholar 

  93. Pouli, D. et al. Label-free, high-resolution optical metabolic imaging of human cervical precancers reveals potential for intraepithelial neoplasia diagnosis. Cell Rep. Med. 1, 100017 (2020).

    Article  Google Scholar 

  94. Gallwas, J. et al. Detection of cervical intraepithelial neoplasia by using optical coherence tomography in combination with microscopy. J. Biomed. Opt. 22, 016013 (2017).

    Article  Google Scholar 

  95. Coole, J. B. et al. Development of a multimodal mobile colposcope for real-time cervical cancer detection. Biomed. Opt. Express 13, 5116 (2022).

    Article  Google Scholar 

  96. Motlokwa, P. K. et al. Disparities in oral cancer stage at presentation in a high HIV prevalence setting in sub-Saharan Africa. JCO Glob. Oncol. 8, e2100439 (2022).

    Article  Google Scholar 

  97. Stanford-Moore, G. et al. Interaction between known risk factors for head and neck cancer and socioeconomic status: the Carolina Head and Neck Cancer Study. Cancer Causes Control. 29, 863–873 (2018).

    Article  Google Scholar 

  98. Gupta, A., Sonis, S., Uppaluri, R., Bergmark, R. W. & Villa, A. Disparities in oral cancer screening among dental professionals: NHANES 2011–2016. Am. J. Prev. Med. 57, 447–457 (2019).

    Article  Google Scholar 

  99. Shabani, S., Turner, K., Nichols, A. C., Wang, X. & Patel, K. B. A review of health care disparities in head and neck squamous cell carcinomas. J. Health Care Poor Underserved 33, 478–491 (2022).

    Article  Google Scholar 

  100. Birur, N. P. et al. Role of community health worker in a mobile health program for early detection of oral cancer. Indian J. Cancer 56, 107 (2019).

    Article  Google Scholar 

  101. Basu, P. et al. A pilot study to evaluate home-based screening for the common non-communicable diseases by a dedicated cadre of community health workers in a rural setting in India. BMC Public Health 19, 14 (2019).

    Article  Google Scholar 

  102. Sankaranarayanan, R. et al. Early findings from a community-based, cluster-randomized, controlled oral cancer screening trial in Kerala, India. The Trivandrum Oral Cancer Screening Study Group. Cancer 88, 664–673 (2000).

    Article  Google Scholar 

  103. Sankaranarayanan, R. et al. Long term effect of visual screening on oral cancer incidence and mortality in a randomized trial in Kerala, India. Oral. Oncol. 49, 314–321 (2013).

    Article  Google Scholar 

  104. Cheung, L. C. et al. Risk-based selection of individuals for oral cancer screening. J. Clin. Oncol. 39, 663–674 (2021).

    Article  Google Scholar 

  105. Uthoff, R. D. et al. Point-of-care, smartphone-based, dual-modality, dual-view, oral cancer screening device with neural network classification for low-resource communities. PLoS One 13, e0207493 (2018).

    Article  Google Scholar 

  106. Maher, N. G. et al. In vivo confocal microscopy for the oral cavity: current state of the field and future potential. Oral. Oncol. 54, 28–35 (2016).

    Article  Google Scholar 

  107. James, B. L. et al. Validation of a point-of-care optical coherence tomography device with machine learning algorithm for detection of oral potentially malignant and malignant lesions. Cancers 13, 3583 (2021).

    Article  Google Scholar 

  108. Simonato, L. E., Tomo, S., Scarparo Navarro, R. & Balbin Villaverde, A. G. J. Fluorescence visualization improves the detection of oral, potentially malignant, disorders in population screening. Photodiagnosis Photodyn. Ther. 27, 74–78 (2019).

    Article  Google Scholar 

  109. Chiang, T.-E. et al. Comparative evaluation of autofluorescence imaging and histopathological investigation for oral potentially malignant disorders in Taiwan. Clin. Oral. Investig. 23, 2395–2402 (2019).

    Article  Google Scholar 

  110. Lima, I. F. P., Brand, L. M., de Figueiredo, J. A. P., Steier, L. & Lamers, M. L. Use of autofluorescence and fluorescent probes as a potential diagnostic tool for oral cancer: a systematic review. Photodiagnosis Photodyn. Ther. 33, 102073 (2021).

    Article  Google Scholar 

  111. Cicciù, M. et al. Early diagnosis on oral and potentially oral malignant lesions: a systematic review on the VELscope® fluorescence method. Dent. J. 7, 93 (2019).

    Article  Google Scholar 

  112. Moffa, A. et al. Accuracy of autofluorescence and chemiluminescence in the diagnosis of oral dysplasia and carcinoma: a systematic review and meta-analysis. Oral. Oncol. 121, 105482 (2021).

    Article  Google Scholar 

  113. Tiwari, L., Kujan, O. & Farah, C. S. Optical fluorescence imaging in oral cancer and potentially malignant disorders: a systematic review. Oral. Dis. 26, 491–510 (2020).

    Article  Google Scholar 

  114. Pierce, M. C. et al. Accuracy of in vivo multimodal optical imaging for detection of oral neoplasia. Cancer Prev. Res. 5, 801–809 (2012).

    Article  Google Scholar 

  115. Colón-López, V. et al. Anal cancer risk among people with HIV infection in the United States. J. Clin. Oncol. 36, 68 (2018).

    Article  Google Scholar 

  116. de Martel, C., Plummer, M., Vignat, J. & Franceschi, S. Worldwide burden of cancer attributable to HPV by site, country and HPV type. Int. J. Cancer 141, 664–670 (2017).

    Article  Google Scholar 

  117. Palefsky, J. M. et al. Treatment of anal high-grade squamous intraepithelial lesions to prevent anal cancer. N. Engl. J. Med. 386, 2273–2282 (2022).

    Article  Google Scholar 

  118. Albuquerque, A., Rios, E. & Schmitt, F. Recommendations favoring anal cytology as a method for anal cancer screening: a systematic review. Cancers 11, 1942 (2019).

    Article  Google Scholar 

  119. Clarke, M. A. & Wentzensen, N. Strategies for screening and early detection of anal cancers: a narrative and systematic review and meta-analysis of cytology, HPV testing, and other biomarkers. Cancer Cytopathol. 126, 447–460 (2018).

    Article  Google Scholar 

  120. Richel, O., Prins, J. M. & de Vries, H. J. C. Screening for anal cancer precursors: what is the learning curve for high-resolution anoscopy? AIDS 28, 1376–1377 (2014).

    Article  Google Scholar 

  121. Silvera, R. et al. The other side of screening: predictors of treatment and follow-up for anal precancers in a large health system. AIDS 35, 2157–2162 (2021).

    Article  Google Scholar 

  122. Han, C., Huangfu, J., Lai, L. L. & Yang, C. A wide field-of-view scanning endoscope for whole anal canal imaging. Biomed. Opt. Express 6, 607 (2015).

    Article  Google Scholar 

  123. Lai, L. L. et al. Feasibility and safety study of a high resolution wide field-of-view scanning endoscope for circumferential intraluminal intestinal imaging. Sci. Rep. 11, 3544 (2021).

    Article  Google Scholar 

  124. Brenes, D. et al. Automated in vivo high-resolution imaging to detect hpv-associated anal precancer in persons living with HIV. Clin. Transl. Gastroenterol. 14, e00558 (2022).

    Article  Google Scholar 

  125. Ferlay, J. et al. Cancer statistics for the year 2020: an overview. Int. J. Cancer 149, 778–789 (2021).

    Article  Google Scholar 

  126. Săftoiu, A. et al. Role of gastrointestinal endoscopy in the screening of digestive tract cancers in Europe: European Society of Gastrointestinal Endoscopy (ESGE) position statement. Endoscopy 52, 293–304 (2020).

    Article  Google Scholar 

  127. Zhu, H. et al. Esophageal cancer in China: practice and research in the new era. Int. J. Cancer 152, 1741–1751 (2022).

    Article  Google Scholar 

  128. Waljee, A. K. et al. Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa. Gut 71, 1259–1265 (2022).

    Article  Google Scholar 

  129. Moon, Y. et al. Cost-effective smartphone-based articulable endoscope systems for developing countries: instrument validation study. JMIR Mhealth Uhealth 8, e17057 (2020).

    Article  Google Scholar 

  130. Mwachiro, M. et al. Gastrointestinal endoscopy capacity in Eastern Africa. Endosc. Int. Open 09, E1827–E1836 (2021).

    Article  Google Scholar 

  131. Grant, R. K., Brindle, W. M., Robertson, A. R., Kalla, R. & Plevris, J. N. Unsedated transnasal endoscopy: a safe, well-tolerated and accurate alternative to standard diagnostic peroral endoscopy. Dig. Dis. Sci. 67, 1937–1947 (2022).

    Article  Google Scholar 

  132. Sharma, G. et al. Smartphone‐based multimodal tethered capsule endoscopic platform for white‐light, narrow‐band, and fluorescence/autofluorescence imaging. J. Biophotonics 14, e202000324 (2021).

    Article  Google Scholar 

  133. Kim, Y. et al. A portable smartphone-based laryngoscope system for high-speed vocal cord imaging of patients with throat disorders: instrument validation study. JMIR Mhealth Uhealth 9, e25816 (2021).

    Article  Google Scholar 

  134. Ozcan, A. & McLeod, E. Lensless imaging and sensing. Annu. Rev. Biomed. Eng. 18, 77–102 (2016).

    Article  Google Scholar 

  135. Shin, J. et al. A minimally invasive lens-free computational microendoscope. Sci. Adv. 5, eaaw5595 (2019).

    Article  Google Scholar 

  136. Fleming, K. A. et al. An essential pathology package for low- and middle-income countries. Am. J. Clin. Pathol. 147, 15–32 (2016).

    Google Scholar 

  137. Reiche, M. A. et al. Imaging Africa: a strategic approach to optical microscopy training in Africa. Nat. Methods 18, 847–855 (2021).

    Article  Google Scholar 

  138. Junaid, M. et al. Toluidine blue: yet another low cost method for screening oral cavity tumour margins in third world countries. J. Pak. Med. Assoc. 63, 835–837 (2013).

    Google Scholar 

  139. Costa, C. et al. Use of a low-cost telecytopathology method for remote assessment of thyroid FNAs. Cancer Cytopathol. 126, 767–772 (2018).

    Article  Google Scholar 

  140. Jiang, P. et al. Development of automatic portable pathology scanner and its evaluation for clinical practice. J. Digit. Imaging 36, 1110–1122 (2023).

    Article  Google Scholar 

  141. Coulibaly, J. T. et al. High sensitivity of mobile phone microscopy screening for schistosoma haematobium in Azaguié, Côte d’Ivoire. Am. J. Trop. Med. Hyg. 108, 41–43 (2023).

    Article  Google Scholar 

  142. Xu, K. et al. A novel digital algorithm for identifying liver steatosis using smartphone-captured images. Transpl. Direct 8, e1361 (2022).

    Article  Google Scholar 

  143. Cheng, S. et al. Robust whole slide image analysis for cervical cancer screening using deep learning. Nat. Commun. 12, 5639 (2021).

    Article  Google Scholar 

  144. Sornapudi, S. et al. DeepCIN: attention-based cervical histology image classification with sequential feature modeling for pathologist-level accuracy. J. Pathol. Inf. 11, 40 (2020).

    Article  Google Scholar 

  145. Liu, Y., Levenson, R. M. & Jenkins, M. W. Slide over: advances in slide-free optical microscopy as drivers of diagnostic pathology. Am. J. Pathol. 192, 180–194 (2022).

    Article  Google Scholar 

  146. Fereidouni, F. et al. Microscopy with ultraviolet surface excitation for rapid slide-free histology. Nat. Biomed. Eng. 1, 957–966 (2017).

    Article  Google Scholar 

  147. Qorbani, A. et al. Microscopy with ultraviolet surface excitation (MUSE): a novel approach to real-time inexpensive slide-free dermatopathology. J. Cutan. Pathol. 45, 498–503 (2018).

    Article  Google Scholar 

  148. Zhu, W. et al. Smartphone epifluorescence microscopy for cellular imaging of fresh tissue in low-resource settings. Biomed. Opt. Express 11, 89 (2020).

    Article  Google Scholar 

  149. Reder, N. P. et al. Open-top light-sheet microscopy image atlas of prostate core needle biopsies. Arch. Pathol. Lab. Med. 143, 1069–1075 (2019).

    Article  Google Scholar 

  150. Chen, Y. et al. Rapid pathology of lumpectomy margins with open-top light-sheet (OTLS) microscopy. Biomed. Opt. Express 10, 1257 (2019).

    Article  Google Scholar 

  151. Xie, W. et al. Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy. J. Biomed. Opt. 25, 126502 (2020).

    Article  Google Scholar 

  152. Barner, L. A., Glaser, A. K., Huang, H., True, L. D. & Liu, J. T. C. Multi-resolution open-top light-sheet microscopy to enable efficient 3D pathology workflows. Biomed. Opt. Express 11, 6605 (2020).

    Article  Google Scholar 

  153. Pitrone, P. G. et al. OpenSPIM: an open-access light-sheet microscopy platform. Nat. Methods 10, 598–599 (2013).

    Article  Google Scholar 

  154. Hedde, P. N. miniSPIM — a miniaturized light-sheet microscope. ACS Sens. 6, 2654–2663 (2021).

    Article  Google Scholar 

  155. Schiffhauer, L. M. et al. Confocal microscopy of unfixed breast needle core biopsies: a comparison to fixed and stained sections. BMC Cancer 9, 265 (2009).

    Article  Google Scholar 

  156. Torres, R. et al. Initial evaluation of rapid, direct-to-digital prostate biopsy pathology. Arch. Pathol. Lab. Med. 145, 583–591 (2021).

    Article  Google Scholar 

  157. Liang, C. et al. A highly potent ruthenium(II)-sonosensitizer and sonocatalyst for in vivo sonotherapy. Nat. Commun. 12, 5001 (2021).

    Article  Google Scholar 

  158. Mohammadi, S. Phototherapy and sonotherapy of melanoma cancer cells using nanoparticles of selenium-polyethylene glycol-curcumin as a dual-mode sensitizer. J. Biomed. Phys. Eng. 10, 597–606 (2020).

    Article  Google Scholar 

  159. Buzzá, H. H. et al. Overall results for a national program of photodynamic therapy for basal cell carcinoma: a multicenter clinical study to bring new techniques to social health care. Cancer Control https://doi.org/10.1177/1073274819856885 (2019).

  160. Inada, N. M. et al. Long term effectiveness of photodynamic therapy for CIN treatment. Pharmaceuticals 12, 107 (2019).

    Article  Google Scholar 

  161. de Castro, C. A., Lombardi, W., Stringasci, M. D., Bagnato, V. S. & Inada, N. M. High-risk HPV clearance and CIN 3 treated with MAL-PDT: a case report. Photodiagnosis Photodyn. Ther. 31, 101937 (2020).

    Article  Google Scholar 

  162. Saini, R., Lee, N., Liu, K. & Poh, C. Prospects in the application of photodynamic therapy in oral cancer and premalignant lesions. Cancers 8, 83 (2016).

    Article  Google Scholar 

  163. Unanyan, A. et al. Efficacy of photodynamic therapy in women with HSIL, LSIL and early stage squamous cervical cancer: a systematic review and meta-analysis. Photodiagnosis Photodyn. Ther. 36, 102530 (2021).

    Article  Google Scholar 

  164. Palamountain, K. M. et al. Perspectives on introduction and implementation of new point-of-care diagnostic tests. J. Infect. Dis. 205, S181–S190 (2012).

    Article  Google Scholar 

  165. Mugambi, M. L., Peter, T., F Martins, S. & Giachetti, C. How to implement new diagnostic products in low-resource settings: an end-to-end framework. BMJ Glob. Health 3, e000914 (2018).

    Article  Google Scholar 

  166. Euliano, E. M., Sklavounos, A. A., Wheeler, A. R. & McHugh, K. J. Translating diagnostics and drug delivery technologies to low-resource settings. Sci. Transl. Med. 14, eabm1732 (2022).

    Article  Google Scholar 

  167. Cocco, P., Ayaz-Shah, A., Messenger, M. P., West, R. M. & Shinkins, B. Target product profiles for medical tests: a systematic review of current methods. BMC Med. 18, 119 (2020).

    Article  Google Scholar 

  168. Sharma, P. et al. The American Society for Gastrointestinal Endoscopy PIVI (preservation and incorporation of valuable endoscopic innovations) on imaging in Barrett’s esophagus. Gastrointest. Endosc. 76, 252–254 (2012).

    Article  Google Scholar 

  169. Mugambi, M., Palamountain, K., Gallarda, J. & Drain, P. Exploring the case for a global alliance for medical diagnostics initiative. Diagnostics 7, 8 (2017).

    Article  Google Scholar 

  170. Niemeier, D., Gombachika, H. & Richards-Kortum, R. How to transform the practice of engineering to meet global health needs. Science 345, 1287–1290 (2014).

    Article  Google Scholar 

  171. Olympus Corporation. Olympus CF Type Q160ZL/I advanced power zoom. Olympus–Uralendomed http://www.olympus-ural.ru/files/CFQ160ZL_I.pdf (2016).

  172. Li, X., He, S. & Ma, B. Autophagy and autophagy-related proteins in cancer. Mol. Cancer 19, 12 (2020).

    Article  Google Scholar 

  173. Kohli, D. R. & Baillie, J. in Clinical Gastrointestinal Endoscopy 3rd edn (eds Chandrasekhara, V. et al.) Ch. 3, 24–31.e2 (Elsevier, 2019).

  174. World Health Organization. Guide to Cancer Early Diagnosis https://apps.who.int/iris/handle/10665/254500 (2017).

  175. World Health Organization. Tackling NCDs: ‘Best Buys’ and Other Recommended Interventions for the Prevention and Control of Noncommunicable Diseases https://apps.who.int/iris/handle/10665/259232 (2017).

  176. World Health Organization. The Selection and Use of Essential In Vitro Diagnostics: Report of the Third Meeting of the Who Strategic Advisory Group of Experts on In Vitro Diagnostics, 2020 (Including the Third Who Model List of Essential In Vitro Diagnostics) (WHO Technical Report Series, 2021).

  177. Huckle, D. Point-of-care diagnostics: an advancing sector with nontechnical issues. Expert Rev. Mol. Diagnostics 8, 679–688 (2008).

    Article  Google Scholar 

  178. Sinha, S. R. & Barry, M. Health technologies and innovation in the global health arena. N. Engl. J. Med. 365, 779–782 (2011).

    Article  Google Scholar 

  179. de Oliveira, C. M. et al. HPV testing for cervical cancer screening in Mozambique: challenges and recommendations.J. Glob. Health Rep. 6, e2022007 (2022).

    Google Scholar 

  180. Land, K. J., Boeras, D. I., Chen, X.-S., Ramsay, A. R. & Peeling, R. W. REASSURED diagnostics to inform disease control strategies, strengthen health systems and improve patient outcomes. Nat. Microbiol. 4, 46–54 (2019).

    Article  Google Scholar 

  181. Ongaro, A. E. et al. Engineering a sustainable future for point-of-care diagnostics and single-use microfluidic devices. Lab. Chip 22, 3122–3137 (2022).

    Article  Google Scholar 

  182. Landes, S. J., McBain, S. A. & Curran, G. M. An introduction to effectiveness-implementation hybrid designs. Psychiatry Res. 280, 112513 (2019).

    Article  Google Scholar 

  183. Bauer, M. S., Damschroder, L., Hagedorn, H., Smith, J. & Kilbourne, A. M. An introduction to implementation science for the non-specialist. BMC Psychol. 3, 32 (2015).

    Article  Google Scholar 

  184. Verbakel, J. Y. et al. Common evidence gaps in point-of-care diagnostic test evaluation: a review of horizon scan reports. BMJ Open 7, e015760 (2017).

    Article  Google Scholar 

  185. Korte, B. J., Rompalo, A., Manabe, Y. C. & Gaydos, C. A. Overcoming challenges with the adoption of point-of-care testing. Point Care 19, 77–83 (2020).

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the contributions of M. Bond and C. Hicheri with the preparation of figures. This research was supported, in part, by the National Cancer Institute of the National Institutes of Health (NIH) under award number R01CA251911 and through the National Academy of Sciences, United States Agency for International Development (Partnerships for Enhanced Engagement in Research, Cooperative Agreement AID-OAA-A-11-00012).

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Authors and Affiliations

  1. Department of Bioengineering, Rice University, Houston, TX, USA

    Rebecca Richards-Kortum

  2. Institute for Global Health Technologies, Rice University, Houston, TX, USA

    Rebecca Richards-Kortum

  3. National Cancer Control Program, Ministry of Health, Maputo, Mozambique

    Cesaltina Lorenzoni

  4. Department of Pathology, Universidade Eduardo Mondlane (UEM), Maputo, Mozambique

    Cesaltina Lorenzoni

  5. Maputo Central Hospital, Maputo, Mozambique

    Cesaltina Lorenzoni

  6. São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil

    Vanderlei S. Bagnato

  7. Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA

    Vanderlei S. Bagnato

  8. Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA

    Kathleen Schmeler

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Related links

Cervical Precancer Planning Tool: https://www.path.org/resources/cervical-precancer-planning-tool-excel-model/

Global Health Expenditure Database: https://apps.who.int/nha/database/country_profile/Index/en

Invention Education Toolkit: https://ive-toolkit.org

Medical Imaging and Data Resource Center: https://data.midrc.org/

OpenSPIM: http://openspim.org/

Screening tests recommended in the USA: https://www.cancer.gov/about-cancer/screening/screening-tests

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Richards-Kortum, R., Lorenzoni, C., Bagnato, V.S. et al. Optical imaging for screening and early cancer diagnosis in low-resource settings. Nat Rev Bioeng 2, 25–43 (2024). https://doi.org/10.1038/s44222-023-00135-4

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