Early detection is important for cancer control, improving patient survival and reducing unnecessary treatment. Methods for early detection of cancer include screening programs, risk stratification, and rapid diagnostic pathways. Nevertheless, limited diagnostic workforces, variability in the added value of screening efficiency, and delays in referral remain impediments to appropriate early detection, especially in post-pandemic healthcare environments. Machine learning (ML) and artificial intelligence (AI) have recently become transformative approaches that can help overcome these limitations for improved diagnostic accuracy, workflow efficiency, and patient selection for screening programs when applied to complex health data. Deep learning algorithms and convolutional neural networks are among the data-driven models that have yielded promising results in manipulating radiological images, pathology slides, and electronic health records for better risk stratification and early diagnosis. AI applications also help to identify cancer recurrence and automate clinical workflows where there are capacity limitations. Even considering these developments, sustainable research into ethical dilemmas, patient data security, biases in AI training datasets, and regulatory compliance represent key areas for ongoing investigations. This review discusses AI in the context of early cancer detection, its use in screening and diagnosis, and the barriers to broader clinical implementation.
References
[1]
McPhail, S., Johnson, S., Greenberg, D., Peake, M. and Rous, B. (2015) Stage at Diagnosis and Early Mortality from Cancer in England. BritishJournalofCancer, 112, S108-S115. https://doi.org/10.1038/bjc.2015.49
[2]
Blandin Knight, S., Crosbie, P.A., Balata, H., Chudziak, J., Hussell, T. and Dive, C. (2017) Progress and Prospects of Early Detection in Lung Cancer. OpenBiology, 7, Article ID: 170070. https://doi.org/10.1098/rsob.170070
[3]
National Cancer Registration and Analysis Service: Staging Data in England.
[4]
Sasieni, P. (2003) Evaluation of the UK Breast Screening Programmes. AnnalsofOncology, 14, 1206-1208. https://doi.org/10.1093/annonc/mdg325
[5]
Maroni, R., Massat, N.J., Parmar, D., Dibden, A., Cuzick, J., Sasieni, P.D., et al. (2020) A Case-Control Study to Evaluate the Impact of the Breast Screening Programme on Mortality in England. BritishJournalofCancer, 124, 736-743. https://doi.org/10.1038/s41416-020-01163-2
[6]
Esserman, L.J., Anton-Culver, H., Borowsky, A., Brain, S., Cink, T., Crawford, B., et al. (2017) The WISDOM Study: Breaking the Deadlock in the Breast Cancer Screening Debate. npjBreastCancer, 3, Article No. 34. https://doi.org/10.1038/s41523-017-0035-5
[7]
McKinney, S.M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., et al. (2020) International Evaluation of an AI System for Breast Cancer Screening. Nature, 577, 89-94. https://doi.org/10.1038/s41586-019-1799-6
[8]
Kim, H., Kim, H.H., Han, B., Kim, K.H., Han, K., Nam, H., et al. (2020) Changes in Cancer Detection and False-Positive Recall in Mammography Using Artificial Intelligence: A Retrospective, Multireader Study. TheLancetDigitalHealth, 2, e138-e148. https://doi.org/10.1016/s2589-7500(20)30003-0
[9]
Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., et al. (2019) Stand-alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison with 101 Radiologists. JNCI: JournaloftheNationalCancerInstitute, 111, 916-922. https://doi.org/10.1093/jnci/djy222
[10]
Schaffter, T., Buist, D.S.M., Lee, C.I., Nikulin, Y., Ribli, D., Guan, Y., et al. (2020) Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMANetworkOpen, 3, e200265. https://doi.org/10.1001/jamanetworkopen.2020.0265
[11]
Bahl, M., Barzilay, R., Yedidia, A.B., Locascio, N.J., Yu, L. and Lehman, C.D. (2018) High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision. Radiology, 286, 810-818. https://doi.org/10.1148/radiol.2017170549
[12]
Dembrower, K., Wåhlin, E., Liu, Y., Salim, M., Smith, K., Lindholm, P., et al. (2020) Effect of Artificial Intelligence-Based Triaging of Breast Cancer Screening Mammograms on Cancer Detection and Radiologist Workload: A Retrospective Simulation Study. TheLancetDigitalHealth, 2, e468-e474. https://doi.org/10.1016/s2589-7500(20)30185-0
[13]
National Health Service (NHS): NHS Long Term Plan: Cancer. https://www.longtermplan.nhs.uk/areas-of-work/cancer/
[14]
Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher, J.J., Peng, L., et al. (2019) End-to-End Lung Cancer Screening with Three-Dimensional Deep Learning on Low-Dose Chest Computed Tomography. NatureMedicine, 25, 954-961. https://doi.org/10.1038/s41591-019-0447-x
[15]
Baldwin, D.R., Gustafson, J., Pickup, L., Arteta, C., Novotny, P., Declerck, J., et al. (2020) External Validation of a Convolutional Neural Network Artificial Intelligence Tool to Predict Malignancy in Pulmonary Nodules. Thorax, 75, 306-312. https://doi.org/10.1136/thoraxjnl-2019-214104
[16]
Christe, A., Peters, A., Drakopoulos, D., Heverhagen, J.T., Geiger, A., Bartholet, C., Woller, S., Kraemer, A. and Poellinger, A. (2020) Computer-Aided Diagnosis of Pulmonary Nodules in Chest CT: Effect of Nodule Characteristics on Detection Performance. European Radiology, 30, 369-383.
[17]
Farjah, F., Halgrim, S., Buist, D.S.M., Gould, M.K., Zeliadt, S.B., Loggers, E.T., et al. (2016) An Automated Method for Identifying Individuals with a Lung Nodule Can Be Feasibly Implemented across Health Systems. eGEMs (GeneratingEvidence&MethodstoImprovePatientOutcomes), 4, 15. https://doi.org/10.13063/2327-9214.1254
[18]
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., et al. (2017) Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542, 115-118. https://doi.org/10.1038/nature21056
[19]
Nam, J.G., Park, S., Hwang, E.J., Lee, J.H., Jin, K.N., Lim, K.Y., Vu, T.H., Sohn, J.H., Hwang, S., Goo, J.M., et al. (2019) Development and Validation of a Deep Learning Model for Screening Pneumonia on Chest Radiograph. Radiology, 292, 211-220.
[20]
Baldwin, D.R., Breathnach, O., Clifton, J., Chavaditiklu, M., Gleeson, F. and Jenkins, D. (2021) The Impact of Artificial Intelligence on Improving the Evidence-Based Use of Imaging: An Expert Opinion. British Journal of Radiology, 94, Article ID: 20210536.
[21]
Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., et al. (2020) Sex and Gender Differences and Biases in Artificial Intelligence for Biomedicine and Healthcare. npjDigitalMedicine, 3, Article No. 81. https://doi.org/10.1038/s41746-020-0288-5
[22]
Morley, J., Machado, C.C.V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., et al. (2020) The Ethics of AI in Health Care: A Mapping Review. SocialScience&Medicine, 260, Article ID: 113172. https://doi.org/10.1016/j.socscimed.2020.113172
[23]
Hindocha, S. and Badea, C. (2021) Moral Exemplars for the Virtuous Machine: The Clinician’s Role in Ethical Artificial Intelligence for Healthcare. AIandEthics, 2, 167-175. https://doi.org/10.1007/s43681-021-00089-6
[24]
Anderson, M., O’Neill, C., Macleod Clark, J., Street, A., Woods, M., Johnston-Webber, C., et al. (2021) Securing a Sustainable and Fit-for-Purpose UK Health and Care Workforce. TheLancet, 397, 1992-2011. https://doi.org/10.1016/s0140-6736(21)00231-2
[25]
National Health Service (NHS): Digital Transformation of Screening-NHSX. https://www.nhsx.nhs.uk/key-tools-and-info/digital-transformation-of-screening/
[26]
Shaheen, N.J., Falk, G.W., Iyer, P.G. and Gerson, L.B. (2016) ACG Clinical Guideline: Diagnosis and Management of Barrett’s Esophagus. AmericanJournalofGastroenterology, 111, 30-50. https://doi.org/10.1038/ajg.2015.322
[27]
World Health Organization (WHO) (2021) Ethics and Governance of Artificial Intelligence for Health: WHO Guidance. 1-148.
[28]
Moore, C.R., Farrag, A. and Ashkin, E. (2017) Using Natural Language Processing to Extract Abnormal Results from Cancer Screening Reports. JournalofPatientSafety, 13, 138-143. https://doi.org/10.1097/pts.0000000000000127
[29]
Nayor, J., Borges, L.F., Goryachev, S., Gainer, V.S. and Saltzman, J.R. (2018) Natural Language Processing Accurately Calculates Adenoma and Sessile Serrated Polyp Detection Rates. DigestiveDiseasesandSciences, 63, 1794-1800. https://doi.org/10.1007/s10620-018-5078-4
[30]
Glaser, A.P., Jordan, B.J., Cohen, J., Desai, A., Silberman, P. and Meeks, J.J. (2018) Automated Extraction of Grade, Stage, and Quality Information from Transurethral Resection of Bladder Tumor Pathology Reports Using Natural Language Processing. JCOClinicalCancerInformatics, 2, 1-8. https://doi.org/10.1200/cci.17.00128
