Journal of Shandong University (Health Sciences) ›› 2021, Vol. 59 ›› Issue (9): 89-96.doi: 10.6040/j.issn.1671-7554.0.2021.1017

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From the perspective of clinicians: the application and reflection of artificial intelligence in cancer precision diagnosis and treatment

WANG Linlin1, SUN Yuping2   

  1. 1. Department of Radiation Oncology;
    2. Department of Phase I Clinical Trial Center, Shandong Cancer Hospital and Institute, Jinan 250117, Shandong, China
  • Published:2021-10-15

Abstract: The artificial intelligence(AI)has shown increasingly potential powers in the precision medicine of cancer treatment. AI can not only decrease routine workload of doctors in departments of radiology and pathology, but also delve the potential complicate patterns hidden in medical data through quantifying the radiologic and pathological images. In this review, we will first summarize the cutting-edge AI technology and then focus on the influence of deep learning, the most popular AI technology, on cancer radiology. Next, we will introduce the new advance of AI tech applied in the cancer pathology, genetics and immunology. Finally, we will discuss the difficulties of clinical application of AI and try to bring some possible solutions for these difficulties.

Key words: Artificial intelligence, Deep learning, Precision medicine, Radiology, Cancer

CLC Number: 

  • R730.5
[1] Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning [J]. Nature, 2015, 518(7540): 529-533.
[2] Moravcík M, Schmid M, Burch N, et al. DeepStack: expert-level artificial intelligence in heads-up no-limit poker [J]. Science, 2017, 356(6337): 508-513.
[3] Xiong W, Droppo J, Huang X, et al. Achieving human parity in conversational speech recognition [EB/OL].(2017-02-17)[2021-05-10]. https://arxiv.org/abs/1610.05256.
[4] Pendleton SD, Andersen H, Du X, et al. Perception, planning, control, and coordination for autonomous vehicles [J]. Machines, 2017, 5(1): 6.
[5] Silver D, Huang A, Maddison CJ, et al. Mastering the game of Go with deep neural networks and tree search [J]. Nature, 2016, 529(7587): 484-489.
[6] Grace K, Salvatier J, Dafoe A, et al. When will AI exceed human performance? evidence from AI experts [EB/OL].(2018-07-31)[2021-05-10]. https://arxiv.org/abs/1705.08807.
[7] Rusk N. Deep learning [J]. Nature Methods, 2016, 13(1): 35-35.
[8] Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks [J]. Nature, 2017, 542(7639): 115-118.
[9] Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs [J]. JAMA, 2016, 316(22): 2402-2410.
[10] Castellino RA. Computer aided detection(CAD): an overview [J]. Cancer Imaging, 2005, 5: 17-19. doi:10.1102/1470-7330.2005.0018.
[11] Cole EB, Zhang Z, Marques HS, et al. Impact of computer-aided detection systems on radiologist accuracy with digital mammography [J]. AJR Am J Roentgenol, 2014, 203(4): 909-916.
[12] Lehman CD, Wellman RD, Buist DS, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection [J]. JAMA Intern Med, 2015, 175(11): 1828-1837.
[13] Huang X, Shan J, Vaidya V. Lung nodule detection in CT using 3D convolutional neural networks[C] //2017 IEEE 14th International Symposium on Biomedical Imaging(ISBI 2017). Piscataway, NJ: IEEE, 2017: 379-383.
[14] Armato SG, Petrick NA, Tsehay YK, et al. SPIE Proceedings [SPIE SPIE Medical Imaging - Orlando, Florida, United States(Saturday 11 February 2017)] Medical Imaging 2017: Computer-Aided Diagnosis - Convolutional neural network based deep-learning architecture for prostate cancer detection on multi [J]. onAcademic, 2017, 10134: 1013405. doi: 10.1117/12.2254423.
[15] Kooi T, Litjens G, van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions [J]. Med Image Anal, 2017, 35: 303-312. doi:10.1016/j.media.2016.07.007.
[16] Champaign JL, Cederbom GJ. Advances in breast cancer detection with screening mammography [J]. Ochsner J, 2000, 2(1): 33-35.
[17] Shiraishi J, Li Q, Appelbaum D, et al. Computer-aided diagnosis and artificial intelligence in clinical imaging [J]. Semin Nucl Med, 2011, 41(6): 449-462.
[18] Cheng JZ, Ni D, Chou YH, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans [J]. Sci Rep, 2016, 6: 24454. doi:10.1038/srep24454.
[19] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation [J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(4): 640-651. doi:10.1109/TPAMI.2016.2572683.
[20] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation [C]. International Conference on Medical Image Computing and Computer-Assisted Intervention. Switzerland: Springer International Publishing, 2015.
[21] Moeskops P, Wolterink JM, van der Velden BHM, et al. Deep learning for multi-task medical image segmentation in multiple modalities [C]. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham:Springer, 2016: 478-486.
[22] Jaffe CC. Measures of response: RECIST, WHO, and new alternatives [J]. J Clin Oncol, 2006, 24(20): 3245-3251.
[23] Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer [J]. JAMA, 2017, 318(22): 2199-2210.
[24] Mobadersany P, Yousefi S, Amgad M, et al. Content summaries of selected best papers for the 2019 IMIA yearbook, section bioinformatics and translational informatics [J]. Proc Natl Acad Sci USA, 2018, 115(13): E2970-E2979.
[25] Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer [J]. JAMA, 2017, 318(22): 2199-2210.
[26] Zou J, Huss M, Abid A, et al. A primer on deep learning in genomics [J]. Nat Genet, 2019, 51(1): 12-18.
[27] F Stephen Hodi, O'Day SJ, McDermott DF, et al. Improved survival with ipilimumab in patients with metastatic melanoma [J]. N Engl J Med, 2010, 363(8): 711-723.
[28] Garon EB, Rizvi NA, Hui R, et al. Pembrolizumab for the treatment of non-small-cell lung cancer [J]. N Engl J Med, 2015, 372(21): 2018-2028.
[29] Borghaei H, Paz-Ares L, Horn L, et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer [J]. N Engl J Med, 2015, 373(17): 1627-1639.
[30] Ferris RL, Blumenschein G, Fayette J, et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck [J]. N Engl J Med, 2016, 375(19): 1856-1867.
[31] Le DT, Durham JN, Smith KN, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade [J]. Science, 2017, 357(6349): 409-413.
[32] Motzer RJ, Escudier B, McDermott DF, et al. Nivolumab versus everolimus in advanced renal-cell carcinoma [J]. N Engl J Med, 2015, 373(19): 1803-1813.
[33] Brahmer J, Reckamp KL, Baas P, et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer [J]. N Engl J Med, 2015, 373(2): 123-135.
[34] Mehra R, Seiwert TY, Gupta S, et al. Efficacy and safety of pembrolizumab in recurrent/metastatic head and neck squamous cell carcinoma: pooled analyses after long-term follow-up in KEYNOTE-012 [J]. Br J Cancer, 2018, 119(2): 153-159.
[35] Hamanishi J, Mandai M, Ikeda T, et al. Safety and antitumor activity of anti-PD-1 antibody, nivolumab, in patients with platinum-resistant ovarian cancer [J]. J Clin Oncol, 2015, 33(34): 4015-4022.
[36] Larkin J, Chiarion-Sileni V, Gonzalez R, et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma [J]. N Engl J Med, 2015, 373(1): 23-34.
[37] Langer CJ, Gadgeel SM, Borghaei H, et al. Carboplatin and pemetrexed with or without pembrolizumab for advanced, non-squamous non-small-cell lung cancer: a randomised, phase 2 cohort of the open-label KEYNOTE-021 study [J]. Lancet Oncol, 2016, 17(11): 1497-1508.
[38] Topalian SL, Taube JM, Anders RA, et al. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy [J]. Nat Rev Cancer, 2016, 16(5): 275-287.
[39] Sunshine J, Taube JM. PD-1/PD-L1 inhibitors[J]. Curr Opin Pharmacol, 2015, 23: 32-38. doi:10.1016/j.coph.2015.05.011.
[40] Xie F, Zhang J, Wang J, et al. Multifactorial deep learning reveals Pan-cancer genomic tumor clusters with distinct immunogenomic landscape and response to immunotherapy [J]. Clin Cancer Res, 2020, 26(12): 2908-2920.
[41] Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology [J]. Nat Rev Cancer, 2018, 18(8): 500-510.
[42] Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development [J]. Nat Rev Drug Discov, 2019, 18(6): 463-477.
[43] Bera K, Schalper KA, Rimm DL, et al. Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology [J]. Nat Rev Clin Oncol, 2019, 16(11): 703-715.
[44] Nagendran M, Chen Y, Lovejoy CA, et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies [J]. BMJ, 2020, 368: m689. doi:10.1136/bmj.m689.
[45] Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis [J]. Lancet Digital Health, 2019, 1(6): e271-e297.
[46] Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information [J]. Ann Intern Med, 1999, 130(6): 515-524.
[47] Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI [J]. Biostatistics, 2020, 21(2): 345-352.
[48] Zech JR, Badgeley MA, Liu M. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study [J]. PLoS Met, 2018, 15(11): e1002683.
[49] Winkler JK, Fink C, Toberer F, et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition [J]. JAMA dermatology, 2019, 155(10): 1135-1141.
[50] Narla A, Kuprel B, Sarin K, et al. Automated classification of skin lesions: from pixels to practice [J]. J Invest Dermatol, 2018, 138(10): 2108-2110.
[51] Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD): explanation and elaboration [J]. Ann Intern Med, 2015, 162(1): W1-73.
[52] Ioannidis JPA, Khoury MJ. Improving validation practices in “omics” research [J]. Science, 2011, 334(6060): 1230-1232.
[53] Obermeyer Z, Emanuel EJ. Predicting the future- big data, machine learning, and clinical medicine [J]. N Engl J Med, 2016, 375(13): 1216-1219.
[54] Keane PA, Topol EJ. With an eye to AI and autonomous diagnosis [J]. NPJ Digit Med, 2018, 1: 40. doi:10.1038/s41746-018-0048-y.
[55] Kawaguchi K, Kaelbling LP, Bengio Y. Generalization in deep learning[EB/OL].(2020-07-27)[2021-05-12]. https://arxiv.org/abs/1710.05468.
[56] LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436-444.
[57] Pfeifer R, Schreter Z, Fogelman-Soulié F,et al. Connectionism in perspective [M]. Amsterdam: Elsevier, 1989.
[58] Pan SJ, Yang Q. A survey on transfer learning[J]. Ieee T Knowl Data En, 2009, 22(10): 1345-1359.
[59] Weiss K, Khoshgoftaar TM, Wang DD. A survey of transfer learning [J]. J Big Data, 2016, 3(1):1-40.
[60] Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database [C]. IEEE Conf Comput Vis Pattern Recog Piscataway, NJ: Ieee, 2009: 248-255.
[61] Shankar S, Halpern Y, Breck E, et al. No classification without representation: assessing geodiversity issues in open data sets for the developing world [EB/OL].(2017-11-22)[2021-05-13]. https://arxiv.org/abs/1711.08536.
[62] Geirhos R, Rubisch P, Michaelis C, et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness [EB/OL].(2019-01-14)[2021-05-13]. https://arxiv.org/abs/1811.12231.
[63] Sun C, Shrivastava A, Singh S, et al. Revisiting unreasonable effectiveness of data in deep learning era [EB/OL].(2017-08-04)[2021-05-13]. https://arxiv.org/abs/1707.02968.
[64] Simard P, Victorri B, LeCun Y, et al. Tangent prop-a formalism for specifying selected invariances in an adaptive network [C]. NIPS, 1991, 91: 895-903.
[65] de Andrade A. Best practices for convolutional neural networks applied to object recognition in images [EB/OL].(2019-10-29)[2021-05-13]. https://arxiv.org/abs/1910.13029.
[66] Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning [J]. J Big Data, 2019, 6(1): 1-48.
[67] Roth HR, Lu L, Liu J, et al. Improving computer-aided detection using convolutional neural networks and random view aggregation [J]. IEEE Trans Med Imaging, 2016, 35(5): 1170-1181.
[68] Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks [J]. Radiology, 2017, 284(2): 574-582.
[69] Zeshan, Hussain, Francisco, et al. Differential data augmentation techniques for medical imaging classification tasks [J]. Amia Annu Symp Proc, 2018, 2017: 979.
[70] Sajjad M, Khan S, Muhammad K, et al. Multi-grade brain tumor classification using deep CNN with extensive data augmentation [J]. J Comput Sci, 2019, 30: 174-182. doi:10.1016/j.jocs.2018.12.003.
[71] Stacke K, Eilertsen G, Unger J, et al. Measuring domain shift for deep learning in histopathology [J]. IEEE J Biomed Health, 2020, 25(2): 325-336.
[72] ellez D, Litjens G, Bándi P, et al. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology [J]. Med Image Anal, 2019, 58: 101544. doi:10.1016/j.media.2019.101544.
[73] van den Hout WB. The area under an ROC curve with limited information [J]. Med Decis Making, 2003, 23(2): 160-166.
[74] Fawcett T. An introduction to ROC analysis[J]. Pattern recognition letters, 2006, 27(8): 861-874.
[75] Harrell FE, Califf RM, Pryor DB, et al. Evaluating the yield of medical tests [J]. Jama, 1982, 247(18): 2543-2546.
[76] Lobo JM, Jiménez-Valverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models [J]. Global Ecol Biogeogr, 2008, 17(2): 145-151.
[77] Ioannidis JPA. What have we(not)learnt from millions of scientific papers with P values? [J]. Am Stat, 2019, 73(Suppl1): 20-25.
[78] Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy [J]. Lancet, 2005, 365(9458): 488-492.
[79] Miller, R.G. Simultaneous statistical inference [M]. 2nd edition. Cham: Springer, 1981.
[80] Hochberg Y, Tamhane AC. Multiple comparison procedures [M]. New Jersey: John Wiley & Sons, Inc, 1987.
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