The role of artificial intelligence in cancer diagnostics - a review
DOI:
https://doi.org/10.12775/JEHS.2021.11.09.016Keywords
artificial intelligence, breast cancer, prostate cancer, skin cancer, melanoma, colorectal cancerAbstract
Introduction and purpose
Artificial intelligence (AI) is more advanced than ever and finds more and more new applications. Attempts are being made to use computer data analysis in medicine. The aim of this study is to summarize the knowledge on the use of AI in the diagnosis of breast, prostate, skin and colorectal cancer with particular emphasis on the applications and effectiveness of AI in making diagnoses.
A brief description of the state of knowledge
The most frequently used form of artificial intelligence in diagnostics are algorithms that analyze databases and recognize patterns. They can capture the features of samples characteristic of tumors, such as abnormal cells in the biopsy material or the alarming size and color of the skin lesion. Additionally, AI is capable of analyzing magnetic resonance images, radiographs, and other standardized test results. In most cases, AI is more effective than clinicians, sometimes as effective as they are, and almost never less effective. As a rule, the most accurate and adequate diagnosis can be obtained by joining the forces of AI and medical specialists. Working with learning algorithms requires the use of very extensive data sets. Every effort should be made to protect sensitive information from patients' medical history.
Conclusions
The results of research on the effectiveness of AI in cancer diagnostics are very promising. Further research and development of information technology systems may positively affect the quality and effectiveness of tumor diagnostics.
References
Kaplan A, Haenlein M. Siri, Siri in my Hand, who’s the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence. Business Horizons. 2019;62(1), 15-25.
Hausman DM. What Is Cancer. Perspect Biol Med. 2019;62(4):778-784.
Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, Xie Z, Ma Y, Robson PM, Chung M, Bernheim A, Mani V, Calcagno C, Li K, Li S, Shan H, Lv J, Zhao T, Xia J, Long Q, Steinberger S, Jacobi A, Deyer T, Luksza M, Liu F, Little BP, Fayad ZA, Yang Y. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat Med. 2020;26(8):1224-1228.
Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H, Feng J. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun. 2020;11(1):5088.
Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D. Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Rev Biomed Eng. 2021;144-15.
Liu X, Chen K, Wu T, Weidman D, Lure F, Li J. Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer’s disease. Transl Res. 2018;19456-67.
Verma RK, Pandey M, Chawla P, Choudhury H, Mayuren J, Bhattamisra SK, Gorain B, Raja MAG, Amjad MW, Obaidur Rahman S. An insight into the role of Artificial Intelligence in the early diagnosis of Alzheimer’s disease. CNS Neurol Disord Drug Targets. 2021
Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett. 2020;47161-71.
Li X, Hu B, Li H, You B. Application of artificial intelligence in the diagnosis of multiple primary lung cancer. Thorac Cancer. 2019;10(11):2168-2174.
Chen PC, Gadepalli K, MacDonald R, Liu Y, Kadowaki S, Nagpal K, Kohlberger T, Dean J, Corrado GS, Hipp JD, Mermel CH, Stumpe MC. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat Med. 2019;25(9):1453-1457.
Lane CA, Hardy J, Schott JM. Alzheimer’s disease. Eur J Neurol. 2018;25(1):59-70.
Eyigoz E, Mathur S, Santamaria M, Cecchi G, Naylor M. Linguistic markers predict onset of Alzheimer's disease. EClinicalMedicine. 2020;28:100583.
Guan Y, Wang P, Wang Q, Li P, Zeng J, Qin P, Meng Y. Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis. Biomed Res Int. 2020;8864756.
Boreak N. Effectiveness of Artificial Intelligence Applications Designed for Endodontic Diagnosis, Decision-making, and Prediction of Prognosis: A Systematic Review. J Contemp Dent Pract. 2020;21(8):926-934.
Zhang D, Liu X, Shao M, Sun Y, Lian Q, Zhang H. The value of artificial intelligence and imaging diagnosis in the fight against COVID-19. Pers Ubiquitous Comput. 2021;1-10.
Neri E, Coppola F, Miele V, Bibbolino C, Grassi R. Artificial intelligence: Who is responsible for the diagnosis? Radiol Med 2020;125(6):517.
Jaremko JL, Azar M, Bromwich R, Lum A, Alicia Cheong LH, Gibert M, Laviolette F, Gray B, Reinhold C, Cicero M, Chong J, Shaw J, Rybicki FJ, Hurrell C, Lee E, Tang A, Canadian AORCARAIWG. Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology. Can Assoc Radiol J. 2019;70(2):107-118.
Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359-86.
Giger ML. Computer-aided diagnosis in radiology. Acad Radiol. 2002;9(1):1-3.
Adachi M, Fujioka T, Mori M, Kubota K, Kikuchi Y, Xiaotong W, Oyama J, Kimura K, Oda G, Nakagawa T, Uetake H, Tateishi U. Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images. Diagnostics (Basel). 2020;10(5):E330
Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system [Internet]. [cited 2021 Sep 1]. Available from: https://www.aimspress.com/article/10.3934/mbe.2021184
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-249.
Mottet N, van den Bergh RCN, Briers E, Van den Broeck T, Cumberbatch MG, De Santis M, Fanti S, Fossati N, Gandaglia G, Gillessen S, Grivas N, Grummet J, Henry AM, van der Kwast TH, Lam TB, Lardas M, Liew M, Mason MD, Moris L, Oprea-Lager DE, van der Poel HG, Rouvière O, Schoots IG, Tilki D, Wiegel T, Willemse PM, Cornford P. EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2021;79(2):243-262.
Alabousi M, Salameh JP, Gusenbauer K, Samoilov L, Jafri A, Yu H, Alabousi A. Biparametric vs multiparametric prostate magnetic resonance imaging for the detection of prostate cancer in treatment-naïve patients: a diagnostic test accuracy systematic review and meta-analysis. BJU Int. 2019;124(2):209-220.
Cuocolo R, Cipullo MB, Stanzione A, Ugga L, Romeo V, Radice L, Brunetti A, Imbriaco M. Machine learning applications in prostate cancer magnetic resonance imaging. Eur Radiol Exp. 2019;3(1):35.
Syer T, Mehta P, Antonelli M, Mallett S, Atkinson D, Ourselin S, Punwani S. Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies. Cancers (Basel). 2021;13(13):3318
Litjens G, Sánchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, Hulsbergen-van de Kaa C, Bult P, van Ginneken B, van der Laak J. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep. 2016;626286.
Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301-1309.
Perincheri S, Levi AW, Celli R, Gershkovich P, Rimm D, Morrow JS, Rothrock B, Raciti P, Klimstra D, Sinard J. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Mod Pathol. 2021;34(8):1588-1595.
Ström P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney DM, Bostwick DG, Evans AJ, Grignon DJ, Humphrey PA, Iczkowski KA, Kench JG, Kristiansen G, van der Kwast TH, Leite KRM, McKenney JK, Oxley J, Pan CC, Samaratunga H, Srigley JR, Takahashi H, Tsuzuki T, Varma M, Zhou M, Lindberg J, Lindskog C, Ruusuvuori P, Wählby C, Grönberg H, Rantalainen M, Egevad L, Eklund M. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 2020;21(2):222-232.
Pantanowitz L, Quiroga-Garza GM, Bien L, Heled R, Laifenfeld D, Linhart C, Sandbank J, Albrecht Shach A, Shalev V, Vecsler M, Michelow P, Hazelhurst S, Dhir R. An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. Lancet Digit Health. 2020;2(8):e407-e416.
S. C. Foundation, "Skin Cancer Facts and Statistics," Online, Jan. 2017. [Internet]. [cited 2021 Sep 31]. Available from: https://www.skincancer.org/skin-cancer-information/skin-cancer-facts/
Ali Z, Yousaf N, Larkin J. Melanoma epidemiology, biology and prognosis. EJC Suppl. 2013;11(2):81-91.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118.
Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, Kalloo A, Hassen ABH, Thomas L, Enk A, Uhlmann L, Reader SL-IAL-IIG, Alt C, Arenbergerova M, Bakos R, Baltzer A, Bertlich I, Blum A, Bokor-Billmann T, Bowling J, Braghiroli N, Braun R, Buder-Bakhaya K, Buhl T, Cabo H, Cabrijan L, Cevic N, Classen A, Deltgen D, Fink C, Georgieva I, Hakim-Meibodi LE, Hanner S, Hartmann F, Hartmann J, Haus G, Hoxha E, Karls R, Koga H, Kreusch J, Lallas A, Majenka P, Marghoob A, Massone C, Mekokishvili L, Mestel D, Meyer V, Neuberger A, Nielsen K, Oliviero M, Pampena R, Paoli J, Pawlik E, Rao B, Rendon A, Russo T, Sadek A, Samhaber K, Schneiderbauer R, Schweizer A, Toberer F, Trennheuser L, Vlahova L, Wald A, Winkler J, Wölbing P, Zalaudek I. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):1836-1842.
Tschandl P, Rosendahl C, Akay BN, Argenziano G, Blum A, Braun RP, Cabo H, Gourhant JY, Kreusch J, Lallas A, Lapins J, Marghoob A, Menzies S, Neuber NM, Paoli J, Rabinovitz HS, Rinner C, Scope A, Soyer HP, Sinz C, Thomas L, Zalaudek I, Kittler H. Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks. JAMA Dermatol. 2019;155(1):58-65.
Fujisawa Y, Otomo Y, Ogata Y, Nakamura Y, Fujita R, Ishitsuka Y, Watanabe R, Okiyama N, Ohara K, Fujimoto M. Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis. Br J Dermatol. 2019;180(2):373-381.
Schadendorf D, van Akkooi ACJ, Berking C, Griewank KG, Gutzmer R, Hauschild A, Stang A, Roesch A, Ugurel S. Melanoma. Lancet. 2018;392(10151):971-984.
Tschandl P. Artificial intelligence for melanoma diagnosis. Ital J Dermatol Venerol. 2021 Jun;156(3):289-299.
Rajpara SM, Botello AP, Townend J, Ormerod AD. Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma. Br J Dermatol. 2009;161(3):591-604
Hekler A, Utikal JS, Enk AH, Hauschild A, Weichenthal M, Maron RC, Berking C, Haferkamp S, Klode J, Schadendorf D, Schilling B, Holland-Letz T, Izar B, von Kalle C, Fröhling S, Brinker TJ, Collaborators. Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer. 2019;120114-121.
Mármol I, Sánchez-de-Diego C, Pradilla Dieste A, Cerrada E, Rodriguez Yoldi MJ. Colorectal Carcinoma: A General Overview and Future Perspectives in Colorectal Cancer. Int J Mol Sci. 2017;18(1):197
Doubeni CA, Corley DA, Quinn VP, Jensen CD, Zauber AG, Goodman M, Johnson JR, Mehta SJ, Becerra TA, Zhao WK, Schottinger J, Doria-Rose VP, Levin TR, Weiss NS, Fletcher RH. Effectiveness of screening colonoscopy in reducing the risk of death from right and left colon cancer: a large community-based study. Gut. 2018;67(2):291-298.
Nie H, Wang Y, Liao Z, Zhou J, Ou C. The function and mechanism of circular RNAs in gastrointestinal tumours. Cell Prolif. 2020;53(7):e12815.
Ling BS, Schoen RE, Trauth JM, Wahed AS, Eury T, Simak DM, Solano FX, Weissfeld JL. Physicians encouraging colorectal screening: a randomized controlled trial of enhanced office and patient management on compliance with colorectal cancer screening. Arch Intern Med. 2009;169(1):47-55.
Zhang R, Zheng Y, Mak TW, Yu R, Wong SH, Lau JY, Poon CC. Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain. IEEE J Biomed Health Inform. 2017;21(1):41-47.
Fernández-Esparrach G, Bernal J, López-Cerón M, Córdova H, Sánchez-Montes C, Rodríguez de Miguel C, Sánchez FJ. Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps. Endoscopy. 2016;48(9):837-842.
Chadebecq F, Tilmant C, Bartoli A. How big is this neoplasia? live colonoscopic size measurement using the Infocus-Breakpoint. Med Image Anal. 2015;19(1):58-74.
Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288(1):62-81.
Sirinukunwattana K, Ahmed Raza SE, Yee-Wah Tsang null, Snead DRJ, Cree IA, Rajpoot NM. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Trans Med Imaging. 2016;35(5):1196–206.
Rathore S, Hussain M, Aksam Iftikhar M, Jalil A. Novel structural descriptors for automated colon cancer detection and grading. Comput Methods Programs Biomed. 2015;121(2):92-108.
Soares F, Becker K, Anzanello MJ. A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening. Artif Intell Med. 2017;821-10.
Hu HP, Niu ZJ, Bai YP, Tan XH. Cancer classification based on gene expression using neural networks. Genet Mol Res. 2015;14(4):17605-17611.
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