Abstract
Background
Artificial intelligence (AI) is revolutionizing early cancer detection, with machine learning (ML) and deep learning (DL) at the forefront. While ML requires human guidance, DL autonomously learns from data using neural networks, excelling in image and speech recognition, pattern detection, and natural language processing, making it highly valuable in oncology. The aim of this research article is to review the application of Artificial Intelligence, particularly DL, in the early detection of cancer, evaluating various models and their effectiveness in improving diagnostic accuracy and prediction.
Methods
A diversified literature review was carried out using PubMed, Google Scholar, and ScienceDirect. Keywords included "Artificial Intelligence," "Machine Learning," "Deep Learning," and "Cancer Detection." Inclusion criteria encompassed articles in English published in the last twenty years, focused on original research. Excluded were non-research articles and those with inaccessible full texts.
Results
AI models under investigation include: Sybil: Accurately predicts lung cancer risk from a single low-dose CT scan. CNN-based models: Effective in classifying cancers from endoscopy, radiology, and histopathology images. CAD colonoscopy: Improves adenoma detection rates in real-time colonoscopy. Paige Prostate Alpha: Detects prostate cancer in whole slide images. CUP-AI-Dx: Recognizes the primary tissue of origin in cancers of unknown primary using RNA-based data. M3Net: Integrates CT images and biomarker data for cancer risk prediction. GANs: Enhance image-based diagnostics through denoising and completion.
Conclusion
DL algorithms demonstrate high accuracy in cancer detection, often matching or surpassing expert evaluations. Despite challenges in model robustness, multimodal data integration, and ethical considerations, AI holds significant promise in oncology. Future research should focus on enhancing model interpretability and clinical integration.
Keywords:
classification, standards, cancer, radiotherapy, ultrastructure, histologyReferences
Chen Z-H, Lin L, Wu C-F, Li C-F, Xu R-H, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun. 2021;41:1100–1115. DOI: 10.1002/cac2.12215
Al-Azri MH. Delay in Cancer Diagnosis: Causes and Possible Solutions. Oman Med J. 2016;31(5):325–326. DOI: 10.5001/omj.2016.65
Kumar Y, Gupta S, Singla R, Hu YC. A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis. Arch Computat Methods Eng. 2022;29(4):2043–2070. DOI: 10.1007/s11831-021-09648-w
Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol. 2021;27(29):4802–4817. DOI: 10.3748/wjg.v27.i29.4802
Zhao Y, Pan Z, Namburi S, Pattison A, Posner A, Balachander S et al. CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence. EBioMedicine. 2020;61:103030. DOI: 10.1016/j.ebiom.2020.103030
Mikhael PG, Wohlwend J, Yala A, Karstens L, Xiang J et al. Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography. J Clin Oncol. 2023;41(12):2191–2200. DOI: 10.1200/JCO.22.01345
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318(22):2199–2210. DOI: 10.1001/jama.2017.14585
Kulkarni PM, Robinson EJ, Pradhan JS, Gartrell-Corrado RD, Rohr BR, Trager MH et al. Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death. Clin. Cancer Res. 2020;26:1126–1134. doi: 10.1158/1078-0432.CCR-19-1495.
Liu WN, Zhang YY, Bian XQ, Wang LJ, Yang Q, Zhang XD et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol. 2020;26(1):13–19. DOI: 10.4103/sjg.SJG_377_19
Milluzzo SM, Cesaro P, Grazioli LM, Olivari N, Spada C. Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective. Clin Endosc. 2021;54(3):329–339. DOI: 10.5946/ce.2020.082
Hassan C, Afshinnekoo E, Li S, Wu S, Mason CE. Genetic and epigenetic heterogeneity and the impact on cancer relapse. Exp Hematol. 2017;54:26–30. DOI: 10.1016/j.exphem.2017.07.002
Raciti P, Sue J, Ceballos R, Godrich R, Kunz JD, Kapur S et al. Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies. Mod Pathol. 2020;33(10):2058–2066. DOI: 10.1038/s41379-020-0551-y
Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 2019;25:1301–1309. DOI: 10.1038/s41591-019-0508-1.
Parwani AV, Patel A, Zhou M, Cheville JC, Tizhoosh H, Humphrey P et al. An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS). J Pathol Inform. 2022;14:100177. DOI: 10.1016/j.jpi.2022.100177
Gao R, Tang Y, Xu K, Kammer MN, Antic SL, Deppen S et al. Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk. Proceedings of SPIE--the International Society for Optical Engineering. 2021;11596:115961E. DOI: 10.1117/12.2580730
Sebastian AM, Peter D. Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions. Life (Basel). 2022;12(12):1991. DOI: 10.3390/life12121991
Swiecicki A, Konz N, Buda M, Mazurowski MA. A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis. Sci Rep. 2021;11(1):10276. DOI: 10.1038/s41598-021-89626-1
Koshino K, Werner RA, Pomper MG, Bundschuh RA, Toriumi F, Higuchi T, Rowe SP. Narrative review of generative adversarial networks in medical and molecular imaging. Ann Transl Med. 2021;9(9):821. DOI: 10.21037/atm-20-6325
Hussain S, Mubeen I, Ullah N, Shah SSUD, Khan BA, Zahoor M, Ullah R, Khan FA, Sultan MA. Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review. Biomed Res Int. 2022;2022:5164970. DOI: 10.1155/2022/5164970
Debelee TG, Kebede SR, Schwenker F, Shewarega ZM. Deep Learning in Selected Cancers' Image Analysis-A Survey. J Imaging. 2020;6(11):121. DOI: 10.3390/jimaging6110121
Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24:1559-1567. doi: 10.1038/s41591-018-0177-5.
Zack TI, Schumacher SE, Carter SL, Cherniack AD, Saksena G, Tabak B et al. Pan-cancer patterns of somatic copy number alteration. Nat Genet. 2013;45(10):1134–1140. DOI: 10.1038/ng.2760
Klempner SJ, Fabrizio D, Bane S, Reinhart M, Peoples T, Ali SM et al. Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence. Oncologist. 2020;25(1):e147–e159. DOI: 10.1634/theoncologist.2019-0244
How to Cite
License
Copyright (c) 2024 Daniyah Zehra Hussain, Amna Zaheer, Ahmad Akhtar
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright© by the author(s). Published by the Evidence Journals. This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.