New advances in artificial intelligence for the diagnosis and treatment of colorectal cancer: a literature review
DOI:
https://doi.org/10.51798/sijis.v5i1.733Keywords:
colorectal cancer; diagnosis; Artificial intelligence (AI) ; treatmentAbstract
Introduction: With 1.93 million new instances of colorectal cancer (CRC) reported in 2020, the disease presents a danger to world health. With its potential to improve CRC management, artificial intelligence (AI) has become increasingly prominent in the medical field. This research attempts to evaluate the current status of AI applications in CRC diagnosis and treatment, considering regional differences in healthcare systems and populations. Methodology: On databases like ScienceDirect, Google Scholar, and PubMed, a systematic literature evaluation was carried out using search phrases including "artificial intelligence," "colorectal cancer," "diagnosis," and "treatment." English-language research on AI applications in CRC diagnosis and treatment that were published during the previous five years met the inclusion criteria. Results: Endoscopic, non-invasive, histological, and radiographic techniques are among the AI applications used in CRC diagnosis. Prognostic forecasts, diagnostic accuracy, and tumor segmentation are all significantly enhanced by AI. AI helps with targeted therapy and chemoradiotherapy decision-making, improves surgical accuracy, and helps with personalized regimens. Conclusion: The use of AI in colorectal cancer management has the potential for timely identification, precise diagnosis, and customized care. Continuous developments in AI algorithms and clinical data support the development of precision medicine, which offers significant gains in CRC treatment and detection.
References
Acs, B., Rantalainen, M., & Hartman, J. (2020). Artificial intelligence as the next step towards precision pathology. Journal of Internal Medicine, 288(1), 62–81. https://doi.org/10.1111/JOIM.13030
Byrne, M. F., Chapados, N., Soudan, F., Oertel, C., Pérez, M. L., Kelly, R., Iqbal, N., Chandelier, F., & Rex, D. K. (2019). Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut, 68(1), 94–100. https://doi.org/10.1136/GUTJNL-2017-314547
Ciardiello, F., Ciardiello, D., Martini, G., Napolitano, S., Tabernero, J., & Cervantes, A. (2022). Clinical management of metastatic colorectal cancer in the era of precision medicine. CA: A Cancer Journal for Clinicians, 72(4), 372–401. https://doi.org/10.3322/CAAC.21728
Echle, A., Grabsch, H. I., Quirke, P., van den Brandt, P. A., West, N. P., Hutchins, G. G. A., Heij, L. R., Tan, X., Richman, S. D., Krause, J., Alwers, E., Jenniskens, J., Offermans, K., Gray, R., Brenner, H., Chang-Claude, J., Trautwein, C., Pearson, A. T., Boor, P., … Kather, J. N. (2020). Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning. Gastroenterology, 159(4), 1406-1416.e11. https://doi.org/10.1053/J.GASTRO.2020.06.021
Ferrando, L., Cirmena, G., Garuti, A., Scabini, S., Grillo, F., Mastracci, L., Isnaldi, E., Marrone, C., Gonella, R., Murialdo, R., Fiocca, R., Romairone, E., Ballestrero, A., & Zoppoli, G. (2020). Development of a long non-coding RNA signature for prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal adenocarcinoma. PLOS ONE, 15(2), e0226595. https://doi.org/10.1371/JOURNAL.PONE.0226595
Ferrari, A., Neefs, I., Hoeck, S., Peeters, M., & Van Hal, G. (2021). Towards novel non-invasive colorectal cancer screening methods: A comprehensive review. Cancers, 13(8), 1820. https://doi.org/10.3390/CANCERS13081820/S1
Ferrari, R., Mancini-Terracciano, C., Voena, C., Rengo, M., Zerunian, M., Ciardiello, A., Grasso, S., Mare, V., Paramatti, R., Russomando, A., Santacesaria, R., Satta, A., Solfaroli Camillocci, E., Faccini, R., & Laghi, A. (2019). MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer. European Journal of Radiology, 118, 1–9. https://doi.org/10.1016/J.EJRAD.2019.06.013
Gonai, T., Kawasaki, K., Nakamura, S., Yanai, S., Akasaka, R., Sato, K., Toya, Y., Asakura, K., Urushikubo, J., Fujita, Y., Eizuka, M., Uesugi, N., Sugai, T., & Matsumoto, T. (2019). Microvascular density under magnifying narrow-band imaging endoscopy in colorectal epithelial neoplasms. Intestinal Research, 18(1), 107–114. https://doi.org/10.5217/IR.2019.00061
Hamabe, A., Ishii, M., Kamoda, R., Sasuga, S., Okuya, K., Okita, K., Akizuki, E., Sato, Y., Miura, R., Onodera, K., Hatakenaka, M., & Takemasa, I. (2022). Artificial intelligence-based technology for semi-automated segmentation of rectal cancer using high-resolution MRI. PLoS ONE, 17(6 June). https://doi.org/10.1371/JOURNAL.PONE.0269931
Hammad, A., Elshaer, M., Tang, X., Hammad, A., Elshaer, M., & Tang, X. (2021). Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning. Mathematical Biosciences and Engineering 2021 6:8997, 18(6), 8997–9015. https://doi.org/10.3934/MBE.2021443
Hassan, C., Spadaccini, M., Iannone, A., Maselli, R., Jovani, M., Chandrasekar, V. T., Antonelli, G., Yu, H., Areia, M., Dinis-Ribeiro, M., Bhandari, P., Sharma, P., Rex, D. K., Rösch, T., Wallace, M., & Repici, A. (2021). Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointestinal Endoscopy, 93(1), 77-85.e6. https://doi.org/10.1016/J.GIE.2020.06.059
He, K., Liu, X., Li, M., Li, X., Yang, H., & Zhang, H. (2020). Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on routine CT imaging. https://doi.org/10.21203/RS.2.22851/V1
Igaki, T., Kitaguchi, D., Kojima, S., Hasegawa, H., Takeshita, N., Mori, K., Kinugasa, Y., & Ito, M. (2022). Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery. Diseases of the Colon and Rectum, 65(5), E329–E333. https://doi.org/10.1097/DCR.0000000000002393
Kamba, S., Tamai, N., Saitoh, I., Matsui, H., Horiuchi, H., Kobayashi, M., Sakamoto, T., Ego, M., Fukuda, A., Tonouchi, A., Shimahara, Y., Nishikawa, M., Nishino, H., Saito, Y., & Sumiyama, K. (2021). Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. Journal of Gastroenterology, 56(8), 746–757. https://doi.org/10.1007/S00535-021-01808-W
Kasahara, K., Katsumata, K., Saito, A., Ishizaki, T., Enomoto, M., Mazaki, J., Tago, T., Nagakawa, Y., Matsubayashi, J., Nagao, T., Hirano, H., Kuroda, M., & Tsuchida, A. (2022). Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer. International Journal of Clinical Oncology, 27(10), 1570–1579. https://doi.org/10.1007/S10147-022-02209-6/METRICS
Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C. A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., Jansen, L., Reyes-Aldasoro, C. C., Zörnig, I., Jäger, D., Brenner, H., Chang-Claude, J., Hoffmeister, M., & Halama, N. (2019). Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine, 16(1). https://doi.org/10.1371/JOURNAL.PMED.1002730
Kleppe, A., Skrede, O. J., De Raedt, S., Hveem, T. S., Askautrud, H. A., Jacobsen, J. E., Church, D. N., Nesbakken, A., Shepherd, N. A., Novelli, M., Kerr, R., Liestøl, K., Kerr, D. J., & Danielsen, H. E. (2022). A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study. The Lancet Oncology, 23(9), 1221–1232. https://doi.org/10.1016/S1470-2045(22)00391-6
Li, H., Lin, J., Xiao, Y., Zheng, W., Zhao, L., Yang, X., Zhong, M., & Liu, H. (2021). Colorectal Cancer Detected by Machine Learning Models Using Conventional Laboratory Test Data. Technology in Cancer Research and Treatment, 20. https://doi.org/10.1177/15330338211058352
Li, J. W., Chia, T., Fock, K. M., Chong, K. D. W., Wong, Y. J., & Ang, T. L. (2021). Artificial intelligence and polyp detection in colonoscopy: Use of a single neural network to achieve rapid polyp localization for clinical use. Journal of Gastroenterology and Hepatology (Australia), 36(12), 3298–3307. https://doi.org/10.1111/JGH.15642
Liu, X., Guo, S., Zhang, H., He, K., Mu, S., Guo, Y., & Li, X. (2019). Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network. Medical Physics, 46(8), 3532–3542. https://doi.org/10.1002/MP.13584
Liu, X., Zhang, D., Liu, Z., Li, Z., Xie, P., Sun, K., Wei, W., Dai, W., Tang, Z., Ding, Y., Cai, G., Tong, T., Meng, X., & Tian, J. (2021). Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study. EBioMedicine, 69. https://doi.org/10.1016/J.EBIOM.2021.103442
Mantas, J., Gallos, P., & Zoulias, E. (2022). Advances in informatics, management and technology in healthcare. https://books.google.com/books/about/Advances_in_Informatics_Management_and_T.html?id=5JGREAAAQBAJ
Mitsala, A., Tsalikidis, C., Pitiakoudis, M., Simopoulos, C., & Tsaroucha, A. K. (2021). Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era. Current Oncology, 28(3), 1581–1607. https://doi.org/10.3390/CURRONCOL28030149
Ngu, J. C. Y., Sim, S., Yusof, S., Ng, C. Y., & Wong, A. S. Y. (2017). Insight into the da Vinci® Xi – technical notes for single-docking left-sided colorectal procedures. International Journal of Medical Robotics and Computer Assisted Surgery, 13(4). https://doi.org/10.1002/RCS.1798
Park, S. H., Park, H. M., Baek, K. R., Ahn, H. M., Lee, I. Y., & Son, G. M. (2020). Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery. World Journal of Gastroenterology, 26(44), 6945–6962. https://doi.org/10.3748/WJG.V26.I44.6945
Qiu, H., Ding, S., Liu, J., Wang, L., & Wang, X. (2022). Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Current Oncology 2022, Vol. 29, Pages 1773-1795, 29(3), 1773–1795. https://doi.org/10.3390/CURRONCOL29030146
Rocca, A., Brunese, M. C., Santone, A., Avella, P., Bianco, P., Scacchi, A., Scaglione, M., Bellifemine, F., Danzi, R., Varriano, G., Vallone, G., Calise, F., & Brunese, L. (2022). Early diagnosis of liver metastases from colorectal cancer through CT radiomics and formal methods: A pilot study. Journal of Clinical Medicine, 11(1). https://doi.org/10.3390/JCM11010031
Russo, V., Lallo, E., Munnia, A., Spedicato, M., Messerini, L., D’Aurizio, R., Ceroni, E. G., Brunelli, G., Galvano, A., Russo, A., Landini, I., Nobili, S., Ceppi, M., Bruzzone, M., Cianchi, F., Staderini, F., Roselli, M., Riondino, S., Ferroni, P., … Peluso, M. (2022). Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis. Cancers, 14(16), 4012. https://doi.org/10.3390/CANCERS14164012/S1
Shao, Y., Cheng, Y., Shah, R. U., Weir, C. R., Bray, B. E., & Zeng-Treitler, Q. (2021). Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes. Journal of Medical Systems, 45(1), 1–9. https://doi.org/10.1007/S10916-020-01701-8/TABLES/3
Sharifi-Azad, M., Fathi, M., Cho, W. C., Barzegari, A., Dadashi, H., Dadashpour, M., & Jahanban-Esfahlan, R. (2022). Recent advances in targeted drug delivery systems for resistant colorectal cancer. Cancer Cell International, 22(1). https://doi.org/10.1186/S12935-022-02605-Y
Sheng, S., Zhao, T., & Wang, X. (2018). Comparison of robot-assisted surgery, laparoscopic-assisted surgery, and open surgery for the treatment of colorectal cancer: A network meta-analysis. Medicine, 97(34). https://doi.org/10.1097/MD.0000000000011817
Song, D., Zhang, Z., Li, W., Yuan, L., & Zhang, W. (2022). Judgment of benign and early malignant colorectal tumors from ultrasound images with deep multi-View fusion. Computer Methods and Programs in Biomedicine, 215, 106634. https://doi.org/10.1016/J.CMPB.2022.106634
Su, Y., Tian, X., Gao, R., Guo, W., Chen, C., Chen, C., Jia, D., Li, H., & Lv, X. (2022). Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis. Computers in Biology and Medicine, 145. https://doi.org/10.1016/J.COMPBIOMED.2022.105409
Sultan, A. S., Elgharib, M. A., Tavares, T., Jessri, M., & Basile, J. R. (2020). The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. Journal of Oral Pathology and Medicine, 49(9), 849–856. https://doi.org/10.1111/JOP.13042
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71(3), 209–249. https://doi.org/10.3322/CAAC.21660
Tanos, R., Tosato, G., Otandault, A., Al Amir Dache, Z., Pique Lasorsa, L., Tousch, G., El Messaoudi, S., Meddeb, R., Diab Assaf, M., Ychou, M., Du Manoir, S., Pezet, D., Gagnière, J., Colombo, P. E., Jacot, W., Assénat, E., Dupuy, M., Adenis, A., Mazard, T., … Thierry, A. R. (2020). Machine Learning-Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening. Advanced Science, 7(18). https://doi.org/10.1002/ADVS.202000486
Viscaino, M., Bustos, J. T., Muñoz, P., Cheein, C. A., & Cheein, F. A. (2021). Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions. World Journal of Gastroenterology, 27(38), 6399–6414. https://doi.org/10.3748/WJG.V27.I38.6399
Wang, K. S., Yu, G., Xu, C., Meng, X. H., Zhou, J., Zheng, C., Deng, Z., Shang, L., Liu, R., Su, S., Zhou, X., Li, Q., Li, J., Wang, J., Ma, K., Qi, J., Hu, Z., Tang, P., Deng, J., … Deng, H. W. (2021). Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Medicine, 19(1). https://doi.org/10.1186/S12916-021-01942-5
Wang, P., Berzin, T. M., Glissen Brown, J. R., Bharadwaj, S., Becq, A., Xiao, X., Liu, P., Li, L., Song, Y., Zhang, D., Li, Y., Xu, G., Tu, M., & Liu, X. (2019). Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study. Gut, 68(10), 1813–1819. https://doi.org/10.1136/GUTJNL-2018-317500
Wei, J., Cheng, J., Gu, D., Chai, F., Hong, N., Wang, Y., & Tian, J. (2021). Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases. Medical Physics, 48(1), 513–522. https://doi.org/10.1002/MP.14563
Xia, C., Dong, X., Li, H., Cao, M., Sun, D., He, S., Yang, F., Yan, X., Zhang, S., Li, N., & Chen, W. (2022). Cancer statistics in China and United States, 2022: Profiles, trends, and determinants. Chinese Medical Journal, 135(5), 584–590. https://doi.org/10.1097/CM9.0000000000002108
Yin, Z., Yao, C., Zhang, L., & Qi, S. (2023). Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Frontiers in Medicine, 10, 1128084. https://doi.org/10.3389/FMED.2023.1128084/BIBTEX
Yu, G., Sun, K., Xu, C., Shi, X. H., Wu, C., Xie, T., Meng, R. Q., Meng, X. H., Wang, K. S., Xiao, H. M., & Deng, H. W. (2021). Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. Nature Communications 2021 12:1, 12(1), 1–13. https://doi.org/10.1038/s41467-021-26643-8
Zhang, W. W., Ming, X. L., Rong, Y., Huang, C. Q., Weng, H., Chen, H., Bian, J. M., & Wang, F. B. (2019). Diagnostic Value Investigation and Bioinformatics Analysis of miR-31 in Patients with Lymph Node Metastasis of Colorectal Cancer. Analytical Cellular Pathology, 2019. https://doi.org/10.1155/2019/9740475
Zhang, X., Yang, Y., Wang, Y., & Fan, Q. (2019). Detection of the BRAF V600E mutation in colorectal cancer by NIR spectroscopy in conjunction with counter propagation artificial neural network. Molecules, 24(12). https://doi.org/10.3390/MOLECULES24122238
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