CGE_2024v12n4

Cancer Genetics and Epigenetics 2024, Vol.12, No.4, 210-222 http://medscipublisher.com/index.php/cge 221 Li R., Wu X., Li A., and Wang M., 2022, HFBSurv: hierarchical multimodal fusion with factorized bilinear models for cancer survival prediction, Bioinformatics, 38(9): 2587-2594. https://doi.org/10.1093/bioinformatics/btac113 PMid:35188177 PMCid:PMC9048674 Li Y., Eresen A., Shangguan J., Yang J., Lu Y., Chen D., Wang J., Velichko Y., Yaghmai V., and Zhang Z., 2019, Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer, American Journal of Cancer Research, 9(11): 2482. Liang M., Ren Z., Yang J., Feng W., and Li B., 2020, Identification of colon cancer using multi-scale feature fusion convolutional neural network based on shearlet transform, IEEE Access, 8: 208969-208977. https://doi.org/10.1109/ACCESS.2020.3038764 Lim Y., Choi S., Oh H. J., Kim C., Song S., Kim S., Song H., Park S., Kim J.W., Kim J.W., Kim J.H., Kang M., Kang S.B., Kim D.W., Oh H.K., Lee H.S., and Lee K.W., 2023, Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes for prediction of prognosis in resected colon cancer, NPJ Precision Oncology, 7(1): 124. https://doi.org/10.1038/s41698-023-00470-0 PMid:37985785 PMCid:PMC10662481 Lobato-Delgado B., Priego-Torres B., and Sanchez-Morillo D., 2022, Combining molecular, imaging, and clinical data analysis for predicting cancer prognosis, Cancers, 14(13): 3215. https://doi.org/10.3390/cancers14133215 PMid:35804988 PMCid:PMC9265023 Mansur A., Saleem Z., Elhakim T., and Daye D., 2023, Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions, Frontiers in Oncology, 13: 1065402. https://doi.org/10.3389/fonc.2023.1065402 PMid:36761957 PMCid:PMC9905815 Mazaki J., Katsumata K., Ohno Y., Udo R., Tago T., Kasahara K., Kuwabara H., Enomoto M., Ishizaki T., Nagakawa Y., and Tsuchida A., 2021, A novel prediction model for colon cancer recurrence using auto-artificial intelligence, Anticancer Research, 41(9): 4629-4636. https://doi.org/10.21873/anticanres.15276 PMid:34475091 Miao Z., Humphreys B.D., McMahon A.P., and Kim J., 2021, Multi-omics integration in the age of million single-cell data, Nature Reviews Nephrology, 17(11): 710-724. https://doi.org/10.1038/s41581-021-00463-x PMid:34417589 PMCid:PMC9191639 Pei L., Bakas S., Vossough A., Reza S.M., Davatzikos C., and Iftekharuddin K.M., 2020, Longitudinal brain tumor segmentation prediction in MRI using feature and label fusion, Biomedical Signal Processing and Control, 55: 101648. https://doi.org/10.1016/j.bspc.2019.101648 PMid:34354762 PMCid:PMC8336640 Preto A.J., Matos-Filipe P., Mourão J., and Moreira I.S., 2022, SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning, GigaScience, 11: giac087. https://doi.org/10.1093/gigascience/giac087 PMid:36155782 PMCid:PMC9511701 Qiu H., Ding S., Liu J., Wang L., and Wang X., 2022, Applications of artificial intelligence in screening, diagnosis, treatment, and prognosis of colorectal cancer, Current Oncology, 29(3): 1773-1795. https://doi.org/10.3390/curroncol29030146 PMid:35323346 PMCid:PMC8947571 Rompianesi G., Pegoraro F., Ceresa C.D., Montalti R., and Troisi R.I., 2022, Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases, World Journal of Gastroenterology, 28(1): 108. https://doi.org/10.3748/wjg.v28.i1.108 PMid:35125822 PMCid:PMC8793013 Shao W., Wang T., Sun L., Dong T., Han Z., Huang Z., Zhang J., Zhang D.Q., and Huang K., 2020, Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers, Medical Image Analysis, 65: 101795. https://doi.org/10.1016/j.media.2020.101795 PMid:32745975 Thakur N., Yoon H., and Chong Y., 2020, Current trends of artificial intelligence for colorectal cancer pathology image analysis: a systematic review, Cancers, 12(7): 1884. https://doi.org/10.3390/cancers12071884 PMid:32668721 PMCid:PMC7408874 Tong D., Tian Y., Zhou T., Ye Q., Li J., Ding K., and Li J., 2020, Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data, BMC Medical Informatics and Decision Making, 20: 1-15. https://doi.org/10.1186/s12911-020-1043-1 PMid:32033604 PMCid:PMC7006213

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