{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:41:55Z","timestamp":1776919315245,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Objective: To evaluate overall survival and to identify clinical, pathological, and demographic factors associated with survival in patients with stage III colon cancer. Methods: This retrospective cross-sectional study included 452 patients with stage III colon cancer who were followed at Ankara Bilkent City Hospital between 2005 and 2025. Patient data, including age, sex, ECOG performance status, comorbidities, tumor characteristics, treatment-related toxicities, and recurrence, were analyzed using PASW Statistics 18.0 (SPSS Inc., Chicago, IL, USA). Kaplan\u2013Meier and log-rank tests were used for survival analysis. Prognostic factors, survival, mortality, and recurrence predictions were evaluated using machine learning algorithms, including coarse tree, bagged trees, support vector machines, and k-nearest neighbors. Furthermore, an explainable artificial intelligence framework was incorporated to improve model transparency and reveal clinically meaningful feature contributions. Model performance was assessed using accuracy, sensitivity, specificity, and F-score. Results: According to statistical analyses, older age, ECOG performance score \u2265 2, stage IIIC disease, N2-level lymph node metastasis, and the presence of comorbidities\u2014particularly diabetes mellitus\u2014were significantly associated with worse survival (p &lt; 0.05). Machine learning analyses identified key prognostic factors, including positive surgical margins, rash, mucositis, thrombocytopenia, number of chemotherapy cycles, pathological tumor subtype, diarrhea, age at diagnosis, and anemia. SHAP analysis further demonstrated that treatment-related variables, particularly surgical margin positivity and chemotherapy-associated toxicities, were among the most influential predictors of survival. Several machine learning models outperformed traditional statistical methods in predicting mortality and recurrence, with the highest accuracy observed in ensemble methods such as coarse tree (87%) and bagged trees. Conclusions: This study identifies key prognostic factors influencing survival in stage III colon cancer and demonstrates that machine learning-based approaches can complement conventional statistical methods. The integration of clinical and treatment-related variables may improve individualized risk stratification and support clinical decision-making. These findings may also guide future large-scale, multicenter, and prospective studies.<\/jats:p>","DOI":"10.3390\/jcm15083091","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:30:36Z","timestamp":1776443436000},"page":"3091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning Framework for Prognostic Modeling in Stage III Colon Cancer"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5464-3470","authenticated-orcid":false,"given":"R\u00fcmeysa","family":"Sungur","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Ankara Bilkent City Hospital, 06800 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Selin","family":"Akt\u00fcrk Esen","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Ankara Bilkent City Hospital, 06800 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6449-6952","authenticated-orcid":false,"given":"Hilal","family":"Arslan","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Ankara Y\u0131ld\u0131r\u0131m Beyaz\u0131t University, 06010 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sevil","family":"Uygun \u0130likhan","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Ankara Bilkent City Hospital, 06800 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3745-5222","authenticated-orcid":false,"given":"Hatice R\u00fcveyda","family":"Ak\u00e7a","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Ankara Y\u0131ld\u0131r\u0131m Beyaz\u0131t University, 06010 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8917-9267","authenticated-orcid":false,"given":"Efnan","family":"Alg\u0131n","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Ankara Bilkent City Hospital, 06800 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6901-2646","authenticated-orcid":false,"given":"\u00d6znur","family":"Bal","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Ankara Bilkent City Hospital, 06800 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u015eebnem","family":"Yaman","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Ankara Bilkent City Hospital, 06800 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0929-3271","authenticated-orcid":false,"given":"Do\u011fan","family":"Uncu","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, Ankara Bilkent City Hospital, 06800 Ankara, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat. Rev. Cancer"},{"key":"ref_4","first-page":"3583","article-title":"Artificial intelligence in colorectal cancer: Applications in pathology and radiology","volume":"13","author":"Sun","year":"2021","journal-title":"Cancers"},{"key":"ref_5","unstructured":"Montgomery, D.C. (2017). Design and Analysis of Experiments, Wiley. [9th ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"143","DOI":"10.11613\/BM.2013.018","article-title":"The chi-square test of independence","volume":"23","author":"McHugh","year":"2013","journal-title":"Biochem. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gibbons, J.D., and Chakraborti, S. (2010). Nonparametric Statistical Inference, Chapman & Hall\/CRC. [5th ed.].","DOI":"10.1201\/9781439896129"},{"key":"ref_8","unstructured":"Breiman, L., Friedman, J., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Wadsworth International Group."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Draper, N.R., and Smith, H. (1998). Applied Regression Analysis, Wiley. [3rd ed.].","DOI":"10.1002\/9781118625590"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., and Lemeshow, S. (2013). Applied Logistic Regression, Wiley. [3rd ed.].","DOI":"10.1002\/9781118548387"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_14","unstructured":"Rish, I. (2001, January 4\u20136). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"10333","DOI":"10.1038\/s41598-020-67178-0","article-title":"A machine learning-based prognostic predictor for stage III colon cancer","volume":"10","author":"Jiang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_18","unstructured":"American Cancer Society (2023). Colorectal Cancer Facts & Figures 2023\u20132025, American Cancer Society."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2624","DOI":"10.1200\/JCO.2011.41.1140","article-title":"Effect of adjuvant chemotherapy on survival of patients with stage III colon cancer diagnosed after age 75 years","volume":"30","author":"Sanoff","year":"2012","journal-title":"J. Clin. Oncol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Warschkow, R., Sulz, M.C., Marti, L., Tarantino, I., Schmied, B.M., Cerny, T., and G\u00fcller, U. (2016). Improved survival in patients with right-sided and left-sided stage I\u2013III colon cancer. BMC Cancer, 16.","DOI":"10.1186\/s12885-016-2412-0"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, X., Lin, H., and Li, S. (2019). Prognoses of different pathological subtypes of colorectal cancer at different stages: A population-based retrospective cohort study. BMC Gastroenterol., 19.","DOI":"10.1186\/s12876-019-1083-0"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1136\/jclinpath-2011-200340","article-title":"Mucinous subtype as prognostic factor in colorectal cancer: A systematic review and meta-analysis","volume":"65","author":"Verhulst","year":"2012","journal-title":"J. Clin. Pathol."},{"key":"ref_23","unstructured":"National Comprehensive Cancer Network (2025). NCCN Clinical Practice Guidelines in Oncology: Colon Cancer, National Comprehensive Cancer Network. Version X.X."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.annonc.2021.07.009","article-title":"Combining tumor deposits with lymph node metastases to improve prognostic accuracy in stage III colon cancer: A post hoc analysis of CALGB\/SWOG 80702 phase III study (Alliance)","volume":"32","author":"Cohen","year":"2021","journal-title":"Ann. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1097\/00000658-200210000-00003","article-title":"A new TNM staging strategy for node-positive (stage III) colon cancer: An analysis of 50,042 patients","volume":"236","author":"Greene","year":"2002","journal-title":"Ann. Surg."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1067\/msy.2000.104674","article-title":"Obstruction and perforation in colorectal adenocarcinoma: An analysis of prognosis and current trends","volume":"127","author":"Chen","year":"2000","journal-title":"Surgery"},{"key":"ref_27","first-page":"166","article-title":"Kolon kanserinde lenfovask\u00fcler ve perin\u00f6ral invazyonun prognostik \u00f6nemi","volume":"54","author":"Gojayev","year":"2021","journal-title":"Acta Haematol. Oncol. Turc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.amjsurg.2013.02.010","article-title":"Prognostic significance of lymphovascular or perineural invasion in patients with locally advanced colorectal cancer","volume":"206","author":"Huh","year":"2013","journal-title":"Am. J. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"17588359211006348","DOI":"10.1177\/17588359211006348","article-title":"Early discontinuation and dose reduction of adjuvant chemotherapy in stage III colon cancer patients","volume":"13","author":"Boakye","year":"2021","journal-title":"Ther. Adv. Med. Oncol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"vi78","DOI":"10.1093\/annonc\/mdr391","article-title":"Management of oral and gastrointestinal mucositis: ESMO Clinical Practice Guidelines","volume":"22","author":"Peterson","year":"2011","journal-title":"Ann. Oncol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1056\/NEJMoa010957","article-title":"A pooled analysis of adjuvant chemotherapy for resected colon cancer in elderly patients","volume":"345","author":"Sargent","year":"2001","journal-title":"N. Engl. J. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1007\/s11831-022-09841-5","article-title":"Role of machine learning in tribology: A systematic review","volume":"30","author":"Paturi","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Uddin, S., and Lu, H. (2024). Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0301541"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6226","DOI":"10.1038\/s41598-025-90884-6","article-title":"Yapay zeka ile hepatosell\u00fcler karsinom sa\u011fkal\u0131m\u0131n\u0131n tahmini","volume":"15","author":"Seven","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","volume":"13","author":"Kourou","year":"2014","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Abbas, S., Soni, R., Nair, S., Heer, R., and Adhikari, K. (2025). AI predicting recurrence in non-muscle-invasive bladder cancer: Systematic review with study strengths and weaknesses. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1509362"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, L., Lai, Q., Li, S., Wu, S., Li, Y., Huang, J., Zeng, Q., and Wei, D. (2024). Performance of AI models for recurrence prediction in hepatocellular carcinoma: A systematic review and meta-analysis. BMC Med. Imaging, 24.","DOI":"10.1186\/s12880-024-01440-z"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, C.C., Tsai, W.C., Lee, H.C., Chang, C.C., Lin, T.C., and Yang, S.F. (2024). A cost-effective model for predicting recurrent gastric cancer using clinical features. Diagnostics, 14.","DOI":"10.20944\/preprints202403.0879.v1"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/8\/3091\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T04:12:47Z","timestamp":1776917567000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/8\/3091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,17]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["jcm15083091"],"URL":"https:\/\/doi.org\/10.3390\/jcm15083091","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,17]]}}}