{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T16:03:47Z","timestamp":1788537827384,"version":"build-2803163510"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T00:00:00Z","timestamp":1752451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study explores the predictive value of clinical and socio-demographic characteristics for postoperative survival in stage III colorectal cancer (CRC) patients and develops a 5-year postoperative survival prediction model using machine learning algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from 13,855 stage III CRC patients who underwent surgery were extracted from the SEER database. Key variables, including marital status, gender, tumor location, histological type, T stage, chemotherapy status, age, tumor size, lymph node ratio, and others, were collected. Data were split into a 7:3 training-validation ratio. Optimal cutoff points for age, tumor diameter, and lymph node ratio were determined using X-tile software. Independent prognostic factors for postoperative survival in stage III colorectal cancer patients were identified through univariate and multivariate logistic regression as well as Lasso regression analyses. These factors were incorporated into machine learning models, including logistic regression, decision tree, LightGBM, and others. ROC curves, calibration curves, and decision curve analysis were used to assess model performance. External validation was performed using data from Shanxi Bethune Hospital.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Optimal cutoff points were identified for age (65, 80 years), tumor size (29 mm, 74 mm), and lymph node ratio (0.11, 0.49). Both multivariate logistic regression and Lasso regression consistently identified marital status, tumor location, histological type, T stage, chemotherapy, radiotherapy, age, maximum tumor diameter, lymph node ratio, serum carcinoembryonic antigen (CEA) level, perineural invasion, and tumor differentiation as independent prognostic factors for 5-year postoperative survival in patients with stage III colorectal cancer (P &amp;lt; 0.05). The models showed excellent predictive performance with AUC values ranging from 0.766 to 0.791 in the validation cohort. Age, lymph node ratio, chemotherapy, and T stage were key factors. External validation confirmed model accuracy and clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study developed and validated an interpretable machine learning model that predicts the 5-year postoperative survival of stage III CRC patients, offering potential for personalized treatment plans.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1604386","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T04:10:23Z","timestamp":1752466223000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of 5-year postoperative survival and analysis of key prognostic factors in stage III colorectal cancer patients using novel machine learning algorithms"],"prefix":"10.3389","volume":"15","author":[{"given":"Wei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghan","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyuan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinxi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660","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":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(19)32319-0","article-title":"Colorectal cancer","volume":"394","author":"Dekker","year":"2019","journal-title":"Lancet (London England)"},{"key":"B3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jncc.2024.01.006","article-title":"Cancer incidence and mortality in China, 2022","volume":"4","author":"Han","year":"2024","journal-title":"J Natl Cancer Center"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdx052","article-title":"Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study","volume":"28","author":"Dienstmann","year":"2017","journal-title":"Ann oncology: Off J Eur Soc Med Oncol"},{"key":"B5","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/s00428-018-2301-9","article-title":"The new TNM-based staging of breast cancer","volume":"472","author":"Cserni","year":"2018","journal-title":"Virchows Archiv: an Int J pathology"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.surg.2020.11.011","article-title":"Association of tumor size with prognosis in colon cancer: A Surveillance, Epidemiology, and End Results (SEER) database analysis","volume":"169","author":"Feng","year":"2021","journal-title":"Surgery"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21388","article-title":"The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more \u201cpersonalized\u201d approach to cancer staging","volume":"67","author":"Amin","year":"2017","journal-title":"CA: Cancer J Clin"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.11622\/smedj.2021054","article-title":"Machine learning in medicine: what clinicians should know","volume":"64","author":"Ting Sim","year":"2023","journal-title":"Singapore Med J"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-02134-1","article-title":"Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer","volume":"29","author":"Foersch","year":"2023","journal-title":"Nat Med"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105409","article-title":"Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis","volume":"145","author":"Su","year":"2022","journal-title":"Comput Biol Med"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/s1470-2045(19)30149-4","article-title":"Big data and machine learning algorithms for health-care delivery","volume":"20","author":"Ngiam","year":"2019","journal-title":"Lancet Oncol"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/j.semcancer.2019.12.011","article-title":"Machine and deep learning approaches for cancer drug repurposing","volume":"68","author":"Issa","year":"2021","journal-title":"Semin Cancer Biol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.29049","article-title":"The surveillance, epidemiology, and end results (SEER) program of the national cancer institute","author":"Cronin","year":"2014","journal-title":"Cancer"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.Ccr-04-0713","article-title":"X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization","volume":"10","author":"Camp","year":"2004","journal-title":"Clin Cancer research: an Off J Am Assoc Cancer Res"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e026057","DOI":"10.1136\/bmjopen-2018-026057","article-title":"Predictors of an early death in patients diagnosed with colon cancer: a retrospective case-control study in the UK","volume":"9","author":"Donnelly","year":"2019","journal-title":"BMJ Open"},{"key":"B16","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1002\/msj.21288","article-title":"Long-term outcomes in elderly surgical patients","volume":"79","author":"Deiner","year":"2012","journal-title":"Mount Sinai J medicine New York"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1001\/jamasurg.2015.3240","article-title":"Marital status and postoperative functional recovery","volume":"151","author":"Neuman","year":"2016","journal-title":"JAMA surgery"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1037\/\/0278-6133.8.2.221","article-title":"Social support and recovery from surgery","volume":"8","author":"Kulik","year":"1989","journal-title":"Health psychology: Off J Division Health Psychology Am psychol Assoc"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1093\/annonc\/mdt591","article-title":"Metastatic pattern in colorectal cancer is strongly influenced by histological subtype","volume":"25","author":"Hugen","year":"2014","journal-title":"Ann oncology: Off J Eur Soc Med Oncol"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1097\/COC.0b013e3181cae8dd","article-title":"Value of tumor size as a prognostic variable in colorectal cancer: a critical reappraisal","volume":"34","author":"Kornprat","year":"2011","journal-title":"Am J Clin Oncol"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-021-10240-6","article-title":"The prognostic value of lymph node ratio in locally advanced esophageal cancer patients who received neoadjuvant chemotherapy","volume":"28","author":"Kano","year":"2021","journal-title":"Ann Surg Oncol"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.08.002","article-title":"Total neoadjuvant treatment for pancreatic ductal adenocarcinoma is associated with limited lymph node yield but improved ratio","volume":"280","author":"Barrak","year":"2022","journal-title":"J Surg Res"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1007\/s00595-021-02386-3","article-title":"Lymph node ratio-based the ypTNrM staging system for gastric cancer after neoadjuvant therapy: a large population-based study","volume":"52","author":"Chen","year":"2022","journal-title":"Surg Today"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-010-1158-1","article-title":"Prognostic value of the lymph node ratio in stage III colorectal cancer: a systematic review","volume":"17","author":"Ceelen","year":"2010","journal-title":"Ann Surg Oncol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1186\/s12957-016-0826-4","article-title":"Prognostic factors in the patients with T2N0M0 colorectal cancer","volume":"14","author":"Xu","year":"2016","journal-title":"World J Surg Oncol"},{"key":"B26","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.ccell.2017.12.004","article-title":"Clinical sequencing defines the genomic landscape of metastatic colorectal cancer","volume":"33","author":"Yaeger","year":"2018","journal-title":"Cancer Cell"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1093\/oncolo\/oyad074","article-title":"A belgian population-based study reveals subgroups of right-sided colorectal cancer with a better prognosis compared to left-sided cancer","volume":"28","author":"Janssens","year":"2023","journal-title":"oncologist"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1084\/jem.122.3.467","article-title":"Specific carcinoembryonic antigens of the human digestive system","volume":"122","author":"Gold","year":"1965","journal-title":"J Exp Med"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.619803","article-title":"Deep convolutional neural network-based lymph node metastasis prediction for colon cancer using histopathological images","volume":"10","author":"Kwak","year":"2020","journal-title":"Front Oncol"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbcan.2020.188411","article-title":"Nerve input to tumours: Pathophysiological consequences of a dynamic relationship","volume":"1874","author":"Hutchings","year":"2020","journal-title":"Biochim Biophys Acta Rev cancer"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.3390\/curroncol28060447","article-title":"Pathological features and prognostication in colorectal cancer","volume":"28","author":"Chen","year":"2021","journal-title":"Curr Oncol (Toronto Ont)"},{"key":"B32","doi-asserted-by":"publisher","first-page":"8874","DOI":"10.1038\/s41598-023-35649-9","article-title":"Machine learning for predicting survival of colorectal cancer patients","volume":"13","author":"Buk Cardoso","year":"2023","journal-title":"Sci Rep"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1080\/01443615.2021.1945006","article-title":"LightGBM: an efficient and accurate method for predicting pregnancy diseases","volume":"42","author":"Liao","year":"2022","journal-title":"J obstetrics gynaecology: J Institute Obstetrics Gynaecology"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1604386\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T04:10:25Z","timestamp":1752466225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1604386\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,14]]},"references-count":33,"alternative-id":["10.3389\/fonc.2025.1604386"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2025.1604386","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,14]]},"article-number":"1604386"}}