{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T06:36:24Z","timestamp":1768026984609,"version":"3.49.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:00:00Z","timestamp":1767916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Colorectal Dis"],"DOI":"10.1007\/s00384-025-05073-8","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T19:35:28Z","timestamp":1767987328000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma"],"prefix":"10.1007","volume":"41","author":[{"given":"Jiaying","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaming","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangqin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,9]]},"reference":[{"key":"5073_CR1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/1092107","volume":"2022","author":"YJ Wu","year":"2022","unstructured":"Wu YJ, Cai ZQ, He RM, Wang XC, Cong LL, Qiu FH (2022) Knockdown of long noncoding RNA 01124 inhibits the malignant behaviors of colon cancer cells via miR-654-5p\/HAX-1. Evid Based Complement Alternat Med 2022:1092107. https:\/\/doi.org\/10.1155\/2022\/1092107","journal-title":"Evid Based Complement Alternat Med"},{"issue":"7","key":"5073_CR2","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1002\/ddr.20276","volume":"69","author":"S Rajamanickam","year":"2008","unstructured":"Rajamanickam S, Agarwal R (2008) Natural products and colon cancer: current status and future prospects. Drug Dev Res 69(7):460\u2013471. https:\/\/doi.org\/10.1002\/ddr.20276","journal-title":"Drug Dev Res"},{"key":"5073_CR3","doi-asserted-by":"publisher","unstructured":"Benson AB, Venook AP, Adam M, Chang G, Chen YJ, Ciombor KK, et al (2024) Colon cancer, version 3.2024, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 22(2 D). https:\/\/doi.org\/10.6004\/jnccn.2024.0029","DOI":"10.6004\/jnccn.2024.0029"},{"key":"5073_CR4","unstructured":"Cuccurullo V, Mansi L (2010) AJCC cancer staging handbook: from the AJCC cancer staging manual Springer, New York 2010, ISBN: 978\u20130\u2013387\u201388442\u20134. Springer"},{"issue":"1","key":"5073_CR5","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s00384-024-04671-2","volume":"39","author":"Z Zheng","year":"2024","unstructured":"Zheng Z, Luo H, Deng K, Li Q, Xu Q, Liu K (2024) Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning. Int J Colorectal Dis 39(1):97. https:\/\/doi.org\/10.1007\/s00384-024-04671-2","journal-title":"Int J Colorectal Dis"},{"issue":"4","key":"5073_CR6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0174944","volume":"12","author":"SF Weng","year":"2017","unstructured":"Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017) Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE 12(4):e0174944. https:\/\/doi.org\/10.1371\/journal.pone.0174944","journal-title":"PLoS ONE"},{"issue":"10270","key":"5073_CR7","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/S0140-6736(20)32519-8","volume":"397","author":"F D'Ascenzo","year":"2021","unstructured":"D\u2019Ascenzo F, De Filippo O, Gallone G, Mittone G, Deriu MA, Iannaccone M et al (2021) Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet 397(10270):199\u2013207. https:\/\/doi.org\/10.1016\/S0140-6736(20)32519-8","journal-title":"Lancet"},{"key":"5073_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.797103","volume":"12","author":"W Li","year":"2022","unstructured":"Li W, Liu Y, Liu W, Tang ZR, Dong S, Li W et al (2022) Machine learning-based prediction of lymph node metastasis among osteosarcoma patients. Front Oncol 12:797103. https:\/\/doi.org\/10.3389\/fonc.2022.797103","journal-title":"Front Oncol"},{"issue":"5","key":"5073_CR9","doi-asserted-by":"publisher","first-page":"e367","DOI":"10.1016\/S2589-7500(24)00047-5","volume":"6","author":"R Han","year":"2024","unstructured":"Han R, Acosta JN, Shakeri Z, Ioannidis JPA, Topol EJ, Rajpurkar P (2024) Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health 6(5):e367\u2013e373. https:\/\/doi.org\/10.1016\/S2589-7500(24)00047-5","journal-title":"Lancet Digit Health"},{"key":"5073_CR10","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.1047433","volume":"13","author":"W Huang","year":"2022","unstructured":"Huang W, Xiao Y, Wang H, Chen G, Li K (2022) Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma. Front Endocrinol (Lausanne) 13:1047433. https:\/\/doi.org\/10.3389\/fendo.2022.1047433","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"8","key":"5073_CR11","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1038\/s41591-023-02482-6","volume":"29","author":"I Moon","year":"2023","unstructured":"Moon I, LoPiccolo J, Baca SC, Sholl LM, Kehl KL, Hassett MJ et al (2023) Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary. Nat Med 29(8):2057\u20132067. https:\/\/doi.org\/10.1038\/s41591-023-02482-6","journal-title":"Nat Med"},{"key":"5073_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fcell.2022.832803","volume":"10","author":"L Zhang","year":"2022","unstructured":"Zhang L, Su Z, Hong F, Wang L (2022) Identification of a methylation-regulating genes prognostic signature to predict the prognosis and aid immunotherapy of clear cell renal cell carcinoma. Front Cell Dev Biol 10:832803. https:\/\/doi.org\/10.3389\/fcell.2022.832803","journal-title":"Front Cell Dev Biol"},{"key":"5073_CR13","unstructured":"Amin MB, Edge SB, Greene FL, et al\u00a0(2017) AJCC cancer staging manual. 8th ed. New York, NY: Springer"},{"key":"5073_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14051199","author":"Y Wu","year":"2022","unstructured":"Wu Y, Cheng M, Huang S, Pei Z, Zuo Y, Liu J et al (2022) Recent advances of deep learning for computational histopathology: principles and applications. Cancers (Basel). https:\/\/doi.org\/10.3390\/cancers14051199","journal-title":"Cancers (Basel)"},{"issue":"1","key":"5073_CR15","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41746-021-00427-2","volume":"4","author":"E Wulczyn","year":"2021","unstructured":"Wulczyn E, Steiner DF, Moran M, Plass M, Reihs R, Tan F et al (2021) Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit Med 4(1):71. https:\/\/doi.org\/10.1038\/s41746-021-00427-2","journal-title":"NPJ Digit Med"},{"key":"5073_CR16","doi-asserted-by":"publisher","unstructured":"Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS\u00a0(2008) Random survival forests. Ann Appl Stat 2(3):841\u2013860. https:\/\/doi.org\/10.1214\/08-AOAS169","DOI":"10.1214\/08-AOAS169"},{"issue":"6","key":"5073_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3214306","volume":"51","author":"P Wang","year":"2019","unstructured":"Wang P, Li Y, Reddy CK (2019) Machine learning for survival analysis: a survey. ACM Comput Surv 51(6):1\u201336","journal-title":"ACM Comput Surv"},{"issue":"7","key":"5073_CR18","doi-asserted-by":"publisher","first-page":"136","DOI":"10.21037\/atm.2016.03.35","volume":"4","author":"Z Zhang","year":"2016","unstructured":"Zhang Z (2016) Variable selection with stepwise and best subset approaches. Ann Transl Med 4(7):136. https:\/\/doi.org\/10.21037\/atm.2016.03.35","journal-title":"Ann Transl Med"},{"key":"5073_CR19","doi-asserted-by":"publisher","DOI":"10.1002\/mdr2.70004","author":"H Zhang","year":"2025","unstructured":"Zhang H, Zhang G, Xu P, Yu F, Li L, Huang R et al (2025) Optimized dynamic network biomarker deciphers a high\u2010resolution heterogeneity within thyroid cancer molecular subtypes. Med Res. https:\/\/doi.org\/10.1002\/mdr2.70004","journal-title":"Med Res"},{"key":"5073_CR20","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2025-012069","author":"P Zhang","year":"2025","unstructured":"Zhang P, Zhang M, Liu J, Zhou Z, Zhang L, Luo P et al (2025) Mitochondrial pathway signature (MitoPS) predicts immunotherapy response and reveals NDUFB10 as a key immune regulator in lung adenocarcinoma. J Immunother Cancer. https:\/\/doi.org\/10.1136\/jitc-2025-012069","journal-title":"J Immunother Cancer"},{"key":"5073_CR21","doi-asserted-by":"publisher","DOI":"10.1080\/13645706.2025.2540482","author":"PA Boland","year":"2025","unstructured":"Boland PA, McEntee PD, Cucek J, Erzen S, Niemiec E, Galligan M et al (2025) Protocol for CLASSICA software as medical device trial. Minim Invasive Ther Allied Technol. https:\/\/doi.org\/10.1080\/13645706.2025.2540482","journal-title":"Minim Invasive Ther Allied Technol"},{"issue":"3","key":"5073_CR22","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1080\/13645706.2025.2454237","volume":"34","author":"M Pescio","year":"2025","unstructured":"Pescio M, Kundrat D, Dagnino G (2025) Endovascular robotics: technical advances and future directions. Minim Invasive Ther Allied Technol 34(3):239\u2013252. https:\/\/doi.org\/10.1080\/13645706.2025.2454237","journal-title":"Minim Invasive Ther Allied Technol"},{"issue":"2","key":"5073_CR23","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1038\/s41571-021-00560-7","volume":"19","author":"K Bera","year":"2022","unstructured":"Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A (2022) Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 19(2):132\u2013146. https:\/\/doi.org\/10.1038\/s41571-021-00560-7","journal-title":"Nat Rev Clin Oncol"},{"issue":"1","key":"5073_CR24","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44\u201356. https:\/\/doi.org\/10.1038\/s41591-018-0300-7","journal-title":"Nat Med"},{"issue":"10","key":"5073_CR25","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1038\/s41591-018-0136-1","volume":"24","author":"P Jiang","year":"2018","unstructured":"Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X et al (2018) Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 24(10):1550\u20131558. https:\/\/doi.org\/10.1038\/s41591-018-0136-1","journal-title":"Nat Med"},{"issue":"2","key":"5073_CR26","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3322\/caac.21388","volume":"67","author":"MB Amin","year":"2017","unstructured":"Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK et al (2017) 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. CA Cancer J Clin 67(2):93\u201399. https:\/\/doi.org\/10.3322\/caac.21388","journal-title":"CA Cancer J Clin"},{"issue":"3","key":"5073_CR27","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1111\/codi.16873","volume":"26","author":"L Hakki","year":"2024","unstructured":"Hakki L, Khan A, Do E, Gonen M, Firat C, Vakiani E et al (2024) Tumour deposits are independently associated with recurrence in colon cancer. Colorectal Dis 26(3):459\u2013465. https:\/\/doi.org\/10.1111\/codi.16873","journal-title":"Colorectal Dis"}],"container-title":["International Journal of Colorectal Disease"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00384-025-05073-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00384-025-05073-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00384-025-05073-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T02:45:55Z","timestamp":1768013155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00384-025-05073-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,9]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["5073"],"URL":"https:\/\/doi.org\/10.1007\/s00384-025-05073-8","relation":{},"ISSN":["1432-1262"],"issn-type":[{"value":"1432-1262","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,9]]},"assertion":[{"value":"11 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Written informed consent to participate in this study was not required from the participants or the participants\u2019 legal guardians\/next of kin in accordance with the national legislation and the institutional requirements.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"All relevant authors agree to publish.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"28"}}