{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:32:19Z","timestamp":1775021539901,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-90191-0","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T02:13:31Z","timestamp":1741313611000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning"],"prefix":"10.1038","volume":"15","author":[{"given":"Ye","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huajun","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shoufeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfu","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxian","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"90191_CR1","doi-asserted-by":"crossref","unstructured":"Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6):394\u2013424. (2018).","DOI":"10.3322\/caac.21492"},{"issue":"8","key":"90191_CR2","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.7150\/ijbs.15438","volume":"12","author":"Y Li","year":"2016","unstructured":"Li, Y. et al. A review of Neoadjuvant Chemoradiotherapy for locally advanced rectal Cancer. Int. J. Biol. Sci. 12 (8), 1022\u20131031. https:\/\/doi.org\/10.7150\/ijbs.15438 (2016).","journal-title":"Int. J. Biol. Sci."},{"key":"90191_CR3","doi-asserted-by":"crossref","unstructured":"Gollins, S. & Sebag-Montefiore, D. Neoadjuvant treatment strategies for locally advanced rectal Cancer. Clin. Oncol. (R. Coll. Radiol.) 28(2):146\u2013151. (2016).","DOI":"10.1016\/j.clon.2015.11.003"},{"key":"90191_CR4","doi-asserted-by":"publisher","unstructured":"Nicholls, R. J., Zinicola, R. & Haboubi, N. Extramural spread of rectal cancer and the AJCC cancer staging manual 8th edition. Ann. Oncol. 30(8):1394\u20131395. https:\/\/doi.org\/10.1093\/annonc\/mdz147. (2017).","DOI":"10.1093\/annonc\/mdz147"},{"issue":"11","key":"90191_CR5","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1016\/j.ejso.2021.11.003","volume":"48","author":"SJA Hazen","year":"2022","unstructured":"Hazen, S. J. A., Sluckin, T. C., Konishi, T. & Kusters, M. Lateral lymph node dissection in rectal cancer: state of the art review. Eur. J. Surg. Oncol. 48 (11), 2315\u20132322 (2022).","journal-title":"Eur. J. Surg. Oncol."},{"key":"90191_CR6","doi-asserted-by":"publisher","unstructured":"Li, J. & Shiomi, A. Lateral lymph node dissection in advanced low rectal cancer treatment. Int. J. Colorectal Dis. 36(11):2361\u20132371.  https:\/\/doi.org\/10.1007\/s00384-021-03975-x (2021).","DOI":"10.1007\/s00384-021-03975-x"},{"issue":"7","key":"90191_CR7","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1016\/j.ejso.2022.04.016","volume":"48","author":"HM Kroon","year":"2022","unstructured":"Kroon, H. M. et al. Systematic review and meta-analysis of long-term oncological outcomes of lateral lymph node dissection for metastatic nodes after neoadjuvant chemoradiotherapy in rectal cancer. Eur. J. Surg. Oncol. 48 (7), 1475\u20131482 (2022).","journal-title":"Eur. J. Surg. Oncol."},{"key":"90191_CR8","doi-asserted-by":"publisher","unstructured":"Law, B. Z. Y., Yusuf, Z., Ng, Y. E. & Aly, E. H. Does adding lateral pelvic lymph node dissection to neoadjuvant chemotherapy improve outcomes in low rectal cancer? Int. J. Colorectal Dis. 35(8):1387\u20131395. https:\/\/doi.org\/10.1007\/s00384-020-03656-1. (2020).","DOI":"10.1007\/s00384-020-03656-1"},{"key":"90191_CR9","doi-asserted-by":"publisher","unstructured":"Gulmez, S. et al. Prognostic significance of the metastatic lymph node ratio compared to the TNM classification in stage III gastric cancer. Niger. J. Clin. Pract. 24(11):1602\u20131608.  https:\/\/doi.org\/10.4103\/njcp.njcp_345_20. (2021).","DOI":"10.4103\/njcp.njcp_345_20"},{"issue":"6","key":"90191_CR10","doi-asserted-by":"publisher","first-page":"1808","DOI":"10.1245\/s10434-011-2189-y","volume":"19","author":"C Tausch","year":"2012","unstructured":"Tausch, C. et al. Prognostic value of number of removed lymph nodes, number of involved lymph nodes, and lymph node ratio in 7502 breast cancer patients enrolled onto trials of the Austrian breast and Colorectal Cancer Study Group (ABCSG). Ann. Surg. Oncol. 19 (6), 1808\u20131817. https:\/\/doi.org\/10.1245\/s10434-011-2189-y (2012).","journal-title":"Ann. Surg. Oncol."},{"key":"90191_CR11","doi-asserted-by":"crossref","unstructured":"Li Destri, G. et al. Predictive Value of the Number of Harvested Lymph Nodes and Cut-Off for Lymph Node Ratio in the Prognosis of Stage II and III Colorectal Cancer Patients. J. Invest. Surg. 32(1):1\u20137. (2019).","DOI":"10.1080\/08941939.2017.1369605"},{"key":"90191_CR12","doi-asserted-by":"publisher","unstructured":"\u0130mamo\u011flu, G. \u0130. et al. The impact of lymph node ratio on overall survival in patients with colorectal cancer. J. Cancer Res. Ther. 17(4):1069\u20131074. https:\/\/doi.org\/10.4103\/jcrt.JCRT_11_19. (2021).","DOI":"10.4103\/jcrt.JCRT_11_19"},{"issue":"2","key":"90191_CR13","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1038\/ajg.2009.578","volume":"105","author":"S Ogino","year":"2010","unstructured":"Ogino, S. et al. Negative lymph node count is associated with survival of colorectal cancer patients, independent of tumoral molecular alterations and lymphocytic reaction. Am. J. Gastroenterol. 105 (2), 420\u2013433. https:\/\/doi.org\/10.1038\/ajg.2009.578 (2010).","journal-title":"Am. J. Gastroenterol."},{"key":"90191_CR14","doi-asserted-by":"publisher","unstructured":"Sun, Y. et al. Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings. J. Magn. Reson. Imaging. https:\/\/doi.org\/10.1002\/jmri.25969. (2018).","DOI":"10.1002\/jmri.25969"},{"issue":"1","key":"90191_CR15","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/0846537119885666","volume":"71","author":"W Ao","year":"2020","unstructured":"Ao, W. et al. Value of apparent diffusion coefficient for assessing preoperative T staging of low rectal Cancer and whether this is correlated with Ki-67 expression. Can. Assoc. Radiol. J. 71 (1), 5\u201311 (2020).","journal-title":"Can. Assoc. Radiol. J."},{"key":"90191_CR16","doi-asserted-by":"publisher","first-page":"895413","DOI":"10.3389\/fonc.2022.895413","volume":"12","author":"T Chen","year":"2022","unstructured":"Chen, T. et al. Predictive value of the log odds of Negative Lymph Nodes\/T Stage as a Novel Prognostic factor in bladder Cancer patients after Radical Cystectomy. Front. Oncol. 12, 895413. https:\/\/doi.org\/10.3389\/fonc.2022.895413 (2022).","journal-title":"Front. Oncol."},{"key":"90191_CR17","doi-asserted-by":"publisher","first-page":"1132687","DOI":"10.3389\/fendo.2023.1132687","volume":"14","author":"X Wang","year":"2023","unstructured":"Wang, X., Wu, Y., Li, X., Hong, J. & Zhang, M. Log odds of negative lymph nodes\/T stage ratio (LONT): a new prognostic tool for differentiated thyroid cancer without metastases in patients aged 55 and older. Front. Endocrinol. (Lausanne). 14, 1132687. https:\/\/doi.org\/10.3389\/fendo.2023.1132687 (2023).","journal-title":"Front. Endocrinol. (Lausanne)"},{"issue":"8","key":"90191_CR18","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1007\/s11605-022-05408-8","volume":"26","author":"W Yang","year":"2022","unstructured":"Yang, W., Lu, S., Ge, F., Hua, Y. & Chen, X. Prognostic and predictive model of the log odds of the Negative Lymph Node\/T Stage Ratio in Resectable gastric adenocarcinoma patients. J. Gastrointest. Surg. 26 (8), 1743\u20131756. https:\/\/doi.org\/10.1007\/s11605-022-05408-8 (2022).","journal-title":"J. Gastrointest. Surg."},{"issue":"8","key":"90191_CR19","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1007\/s00432-021-03654-y","volume":"147","author":"J Xie","year":"2021","unstructured":"Xie, J., Pang, Y., Li, X. & Wu, X. The log odds of negative lymph nodes\/T stage: a new prognostic and predictive tool for resected gastric cancer patients. J. Cancer Res. Clin. Oncol. 147 (8), 2259\u20132269. https:\/\/doi.org\/10.1007\/s00432-021-03654-y (2021).","journal-title":"J. Cancer Res. Clin. Oncol."},{"issue":"5","key":"90191_CR20","doi-asserted-by":"publisher","first-page":"e2211973","DOI":"10.1001\/jamanetworkopen.2022.11973","volume":"5","author":"Y Ren","year":"2022","unstructured":"Ren, Y. et al. Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative complications and Report on a Mobile platform. JAMA Netw. Open. 5 (5), e2211973 (2022).","journal-title":"JAMA Netw. Open."},{"key":"90191_CR21","unstructured":"Bertsimas, D., Dunn, J., Velmahos, G. C. & Kaafarani, H. M. A. Surgical Risk Is Not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator. Ann Surg."},{"issue":"5","key":"90191_CR22","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1097\/XCS.0000000000000556","volume":"236","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Ko, C. Y., Hall, B. L. & Cohen, M. E. American College of Surgeons NSQIP Risk Calculator Accuracy using a machine learning Algorithm compared with regression. J. Am. Coll. Surg. 236 (5), 1024\u20131030 (2023).","journal-title":"J. Am. Coll. Surg."},{"issue":"1","key":"90191_CR23","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1148\/radiol.2020190936","volume":"296","author":"XY Zhang","year":"2020","unstructured":"Zhang, X. Y. et al. Predicting rectal Cancer response to Neoadjuvant Chemoradiotherapy using Deep Learning of Diffusion Kurtosis MRI. Radiology 296 (1), 56\u201364 (2020).","journal-title":"Radiology"},{"issue":"11","key":"90191_CR24","doi-asserted-by":"publisher","first-page":"6263","DOI":"10.1007\/s00330-020-06968-6","volume":"30","author":"H Shaish","year":"2020","unstructured":"Shaish, H. et al. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. Eur. Radiol. 30 (11), 6263\u20136273. (2020).","journal-title":"Eur. Radiol."},{"issue":"1","key":"90191_CR25","doi-asserted-by":"publisher","first-page":"e8","DOI":"10.1016\/S2589-7500(21)00215-6","volume":"4","author":"L Feng","year":"2022","unstructured":"Feng, L. et al. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study. Lancet Digit. Health. 4 (1), e8\u2013e17 (2022).","journal-title":"Lancet Digit. Health"},{"issue":"7","key":"90191_CR26","doi-asserted-by":"publisher","first-page":"806","DOI":"10.6004\/jnccn.2020.0032","volume":"18","author":"AB Benson","year":"2020","unstructured":"Benson, A. B. et al. NCCN guidelines insights: rectal Cancer, Version 6.2020. J. Natl. Compr. Cancer Netw. 18 (7), 806\u2013815. https:\/\/doi.org\/10.6004\/jnccn.2020.0032 (2020).","journal-title":"J. Natl. Compr. Canc Netw."},{"issue":"1","key":"90191_CR27","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/s00384-023-04474-x","volume":"38","author":"S Li","year":"2023","unstructured":"Li, S., Wang, Y., Cai, H., Pan, Z. & Guan, G. A good preoperative immune prognostic index is precits a better prognosis for locally advanced rectal cancer patients with ypTNM stage II who underwent radical resection after neoadjuvant chemoradiotherapy. Int. J. Colorectal Dis. 38 (1), 184 (2023).","journal-title":"Int. J. Colorectal Dis."},{"issue":"7","key":"90191_CR28","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1016\/j.ejso.2022.04.016","volume":"48","author":"HM Kroon","year":"2022","unstructured":"Kroon, H. M. et al. Systematic review and meta-analysis of long-term oncological outcomes of lateral lymph node dissection for metastatic nodes after neoadjuvant chemoradiotherapy in rectal cancer. Eur. J. Surg. Oncol. 48 (7), 1475\u20131482 (2022).","journal-title":"Eur. J. Surg. Oncol."},{"issue":"7","key":"90191_CR29","doi-asserted-by":"publisher","first-page":"2280","DOI":"10.1245\/s10434-014-3559-z","volume":"21","author":"HK Oh","year":"2014","unstructured":"Oh, H. K. et al. Neoadjuvant chemoradiotherapy affects the indications for lateral pelvic node dissection in mid\/low rectal cancer with clinically suspected lateral node involvement: a multicenter retrospective cohort study. Ann. Surg. Oncol. 21 (7), 2280\u20132287 (2014).","journal-title":"Ann. Surg. Oncol."},{"issue":"1","key":"90191_CR30","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1245\/s10434-010-1238-2","volume":"18","author":"JT Liang","year":"2011","unstructured":"Liang, J. T. Technical feasibility of laparoscopic lateral pelvic lymph node dissection for patients with low rectal cancer after concurrent chemoradiation therapy. Ann. Surg. Oncol. 18 (1), 153\u2013159 (2011).","journal-title":"Ann. Surg. Oncol."},{"issue":"2","key":"90191_CR31","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.ijrobp.2023.04.013","volume":"117","author":"TC Sluckin","year":"2023","unstructured":"Sluckin, T. C. et al. Coverage of lateral lymph nodes in rectal cancer patients with routine radiation therapy practice and associated locoregional recurrence rates. Int. J. Radiat. Oncol. Biol. Phys. 117 (2), 422\u2013433 (2023).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"90191_CR32","doi-asserted-by":"crossref","unstructured":"Chen, T. & Guestrin, C. ACM Press the 22nd ACM SIGKDD International Conference - San Francisco, California, USA. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD \\16 - XGBoost. (2016).","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"90191_CR33","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1186\/s12874-023-01965-x","volume":"23","author":"ML Wallace","year":"2023","unstructured":"Wallace, M. L. et al. Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction. BMC Med. Res. Methodol. 23 (1), 144 (2023).","journal-title":"BMC Med. Res. Methodol."},{"key":"90191_CR34","unstructured":"Breiman, L. Classification and Regression Trees 1st edition (Routledge, 1984)."},{"key":"90191_CR35","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189\u20131232 (2001).","journal-title":"Ann. Stat."},{"issue":"12","key":"90191_CR36","doi-asserted-by":"publisher","first-page":"4090","DOI":"10.1111\/ene.15071","volume":"28","author":"A Shek","year":"2021","unstructured":"Shek, A. et al. Machine learning-enabled multitrust audit of stroke comorbidities using natural language processing. Eur. J. Neurol. 28 (12), 4090\u20134097 (2021).","journal-title":"Eur. J. Neurol."},{"issue":"5","key":"90191_CR37","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","volume":"29","author":"S Zhang","year":"2018","unstructured":"Zhang, S., Li, X., Zong, M., Zhu, X. & Wang, R. Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans. Neural Netw. Learn. Syst. 29 (5), 1774\u20131785 (2018).","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"90191_CR38","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neunet.2014.02.002","volume":"53","author":"M Lapin","year":"2014","unstructured":"Lapin, M., Hein, M. & Schiele, B. Learning using privileged information: SVM\u2009+\u2009and weighted SVM. Neural Netw. 53, 95\u2013108 (2014).","journal-title":"Neural Netw."},{"issue":"12","key":"90191_CR39","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.3390\/e23121703","volume":"23","author":"S Sugahara","year":"2021","unstructured":"Sugahara, S. & Ueno, M. Exact learning augmented naive bayes classifier. Entropy. 23 (12), 1703 (2021).","journal-title":"Entropy (Basel)"},{"issue":"1","key":"90191_CR40","doi-asserted-by":"publisher","first-page":"e0244029","DOI":"10.1371\/journal.pone.0244029","volume":"16","author":"M Mansour","year":"2021","unstructured":"Mansour, M., Alsulamy, S. & Dawood, S. Prediction of implementing ISO 14031 guidelines using a multilayer perceptron neural network approach. PloS One. 16 (1), e0244029 (2021).","journal-title":"PloS One"},{"key":"90191_CR41","doi-asserted-by":"publisher","first-page":"8065972","DOI":"10.1155\/2020\/8065972","volume":"2020","author":"P Lei","year":"2020","unstructured":"Lei, P. et al. Prognostic impact of the number of examined lymph nodes in stage II colorectal adenocarcinoma: a retrospective study. Gastroenterol. Res. Pract. 2020, 8065972. https:\/\/doi.org\/10.1155\/2020\/8065972 (2020).","journal-title":"Gastroenterol. Res. Pract."},{"issue":"1","key":"90191_CR42","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1177\/00031348231192069","volume":"90","author":"S Benli","year":"2024","unstructured":"Benli, S., Tazeo\u011flu, D. & \u00c7olak, T. Combination of the ratio between negative and harvested lymph nodes and Metastatic Lymph Node Count as a Prognostic Indicator in Stage III Colon cancer: a retrospective cohort study. Am. Surg. 90 (1), 38\u201345. (2024).","journal-title":"Am. Surg."},{"key":"90191_CR43","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.semcancer.2023.01.006","volume":"89","author":"S Huang","year":"2023","unstructured":"Huang, S., Yang, J., Shen, N., Xu, Q. & Zhao, Q. Artificial intelligence in lung cancer diagnosis and prognosis: current application and future perspective. Semin. Cancer Biol. 89, 30\u201337 (2023).","journal-title":"Semin Cancer Biol."},{"issue":"2","key":"90191_CR44","doi-asserted-by":"publisher","first-page":"475","DOI":"10.7150\/ijbs.55716","volume":"17","author":"Z Peng","year":"2021","unstructured":"Peng, Z. et al. Application of radiomics and machine learning in head and neck cancers. Int. J. Biol. Sci. 17 (2), 475\u2013486 (2021).","journal-title":"Int. J. Biol. Sci."},{"issue":"5","key":"90191_CR45","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1016\/j.ijrobp.2015.07.2286","volume":"93","author":"J Kang","year":"2015","unstructured":"Kang, J., Schwartz, R., Flickinger, J. & Beriwal, S. Machine Learning approaches for Predicting Radiation Therapy outcomes: a clinician\u2019s perspective. Int. J. Radiat. Oncol. Biol. Phys. 93 (5), 1127\u20131135 (2015).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"8","key":"90191_CR46","doi-asserted-by":"publisher","first-page":"1772","DOI":"10.1016\/j.cell.2023.01.035","volume":"186","author":"K Swanson","year":"2023","unstructured":"Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A. & Zou, J. From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell 186 (8), 1772\u20131791 (2023).","journal-title":"Cell"},{"issue":"2","key":"90191_CR47","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1109\/JBHI.2021.3095476","volume":"26","author":"J Cheng","year":"2022","unstructured":"Cheng, J. et al. Multimodal disentangled variational autoencoder with game theoretic interpretability for glioma grading. IEEE J. Biomed. Health Inf. 26 (2), 673\u2013684. https:\/\/doi.org\/10.1109\/JBHI.2021.3095476 (2022).","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"1","key":"90191_CR48","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1186\/s12967-023-04119-1","volume":"21","author":"J Liu","year":"2023","unstructured":"Liu, J. et al. Identification of colorectal cancer progression-associated intestinal microbiome and predictive signature construction. J. Transl. Med. 21 (1), 373. (2023).","journal-title":"J. Transl Med."},{"issue":"11","key":"90191_CR49","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1002\/jum.16297","volume":"42","author":"W Huang","year":"2023","unstructured":"Huang, W. et al. Utility of Machine Learning algorithms in Predicting Preoperative Lymph Node Metastasis in patients with rectal Cancer based on three-Dimensional Endorectal Ultrasound and Clinical and Laboratory Data. J. Ultrasound Med. 42 (11), 2615\u20132627. (2023).","journal-title":"J. Ultrasound Med."},{"key":"90191_CR50","doi-asserted-by":"publisher","first-page":"103761","DOI":"10.1016\/j.compbiomed.2020.103761","volume":"121","author":"B Ma","year":"2020","unstructured":"Ma, B. et al. Diagnostic classification of cancers using extreme gradient boosting algorithm and multi-omics data. Comput. Biol. Med. 121, 103761 (2020).","journal-title":"Comput. Biol. Med."},{"key":"90191_CR51","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Y. & Zhang, J. Information Computing and Applications. In: New. Machine Learning Algorithm. Random Forest. Springer, 246\u2013252. (2012)","DOI":"10.1007\/978-3-642-34062-8_32"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-90191-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-90191-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-90191-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T13:56:38Z","timestamp":1741355798000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-90191-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,7]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["90191"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-90191-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,7]]},"assertion":[{"value":"28 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"7924"}}