Machine learning models for predicting postoperative peritoneal metastasis after hepatocellular carcinoma rupture: a multicenter cohort study in China.
Background Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognosis. Machine learning models have shown great potential in predicting clinical outcomes; however, the optimal model for this specific problem remains un...
| Publicado en: | Oncologist Vol. 30; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=182886360&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 182886360 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10837159 IJ7 jtl: Oncologist issn: 10837159 maglogo: N pubinfo: dt: Jan2025 vid: 30 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 182886360 182886360 182886360 10.1093/oncolo/oyae341 182886360 ppf: 1 ppct: 12 formats: tig: atl: Machine learning models for predicting postoperative peritoneal metastasis after hepatocellular carcinoma rupture: a multicenter cohort study in China. aug: au: Xia, Feng Chen, Qian Liu, Zhicheng Zhang, Qiao Guo, Bin Fan, Feimu Huang, Zhiyuan Zheng, Jun Gao, Hengyi Xia, Guobing Ren, Li Mei, Hongliang Chen, Xiaoping Cheng, Qi Zhang, Bixiang Zhu, Peng affil: Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China sug: subj: Machine Learning Prediction Models Neoplasm Metastasis Risk Factors Peritoneal Neoplasms Risk Factors Risk Assessment Carcinoma, Hepatocellular Complications Postoperative Complications Carcinoma, Hepatocellular Surgery Peritoneal Neoplasms Prognosis Human Multicenter Studies Prospective Studies Cancer Patients Logistic Regression Support Vector Machine Classification Algorithms Random Forest Deep Learning Tumor Burden Hepatectomy alpha Fetoproteins Retrospective Design Male Female Middle Age Aged China Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Background Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognosis. Machine learning models have shown great potential in predicting clinical outcomes; however, the optimal model for this specific problem remains unclear. Methods Clinical data were collected and analyzed from 522 patients with ruptured HCC who underwent surgery at 7 different medical centers. Patients were assigned to the training, validation, and test groups in a random manner, with a distribution ratio of 7:1.5:1.5. Overall, 78 (14.9%) patients experienced postoperative PM. Five different types of models, including logistic regression, support vector machines, classification trees, random forests, and deep learning (DL) models, were trained using these data and evaluated based on their receiver operating characteristic curve and area under the curve (AUC) values and F1 scores. Results The DL models achieved the highest AUC values (10-fold training cohort: 0.943, validation set: 0.928, and test set: 0.892) and F1 scores (10-fold training set: 0.917, validation cohort: 0.908, and test set:0.899) The results of the analysis indicate that tumor size, timing of hepatectomy, alpha-fetoprotein levels, and microvascular invasion are the most important predictive factors closely associated with the incidence of postoperative PM. Conclusion The DL model outperformed all other machine learning models in predicting postoperative PM after the rupture of HCC based on clinical data. This model provides valuable information for clinicians to formulate individualized treatment plans that can improve patient outcomes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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