Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.
Purpose: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data. Materials and methods: In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed...
| Publicado en: | Abdominal Radiology Vol. 49; no. 7; pp. 2311 - 2325 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2024
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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=178678147&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178678147 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jul2024 vid: 49 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178678147 177838257 10.1007/s00261-024-04418-1 178678147 ppf: 2311 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study. aug: au: Yang, YouChang Wang, JiaJia Ren, QingGuo Yu, Rong Yuan, ZiYi Jiang, QingJun Guan, Shuai Tang, XiaoQiang Duan, TongTong Meng, XiangShui affil: https://ror.org/0207yh398 Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, 266035, Qingdao, China sug: ab: Purpose: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data. Materials and methods: In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included area under the receiver operating characteristic curve (AUC) value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and decision curve analysis (DCA) curve. Results: A total of 251 patients were evaluated. Patients (n = 166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n = 37) from Shandong University Qilu Hospital (Qingdao) were used as internal testing, of which 15 patients developed metastases; patients (n = 48) from Changzhou Second People's Hospital were used as external testing, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (AUC, 0.924) in predicting lymph node metastasis (LNM), while the clinical and radiomics models both had AUCs of 0.845 and 0.870, respectively. In the internal testing, the combined model had the highest performance (AUC, 0.877) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the external testing, the combined model had the highest performance (AUC, 0.849) for predicting LNM, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of LNM in ccRCC patients compared with the clinical model or the radiomics model. Conclusion: The combined model was superior to the clinical and radiomics models in predicting LNM in ccRCC patients. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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