A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.
Objective: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images. Methods: A retrospective analysis was conducted involving 201 bladder cancer patie...
| Publicado en: | Abdominal Radiology Vol. 50; no. 7; pp. 3049 - 3060 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2025
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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=186105102&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186105102 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jul2025 vid: 50 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 186105102 181697725 10.1007/s00261-024-04748-0 186105102 ppf: 3049 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer. aug: au: Zhao, Ting He, Jian Zhang, Licui Li, Hongyang Duan, Qinghong affil: https://ror.org/02kstas42 Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guizhou, China sug: ab: Objective: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images. Methods: A retrospective analysis was conducted involving 201 bladder cancer patients with definite pathological grading results after surgical excision at the organization between January 2019 and June 2023. The cohort was classified into a test set of 81 cases and a training set of 120 cases. Hand-crafted radiomics (HCR) and features derived from deep-learning (DL) were obtained from computed tomography (CT) images. The research builds a prediction model using 12 machine-learning classifiers, which integrate HCR, DL features, and clinical data. Model performance was estimated utilizing decision-curve analysis (DCA), the area under the curve (AUC), and calibration curves. Results: Among the classifiers tested, the logistic regression model that combined DL and HCR characteristics demonstrated the finest performance. The AUC values were 0.912 (training set) and 0.777 (test set). The AUC values of clinical model achieved 0.850 (training set) and 0.804 (test set). The AUC values of the combined model were 0.933 (training set) and 0.824 (test set), outperforming both the clinical and HCR-only models. Conclusion: The CT-based combined model demonstrated considerable diagnostic capability in differentiating high-grade from low-grade bladder cancer, serving as a valuable noninvasive instrument for preoperative pathological evaluation. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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