A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer

• Published in: Abdominal imaging Vol. 50; no. 7; pp. 3049 - 3059
• Main Authors: Zhao, Ting, He, Jian, Zhang, Licui, Li, Hongyang, Duan, Qinghong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2025; Springer Nature B.V
• Subjects: Accuracy; Aged; Aged, 80 and over; Biopsy; Bladder; Bladder cancer; Cancer; Cancer therapies; Computed tomography; Contrast agents; Deep Learning; Female; Gastroenterology; Gender; Hematuria; Hepatology; Humans; Imaging; Lymphocytes; Machine learning; Male; Medical imaging; Medical prognosis; Medical screening; Medicine; Medicine & Public Health; Middle Aged; Neoplasm Grading; Neutrophils; Pathology; Patients; Performance evaluation; Prediction models; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Radiomics; Regression analysis; Regression models; Retrospective Studies; Review; Risk factors; Software; Test sets; Tomography; Tomography, X-Ray Computed - methods; Tumors; Urinary Bladder - diagnostic imaging; Urinary Bladder - pathology; Urinary Bladder Neoplasms - diagnostic imaging; Urinary Bladder Neoplasms - pathology; Combined model; Pathological grading; Deep-learning; Bladder cancer; Radiomics
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-024-04748-0

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.