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 patient...