Value of the application of computed tomography‐based radiomics for preoperative prediction of unfavorable pathology in initial bladder cancer

Author:

Xiong Situ12ORCID,Dong Wentao3ORCID,Deng Zhikang4,Jiang Ming12,Li Sheng12,Hu Bing12,Liu Xiaoqiang12,Chen Luyao12,Xu Songhui12,Fan Bing3,Fu Bin12ORCID

Affiliation:

1. Department of Urology The First Affiliated Hospital of Nanchang University Nanchang China

2. Jiangxi Institute of Urology Nanchang China

3. Department of Radiology Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College Nanchang China

4. Department of Nuclear Medicine, Jiangxi Provincial People's Hospital The First Affiliated Hospital of Nanchang Medical College Nanchang China

Abstract

AbstractObjectivesTo construct and validate unfavorable pathology (UFP) prediction models for patients with the first diagnosis of bladder cancer (initial BLCA) and to compare the comprehensive predictive performance of these models.Materials and MethodsA total of 105 patients with initial BLCA were included and randomly enrolled into the training and testing cohorts in a 7:3 ratio. The clinical model was constructed using independent UFP‐risk factors determined by multivariate logistic regression (LR) analysis in the training cohort. Radiomics features were extracted from manually segmented regions of interest in computed tomography (CT) images. The optimal CT‐based radiomics features to predict UFP were determined by the optimal feature filter and the least absolute shrinkage and selection operator algorithm. The radiomics model consist with the optimal features was constructed by the best of the six machine learning filters. The clinic‐radiomics model combined the clinical and radiomics models via LR. The area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive value, calibration curve and decision curve analysis were used to evaluate the predictive performance of the models.ResultsPatients in the UFP group had a significantly older age (69.61 vs. 63.93 years, p = 0.034), lager tumor size (45.7% vs. 11.1%, p = 0.002) and higher neutrophil to lymphocyte ratio (NLR; 2.76 vs. 2.33, p = 0.017) than favorable pathologic group in the training cohort. Tumor size (OR, 6.02; 95% CI, 1.50–24.10; p = 0.011) and NLR (OR, 1.50; 95% CI, 1.05–2.16; p = 0.026) were identified as independent predictive factors for UFP, and the clinical model was constructed using these factors. The LR classifier with the best AUC (0.817, the testing cohorts) was used to construct the radiomics model based on the optimal radiomics features. Finally, the clinic‐radiomics model was developed by combining the clinical and radiomics models using LR. After comparison, the clinic‐radiomics model had the best performance in comprehensive predictive efficacy (accuracy = 0.750, AUC = 0.817, the testing cohorts) and clinical net benefit among UFP‐prediction models, while the clinical model (accuracy = 0.625, AUC = 0.742, the testing cohorts) was the worst.ConclusionOur study demonstrates that the clinic‐radiomics model exhibits the best predictive efficacy and clinical net benefit for predicting UFP in initial BLCA compared with the clinical and radiomics model. The integration of radiomics features significantly improves the comprehensive performance of the clinical model.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology

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