Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer

Abstract

ObjectiveThe purpose of this research was to develop a radiomics model that combines several clinical features for preoperative prediction of the pathological grade of bladder cancer (BCa) using non-enhanced computed tomography (NE-CT) scanning images.Materials and methodsThe computed tomography (CT), clinical, and pathological data of 105 BCa patients attending our hospital between January 2017 and August 2022 were retrospectively evaluated. The study cohort comprised 44 low-grade BCa and 61 high-grade BCa patients. The subjects were randomly divided into training (n = 73) and validation (n = 32) cohorts at a ratio of 7:3. Radiomic features were extracted from NE-CT images. A total of 15 representative features were screened using the least absolute shrinkage and selection operator (LASSO) algorithm. Based on these characteristics, six models for predicting BCa pathological grade, including support vector machine (SVM), k-nearest neighbor (KNN), gradient boosting decision tree (GBDT), logical regression (LR), random forest (RF), and extreme gradient boosting (XGBOOST) were constructed. The model combining radiomics score and clinical factors was further constructed. The predictive performance of the models was evaluated based on the area under the receiver operating characteristic (ROC) curve, DeLong test, and decision curve analysis (DCA).ResultsThe selected clinical factors for the model included age and tumor size. LASSO regression analysis identified 15 features most linked to BCa grade, which were included in the machine learning model. The SVM analysis revealed that the highest AUC of the model was 0.842. A nomogram combining the radiomics signature and selected clinical variables showed accurate prediction of the pathological grade of BCa preoperatively. The AUC of the training cohort was 0.919, whereas that of the validation cohort was 0.854. The clinical value of the combined radiomics nomogram was validated using calibration curve and DCA.ConclusionMachine learning models combining CT semantic features and the selected clinical variables can accurately predict the pathological grade of BCa, offering a non-invasive and accurate approach for predicting the pathological grade of BCa preoperatively.

A computed tomography urography‐based machine learning model for predicting preoperative pathological grade of upper urinary tract urothelial carcinoma

“…In patients with breast cancer, a substantial association has been shown between GLCM idmn and one‐year relapse‐free survival 42 . Moreover, it was discovered that GLRLM may be used to predict acute pulmonary thromboembolism, 43 whereas NGTDM is linked to the prediction of pathological grade in bladder cancer 44 . The aforementioned studies have demonstrated that radiomics features not only enable the expression of tumor heterogeneity, which is challenging to detect visually, through digital information but also exhibit potential for evaluating tumor prognosis 12 .…”

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

“…Our study confirmed this association, as the radiomics scores derived from planning CT images were significantly higher in the pCR group, with all corresponding AUC values being greater than or nearly 80%. While some studies favored SVM classifiers over the LASSO or RF methods [32][33][34][35] in the development of radiomics models, other studies reported the superior performance of the LASSO or RF classifiers [27,[36][37][38][39]. These divergent findings suggest that the choice of machine learning model influenced the predictive performance.…”

Imaging in Upper Tract Urothelial Carcinoma: A Review

“…There has been promising recent work applying deep learning and multiomics methods to UTUC. Future work may incorporate the direct use of radiologic imaging in these machine learning models for UTUC detection, subtyping, and prognostication, as has been performed in abundance for urothelial cancer of the bladder [32,33].…”