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• W3136203106 abstract "•Use of radiomics signature may predict pathologic complete response. •Radiomics-based model showed higher net benefit than no model use. •Radiomics-based model provides a tool for optimizing treatment plans. AIM To develop and validate a radiomics-based model for predicting response to neoadjuvant chemotherapy (NAC) using baseline computed tomography (CT) images in patients with muscle-invasive bladder cancer (MIBC). MATERIALS AND METHODS A radiomics signature for predicting pathological complete response (pCR) was developed using radiomics features selected by a random forest classifier on baseline CT images, and imaging predictors were identified in the training set (87 patients). By incorporating imaging predictors and radiomics signature, an imaging-based model was constructed using multivariate logistic regression analysis and validated in an independent validation set consisting of 48 patients with CT from outside institutions. The performance and clinical usefulness of the imaging-based model for predicting pCR were evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis. Using a cut-off determined in the training set, the positive likelihood ratios of the imaging-based model were calculated and compared with imaging and histological predictors. RESULTS The radiomics signature was developed based on six stable radiomics features. An imaging-based model incorporating radiomics signature, tumour shape, tumour size, and clinical stage showed good performance for predicting pCR in both the training (AUC, 0.85; 95% confidence interval [CI], 0.78–0.93) and validation (AUC, 0.75; 95% CI, 0.60–0.86) sets, providing a larger net benefit in decision curve analysis. The imaging-based model showed a higher positive likelihood ratio (1.91) for pCR than imaging and histological predictors (1.33–1.63). CONCLUSIONS The radiomics-based model using baseline CT images may predict the response of patients with MIBC to NAC. To develop and validate a radiomics-based model for predicting response to neoadjuvant chemotherapy (NAC) using baseline computed tomography (CT) images in patients with muscle-invasive bladder cancer (MIBC). A radiomics signature for predicting pathological complete response (pCR) was developed using radiomics features selected by a random forest classifier on baseline CT images, and imaging predictors were identified in the training set (87 patients). By incorporating imaging predictors and radiomics signature, an imaging-based model was constructed using multivariate logistic regression analysis and validated in an independent validation set consisting of 48 patients with CT from outside institutions. The performance and clinical usefulness of the imaging-based model for predicting pCR were evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis. Using a cut-off determined in the training set, the positive likelihood ratios of the imaging-based model were calculated and compared with imaging and histological predictors. The radiomics signature was developed based on six stable radiomics features. An imaging-based model incorporating radiomics signature, tumour shape, tumour size, and clinical stage showed good performance for predicting pCR in both the training (AUC, 0.85; 95% confidence interval [CI], 0.78–0.93) and validation (AUC, 0.75; 95% CI, 0.60–0.86) sets, providing a larger net benefit in decision curve analysis. The imaging-based model showed a higher positive likelihood ratio (1.91) for pCR than imaging and histological predictors (1.33–1.63). The radiomics-based model using baseline CT images may predict the response of patients with MIBC to NAC." @default.
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• W3136203106 date "2021-08-01" @default.
• W3136203106 modified "2023-10-11" @default.
• W3136203106 title "Radiomics-based model for predicting pathological complete response to neoadjuvant chemotherapy in muscle-invasive bladder cancer" @default.
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• W3136203106 doi "https://doi.org/10.1016/j.crad.2021.03.001" @default.
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