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Objectives: To develop a radiomics model using preoperative multiphasic CT for predicting distant metastasis after surgical resection in patients with localized clear cell renal cell carcinoma (ccRCC) and to identify key biological pathways underlying the predictive radiomics features using RNA sequencing data. Methods: In this multi-institutional retrospective study, a CT radiomics metastasis score (RMS) was developed from a radiomics analysis cohort (n = 184) for distant metastasis prediction. Using a gene expression analysis cohort (n = 326), radiomics-associated gene modules were identified. Based on a radiogenomics discovery cohort (n = 42), key biological pathways were enriched from the gene modules. Furthermore, a multigene signature associated with RMS was constructed and validated on an independent radiogenomics validation cohort (n = 37). Results: The 9-feature-based RMS predicted distant metastasis with an AUC of 0.861 in validation set and was independent with clinical factors (p < 0.001). A gene module comprising 114 genes was identified to be associated with all nine radiomics features (p < 0.05). Four enriched pathways were identified, including ECM-receptor interaction, focal adhesion, protein digestion and absorption, and PI3K-Akt pathways. Most of them play important roles in tumor progression and metastasis. A 19-gene signature was constructed from the radiomics-associated gene module and predicted metastasis with an AUC of 0.843 in the radiogenomics validation cohort. Conclusions: CT radiomics features can predict distant metastasis after surgical resection of localized ccRCC while the predictive radiomics phenotypes may be driven by key biological pathways related to cancer progression and metastasis. Key Points: • Radiomics features from primary tumor in preoperative CT predicted distant metastasis after surgical resection in patients with localized ccRCC. • CT radiomics features predictive of distant metastasis were associated with key signaling pathways related to tumor progression and metastasis. • Gene signature associated with radiomics metastasis score predicted distant metastasis in localized ccRCC. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the value of radiomics features from diffusion-weighted imaging (DWI) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC).Methods: This retrospective study included 218 pathologically confirmed bladder cancer patients (training set: 131 patients, 86 MIBC; validation set: 87 patients, 55 MIBC) who underwent DWI before biopsy through transurethral resection (TUR) between July 2014 and December 2018. Radiomics models based on DWI for discriminating state of muscle-invasive were built using random forest (RF) and all-relevant (AR) methods on the training set and were tested on validation set. Combination models based on TUR data were also built. Discrimination performances were evaluated with the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and F1 and F2 scores. Qualitative MRI evaluation based on morphology was performed for comparison.Results: No significant difference was found between RF and AR models. RF model was more sensitive than TUR (0.873 vs 0.655, p = 0.019) for discriminating muscle-invasive bladder cancer. When combining RF with TUR, the sensitivity increased to 0.964, significantly higher than TUR (0.655, p < 0.001), MRI evaluation (0.764, p = 0.006), and the combination of TUR and MRI (0.836, p = 0.046). Combining RF and TUR achieved the highest accuracy of 0.897 and F2 score of 0.946.Conclusion: Combining DWI radiomics features with TUR could improve the sensitivity and accuracy in discriminating the presence of muscle invasion in bladder cancer for clinical practice. Multicenter, prospective studies are needed to confirm our results.Key Points: • Twenty-seven to 51% of superficial bladder cancers diagnosed by transurethral resection are upstaged to muscle-invasive at radical cystectomy, suggesting its poor sensitivity for discriminating muscle-invasive bladder cancer. • A small subset of selected all-relevant radiomics features exhibited an equivalent performance compared to that of all the extracted features, confirming that radiomics data contained redundant or irrelevant features and that feature selection should be performed in building radiomics models. • Combining DWI radiomics features with transurethral resection could improve in clinical practice the sensitivity and accuracy for the detection of muscle invasion in bladder cancer. [ABSTRACT FROM AUTHOR]