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Objectives: To develop an artificial intelligence (AI) model for prostate segmentation and prostate cancer (PCa) detection, and explore the added value of AI-based computer-aided diagnosis (CAD) compared to conventional PI-RADS assessment. Methods: A retrospective study was performed on multi-centers and included patients who underwent prostate biopsies and multiparametric MRI. A convolutional-neural-network-based AI model was trained and validated; the reliability of different CAD methods (concurrent read and AI-first read) were tested in an internal/external cohort. The diagnostic performance, consistency and efficiency of radiologists and AI-based CAD were compared. Results: The training/validation/internal test sets included 650 (400/100/150) cases from one center; the external test included 100 cases (25/25/50) from three centers. For diagnosis accuracy, AI-based CAD methods showed no significant differences and were equivalent to the radiologists in the internal test (127/150 vs. 130/150 vs. 125/150 for reader 1; 127/150 vs.132/150 vs. 131/150 for reader 2; all p > 0.05), whereas in the external test, concurrent-read methods were superior/equal to AI-first read (87/100 vs. 71/100, p < 0.001, for reader 2; 79/100 vs. 69/100, p = 0.076, for reader 1) and better than/equal to radiologists (79/100 vs. 72/100, p = 0.039, for reader 1; 87/100 vs. 86/100, p = 1.000, for reader 2). Moreover, AI-first read/concurrent read improved consistency in both internal test (κ = 1.000, 0.830) and external test (κ = 0.958, 0.713) compared to radiologists (κ = 0.747, 0.600); AI-first read method (8.54 s/7.66 s) was faster than readers (92.72 s/89.54 s) and concurrent-read method (29.15 s/28.92 s), respectively. Conclusion: AI-based CAD could improve the consistency and efficiency for accurate diagnosis; the concurrent-read method could enhance the diagnostic capabilities of an inexperienced radiologist in unfamiliar situations. Key Points: • For prostate cancer segmentation, the performance of multi-small Vnet displays optimal compared to small Vnet and Vnet (DSCmsvnet vs. DSCsvnet, p = 0.021; DSCmsvnet vs. DSCvnet, p < 0.001). • For prostate gland segmentation, the mean/median DSCs for fine and coarse segmentation were 0.91/0.91 and 0.88/0.89, respectively. Fine segmentation displays superior performance compared to coarse (DSCcoarse vs. DSCfine, p < 0.001). • For PCa diagnosis, AI-based CAD methods improve consistency in internal (κ = 1.000; 0.830) and external (κ = 0.958; 0.713) tests compared to radiologists (κ = 0.747; 0.600); the AI-first read (8.54 s/7.66 s) was faster than the readers (92.72 s/89.54 s) and the concurrent-read method (29.15 s/28.92 s). [ABSTRACT FROM AUTHOR]

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]

Objectives: To assess the ability of renal blood oxygen level dependent (BOLD) MRI metrics to predict post-operative renal function.Methods: We studied 152 patients who underwent laparoscopic partial nephrectomy (LPN) and renal MRI examination including BOLD. Short-term and long-term renal function was evaluated using the glomerular filtration rate (GFR) derived from renal scintigraphy. Renal function decline was assessed as the absolute decline (AD), percentage decline (PD) and optimal renal function preservation (OP). T2* values were analysed in the renal cortex and medulla ipsilateral and contralateral to the tumour. Clinical characteristics and imaging metrics were evaluated using univariate and multivariate linear regression analyses. Risk factors obtained using BOLD metrics (determined by multivariate regression) were then combined and compared with RENAL scores to predict OP.Results: Increasing warm ischaemia time (WIT), resected and ischaemic volume (RAIV), larger tumour size, higher RENAL score and lower preoperative GFR were short-term risk factors for AD, while increasing WIT and lower preoperative GFR were significant for long-term outcomes. Increasing WIT, RAIV, lower T2* value in the cortex and higher T2* value in the medulla on the ipsilateral side were short-term risk factors for PD, while all of the above factors (except WIT and RAIV) were significant for long-term outcomes. The performance of the combination of T2* values in the cortex and medulla on the ipsilateral side to tumour in predicting OP was better than RENAL score (AUC 0.762 vs 0.634, p = 0.013).Conclusions: Renal BOLD-MRI metrics could provide useful information to the clinician in predicting post-operative renal function outcomes.Key Points: • Renal fMRI metrics may be useful for prediction of renal functional outcomes and merit further study. • Renal fMRI metrics may reflect degree of baseline disease and ability to tolerate warm ischaemia. • Combination of T2* values was better than RENAL score for predicting OP. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the utility of MR R2*-mapping and the optimal time-point for assessing the response of pulmonary metastatic renal cell carcinoma (mRCC) to anti-angiogenic targeted therapy (aATT).Materials and Methods: The exploration-sample group and the validation-sample group consisted of 22 and 16 patients. The parameters of MR R2*-mapping, including the R2* value at each time-point (R2*base, R2*1cyc and R2*2cyc) and change between different time-points (R2*(1cyc-base)/base, R2*(2cyc-base)/base and R2*(2cyc-1cyc)/1cyc), were evaluated with a receiver-operating-characteristic analysis, and a cut-off value derived from the clinical outcome was applied to the Kaplan-Meier method to assess the value of R2* mapping and Response-Evaluation-Criteria in Solid Tumours (RECIST) during treatment evaluation.Results: The inter-, intra-observer agreements and inter-scan consistency were excellent (p > 0.80). For the exploration-sample group, the areas under the curve for the parameters of MR R2* mapping were 0.55, 0.60, 0.83, 0.64, 0.88 and 0.83 for R2*base, R2*1cyc, R2*2cyc, R2*(1cyc-base)/base, R2*(2cyc-base)/base and R2*(2cyc-1cyc)/1cyc. For the validation-sample, R2*(2cyc-base)/base better predicted progression-free survival (p = 0.03) than RECIST and other R2* mapping parameters with a lower p value.Conclusion: Assessing aATT outcome based on changes in the R2* value between baseline and second treatment is more accurate than assessment at other time-points and assessment based on the RECIST.Key Points: • The inter-scan consistency of R2*-mapping in pulmonary mRCC are excellent. • The intra-/inter-observer agreement of R2* mapping in pulmonary mRCC are excellent. • Using changes in R2* value between baseline/after second-treatment is better than RECIST. • The choice of baseline/after second treatment is better than other time-points. [ABSTRACT FROM AUTHOR]