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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]

Purpose: To compare various models of diffusion-weighted imaging including mono-exponential, bi-exponential, diffusion kurtosis (DK) and fractional-order calculus (FROC) models in diagnosing prostate cancer (PCa) in transition zone (TZ) and distinguish the high-grade PCa [Gleason score (GS) ≥ 7] lesions from the total of low-grade PCa (GS ≤ 6) lesions and benign prostatic hyperplasia (BPH) in TZ. Methods: 80 Patients with 103 lesions were included in this study. Nine metrics [including apparent diffusion coefficient (ADC) derived from mono-exponential model, slow diffusion coefficient (Ds), fast diffusion coefficient (Df),, and f (the fraction of fast diffusion) from bi-exponential model; mean diffusivity (MD) and mean kurtosis (MK) from DK model; diffusion coefficient (D), fractional-order derivative in space (β), and spatial metric (μ) from FROC model] were calculated. Comparisons between BPH and PCa lesions as well as between clinically significant PCa (CsPCa) (GS ≥ 7, n = 31) and clinically insignificant lesions (Cins) (GS ≤ 6 and BPH, n = 72) of these metrics were conducted. Mann–Whitney U-test and receiver operating characteristic (ROC) analysis were used for statistical evaluations. Results: The areas under the ROC curve (AUC) values of β derived from FROC model were 0.778 and 0.853 in differentiating PCa from BPH and in differentiating CS (GS ≥ 7) from Cins (GS ≤ 6 and BPH), both were the highest compared to other metrics. The AUC value of β was significantly higher than that of ADC (P = 0.009) in differentiating CS from Cins, while the differentiation between BPH and PCa did not reach the statistical significance when comparing with ADC (P = 0.089). Conclusion: Although no significant difference was found in distinguishing PCa from BPH, the metric β derived from FROC model was superior to other diffusion metrics in differentiation between CS and Cins in TZ. [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]