• Based on existing study quality standard frameworks such as SPIRIT and STARD, we propose a list of quality criteria for ML studies in radiology. • The cardiovascular imaging research community should strive for the compilation of multicenter datasets for the development, evaluation, and benchmarking of ML algorithms.
  Patients with hepatocellular carcinoma (HCC) receiving different treatments might have specific prognostic factors that can be captured in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced magnetic resonance imaging (GA-MRI). We aimed to identify the clinical findings and HBP features with prognostic value in patients with HCC.
 
  In this retrospective, single-institution study, we included patients with Barcelona Clinic Liver Cancer very early/early stage HCC who underwent GA-MRI before treatment. After performing propensity score matching, 183 patients received the following treatments resection, radiofrequency ablation (RFA), and transarterial chemoembolization (TACE) (n = 61 for each). Cox regression models were used to identify clinical factors and HBP features associated with disease-free survival (DFS) and overall survival (OS).
 
  In the resection group, large tumor size was associated with poor DFS (hazard ratio [HR] 4.159 per centimeter; 95% confidence interval [CI], 1.669-10.365) and poor OS, no clinical or HBP imaging features were associated with disease-free survival or overall survival.
 • In patients who underwent resection for HCC, a large tumor size on HBP images was associated with poor disease-free survival and overall survival. • In the RFA group, satellite nodules and peritumoral hypointensity on HBP images, along with decreased serum albumin levels and PT-INR, were associated with poor disease-free survival and/or overall survival. • In the TACE group, no clinical or HBP imaging features were associated with disease-free survival or overall survival.
  Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT.
 
  A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets.
 
  Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness.
 
  The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process.
 
  • Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.
 • Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.
  To illustrate tumor contour irregularity on preoperative imaging with a practical method and further determine its value in predicting disease-free survival (DFS) in patients with pRCC (papillary renal cell carcinoma).
 
  We performed a retrospective single-institution review of 267 Chinese pRCC patients between March 2009 and May 2019. Contour irregularity on cross-section was classified into smooth but distorted margin, unsmooth and sharply nodular margin, and blurred margin. Then, the ratio of the cross-section numbers of irregularity and the total tumor was defined as the contour irregular degree (CID). Cox regression and Kaplan-Meier analysis were performed to analyze the impact of CID on DFS.  https://www.selleckchem.com/products/PP242.html  Then, the prognostic performance of CID was compared with pRCC risk stratification published by Leibovich et al. RESULTS The median follow-up was 45 months (IQR 23-69), in which 27 (10%) patients had metastasis or recurrence. Observed DFS rates were 95%, 90%, and 88% at 1, 3, and 5 years. The CID was an independense-free survival. • Tumor contour irregularity in pRCC risk stratification outperformed Leibovich's model from our cohort.
  (1) To investigate whether a contrast-free biparametric MRI (bp-MRI) including T2-weighted images (T2W) and diffusion-weighted images (DWI) can be considered an accurate alternative to the standard multiparametric MRI (mp-MRI), consisting of T2, DWI, and dynamic contrast-enhanced (DCE) imaging for the muscle-invasiveness assessment of bladder cancer (BC), and (2) to evaluate how the diagnostic performance of differently experienced readers is affected according to the type of MRI protocol.
 
  Thirty-eight patients who underwent a clinically indicated bladder mp-MRI on a 3-T scanner were prospectively enrolled. Trans-urethral resection of bladder was the gold standard. Two sets of images, set 1 (bp-MRI) and set 2 (mp-MRI), were independently reviewed by four readers. Descriptive statistics, including sensitivity and specificity, were calculated for each reader. Receiver operating characteristic (ROC) analysis was performed, and the areas under the curve (AUCs) were calculated for the bp-MRI and the standard mp-MRI.