Cardiovascular magnetic resonance of cardiac tumors and masses

In the setting of troponin-positive acute chest pain syndrome, the addition of late contrast enhancement CT significantly improved the diagnostic rate of triple-rule-out CT. Background Acute chest pain with mild troponin rise and inconclusive diagnosis after clinical evaluation represents a diagnostic challenge. Triple-rule-out (TRO) CT may exclude coronary artery dise...

Bone represents the second most common site of distant metastases in differentiated thyroid cancer (DTC). The clinical course of DTC patients with bone metastases (BM) is quite heterogeneous, but generally associated with low survival rates. Skeletal-related events might be a serious complication of BM, resulting in high morbidity and impaired quality of life. To achieve disease control and symptoms relief, multimodal treatment is generally required: radioiodine therapy, local procedures—including surgery, radiotherapy and percutaneous techniques—and systemic therapies, such as kinase inhibitors and antiresorptive drugs. The management of DTC with BM is challenging: a careful evaluation and a personalized approach are essential to improve patients’ outcomes. To date, prospective studies focusing on the main clinical aspects of DTC with BM are scarce; available analyses mainly include cohorts assembled over multiple decades, small samples sizes and data about BM not always separated from those regarding other distant metastases. The aim of this review is to summarize the most recent evidences and the unsolved questions regarding BM in DTC, analyzing several key issues: pathophysiology, prognostic factors, role of anatomic and functional imaging, and clinical management.

Objectives: To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions. Methods: Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions’ data and compared with a baseline reference and expert radiologist assessment of EPE. Results: In total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81–83%, p = 0.39–1) and outperforming the baseline reference (p = 0.001–0.02). Conclusions: A ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task. Key Points: • Predicting the presence of EPE in prostate cancer patients is a challenging task for radiologists. • A support vector machine algorithm achieved high diagnostic accuracy for EPE detection, with good generalizability when tested on multiple external datasets. • The performance of the algorithm was not significantly different from that of an experienced radiologist.