Citations

Abstract Minimally invasive image-guided tumor ablation techniques have been established as safe, effective methods to treat a variety of unresectable soft tissue tumors. Standard thermal ablation methods include radiofrequency ablation, microwave ablation, and cryoablation. However, newer non-thermal and/or non-invasive ablation techniques are now available as alternative options to treat soft tissue tumors, particularly those that are near critical structures or otherwise susceptible to thermal energy sink effects. The 4 types of emerging ablation techniques discussed in this review are as follows: irreversible electroporation, pulsed electric field, high-intensity focused ultrasound, and histotripsy. While the clinical trials evaluating the safety and efficacy of these ablation techniques are in their early stages, initial results are promising in the treatment of various cancers at different stages. These include potential synergistic effects when combined with chemotherapy and immunotherapy.

AbstractAlthough bronchial artery embolization (BAE) initially emerged as a critical minimally invasive image-guided procedure to manage massive hemoptysis regardless of underlying etiologies, there has been a growing interest in BAE to treat pulmonary malignancies within recent years. This article provides an in-depth exploration of bronchial artery anatomy, pathologies, imaging modalities in guiding effective BAE, and procedural pearls for successful intervention. The complexity of bronchial artery anatomy, including its variations and potential complications during embolization, necessitates thorough understanding and precise procedural techniques to optimize patient outcomes and minimize risks.

AbstractAcute pancreatitis (AP) is a severe inflammatory condition of the pancreas with significant morbidity and mortality, particularly in its necrotizing form. This review explores the management of peripancreatic collections and in particular necrotizing pancreatitis, focusing on the evolution from traditional open surgical methods to contemporary minimally invasive image-guided techniques. AP can lead to various local complications, including pancreatic pseudocysts, acute necrotic collections, and walled-off necrosis. Imaging plays a crucial role in diagnosing complications of AP, while treatment approaches have shifted toward a step-up strategy involving percutaneous, endoscopic, and minimally invasive methods. Percutaneous necrosectomy, combining mechanical and chemical debridement, has shown promising results. Mechanical necrosectomy uses devices like baskets and retrieval nets, while chemical necrosectomy adds agents like hydrogen peroxide and streptokinase. Current evidence suggests that the step-up approach, starting with percutaneous drainage or necrosectomy and escalating to endoscopic or surgical interventions, if necessary, improves patient outcomes. The review underscores the need for a multidisciplinary approach in managing complicated AP and highlights ongoing advancements in minimally invasive techniques.

BackgroundPretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision‐making.PurposeTo evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low‐grade (grade I–II) from high‐grade (grade III–IV) in stage I and II renal cell carcinoma.Study TypeRetrospective.PopulationIn all, 376 patients with 430 renal cell carcinoma lesions from 2008–2019 in a multicenter cohort were acquired. The 353 Fuhrman‐graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set.Field Strength/Sequence1.5T and 3.0T/T2‐weighted and T1 contrast‐enhanced sequences.AssessmentThe accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision‐recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model.Statistical TestsMann–Whitney U‐test for continuous data and the chi‐square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low‐ and high‐grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity.ResultsThe final deep‐learning model achieved a test accuracy of 0.88 (95% CI: 0.73–0.96), sensitivity of 0.89 (95% CI: 0.74–0.96), and specificity of 0.88 (95% CI: 0.73–0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73–0.90), sensitivity of 0.92 (95% CI: 0.84–0.97), and specificity of 0.78 (95% CI: 0.68–0.86) in the WHO/ISUP test set.Data ConclusionDeep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy.Level of Evidence3Technical Efficacy Stage2

Abstract Purpose: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging. Experimental Design: Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model. Results: Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770). Conclusions: Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.