Clinical feasibility of MRI-guided in-bore prostate biopsies at 0.55T.

Objective: In-bore MRI-guided biopsy allows direct visualization of suspicious lesions, biopsy needles, and trajectories, allowing accurate sampling when MRI-ultrasound fusion biopsy is not feasible. However, its use has been limited. Wide-bore, lower-field, and lower-cost scanners could help address these issues, but their feasibility for prostate biopsy is unknown.

Outcome Prediction Using Multi-Modal Information: Integrating Large Language Model-Extracted Clinical Information and Image Analysis.

Survival prediction post-cystectomy is essential for the follow-up care of bladder cancer patients. This study aimed to evaluate artificial intelligence (AI)-large language models (LLMs) for extracting clinical information and improving image analysis, with an initial application involving predicting five-year survival rates of patients after radical cystectomy for bladder cancer. Data were retrospectively collected from medical records and CT urograms (CTUs) of bladder cancer patients between 2001 and 2020.

Ovarian Cancer surgical consideration is markedly improved by the neural network powered-MIA3G multivariate index assay.

Background: Surgery remains the main treatment option for an adnexal mass suspicious of ovarian cancer. The malignancy rate is, however, only 10-15% in women undergoing surgery. This results in a high number of unnecessary surgeries.

Performance of CT With Adrenal-Washout Protocol in Heterogeneous Adrenal Nodules: A Multiinstitutional Study.

CT with adrenal-washout protocol (hereafter, adrenal-protocol CT) is commonly performed to distinguish adrenal adenomas from other adrenal tumors. However, the technique's utility among heterogeneous nodules is not well established, and the optimal method for placing ROIs in heterogeneous nodules is not clearly defined. The purpose of our study was to determine the diagnostic performance of adrenal-protocol CT to distinguish adenomas from nonadenomas among heterogeneous adrenal nodules and to compare this performance among different methods for ROI placement.

Survival Prediction of Patients with Bladder Cancer after Cystectomy Based on Clinical, Radiomics, and Deep-Learning Descriptors.

Accurate survival prediction for bladder cancer patients who have undergone radical cystectomy can improve their treatment management. However, the existing predictive models do not take advantage of both clinical and radiological imaging data. This study aimed to fill this gap by developing an approach that leverages the strengths of clinical (C), radiomics (R), and deep-learning (D) descriptors to improve survival prediction.