Optimizing Multiparametric MRI Protocols for Prostate Cancer Detection: A Comprehensive Assessment Aligned with PI-RADS Guidelines.

Background And Aim: Multiparametric magnetic resonance imaging (mpMRI) is recognized as the most indicative method for diagnosing prostate cancer. The purpose of this narrative review is to provide a comprehensive evaluation aligned with the Prostate Imaging and Reporting Data System (PI-RADS) guidelines, offering an in-depth insight into the various MRI sequences used in a standard mpMRI protocol. Additionally, it outlines the critical technical requirements necessary to perform a standard mpMRI examination of the prostate, as defined by the PI-RADS specifications.

Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation.

Rationale And Objectives: Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification problem.

Materials And Methods: T1-CE, T2WI, and FLAIR 3D-segmented masks of 307 patients (157 GB and 150 BM) were generated post resampling, co-registration normalization and semi-automated 3D-segmentation and used for internal model development.

AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods.

Purpose: To determine if machine learning (ML) or deep learning (DL) pipelines perform better in AI-based three-class classification of glioblastoma (GBM), intracranial metastatic disease (IMD) and primary CNS lymphoma (PCNSL).

Methodology: Retrospective analysis included 502 cases for training (208 GBM, 67 PCNSL and 227 IMD), with external validation on 86 cases (27:27:32). Multiparametric MRI images (T1W, T2W, FLAIR, DWI and T1-CE) were co-registered, resampled, denoised and intensity normalized, followed by semiautomatic 3D segmentation of the enhancing tumor (ET) and peritumoral region (PTR).

Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification.

Rationale And Objectives: Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex.

Materials And Methods: In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation.