Sarcopenia Assessment Using Fully Automated Deep Learning Predicts Cardiac Allograft Survival in Heart Transplant Recipients.

Background: Sarcopenia is associated with adverse outcomes in patients with end-stage heart failure. Muscle mass can be quantified via manual segmentation of computed tomography images, but this approach is time-consuming and subject to interobserver variability. We sought to determine whether fully automated assessment of radiographic sarcopenia by deep learning would predict heart transplantation outcomes.

CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray.

The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurability of state-of-the-art techniques. The first event, CXR-LT 2023, aimed to achieve these goals by providing high-quality benchmark CXR data for model development and conducting comprehensive evaluations to identify ongoing issues impacting lung disease classification performance.

One Year On: Assessing Progress of Multimodal Large Language Model Performance on RSNA 2024 Case of the Day Questions.

Background With the growing use of multimodal large language models (LLMs), numerous vision-enabled models have been developed and made available to the public. Purpose To assess and quantify the advancements of multimodal LLMs in interpreting radiologic quiz cases by examining both image and textual content over the course of 1 year, and to compare model performance with that of radiologists. Materials and Methods For this retrospective study, 95 questions from Case of the Day at the RSNA 2024 Annual Meeting were collected.

CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray.

The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurability of state-of-the-art techniques. The first event, CXR-LT 2023, aimed to achieve these goals by providing high-quality benchmark CXR data for model development and conducting comprehensive evaluations to identify ongoing issues impacting lung disease classification performance.

3D Universal Lesion Detection and Tagging in CT with Self-Training.

Radiologists routinely perform the tedious task of lesion localization, classification, and size measurement in computed tomography (CT) studies. Universal lesion detection and tagging (ULDT) can simultaneously help alleviate the cumbersome nature of lesion measurement and enable tumor burden assessment. Previous ULDT approaches utilize the publicly available DeepLesion dataset, however it does not provide the full volumetric (3D) extent of lesions and also displays a severe class imbalance.

LEAVS: An LLM-based Labeler for Abdominal CT Supervision.

Extracting structured labels from radiology reports has been employed to create vision models to simultaneously detect several types of abnormalities. However, existing works focus mainly on the chest region. Few works have been investigated on abdominal radiology reports due to more complex anatomy and a wider range of pathologies in the abdomen.

Classification of Multi-Parametric Body MRI Series Using Deep Learning.

Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional technologist errors. To address this, we present a deep learning-based classification model to classify 8 different body mpMRI series types so that radiologists read the exams efficiently.

Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation.

Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases, such as cancer. However, the CT studies can be acquired with low radiation doses, different scanners, and are frequently affected by motion and metal artifacts. Prior approaches have targeted the quality improvement of one specific CT phase (e.

Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT.

Radiologists routinely detect and size lesions in CT to stage cancer and assess tumor burden. To potentially aid their efforts, multiple lesion detection algorithms have been developed with a large public dataset called DeepLesion (32,735 lesions, 32,120 CT slices, 10,594 studies, 4,427 patients, 8 body part labels). However, this dataset contains missing measurements and lesion tags, and exhibits a severe imbalance in the number of lesions per label category.

Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation.

Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely measure the size of LN to distinguish benign from malignant nodes, which would require subsequent cancer staging. Sizing is a cumbersome task compounded by the diverse appearances of LNs in mpMRI, which renders their measurement difficult.

Correcting Class Imbalances with Self-Training for Improved Universal Lesion Detection and Tagging.

Universal lesion detection and tagging (ULDT) in CT studies is critical for tumor burden assessment and tracking the progression of lesion status (growth/shrinkage) over time. However, a lack of fully annotated data hinders the development of effective ULDT approaches. Prior work used the DeepLesion dataset (4,427 patients, 10,594 studies, 32,120 CT slices, 32,735 lesions, 8 body part labels) for algorithmic development, but this dataset is not completely annotated and contains class imbalances.

Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI.

The segmentation of multiple organs in multi-parametric MRI studies is critical for many applications in radiology, such as correlating imaging biomarkers with disease status (e.g., cirrhosis, diabetes).

Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost.

Purpose: To automate contrast phase classification in CT using organ-specific features extracted from a widely used segmentation tool with a lightweight decision tree classifier.

Materials And Methods: This retrospective study utilized three public CT datasets from separate institutions. The phase prediction model was trained on the WAW-TACE (median age: 66 [60,73]; 185 males) dataset, and externally validated on the VinDr-Multiphase (146 males; 63 females; 56 unk) and C4KC-KiTS (median age: 61 [50.

Improved muscle and fat segmentation for body composition measures on quantitative CT.

Purpose: Body composition analysis on abdominal CT scans is useful for opportunistic screening. It also offers prognostic insights into mortality and cardiovascular risk. However, current segmentation methods for muscle and fat often fail on quantitative CT scans used for bone densitometry.

How well do multimodal LLMs interpret CT scans? An auto-evaluation framework for analyses.

Objective: This study introduces a novel evaluation framework, GPTRadScore, to systematically assess the performance of multimodal large language models (MLLMs) in generating clinically accurate findings from CT imaging. Specifically, GPTRadScore leverages LLMs as an evaluation metric, aiming to provide a more accurate and clinically informed assessment than traditional language-specific methods. Using this framework, we evaluate the capability of several MLLMs, including GPT-4 with Vision (GPT-4V), Gemini Pro Vision, LLaVA-Med, and RadFM, to interpret findings in CT scans.

Landmark-Based Pancreas Sub-region Segmentation in CT.

CT-based imaging biomarkers can be derived from the pancreas for detecting pancreatic pathologies. However, current approaches using full pancreas segmentations are unable to provide region-specific biomarkers that are crucial in predicting disease severity for many conditions, such as pancreatic adenocarcinomas. This study aims to develop an automated 3D tool to detect and segment the pancreatic sub-regions (the head, body, and tail) on CT volumes.

Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.

Accurate, reproducible body composition analysis from abdominal computed tomography (CT) images is critical for both clinical research and patient care. We present a fully automated, artificial intelligence (AI)-based pipeline that streamlines the entire process-from data normalization and anatomical landmarking to automated tissue segmentation and quantitative biomarker extraction. Our methodology ensures standardized inputs and robust segmentation models to compute volumetric, density, and cross-sectional area metrics for a range of organs and tissues.

MRISegmenter: A Fully Accurate and Robust Automated Multiorgan and Structure Segmentation Tool for T1-weighted Abdominal MRI.

Background There is a pressing demand to develop an automated segmentation tool for abdominal MRI that can provide accurate and robust segmentation in more than 60 abdominal organs and structures. Purpose To develop and evaluate the accuracy and robustness of an automated multiorgan and structure segmentation tool for T1-weighted abdominal MRI. Materials and Methods In this retrospective study, a T1-weighted abdominal MRI dataset composed of axial precontrast T1-weighted and contrast-enhanced T1-weighted arterial, portal venous, and delayed phases for each patient in a randomly selected sample was included at the National Institutes of Health Clinical Center.

Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports.

In current radiology practice, radiologists identify a finding in the current imaging exam, manually match it against the description from the prior exam report and assess interval changes. Large Language Models (LLMs) can identify report findings, but their ability to track interval changes has not been tested. The goal of this study was to determine the utility of a privacy-preserving LLM for matching findings between two reports (prior and follow-up) and tracking interval changes in size.

Automated abdominal CT imaging biomarkers and clinical frailty measures associated with postoperative deceased-donor liver transplant outcomes.

Objective: To quantify the potential of fully automated CT-based body composition metrics and clinical frailty data in predicting liver transplant recipient postoperative outcomes.

Methods: AI-enabled body composition tools were applied to pre-transplant abdominal CT scans in a retrospective cohort of first-time deceased-donor liver transplant recipients. Clinical frailty data (Fried frailty score) was obtained from an established transplant database.

Assessing the Reliability of Pancreatic CT Imaging Biomarkers for Diabetes Prediction: A Dual Center Retrospective Study.

Rationale And Objectives: Pancreatic imaging biomarkers on CT imaging are known to be associated with diabetes. However, no studies have examined if these imaging biomarkers are resilient to changes in segmentation quality and contrast status. Here, we assess if imaging biomarkers are robust to variations in pancreatic segmentation quality and contrast status, and how these factors affect their ability to predict diabetes.

AI-enabled body composition biomarkers at post-mortem CT for enriching autopsy: analysis of a large decedent cohort.

Objective: To correlate fully-automated PMCT-based body composition measures with causes of death and comorbidities.

Materials And Methods: Retrospective study of New Mexico Decedent Image Database (NMDID) with non-contrast PMCT scans between 2010 and 2017. Automated pipeline of AI-driven algorithms for quantifying skeletal muscle, subcutaneous/visceral fat, and aortic calcification from the abdominal component of PMCT scans was used.

A unified approach to medical image segmentation by leveraging mixed supervision and self and transfer learning (MIST).

Medical image segmentation is important for quantitative disease diagnosis and treatment but relies on accurate pixel-wise labels, which are costly, time-consuming, and require domain expertise. This work introduces MIST (MIxed supervision, Self, and Transfer learning) to reduce manual labeling in medical image segmentation. A small set of cases was manually annotated ("strong labels"), while the rest used automated, less accurate labels ("weak labels").

Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity.

We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median follow-up, 5.

Demystifying Large Language Models for Medicine: A Primer.

Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare by generating human-like responses across diverse contexts and adapting to novel tasks following human instructions. Their potential application spans a broad range of medical tasks, such as clinical documentation, matching patients to clinical trials, and answering medical questions. In this primer paper, we propose an actionable guideline to help healthcare professionals more efficiently utilize LLMs in their work, along with a set of best practices.