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.

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.

SCGAN: Sparse CounterGAN for Counterfactual Explanations in Breast Cancer Prediction.

Imaging phenotypes extracted via radiomics of magnetic resonance imaging have shown great potential in predicting the treatment response in breast cancer patients after administering neoadjuvant systemic therapy (NST). Understanding the causal relationships between the treatment response and Imaging phenotypes, Clinical information, and Molecular (ICM) features are critical in guiding treatment strategies and management plans. Counterfactual explanations provide an interpretable approach to generating causal inference.

Erratum to "Automatic classification of cancer pathology reports: A systematic review" [Journal of Pathology Informatics Volume 13, 2022, 100003].

[This corrects the article DOI: 10.1016/j.jpi.

Open-source domain adaptation to handle data shift for volumetric segmentation-use case kidney segmentation.

Objectives: Current development of kidney segmentation models has focused on using a single-phase CT, resulting in significant performance degradation caused by simple characteristic drift in testing datasets, e.g., difference in contrast phase appearance.

Artificial Intelligence for Identification of Patients with Increased Risk of Severe Cancer Therapy-Related Cardiac Dysfunction Following Anthracycline Therapy.

Background: Early detection of cancer therapy-related cardiac dysfunction (CTRCD) after anthracycline exposure is critically important in minimizing morbidity and mortality. Artificial intelligence models applied to electrocardiograms (ECG-AI) may allow for early identification of CTRCD and improved outcomes.

Methods: Patients treated with anthracycline therapy between 2002 and 2022 across three tertiary centers were evaluated.

Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From Free-Text Clinical Notes.

Purpose: Automated curation of breast cancer treatment data with minimal human involvement could accelerate the collection of statewide and nationwide evidence for patient management and assessing the effectiveness of treatment pathways. The primary challenges are the complexity and inconsistency of structured clinical data streams and accurate extraction of this information from free-text clinical narratives.

Materials And Methods: We proposed a hybrid two-phase information extraction framework that combined a Unified Medical Language System parser (phase-1) with a fine-tuned large language model (LLM; phase-2) to extract longitudinal treatment timelines from time-stamped clinical notes.

Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports.

Purpose: Radiology reports are essential for communicating imaging findings to guide diagnosis and treatment. Although most radiology reports are accurate, errors can occur in the final reports due to high workloads, use of dictation software, and human error. Advanced artificial intelligence models, such as GPT-4, show potential as tools to improve report accuracy.

Foundation versus domain-specific models for left ventricular segmentation on cardiac ultrasound.

The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99 patients: 58 TTE, 41 POCUS). Fine-tuned SAM was superior or comparable to MedSAM. The fine-tuned SAM also outperformed EchoNet and U-Net models, demonstrating strong generalization, especially on apical 2-chamber (A2C) images (fine-tuned SAM vs.

Exposing Vulnerabilities in Clinical LLMs Through Data Poisoning Attacks: Case Study in Breast Cancer.

Training Large Language Models (LLMs) with billions of parameters on a dataset and publishing the model for public access is the current standard practice. Despite their transformative impact on natural language processing (NLP), public LLMs present notable vulnerabilities given the source of training data is often web-based or crowdsourced, and hence can be manipulated by perpetrators. We delve into the vulnerabilities of clinical LLMs, particularly BioGPT which is trained on publicly available biomedical literature and clinical notes from MIMIC-III, in the realm of data poisoning attacks.

A Multi-Task Learning Approach for Segmentation of Breast Arterial Calcifications in Screening Mammograms.

Screening mammogram is a standard and cost-efficient imaging procedure to measure breast cancer risk among 45+ year old women. Quantifying breast arterial calcification (BAC) from screening mammograms is a non-invasive and cost-efficient approach to assess the future risk of adverse cardiovascular events among women, such as heart attack and stroke. However, segmentation of breast arterial calcification is an involved task and poses several technical challenges such as extremely small BAC finding, low breast arteries to breast area ratio in the mammogram images, tissue features such as breast folds and heterogeneous density, have very similar imaging appearance.

Evaluating large language models in echocardiography reporting: opportunities and challenges.

Aims: The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored.

Methods And Results: Adult echocardiography studies, conducted at the Mayo Clinic from 1 January 2017 to 31 December 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets.

Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology.

Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune checkpoint inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds.

Weakly supervised language models for automated extraction of critical findings from radiology reports.

Critical findings in radiology reports are life threatening conditions that need to be communicated promptly to physicians for timely management of patients. Although challenging, advancements in natural language processing (NLP), particularly large language models (LLMs), now enable the automated identification of key findings from verbose reports. Given the scarcity of labeled critical findings data, we implemented a two-phase, weakly supervised fine-tuning approach on 15,000 unlabeled Mayo Clinic reports.

Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with most cases diagnosed at stage IV and a five-year overall survival rate below 5%. Early detection and prognosis modeling are crucial for improving patient outcomes and guiding early intervention strategies. In this study, we developed and evaluated a deep learning fusion model that integrates radiology reports and CT imaging to predict PDAC risk.

Effects of patient survey feedback on improving patient experience with outpatient magnetic resonance imaging: Patient Feedback Helps Improve MRI Experience.

Objective: We examined the feasibility of collecting timely patient feedback after outpatient magnetic resonance imaging (MRI) and the effect of radiology staff responses or actions on patient experience scores.

Methods: This study included 6043 patients who completed a feedback survey via email after undergoing outpatient MRI at a tertiary care medical center between April 2021 and September 2022. The survey consisted of the question "How was your radiology visit?" with a 5-point emoji-Likert scale, an open-text feedback box, and an option to request a response.

Global disparities in artificial intelligence-based mammogram interpretation for breast cancer: A scientometric analysis of representation, trends, and equity.

Background: Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer death among women worldwide. Artificial intelligence (AI) shows promise for improving mammogram interpretation, especially in resource-limited settings. However, concerns remain regarding the diversity of datasets and the representation of researchers in AI model development, which may affect the models' generalizability, fairness, and equity.

Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography.

This study evaluates a deep learning model for classifying normal versus potentially abnormal regions of interest (ROIs) on mammography, aiming to identify imaging, pathologic, and demographic characteristics that may induce suboptimal model performance in certain patient subgroups. We utilized the EMory BrEast imaging Dataset (EMBED), containing 3.4 million mammographic images from 115,931 patients.

Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients.

Predicting post-Percutaneous Coronary Intervention (PCI) outcomes is crucial for effective patient management and quality improvement in healthcare. However, achieving accurate predictions requires the integration of multimodal clinical data, including physiological signals, demographics, and patient history, to estimate prognosis. The integration of such high-dimensional, multi-modal data presents a significant challenge due to its complexity and the need for sophisticated analytical methods.

Applications of Artificial Intelligence for the Prediction and Diagnosis of Cancer Therapy-Related Cardiac Dysfunction in Oncology Patients.

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship.

Adaptable graph neural networks design to support generalizability for clinical event prediction.

Objective: While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges when tested outside of their institution of training. These challenges are rooted not only in differences in patient population characteristics, but medical practice patterns of different institutions.

Method: We propose a solution to this problem through systematically adaptable design of graph-based convolutional neural networks (GCNN) for clinical event prediction.

Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.

Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.

Causal debiasing for unknown bias in histopathology-A colon cancer use case.

Advancement of AI has opened new possibility for accurate diagnosis and prognosis using digital histopathology slides which not only saves hours of expert effort but also makes the estimation more standardized and accurate. However, preserving the AI model performance on the external sites is an extremely challenging problem in the histopathology domain which is primarily due to the difference in data acquisition and/or sampling bias. Although, AI models can also learn spurious correlation, they provide unequal performance across validation population.