Search

Lung cancer screening (LCS) reduces mortality and involves vast multimodal data such as text, tables, and images. Fully mining such big data requires multitasking; otherwise, occult but important features may be overlooked, adversely affecting clinical management and healthcare quality. Here we propose a medical multimodal-multitask foundation model (M3FM) for three-dimensional low-dose computed tomography (CT) LCS. After curating a multimodal multitask dataset of 49 clinical data types, 163,725 chest CT series, and 17 tasks involved in LCS, we develop a scalable multimodal question-answering model architecture for synergistic multimodal multitasking. M3FM consistently outperforms the state-of-the-art models, improving lung cancer risk and cardiovascular disease mortality risk prediction by up to 20% and 10% respectively. M3FM processes multiscale high-dimensional images, handles various combinations of multimodal data, identifies informative data elements, and adapts to out-of-distribution tasks with minimal data. In this work, we show that M3FM advances various LCS tasks through large-scale multimodal and multitask learning. Lung cancer screening (LCS) requires effectively and efficiently mining big, multimodal datasets. Here, the authors develop a medical multimodal-multitask foundation model (M3FM) for LCS from 3D low-dose computed tomography and medical multimodal data, outperforming state-of-the-art methods and allowing the identification of informative data elements. [ABSTRACT FROM AUTHOR]

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77–0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4–6 months: 0.82; 7–9 months: 0.75; 10–12 months: 0.71), accuracy (0.68–0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology. [ABSTRACT FROM AUTHOR]

IgA nephropathy (IgAN) is a common glomerular disease affecting individuals across the age spectrum. However, there are differences in the presentation and prognosis of IgAN between elderly and young adults. Elderly patients with IgAN tend to present with more severe kidney disturbances, worse baseline renal function, and a poorer prognosis compared to young adults. The Oxford classification contributes to the overall prognosis of IgAN in elderly patients by improving prognostic assessment, providing a better determinant of kidney survival, and combining histopathologic findings with clinical features. It offers a more comprehensive approach to understanding the disease course and predicting outcomes in elderly individuals with IgAN. Understanding these differences is a fundamental basis for pinpointing potential therapeutic targets to mitigate age-related renal pathology in IgAN patients. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICIs) constitute a class of drugs that stimulate the immune system to fight cancer cells. However, they can also induce immune-related adverse events (irAEs) in various organs, including the kidneys. One of the infrequent irAEs associated with ICIs is sarcoidosis, an inflammatory disease that can impact multiple organs, such as the lungs, skin, and lymph nodes. Sarcoidosis is characterized by the formation of granulomas, clusters of immune cells that can potentially harm tissues. In some cases, ICIs can trigger kidney sarcoidosis, leading to impaired renal function. The mechanism through which ICIs initiate sarcoidosis is believed to involve activating T cells and cytokines that foster inflammation. [ABSTRACT FROM AUTHOR]

The article titled "Oncohypertension; treatment of high blood pressure in cancer patients" explores the connection between high blood pressure and cancer. It highlights that high blood pressure is a common comorbidity in cancer patients and that certain anti-cancer drugs can worsen or cause hypertension. The article emphasizes the need for further investigation into the relationship between hypertension and cardiovascular mortality, morbidity, and renal function. The concept of oncohypertension is introduced as a new entity among oncologists, nephrologists, and cardiologists. [Extracted from the article]

Large Language Models (LLMs) such as ChatGPT have been increasingly integrated into radiology research, revolutionizing the research landscape. The Radiology Research Alliance (RRA) of the Association for Academic Radiology (AAR) has convened a Task Force to develop this guide to help radiology researchers responsibly adopt LLM technologies. LLMs can improve various phases of the research process by helping to automate literature reviews, generate research questions, analyze complex datasets, and draft manuscripts. Despite its potential to improve research efficiency, implementation of LLMs poses challenges, especially for users with limited artificial intelligence (AI) experience. This review focuses on approaches to using LLMs in each phase of the research process and addresses prompt engineering to improve interaction with LLMs as well as ethical concerns to ensure scientific integrity. By combining human expertize with AI-driven efficiency, radiology researchers can foster innovation, advance knowledge, and enhance patient care., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Michele Retrouvey, MD reports a relationship with Elsevier that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)