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Background: CT-based abdominal body composition measures have shown associations with important health outcomes. Artificial intelligence (AI) advances now allow deployment of tools that measure body composition in large patient populations. Objective: To assess associations of age, sex, and common systemic diseases on CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample. Methods: This retrospective study included 140,606 adult patients (mean age 53.1±17.6 years; 67,613 male, 72,992 female) who underwent abdominal CT at a single academic institution between January 1, 2000, and February 28, 2021. CT examinations were not restricted based on patient setting, clinical indication, or IV contrast media utilization. Thirteen fully automated AI body composition tools quantifying liver, spleen, and kidney volume and attenuation, vertebral trabecular attenuation, skeletal muscle area and attenuation, and abdominal fat area and attenuation were applied to each patient's first available abdominal CT examination. EHR review was performed to identify common systemic diseases, including cancer, cardiovascular disease (CVD), diabetes mellitus (DM), and cirrhosis, based on relevant ICD-10 codes; 64,789 (46.1%) patients had a diagnosis of at least one systemic disease. Multiple linear regression models were performed in the 118,141 (84.0%) patients with no systemic disease or a single systemic disease, to assess age, sex, and presence of systemic disease as predictors of body composition measures; effect sizes were characterized using the unstandardized regression coefficient B. Results: Multiple linear regression models using age, sex, and systemic disease as predictors were overall significant for all 13 body composition measures (all p<.001) with variable goodness of fit (R2=0.03-0.43 across models). In the models, age was predictive of all 13 body composition measures, sex of 12 measures, cancer of nine measures, CVD of 11 measures, DM of 13 measures, and cirrhosis of 12 measures (all p<.05). Conclusion: Age, sex, and the presence of common systemic diseases were predictors of AI-derived CT-based body composition measures. Clinical Impact: An understanding of the identified associations with common systemic diseases will be critical for establishing normative reference ranges as CT-based AI body composition tools are developed for clinical use.

Multiple intravenous contrast phases of CT scans are commonly used in clinical practice to facilitate disease diagnosis. However, contrast phase information is commonly missing or incorrect due to discrepancies in CT series descriptions and imaging practices. This work aims to develop a classification algorithm to automatically determine the contrast phase of a CT scan. We hypothesize that image intensities of key organs (e.g. aorta, inferior vena cava) affected by contrast enhancement are inherent feature information to decide the contrast phase. These organs are segmented by TotalSegmentator followed by generating intensity features on each segmented organ region. Two internal and one external dataset were collected to validate the classification accuracy. In comparison with the baseline ResNet classification method that did not make use of key organs features, the proposed method achieved the comparable accuracy of 92.5% and F1 score of 92.5% in one internal dataset. The accuracy was improved from 63.9% to 79.8% and F1 score from 43.9% to 65.0% using the proposed method on the other internal dataset. The accuracy improved from 63.5% to 85.1% and the F1 score from 56.4% to 83.9% on the external dataset. Image intensity features from key organs are critical for improving the classification accuracy of contrast phases of CT scans. The classification method based on these features is robust to different scanners and imaging protocols from different institutes. Our results suggested improved classification accuracy over existing approaches, which advances the application of automatic contrast phase classification toward real clinical practice. The code for this work can be found here: (https://github.com/rsummers11/CT&#95;Contrast&#95;Phase&#95;Classifier)., Competing Interests: Declaration of competing interest Author RMS receives royalties for patents or software licenses from iCAD, PingAn, ScanMed, Philips, MGB, and Translation Holdings. His lab had a CRADA with PingAn. The other authors 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 © 2024. Published by Elsevier Ltd.)

In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in label quality. These labelers typically output only presence labels, sometimes with binary uncertainty indicators, which limits their usefulness. Supervised deep learning models have also been developed for report labeling but lack adaptability, similar to rule-based systems. In this work, we present MAPLEZ (Medical report Annotations with Privacy-preserving Large language model using Expeditious Zero shot answers), a novel approach leveraging a locally executable Large Language Model (LLM) to extract and enhance findings labels on CXR reports. MAPLEZ extracts not only binary labels indicating the presence or absence of a finding but also the location, severity, and radiologists' uncertainty about the finding. Over eight abnormalities from five test sets, we show that our method can extract these annotations with an increase of 3.6 percentage points (pp) in macro F1 score for categorical presence annotations and more than 20 pp increase in F1 score for the location annotations over competing labelers. Additionally, using the combination of improved annotations and multi-type annotations in classification supervision in a dataset of limited-resolution CXRs, we demonstrate substantial advancements in proof-of-concept classification quality, with an increase of 1.1 pp in AUROC over models trained with annotations from the best alternative approach. We share code and annotations., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ronald Summers reports a relationship with PingAn that includes: funding grants, royalties for patents or software licenses. Ronald Summers reports a relationship with iCAD that includes: royalties for patents or software licenses. Ronald Summers reports a relationship with Philips that includes: royalties for patents or software licenses. Ronald Summers reports a relationship with ScanMed that includes: royalties for patents or software licenses. Ronald Summers reports a relationship with Translation Holdings that includes: royalties for patents or software licenses. Ronald Summers reports a relationship with MGB that includes: royalties for patents or software licenses. 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 © 2024. Published by Elsevier B.V.)

Objectives: To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population., Methods: Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC and time-to-event analyses were performed to generate AUCs and hazard ratios (HR) binned by octile., Results: A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm 2 , HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT., Conclusion: Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures., Clinical Relevance Statement: Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients., Key Points: Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measures uncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is M.H.L., MD. Conflict of interest: The authors of this manuscript declare relationships with the following companies: Dr. Pickhardt serves as an advisor to Bracco and Nanox; Dr. Summers receives royalties from iCAD, PingAn, Philips, Translation Holdings, and ScanMed and research support from PingAn and NVIDIA. Statistics and biometry: One of the authors, Mr. Zea, has significant statistical expertise. Informed consent: Written informed consent was waived by the Institutional Review Board. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: Some study subjects or cohorts have been previously reported in prior studies comparing the predictive value of our panel of fully automated CT-based measures with clinical tools for predicting cardiometabolic risk. Importantly, however, we feel our work is unique and innovative in the following ways: we uncover trends in abdominal fat deposition and ectopic fat deposition associated with increased mortality and cardiometabolic disease risk, which can be readily derived from existing CT data for additional value-added “opportunistic” screening. Our fully automated AI tools address a need and have the potential to improve standardization and methodological heterogeneity and decrease barriers to clinical implementation. The “U-shaped” relationship we observed for survival and subcutaneous fat provides new insight into the so-called “obesity paradox,” whereby more favorable outcomes are observed in overweight and obese patients. Methodology: Retrospective Observational cohort study Performed at one institution, (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)