AIMC Topic: Lung Neoplasms
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Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.
JNCI cancer spectrum
Sep 2, 2024
BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided...
Transforming Lung Cancer Screening with AI: Comprehensive Evaluation and Personalized Medicine Prospects.
Radiology
Sep 1, 2024
Validation of Non-Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model.
JCO clinical cancer informatics
Sep 1, 2024
PURPOSE: Limited studies have used natural language processing (NLP) in the context of non-small cell lung cancer (NSCLC). This study aimed to validate the application of an NLP model to an NSCLC cohort by extracting NSCLC concepts from free-text med...
Automated Interstitial Lung Abnormality Probability Prediction at CT: A Stepwise Machine Learning Approach in the Boston Lung Cancer Study.
Radiology
Sep 1, 2024
Background It is increasingly recognized that interstitial lung abnormalities (ILAs) detected at CT have potential clinical implications, but automated identification of ILAs has not yet been fully established. Purpose To develop and test automated I...
Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.
Clinical and translational science
Sep 1, 2024
Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there...
Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness.
Korean journal of radiology
Sep 1, 2024
OBJECTIVE: To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone.
External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs.
Radiology. Artificial intelligence
Sep 1, 2024
Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radio...
Assessment of Follow-Up for Pulmonary Nodules from Radiology Reports with Natural Language Processing.
Studies in health technology and informatics
Aug 22, 2024
Radiology reports are an essential communication method for ensuring smooth workflow in healthcare. However, many of these reports are described in free text, and findings documented by radiologists may not be adequately addressed. In this study, foc...
Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients.
Studies in health technology and informatics
Aug 22, 2024
This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Machine Learning for Health Outcomes (MLHO) framework, emphasizing best practices and pitfalls in its ap...
A preliminary study on the diagnostic performance of the uPath PD-L1 (SP263) artificial intelligence (AI) algorithm in patients with NSCLC treated with PD-1/PD-L1 checkpoint blockade.
Pathologica
Aug 1, 2024
OBJECTIVE: The uPath PD-L1 (SP263) is an AI-based platform designed to aid pathologists in identifying and quantifying PD-L1 positive tumor cells in non-small cell lung cancer (NSCLC) samples stained with the SP263 assay.
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