AIMC Topic: Lung Neoplasms
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Large Language Models in Summarizing Radiology Report Impressions for Lung Cancer in Chinese: Evaluation Study.
Journal of medical Internet research
Apr 3, 2025
BACKGROUND: Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various natural language processing tasks, particularly in text generation. However, their effectiveness in summarizing radiology report impressio...
Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.
Clinical radiology
Apr 2, 2025
AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients and estimating progression-free survival...
Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma.
Frontiers in immunology
Apr 2, 2025
BACKGROUND: Unlike lung adenocarcinoma, patients with advanced squamous carcinoma exhibit a low proportion of driver gene positivity, with fewer effective treatment strategies available. Chemoimmunotherapy has now become the standard first-line treat...
Development and validation of machine learning models for early diagnosis and prognosis of lung adenocarcinoma using miRNA expression profiles.
Cancer biomarkers : section A of Disease markers
Apr 2, 2025
ObjectiveStudy aims to develop diagnostic and prognostic models for lung adenocarcinoma (LUAD) using Machine learning（ML）algorithms, aiming to enhance clinical decision-making accuracy.MethodsData from The Cancer Genome Atlas (TCGA) for LUAD patients...
Improved unsupervised 3D lung lesion detection and localization by fusing global and local features: Validation in 3D low-dose computed tomography.
Medical image analysis
Apr 1, 2025
Unsupervised anomaly detection (UAD) is crucial in low-dose computed tomography (LDCT). Recent AI technologies, leveraging global features, have enabled effective UAD with minimal training data of normal patients. However, this approach, devoid of ut...
A CNN-transformer fusion network for predicting high-grade patterns in stage IA invasive lung adenocarcinoma.
Medical physics
Apr 1, 2025
BACKGROUND: Invasive lung adenocarcinoma (LUAD) with the high-grade patterns (HGPs) has the potential for rapid metastasis and frequent recurrence. Therefore, accurately predicting the presence of high-grade components is crucial for doctors to devel...
Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT-based radiomics features.
Medical physics
Apr 1, 2025
BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (NSCLC) diagnoses, where epidermal growth factor receptor (EGFR) mutations play a pivotal role as indicators for therapeutic intervention with targeted...
Clinically applicable semi-supervised learning framework for multiple organs at risk and tumor delineation in lung cancer brachytherapy.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Apr 1, 2025
PURPOSE: The generalization ability of deep learning-based automatic segmentation techniques for lung cancer in practical clinical applications remains under-validated. We reported an investigation that validated a robust semi-supervised conditional ...
Deep Learning-driven Microfluidic-SERS to Characterize the Heterogeneity in Exosomes for Classifying Non-Small Cell Lung Cancer Subtypes.
ACS sensors
Apr 1, 2025
Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to deliver personalized medicines by tailoring therapy regimens. Tissue biopsy, albeit the gold standar...
Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.
PloS one
Apr 1, 2025
BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thromboembolism (VTE) risk. This study explored the VTE risk factors and established a machine-learning model to predict a failure of postoperative throm...
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