AIMC Topic: Adenocarcinoma of Lung
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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...
Preoperative Prediction of STAS Risk in Primary Lung Adenocarcinoma Using Machine Learning: An Interpretable Model with SHAP Analysis.
Academic radiology
Mar 25, 2025
BACKGROUND: Accurate preoperative prediction of spread through air spaces (STAS) in primary lung adenocarcinoma (LUAD) is critical for optimizing surgical strategies and improving patient outcomes.
A tumor-infiltrating B lymphocytes -related index based on machine-learning predicts prognosis and immunotherapy response in lung adenocarcinoma.
Frontiers in immunology
Mar 24, 2025
INTRODUCTION: Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progression of lung adenocarcinoma (LUAD). However, there remains a scarcity of research that has thoroughly...
Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.
BMC cancer
Mar 12, 2025
BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep...
Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.
Respiratory research
Mar 8, 2025
BACKGROUND: To evaluate the clinical applicability of deep learning (DL) models based on automatic segmentation in preoperatively predicting tumor spread through air spaces (STAS) in peripheral stage I lung adenocarcinoma (LUAD).
Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study.
BMC cancer
Mar 7, 2025
BACKGROUND AND OBJECTIVE: Spread through air spaces (STAS) is an important factor in determining the aggressiveness and recurrence risk of lung cancer, especially in early-stage adenocarcinoma. Preoperative identification of STAS is crucial for optim...
Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma.
International journal of medical sciences
Feb 18, 2025
Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patients with lung adenocarcinoma (LUAD) still remains unclear. In this study, an integrated framework of...
Unveiling the crucial role of glycosylation modification in lung adenocarcinoma metastasis through artificial neural network-based spatial multi-omics single-cell analysis and Mendelian randomization.
BMC cancer
Feb 13, 2025
BACKGROUND: Investigations into the intricacies of glycosylation modifications, a prevalent post-translational alteration observed in neoplasms, especially remain elusive in the context of lung adenocarcinoma. Through the integration of multiple omic...
Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study.
Journal of cardiothoracic surgery
Feb 11, 2025
BACKGROUND: Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + ...
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