AIMC Topic: Lung Neoplasms
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Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks.
BMC cancer
Apr 10, 2025
OBJECTIVE: The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment ...
Lung nodule detection using a multi-scale convolutional neural network and global channel spatial attention mechanisms.
Scientific reports
Apr 10, 2025
Early detection of lung nodules is crucial for the prevention and treatment of lung cancer. However, current methods face challenges such as missing small nodules, variations in nodule size, and high false positive rates. To address these challenges,...
MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines.
Journal of medical systems
Apr 10, 2025
Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has greatly contributed to addressing DRP challenges. Notably, the prediction of DRP for cancer cell lines be...
Exploring 4 generation EGFR inhibitors: A review of clinical outcomes and structural binding insights.
European journal of pharmacology
Apr 9, 2025
Epidermal growth factor receptor (EGFR) is a potential target for anticancer therapies and plays a crucial role in cell growth, survival, and metastasis. EGFR gene mutations trigger aberrant signaling, leading to non-small cell lung cancer (NSCLC). T...
Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.
Biomedical physics & engineering express
Apr 8, 2025
. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that ...
Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study.
Annals of medicine
Apr 7, 2025
BACKGROUND: Reducing postoperative cardiovascular and neurological complications (PCNC) during thoracic surgery is the key to improving postoperative survival.
Virtual lung screening trial (VLST): An in silico study inspired by the national lung screening trial for lung cancer detection.
Medical image analysis
Apr 5, 2025
Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-f...
MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.
Science advances
Apr 4, 2025
Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and ...
An extension to the OVH concept for knowledge-based dose volume histogram prediction in lung tumor volumetric-modulated arc therapy.
Journal of applied clinical medical physics
Apr 3, 2025
PURPOSE: Volumetric-modulated arc therapy (VMAT) treatment planning allows a compromise between a sufficient coverage of the planning target volume (PTV) and a simultaneous sparing of organs-at-risk (OARs). Particularly in the case of lung tumors, de...
A Novel Theranostic Strategy for Malignant Pulmonary Nodules by Targeted CECAM6 with Zr/I-Labeled Tinurilimab.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Apr 3, 2025
Lung adenocarcinoma (LUAD) constitutes a major cause of cancer-related fatalities worldwide. Early identification of malignant pulmonary nodules constitutes the most effective approach to reducing the mortality of LUAD. Despite the wide application o...
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