AIMC Topic: Lung Neoplasms
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Multiomic machine learning on lactylation for molecular typing and prognosis of lung adenocarcinoma.
Scientific reports
Jan 24, 2025
To integrate machine learning and multiomic data on lactylation-related genes (LRGs) for molecular typing and prognosis prediction in lung adenocarcinoma (LUAD). LRG mRNA and long non-coding RNA transcriptomes, epigenetic methylation data, and somati...
Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO clinical cancer informatics
Jan 24, 2025
PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evalu...
Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation.
JMIR cancer
Jan 23, 2025
BACKGROUND: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology...
Colorimetric aptasensor coupled with a deep-learning-powered smartphone app for programmed death ligand-1 expressing extracellular vesicles.
Frontiers in immunology
Jan 23, 2025
Lung cancer is a devastating public health threat and a leading cause of cancer-related deaths. Therefore, it is imperative to develop sophisticated techniques for the non-invasive detection of lung cancer. Extracellular vesicles expressing programme...
Deep learning-based synthetic CT for dosimetric monitoring of combined conventional radiotherapy and lattice boost in large lung tumors.
Radiation oncology (London, England)
Jan 22, 2025
PURPOSE: Conventional radiotherapy (CRT) has limited local control and poses a high risk of severe toxicity in large lung tumors. This study aimed to develop an integrated treatment plan that combines CRT with lattice boost radiotherapy (LRT) and mon...
Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study.
BMC cancer
Jan 22, 2025
BACKGROUND: Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitat...
Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules.
Scientific reports
Jan 20, 2025
Percutaneous transthoracic puncture of small pulmonary nodules is technically challenging. We developed a novel electromagnetic navigation puncture system for the puncture of sub-centimeter lung nodules by combining multiple deep learning models with...
Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow-up.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Jan 17, 2025
OBJECTIVES: For emergency department (ED) patients, lung cancer may be detected early through incidental lung nodules (ILNs) discovered on chest CTs. However, there are significant errors in the communication and follow-up of incidental findings on E...
Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.
BMC medical imaging
Jan 17, 2025
OBJECTIVE: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aim...
Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods.
NPJ systems biology and applications
Jan 17, 2025
Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinical tests and biopsies that delay treatment initiation. To address this, we developed a machine learni...
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