AIMC Topic: Carcinoma, Non-Small-Cell Lung

Clear Filters Showing 11 to 20 of 404 articles

Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.

Journal for immunotherapy of cancer
PURPOSE: This study evaluated the dynamic changes in the tumor microenvironment (TME) in patients with non-small cell lung cancer (NSCLC) and acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) using an ar...

Machine learning-enhanced direct mass spectrometry analysis of non-volatile breath metabolites for rapid and accurate lung cancer screening.

Analytical methods : advancing methods and applications
Breath analysis by direct mass spectrometry faces significant challenges due to the inherent complexities in sample collection, low analyte concentrations, and accurate compound identification. While current breath analysis primarily focuses on volat...

Comparing radiomics, deep learning, and fusion models for predicting occult pleural dissemination in patients with non-small cell lung cancer: a retrospective multicenter study.

BMC cancer
BACKGROUND: Occult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery. This study aimed ...

Machine learning identifies exosome related gene signatures for early prediction of non-small cell lung cancer.

Scientific reports
Non-small cell lung cancer (NSCLC) remains a major health challenge worldwide, mainly due to the lack of effective early diagnostic biomarkers. Exosome-related genes have recently emerged as potential diagnostic markers due to their roles in tumor pr...

Personalized Cancer-Specific Protein-Aptamer Corona for Orthogonal Multiplex Cancer Diagnosis.

Journal of the American Chemical Society
Aptamers are powerful synthetic recognition elements for biosensing, yet their application in complex biofluids, such as human serum, is critically limited by enzymatic degradation. To overcome this fundamental challenge, we introduce a novel analyti...

AI-driven 3D CT imaging prediction model for improving preoperative detection of visceral pleural invasion in early-stage lung cancer.

PloS one
Visceral pleural invasion (VPI) is a critical prognostic factor in early-stage non-small-cell lung cancer (NSCLC), significantly affecting patient outcomes. Conventional computed tomography (CT) often fails to diagnose VPI accurately. This retrospect...

Machine learning-based prediction of N2 lymph node metastasis in non-small cell lung cancer.

BMC pulmonary medicine
BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide. Accurate staging of mediastinal lymph nodes is a crucial step in determining appropriate treatment approaches. Current noninvasive diagnostic methods do not provide suf...

Transformer-based AI approach to unravel long-term, time-dependent prognostic complexity in patients with advanced NSCLC and PD-L1 ≥50%: insights from the pembrolizumab 5-year global registry.

Journal for immunotherapy of cancer
BACKGROUND: With nearly one-third of patients with advanced non-small cell lung cancer (NSCLC) and PD-L1 Tumor Proportion Score≥50% surviving beyond 5 years following first-line pembrolizumab, long-term outcomes challenge traditional paradigms of can...

AI cancer driver mutation predictions are valid in real-world data.

Nature communications
Characterizing and validating which mutations influence development of cancer is challenging. Artificial intelligence (AI) has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less expl...

Construction of an automated machine learning-based predictive model for postoperative pulmonary complications risk in non-small cell lung cancer patients undergoing thoracoscopic surgery.

PloS one
OBJECTIVE: To develop a predictive framework integrating machine learning and clinical parameters for postoperative pulmonary complications (PPCs) in non-small cell lung cancer (NSCLC) patients undergoing video-assisted thoracic surgery (VATS).