AIMC Topic: Lung Neoplasms

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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...

PixelPrint 4D : A 3D Printing Method of Fabricating Patient-Specific Deformable CT Phantoms for Respiratory Motion Applications.

Investigative radiology
OBJECTIVES: Respiratory motion poses a significant challenge for clinical workflows in diagnostic imaging and radiation therapy. Many technologies such as motion artifact reduction and tumor tracking have been developed to compensate for its effect. ...

Automated contouring of gross tumor volume lymph nodes in lung cancer by deep learning.

BMC cancer
PURPOSE: The precise contouring of gross tumor volume lymph nodes (GTVnd) is an essential step in clinical target volume delineation. This study aims to propose and evaluate a deep learning model for segmenting GTVnd specifically in lung cancer, repr...

Large Language Models in Lung Cancer: Systematic Review.

Journal of medical Internet research
BACKGROUND: In the era of data and intelligence, artificial intelligence has been widely applied in the medical field. As the most cutting-edge technology, the large language model (LLM) has gained popularity due to its extraordinary ability to handl...

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).

Integrative single-cell and machine learning approach to characterize immunogenic cell death and tumor microenvironment in LUAD.

Journal of translational medicine
BACKGROUND: Immunogenic cell death (ICD) triggers antitumor immune responses and plays a critical role in shaping the tumor microenvironment (TME). However, its specific contribution to lung adenocarcinoma (LUAD) progression and immunotherapy respons...

SERS mapping combined with explainable deep learning for exosome analysis to enhance lung cancer detection.

The Analyst
Exosomes are critical biomarkers for early cancer diagnosis and prognosis due to their rich biological information. Nevertheless, analyzing exosomal biomarkers comprehensively remains challenging. Surface-enhanced Raman scattering (SERS) has been emp...

Learning the cellular origins across cancers using single-cell chromatin landscapes.

Nature communications
Deciphering the pre-malignant cell of origin (COO) of different cancers is critical for understanding tumor development and improving diagnostic and therapeutic strategies in oncology. Prior work demonstrates that somatic mutations preferentially acc...