AIMC Topic: Lung Neoplasms

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Constructing a sixteen lactate-related gene risk signature for LUAD to predict the prognosis and TME by machine learning.

Scientific reports
Although it is the most common subtype of lung cancer in clinical practice, lung adenocarcinoma (LUAD) was proven to be associated with a poor prognosis. In recent years, lactate metabolism has been considered an important biological mechanism in lun...

Macrophage mitophagy-related genes predict prognosis and therapeutic response in lung adenocarcinoma.

Scientific reports
Mitochondrial autophagy (mitophagy) in macrophages is crucial yet poorly understood within the lung adenocarcinoma (LUAD) tumor microenvironment. This study aimed to identify key macrophage mitophagy-related genes and develop a robust prognostic mode...

Fusion of classical and deep learning features with incremental learning for improved classification of lung and colon cancer.

Scientific reports
Correct histopathological image classification of lung and colon cancer is a stringent challenge for clinical pathology. This work introduces a hybrid deep learning network by combining traditional handcrafted features of LBP, GLCM, wavelet, color, a...

Development and internal validation of a preoperative prediction model for postoperative pneumonia in lung cancer patients: a retrospective study.

BMC surgery
PURPOSE: To evaluate the postoperative pneumonia (POP) risk of patients with non-small cell lung cancer (NSCLC), identify influencing factors, develop a LASSO regression-based model to predict POP risk and identify critical influencing factors.

Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration.

Medical oncology (Northwood, London, England)
Radiogenomics links quantitative features from routine CT and PET/CT with tumor genomics to non-invasively profile non-small cell lung cancer (NSCLC). This review synthesizes the current workflow-from image acquisition and segmentation to feature ext...

Impact of patient-specific deep learning lung organs-at-risk segmentation on accumulated dose in online adaptive 0.35 T MR-guided radiotherapy.

Physics in medicine and biology
Online adaptation in magnetic resonance imaging-guided radiotherapy (MRgRT) for lung cancer is hindered by time-consuming organs-at-risk (OARs) recontouring on daily MR images (dMRIs) and inter-/intra-observer variability. Deep learning auto-segmenta...

DCN, NPM3 and SULF1 are hub genes related to vasculogenic mimicry in lung adenocarcinoma.

Journal of cancer research and clinical oncology
AIM: Vasculogenic mimicry (VM), a process in which cancer cells form endothelial cell-independent vascular networks, is a hallmark of tumor aggressiveness in lung adenocarcinoma (LUAD) and supports tumor growth and metastasis. This study aims to iden...

Construction and validation of an anoikis-related prognostic model for lung adenocarcinoma based on bulk and single-cell transcriptomic data.

PloS one
Lung adenocarcinoma (LUAD) is a highly aggressive lung cancer with poor prognosis due to lack of reliable biomarkers. Resistance to anoikis drives tumor progression and metastasis. This study aims to develop and validate an anoikis-related prognostic...

AI-powered spatial cell phenomics enhances risk stratification in non-small cell lung cancer.

Nature communications
Risk stratification remains a critical challenge in non-small cell lung cancer patients for optimal therapy selection. In this study, we develop an artificial intelligence-powered spatial cellomics approach that combines histology, multiplex immunofl...