AIMC Topic: Carcinoma, Non-Small-Cell Lung

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PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer.

Military Medical Research
BACKGROUND: Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients t...

Unraveling tissue-specific molecular targets of dihydroartemisinin in non-small cell lung cancer: an integrative machine learning and network pharmacology approach.

Medical oncology (Northwood, London, England)
Non-small cell lung cancer (NSCLC) presents significant therapeutic challenges due to resistance and immune evasion. Dihydroartemisinin (DHA), a derivative of artemisinin, exhibits broad anti-tumor activity, but its molecular targets and mechanisms i...

The - 216G/T polymorphism in the EGFR gene: A review focusing on Non-Small lung cancer.

Molecular biology reports
The epidermal growth factor receptor (EGFR) is a key regulator of cell proliferation and a well-established therapeutic target in non-small-cell lung cancer (NSCLC). Somatic mutations in the EGFR gene have been widely studied in the context of tyrosi...

Foundation model based prediction of lung cancer survival using temporal changes in dual time point CT scans.

Scientific reports
Lung cancer remains a significant cause of mortality, with non-small cell lung cancer (NSCLC) representing most cases. Currently, clinical data based models fall short in predicting survival while more advanced deep learning based image models requir...

Single cell and machine learning identify type II pneumocyte-derived biomarkers HN1/OCIAD2/SFTA2 for non-small cell lung cancer prognosis and immune regulation.

European journal of medical research
BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the most prevalent malignancies and currently shows a poor clinical prognosis. Type II pneumocyte, as one of the main sources of cancer cells in NSCLC, is important to explore the molecular fun...

Development and validation of a machine learning model to predict early recurrence after surgery in NSCLC patients.

Scientific reports
To develop and validate a machine learning (ML) model for predicting early recurrence (ER) within two years post-surgery in non-small cell lung cancer (NSCLC) patients. This multicenter cohort study included 3,171 NSCLC patients who underwent radical...

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...

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...

Primary tumor resection: a new hope or an old illusion for patients with metastatic non-small cell lung neuroendocrine tumors?

World journal of surgical oncology
OBJECTIVES: This study aimed to investigate the impact of primary tumor resection (PTR) on survival outcomes for patients with metastatic non-small cell neuroendocrine tumors (mNSCLC-NETs), develop a predictive model to identify which patients may be...