AIMC Topic: Lung Neoplasms

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Advancements in lung cancer: molecular insights, innovative therapies, and future prospects.

Medical oncology (Northwood, London, England)
Still among the most common and deadly cancers worldwide, lung cancer causes major morbidity and death. Thanks to late-stage diagnosis, tumor heterogeneity, and resistance to traditional treatments, the prognosis for lung cancer patients is still poo...

Leveraging readily available clinical data with machine learning to predict first-line immunotherapy outcomes in non-small cell lung cancer.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitors (ICIs) are essential first-line treatments for recurrent or metastatic non-small cell lung cancer (NSCLC). However, predicting their effectiveness and the occurrence of immunotherapy-related adverse events (ir...

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.

Nature communications
Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remain...

Association of ambient fine particulate matter (PM) and its constituents with risk of pulmonary nodules in a lung cancer screening project.

Environmental research
BACKGROUND: Ambient fine particulate matter (PM) is a significant global public health concern. However, large-scale research has yet to explore the effects of long-term PM exposure on the pulmonary nodule characteristics.

Collaborative assessment of the risk of postoperative progression in early-stage non-small cell lung cancer: a robust federated learning model.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: While the TNM staging system provides valuable insights into the extent of disease, predicting postoperative progression in early-stage non-small cell lung cancer (NSCLC) remains a significant challenge. An effective bioimaging prognostic...

Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules.

BMC medical informatics and decision making
BACKGROUND: Pulmonary Nodules (PNs) are a trend considered as the early manifestation of lung cancer. Among them, PNs that remain stable for more than two years or whose pathological results suggest not being lung cancer are considered benign PNs (BP...

Determination of lung cancer exhaled breath biomarkers using machine learning-a new analysis framework.

Scientific reports
Exhaled breath samples of lung cancer patients (LC), tuberculosis (TB) patients and asymptomatic controls (C) were analyzed using gas chromatography-mass spectrometry (GC-MS). Ten volatile organic compounds (VOCs) were identified as possible biomarke...

Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis.

Science advances
Current approaches to estimating cell trajectories, tumor progression dynamics, and cell population diversity of tumor microenvironment often depend on single-cell RNA sequencing, which is costly and resource intensive. To address this limitation, we...

Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.

BMC medical imaging
BACKGROUND: PD-1/PD-L1 immunotherapy represents the primary treatment for advanced NSCLC patients; however, response rates to this therapy vary among individuals. This dual-center study aimed to integrate habitat radiomics and multi-instance deep lea...

Machine Learning Prediction of Financial Toxicity in Patients with Resected Lung Cancer.

Journal of the American College of Surgeons
BACKGROUND: Financial toxicity (FT) refers to the financial stress and detrimental impact on quality of life experienced by patients due to treatment cost. In patients with resected lung cancer (LC), we sought to identify those at risk of developing ...