AIMC Topic: Lung Neoplasms
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Anti-ceramide antibody and sphingosine-1-phosphate as potential biomarkers of unresectable non-small cell lung cancer.
Pathology oncology research : POR
Jan 6, 2025
OBJECTIVES: Spingosine-1-phosphate (S1P) and ceramides are bioactive sphingolipids that influence cancer cell fate. Anti-ceramide antibodies might inhibit the effects of ceramide. The aim of this study was to assess the potential role of circulating ...
Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study.
Academic radiology
Jan 4, 2025
RATIONALE AND OBJECTIVES: This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat of the tumor and its peritumoral microenvironment to predict progression-free survival (PFS) in patie...
Assessing the feasibility and external validity of natural language processing-extracted data for advanced lung cancer patients.
Lung cancer (Amsterdam, Netherlands)
Jan 4, 2025
BACKGROUND: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with ad...
Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology.
BMC cancer
Jan 3, 2025
OBJECTIVE: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained...
Explainable Machine Learning to Predict Treatment Response in Advanced Non-Small Cell Lung Cancer.
JCO clinical cancer informatics
Jan 3, 2025
PURPOSE: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non-small cell...
Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups.
European radiology
Jan 2, 2025
OBJECTIVES: Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-netw...
Attention-guided CenterNet deep learning approach for lung cancer detection.
Computers in biology and medicine
Jan 2, 2025
Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction,...
Tumor detection on bronchoscopic images by unsupervised learning.
Scientific reports
Jan 2, 2025
The diagnosis and early identification of intratracheal tumors relies on the experience of the operators and the specialists. Operations by physicians with insufficient experience may lead to misdiagnosis or misjudgment of tumors. To address this iss...
Identification of key gene signatures for predicting chemo-immunotherapy efficacy in extensive-stage small-cell lung cancer using machine learning.
Lung cancer (Amsterdam, Netherlands)
Jan 2, 2025
OBJECTIVES: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-SCLC). We aimed to identify key genes associated with chemo-immunotherapy efficacy in ES-SCLC through...
Humans
Middle Aged
Immunotherapy
Neoplasm Staging
Small Cell Lung Carcinoma
Machine Learning
Gene Expression Regulation, Neoplastic
Gene Expression Profiling
Aged
Antineoplastic Combined Chemotherapy Protocols
Transcriptome
Prospective Studies
Biomarkers, Tumor
Lung Neoplasms
Treatment Outcome
Prognosis
Male
Female
Analyzing Secondary Cancer Risk: A Machine Learning Approach.
Asian Pacific journal of cancer prevention : APJCP
Jan 1, 2025
OBJECTIVE: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the potential risk of developing secondary cancer (SC), which can be influenced by several factors incl...
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