AIMC Topic: Lung Neoplasms

Clear Filters Showing 1461 to 1470 of 1778 articles

A robust machine learning model based on ribosomal-subunit-derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large-scale of sequencing data.

Clinical and translational medicine
Nonsmall cell lung cancer (NSCLC) is a lethal cancer and lacks robust biomarkers for noninvasive clinical diagnosis. Detecting NSCLC at the early stage can decrease the mortality rate and minimise harm caused by various treatments. We curated 2050 sa...

Comprehensive Characterization of Somatic Mutation Timing Reveals the Evolutionary Trajectory of Lung Adenocarcinoma in Chinese Patients.

Cancer research
UNLABELLED: Lung adenocarcinoma (LUAD) is a heterogeneous disease with substantial genomic differences between individuals of Chinese and European ancestries. Deciphering the timing of driver mutations may lead to insights into tumor evolution that c...

Non-invasive CT based multiregional radiomics for predicting pathologic complete response to preoperative neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

European journal of radiology
PURPOSE: This study aims to develop and validate a multiregional radiomics model to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC), and further evaluate the performance of the mode...

Development and Validation of a Machine Learning-Based Predictive Model for Postoperative Frailty in Patients with Non-Small Cell Lung Cancer and Its Relation to Early Recovery.

Annals of surgical oncology
PURPOSE: This study was designed to evaluate the postoperative frailty status of patients with non-small cell lung cancer, identify influencing factors, establish a machine learning-based prediction model, and explore the correlation between frailty ...

Application of a pulmonary nodule detection program using AI technology to ultra-low-dose CT: differences in detection ability among various image reconstruction methods.

Japanese journal of radiology
PURPOSE: This study aimed to investigate the performance of an artificial intelligence (AI)-based lung nodule detection program in ultra-low-dose CT (ULDCT) imaging, with a focus on the influence of various image reconstruction methods on detection a...

Interpretable lung cancer risk prediction using ensemble learning and XAI based on lifestyle and demographic data.

Computational biology and chemistry
Lung cancer is a leading cause of cancer-related death worldwide. The early and accurate detection of lung cancer is crucial for improving patient outcomes. Traditional predictive models often lack the accuracy and interpretability required in clinic...

Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model.

Computational biology and chemistry
Lung cancer, with its high mortality rate, is one of the deadliest diseases globally. The alarming increase in lung cancer deaths and its widespread prevalence have led to the development of various cancer control research and early detection methods...

Application of Fourier transform infrared (FTIR) spectroscopy in liquid biopsy to predict the response to the first-line immunotherapy in non-small-cell lung cancer (NSCLC) patients.

Biochemical and biophysical research communications
The direction of anticancer therapies has changed in recent years, including the increasing use of immunotherapy. However, around 50 % of non-small-cell lung cancer (NSCLC) patients do not respond to immunotherapy. Therefore, it is important to find ...

Deep learning-based contour propagation in magnetic resonance imaging-guided radiotherapy of lung cancer patients.

Physics in medicine and biology
Fast and accurate organ-at-risk (OAR) and gross tumor volume (GTV) contour propagation methods are needed to improve the efficiency of magnetic resonance (MR) imaging-guided radiotherapy. We trained deformable image registration networks to accuratel...

Mid-level data fusion of pleural effusion SERS spectra and serum CEA levels using machine learning algorithms for precise lung cancer detection.

Nanoscale
Accurate identification of clinically malignant pleural effusions is critical for cancer diagnosis and subsequent treatment planning. Here, surface-enhanced Raman spectroscopy (SERS) data of pleural effusions and serum carcinoembryonic antigen (CEA) ...