Lung Cancer

Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.

OBJECTIVE: To critically evaluate machine learning (ML) models developed for predicting radiation-induced oral mucositis (OM) in head and neck cancer (HNC) patients, emphasizing the contribution of advanced imaging biomarkers such as radiomic and dosiomic features. MATERIALS AND METHODS: This systematic review followed the PRISMA 2020 guidelines and was registered in PROSPERO (CRD420251075245). Th...

Predicting 5-Year Mortality in Non-Small-Cell Lung Cancer Using the Korean Central Cancer Registry: Model Development and Validation Study.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is one of the most common cancers and a leading cause of cancer-related mortality, making prognostic prediction clinically essential. Machine learning models are increasingly used to assess prognosis; however, developing systems that combine high discrimination with clear, clinically interpretable reasoning remains challenging. OBJECTIVE: This study a...

Integrated transcriptomics, network pharmacology and clinical expression validation reveal the prognostic significance of PANoptosis-related genes in cordycepin-treated lung adenocarcinoma.

BACKGROUND: Studies have shown that PANoptosis is increasingly involved in cancer and cancer treatment. The Cordycepin has also been found to be invol...

Integrative single-cell and spatial transcriptomics analysis reveals a baicalein-responsive 10-gene signature for non-small cell lung cancer.

BACKGROUND: Non‑small cell lung cancer (NSCLC) remains a leading cause of cancer‑related mortality worldwide. Baicalein, a natural flavonoid, has show...

Explainable AI reveals squamous histology and U-shaped PD-L1 patterns as primary subgroup predictors of neoadjuvant and perioperative immunotherapy benefit in NSCLC: a machine learning analysis.

BACKGROUND: The introduction of neoadjuvant and perioperative immunotherapy has broadened treatment options for resectable non-small cell lung cancer ...

Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning.

Prognostic assessment of diabetic kidney disease (DKD) is essential for personalized management. This study developed eight machine learning models us...

Multimodal deep learning integration of clinical, dosiomics, and radiomics features to predict toxicity in breast cancer patients undergoing hypofractionated radiotherapy.

BACKGROUND AND PURPOSE: Radiation dermatitis (RD) and superficial soft tissue fibrosis are common toxicities among the patients with breast cancer rec...

Virtual photon-counting micro-CT platform for simulation of head and neck cancer imaging in mice.

OBJECTIVE: Our goal was to develop a simulation platform for photon-counting CT (PCCT) imaging in mouse models of head and neck squamous cell carcinom...

Prediction of delirium in trauma patients using interpretable machine learning.

This study aimed to identify key risk factors for delirium in trauma patients and to develop an interpretable machine learning model using routinely a...

Predicting Treatment Response After Total Neoadjuvant Therapy for Locally Advanced Rectal Cancer.

OBJECTIVE: To develop a predictive model for pathological complete response (pCR) after total neoadjuvant therapy (TNT) to inform selection for watch-...

Longitudinal serum uric acid transition patterns and their clinical, genetic, and dietary determinants: prospective prediction of incident hyperuricemia and gout in a Korean population-based cohort.

BACKGROUND: Longitudinal serum uric acid (SUA) transition patterns and their clinical, genetic, and dietary determinants remain poorly characterized. ...

Peristaltic transport and thermodynamic analysis of hybrid nanofluids in porous media using physics-informed neural networks.

This study presents the wavelet-based physics-informed neural networks (PINNs) simulation to analyse entropy generation in hybrid nanofluid peristalti...

3D chromatin architecture-related genes orchestrate LUAD evolution and therapy resistance: insights from integrative machine learning and spatial single-cell mapping.

BACKGROUND: Lung adenocarcinoma (LUAD) is a prevalent and lethal malignancy. The three-dimensional (3D) chromatin architecture significantly influence...

Radiation dose-aware sinogram knowledge library transformer with feature modulation for low-dose medical image segmentation.

Low-dose computed tomography (LDCT) and low-dose positron emission tomography (LDPET) enable shorter acquisition times and lower radiation exposure. H...

Plasma signals of lung tumor promotion for molecular cancer prevention.

Predicting lung cancer risk would enhance prevention trials. Although the Canakinumab Anti-inflammatory Thrombosis Outcome Study (CANTOS) trial demons...

CT-based deep learning radiogenomics for predicting key glioma genotypes (IDH, ATRX, EGFR, TP53).

PURPOSE: Molecular subtyping guides diagnosis and targeted therapy for gliomas. Although MRI-the current imaging standard-can be time-consuming and is...

Low-kVp techniques for radiation dose optimization in abdominopelvic contrast-enhanced CT: a scoping review.

Contrast-enhanced computed tomography (CECT) of the abdomen and pelvis is widely used for diagnostic imaging but contributes substantially to cumulati...

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

INTRODUCTION: The survival rate of patients with life-threatening diseases primarily depends on the speed of diagnosis. Too often, diseases are detect...

Prospective germline exome and machine learning-based risk score identify predictive and prognostic biomarkers of immunotherapy outcomes in advanced non-small cell lung cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC). However, a su...

A Foundation Model Based CT Biomarker for Non-Invasive Prediction of Response to Neoadjuvant Immunochemotherapy in Non-Small Cell Lung Cancer.

Predicting pathological complete response (pCR) to neoadjuvant immunochemotherapy in non-small cell lung cancer (NSCLC) is clinically important yet re...