Lung Cancer

Prediction of Lymph Node Metastasis in Non-Small Cell Lung Carcinoma Using Primary Tumor Somatic Mutation Data.

PURPOSE: Lymph node metastasis (LNM) significantly affects prognosis and treatment strategies in non...

Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph.

Lung cancer remains a leading cause of global cancer mortality, demanding precise diagnostic tools f...

Predicting NSCLC surgical outcomes using deep learning on histopathological images: development and multi-omics validation of Sr-PPS model.

BACKGROUND: Currently, there remains a critical need for reliable tools to accurately predict post-s...

CT-Based Radiomics for Predicting PD-L1 Expression in Non-small Cell Lung Cancer: A Systematic Review and Meta-analysis.

BACKGROUND AND PURPOSE: The efficacy of immunotherapy in non-small cell lung cancer (NSCLC) is intri...

Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer.

INTRODUCTION: A significant challenge in treating non-small cell lung cancer (NSCLC) is its inherent...

Comparative evaluation of deep learning-based and conventional reconstruction techniques for image quality enhancement in low-dose chest computed tomography.

BACKGROUND: With the increasing use of computed tomography (CT) in clinical practice, greater attent...

Evaluating generative AI models for explainable pathological feature extraction in lung adenocarcinoma grading assessment and prognostic model construction.

BACKGROUND: Given the increasing prevalence of generative AI (GenAI) models, a systematically evalua...

A Bayesian deep learning model with consolidation-to-tumor ratio (CTR) prior revolutionizes the prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma: a large-scale diagnostic study.

BACKGROUND: The preoperative prediction of spread through air spaces (STAS) in patients with early-s...

Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.

Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vesse...

ASO Author Reflections: Machine Learning for Postoperative Frailty Prediction in NSCLC-From Model Development to Clinical Translation.

Validity and Reliability of an Artificial Intelligence-Based Posture Estimation Software for Measuring Cervical and Lower-Limb Alignment Versus Radiographic Imaging.

: Accurate postural assessment is essential for managing musculoskeletal disorders; however, routine...

Evaluation of locoregional invasiveness of early lung adenocarcinoma manifesting as ground-glass nodules via [Ga]Ga-FAPI-46 PET/CT imaging.

PURPOSE: Accurate differentiation of the histologic invasiveness of early-stage lung adenocarcinoma ...

The predictive value of F-FDG PET/CT radiomics for pleural invasion in non-small cell lung cancer.

OBJECTIVE: This study aims to develop and validate a PET/CT radiomics fusion model for preoperative ...

Deep learning and iterative image reconstruction for head CT: Impact on image quality and radiation dose reduction-Comparative study.

This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Ima...

Shining the Beam on the Next Generation: A Program Evaluation of a National Workshop Focusing on Medical Student Engagement in Radiation Oncology.

The rising cancer incidence has increased demand for radiation oncologists, surpassing current staff...

Single-cell multimodal analysis reveals tumor microenvironment predictive of treatment response in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) constitutes over 80% of lung cancer cases and remains a leading c...

Process management method in head and neck cancer patient care.

Management of patients with head and neck cancer (HNC) is a complex process that involves extensive ...

Machine learning explores the prognostic and immuno-oncological impact of mitochondrial unfolded protein response in CESC.

BACKGROUND: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) pose significant...

Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adenocarcinoma: a retrospective pilot study.

PURPOSE: This study aims to develop an interpretable predictive model combining contrast-enhanced CT...

Assessment of contour accuracy in head and neck replanning: Deep learning trained model compared with deformable image registration propagation technique.

Accurate contouring is crucial for optimal treatment outcomes, whether for nonadaptive radiotherapy ...

Deep Learning-Based Multimodal Feature Interaction-Guided Fusion: Enhancing the Evaluation of EGFR in Advanced Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: The aim of this study is to develop a deep learning-based multimodal featu...