Lung Cancer

18F-FDG PET/CT and receptor-positive circulating tumor cells-based machine learning model for predicting poorly differentiated lung adenocarcinoma.

INTRODUCTION: This study aimed to develop and validate a machine learning model that integrates radiomic features from 2-[18F]fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) with folate receptor-positive circulating tumor cells (FR+-CTCs) for the preoperative prediction of tumor differentiation grade, as defined by the International Association for the ...

Artificial Intelligence Integration in Radiation Therapy Education: A Multi-Modal Approach.

INTRODUCTION: Critical workforce shortages in radiation oncology have led tertiary institutions to rapidly expand their radiation therapy (RT) student cohorts. The increase in students entering university created the challenge of scaling educational delivery while preserving the quantity and quality of learning essential for developing well-prepared, competent clinicians. Artificial Intelligence (...

A Deep Learning Framework Integrating Tumor Microenvironmental Features Accurately Predicts Multiple Driver Gene Mutations in Lung Cancer Pathology Images.

UNLABELLED: Deep learning (DL) has the potential to enable the prediction of gene mutations directly from routine histopathology slides in lung cancer...

Deep Learning-Derived Sarcopenia Marker Predicts Benefit from Anti-EGFR Therapy in Patients with RAS Wild-type Metastatic Colorectal Cancer.

PURPOSE: The benefit of treatment intensification in metastatic colorectal cancer (mCRC) may be influenced by host-related factors that are not accoun...

Computational exploration and discovery of dual EGFR-CDK2 kinase inhibitors: AI-ML powered bioisosteric design, 3D QSAR, docking, DFT and ADMET analysis of novel phthalimide derivatives.

AI-ML approaches emerged as transformative technologies in cancer drug discovery by accelerating the target identification and lead optimization. EGFR...

Thermal analysis of flat plate solar air heater system with radiation reflectors and W-shaped roughness: artificial neural network & machine learning approach.

The lower thermal behavior of solar-based thermal systems limits the contribution of solar systems to meet current energy demand of industries. The Fl...

Characterization of the Sequence of Cytopenia after Total-body Irradiation using Unsupervised Machine Learning.

Hematopoietic acute radiation syndrome (H-ARS) elicits multidimensional effects, as total-body irradiation (TBI) induced myelosuppression results in d...

Developing a Metabolic-Associated Prognostic Index for Risk Stratification and Therapeutic Guidance in Stage I Lung Adenocarcinoma via Multiomics Analysis.

PURPOSE: Early-stage lung adenocarcinoma (LUAD) exhibits substantial clinical heterogeneity that is not fully explained by TNM staging, highlighting t...

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This revi...

Diagnostic and Technological Advances in Magnetic Resonance (Focusing on Imaging Technique and the Gadolinium-Based Contrast Media), Computed Tomography (Focusing on Photon Counting CT), and Ultrasound-State of the Art.

Magnetic resonance continues to evolve and advance as a critical imaging modality for disease diagnosis and monitoring. Hardware and software advances...

Renal interstitial inflammation predicts IgA nephropathy progression via multiple machine learning models.

BACKGROUND: Renal interstitial inflammation (RII) is a frequent pathological feature in IgA nephropathy (IgAN), but its prognostic value remains uncer...

A Self-Supervised Foundation Model Based on Three-Dimensional Chest CT Scans for Lung Cancer Diagnosis and Prognosis Prediction.

Purpose To develop a self-supervised chest CT foundation model and evaluate its performance in lung cancer clinical tasks. Materials and Methods In th...

Cancer-associated fibroblasts are associated with CD8+ T cell depletion and poor prognosis in colorectal adenocarcinoma: a multi-omics and machine learning analysis.

Fibroblastic proliferation in various tumor microenvironments influences cancer survival through complex interactions with diverse immune responses. T...

Evaluating the cost-effectiveness of artificial intelligence in Barrett's surveillance.

Artificial intelligence (AI) has emerged as a promising tool to detect early dysplasia in Barrett's esophagus (BE). However, the cost-effectiveness of...

Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.

BACKGROUND: Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing ...

Thermal signatures in breast cancer: Deciphering latent biomarkers through deep learning and explainable AI.

Breast cancer, characterized by its aggressive pro-gression and high mortality rates, continues to be among the most common types of cancer. While ear...

Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks - Foundations and Emerging Opportunities in Spatial Pathobiology.

Cytology cell block specimens are essential diagnostic materials in patients with advanced-stage malignancy and often represent the only available sub...

Signature-aware deep learning reveals distinct driver gene programs and mutational processes in glioblastoma and colon adenocarcinoma.

The precise identification of cancer driver mutations is essential for precision oncology; however, it remains a significant challenge because of the ...

Identification of Gene Signatures Differentiating Cancer from Normal Tissues Across Histological Classifications of Gastric Adenocarcinoma via Machine Learning Methods.

Gastric adenocarcinoma is a significant global health concern. Among the myriad histologic classification methods for this cancer, the Lauren classifi...

Artificial intelligence model for cardiovascular disease risk prediction in breast cancer patients using electronic health records and computed tomography scans.

BACKGROUND AND PURPOSE: Cardiovascular disease (CVD) is the leading cause of death globally [1] as well as the leading cause of death among cancer sur...