Lung Cancer

FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images.

Cancer is among the most dangerous diseases contributing to rising global mortality rates. Lung cancer, particularly adenocarcinoma, is one of the deadliest forms and severely impacts human life. Early diagnosis and appropriate treatment significantly increase patient survival rates. Computed Tomography (CT) is a preferred imaging modality for detecting lung cancer, as it offers detailed visualiza...

Current AI technologies in cancer diagnostics and treatment.

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the...

Multi-omics identifies OSM-OSMR as a key receptor-ligand in the tumor environment of endometrial adenocarcinoma.

Endometrial adenocarcinoma carries a bleak prognosis, and the molecular markers that evaluate the progression of endometrial adenocarcinoma to advance...

NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC.

BACKGROUND: Accurate preoperative prediction of major pathological response or pathological complete response after neoadjuvant chemo-immunotherapy re...

Monte Carlo and Machine Learning-Based Evaluation of Fe-Enriched Al Alloys for Nuclear Radiation Shielding Applications.

This study presents a hybrid computational investigation into the radiation shielding behavior of Fe-enriched Al-based alloys (Al-Fe-Mo-Si-Zr) for pot...

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-small cell lung cancer (NSCLC). Current diagnosti...

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 for accurate subtype classification. This paper int...

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-surgical outcomes in non-small cell lung cancer (NS...

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 intricately associated with baseline PD-L1 expression r...

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 resistance to radiation therapy, leading to poor ...

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 attention is being paid to the radiation dose. There are...

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 evaluation of their performance in lung adenocarcinoma h...

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-stage lung adenocarcinoma (LUAD) is crucial for sel...

Applications of artificial intelligence in abdominal imaging.

The rapid advancements in artificial intelligence (AI) carry the promise to reshape abdominal imaging by offering transformative solutions to challeng...

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 vessels, particularly when cancer is in its early stage...

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 screening is often limited by radiation exposure,...

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 is crucial for determining surgical strategies. Th...

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 predicting pleural invasion (PI) in non-small cell...

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 Image Reconstruction (DLIR)-ability to improve image ...