Lung Cancer

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 histopathological assessment is crucial. This study aimed to evaluate and compare three visual-capable GenAI models (GPT-4o, Claude-3.5-Sonnet, and Gemini-1.5-Pro) for lung adenocarcinoma histological pattern recognition and grading, as well as to exp...

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 selecting the appropriate surgical approach and improving patient outcomes. Previous research has confirmed that there is a significant correlation between consolidation-to-tumor ratio (CTR) and STAS. This study aimed to develop a Bayesian deep learning...

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 ...

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 staffing expansion estimates. Enhancing radiation oncol...

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 cause of cancer-related mortality worldwide. Despit...

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 knowledge on (at least) the discipline of surgery,...

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 global health challenges. While the mitochondrial...

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 (CECT) radiomics features with clinicopathologica...

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 with single images or adaptive radiotherapy (ART) ...

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 feature interaction-guided fusion (DL-MFIF) framework t...

Deep learning dosiomics for the pretreatment prediction of radiation dermatitis in nasopharyngeal carcinoma patients treated with radiotherapy.

PURPOSE: To develop a combined dosiomics and deep learning (DL) model for predicting radiation dermatitis (RD) of grade ≥ 2 in patients with nasophary...

Plasma and Urine Circulating Tumor DNA Methylation Profiles for Non-Invasive Pancreatic Ductal Adenocarcinoma Detection: Significant Findings in Plasma Only.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with limited treatment options, and early detection remains challenging due to the lack...

Lung MRI: Indications, Capabilities, and Techniques- Expert Panel Narrative Review.

Lung MRI provides both structural and functional information across a spectrum of parenchymal and airway pathologies. MRI, using current widely availa...

Arginine methylation patterns in LUAD: defining prognostic subtypes and relevance to immunotherapy.

BACKGROUND: Lung cancer remains the leading cause of cancer-related death worldwide, with lung adenocarcinoma (LUAD) being the most common subtype. Ar...