Lung Cancer

Robotic radiation shielding system reduces radiation-induced DNA damage in operators performing electrophysiological procedures.

Fluoroscopically guided electrophysiology (EP) procedures expose operators to low doses of ionizing radiation, which can induce DNA double-strand breaks (DSBs) and raises increasing concerns regarding potential health risks. A novel robotic radiation shielding system (RSS) was developed to provide full-body protection by encapsulating the imaging beam and blocking scattered radiation. This study a...

Machine learning-driven imaging data for early prediction of lung toxicity in breast cancer radiotherapy.

One possible adverse effect of breast irradiation is the development of pulmonary fibrosis. The aim of this study was to determine whether planning CT scans can predict which patients are more likely to develop lung lesions after treatment. A retrospective analysis of 242 patient records was performed using different machine learning models. These models showed a remarkable correlation between the...

Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.

INTRODUCTION: Cervical cancer, which includes squamous cell carcinoma and adenocarcinoma, is a leading cause of cancer-related deaths globally, partic...

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning.

Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the no...

Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strat...

Pixels to Prognosis: Harmonized Multi-Region CT-Radiomics and Foundation-Model Signatures Across Multicentre NSCLC Data

Purpose: To evaluate the impact of harmonization and multi-region CT image feature integration on survival prediction in non-small cell lung cancer ...

Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.

BACKGROUND: As machine learning (ML) continuously develops in cancer diagnosis and treatment, some researchers have attempted to predict the expressio...

Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.

BACKGROUND: Esophageal cancer is the sixth most common cancer worldwide, with a high mortality rate. Early prognosis of esophageal abnormalities can i...

Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy

Background: Deep learning has potential to improve the efficiency and consistency of radiation therapy planning, but clinical adoption is hindered b...

AI-Driven Multiscale Study on the Mechanism of Polygonati Rhizoma in Regulating Immune Function in STAD.

Polygonati Rhizoma, a traditional Chinese medicine, has demonstrated immunomodulatory and anticancer properties, yet its precise mechanisms in stomach...

A Digital Score Based on Circulating-Tumor-Cells-Derived mRNA Quantification and Machine Learning for Early Colorectal Cancer Detection.

Circulating tumor cells (CTCs) serve as valuable biomarkers in tumor circulation, carrying essential primary tumor information. The purification of CT...

Reconstruction of partially obscured objects with a physics-driven self-training neural network.

We investigate artificial-intelligence-supported in-line holographic imaging with coherent terahertz (THz) radiation. The goal is to reconstruct three...

Current trends and emerging themes in utilizing artificial intelligence to enhance anatomical diagnostic accuracy and efficiency in radiotherapy.

Artificial intelligence (AI) incorporation into healthcare has proven revolutionary, especially in radiotherapy, where accuracy is critical. The purpo...

Patient-Specific Autoregressive Models for Organ Motion Prediction in Radiotherapy

Radiotherapy often involves a prolonged treatment period. During this time, patients may experience organ motion due to breathing and other physiolo...

Pretrained hybrid transformer for generalizable cardiac substructures segmentation from contrast and non-contrast CTs in lung and breast cancers

AI automated segmentations for radiation treatment planning (RTP) can deteriorate when applied in clinical cases with different characteristics than...

Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer.

Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patients' quality of life. This study aimed to establish...

The role of artificial intelligence in occupational health in radiation exposure: a scoping review of the literature.

INTRODUCTION: Artificial intelligence (AI) has the potential to significantly enhance workplace safety and mitigate occupational radiation exposure ri...

RadField3D: a data generator and data format for deep learning in radiation-protection dosimetry for medical applications.

In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating three-dimensional...

Photon-counting CT in cancer radiotherapy: technological advances and clinical benefits.

Photon-counting computed tomography (PCCT) marks a significant advancement over conventional Energy-integrating detector CT systems. This review highl...

Clinical Implications of The Cancer Genome Atlas Molecular Classification System in Esophagogastric Cancer.

PURPOSE: The Cancer Genome Atlas (TCGA) project defined four distinct molecular subtypes of esophagogastric adenocarcinoma: microsatellite instable (M...