Cardiovascular

Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population

This study presents a deep learning system for breast cancer detection in mammography, developed using a modified EfficientNetV2 architecture with enhanced attention mechanisms. The model was trained on mammograms from a major Thai medical center and validated on three distinct datasets: an in-domain test set (9,421 cases), a biopsy-confirmed set (883 cases), and an out-of-domain generalizabilit...

Machine learning identifies SRD5A3 as a propionate-related prognostic biomarker in triple-negative breast cancer.

The increased risk of recurrence and metastasis are obstacles to treating TNBC. Propionate-related genes play an important role in tumor development and immune cell infiltration. The study was to identify the association between propionate-related genes and the prognosis of TNBC patients. Propionate-related genes were collected and analyzed to establish propionate-related gene characteristics. The...

Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy.

Low-dose radiation therapy has emerged as a promising modality for cancer treatment because of its ability to stimulate antitumor immune responses whi...

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 brea...

STA-Risk: A Deep Dive of Spatio-Temporal Asymmetries for Breast Cancer Risk Prediction

Predicting the risk of developing breast cancer is an important clinical tool to guide early intervention and tailoring personalized screening strat...

Developing hybrid mechanistic and data-driven personalized prediction models for platelet dynamics

Hematotoxicity, drug-induced damage to the blood-forming system, is a frequent side effect of cytotoxic chemotherapy and poses a significant challen...

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...

[Integrated diagnosis and treatment of peritoneal metastasis in gastric cancer].

The high incidence and mortality rates of gastric cancer pose a significant burden on human health and public health systems. Peritoneal metastasis is...

Identification of molecular subtypes and a prognostic signature based on machine learning and purine metabolism-related genes in breast cancer.

Breast cancer (BC), one of the most prevalent malignant tumors worldwide, lacks efficacious diagnostic biomarkers and therapeutic targets. This study ...

Predicting Neo-Adjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images

Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human e...

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...

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...

Artificial intelligence generated 3D body composition predicts dose modifications in patients undergoing neoadjuvant chemotherapy for rectal cancer.

PURPOSE: Chemotherapy administration is a balancing act between giving enough to achieve the desired tumour response while limiting adverse effects. C...

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...