Breast Cancer

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...

[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 one of the main routes of metastasis in gastric cancer, and patients with this condition have poor prognosis. With advancements in medicine and sociology, the concept of integrated medicine emerged in the late 1980s. It emphasizes treating patients ...

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...

Machine Learning Models Predicting Hospital Admissions During Chemotherapy Utilising Longitudinal Symptom Severity Reports and Patient-Reported Outcome Measures.

Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients' well-being. Machine learning (ML...

Longitudinal changes in body composition during palliative systemic chemotherapy and survival outcomes in metastatic colorectal cancer.

BACKGROUND: In patients with metastatic colorectal cancer, chemotherapy may lead to changes in body composition, including skeletal muscle quantity an...

Prognostic value of circadian rhythm-associated genes in breast cancer.

OBJECTIVE: Breast cancer (BC) remains the most prevalent malignancy among women. Clinical evidence indicates that genetic variations related to circad...

Integrating Bulk and Single-Cell Transcriptomics with Machine Learning Reveals a Heme Metabolism-Based Panel for Lung Adenocarcinoma Chemotherapy Resistance.

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality, with heme metabolism playing a critical role in tumor progression and treat...

A customizable P hydrogel applicator for brachytherapy of skin hemangioma based on machine learning and 3D-printing.

Skin hemangioma is a tumor originating from skin blood vessels, which often occurs in infants and children. Brachytherapy with the P-based radionuclid...

An automated cascade framework for glioma prognosis via segmentation, multi-feature fusion and classification techniques.

Glioma is one of the most lethal types of brain tumors, accounting for approximately 33% of all diagnosed brain tumor cases. Accurate segmentation and...