Cardiovascular

Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.

BACKGROUND: Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical approach after neoadjuvant chemotherapy (NAC). We previously developed a deep neural network-based endoscopic evaluation to determine pCR preoperatively. Its quality warrants improvement with a larger data series for clinical application.

Mexican dataset of digital mammograms (MEXBreast) with suspicious clusters of microcalcifications.

Breast cancer is one of the most prevalent cancers affecting women worldwide. Early detection and treatment are crucial in significantly reducing mortality rates Microcalcifications (MCs) are of particular importance among the various breast lesions. These tiny calcium deposits within breast tissue are present in approximately 30% of malignant tumors and can serve as critical indirect indicators o...

Deep learning for malignant lymph node segmentation and detection: a review.

Radiation therapy remains a cornerstone in the treatment of cancer, with the delineation of Organs at Risk (OARs), tumors, and malignant lymph nodes p...

Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.

Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor R...

Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer.

BACKGROUND: Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critical prognostic marker for patients with breast canc...

Reinforcement learning-driven automated head and neck simultaneous integrated boost (SIB) radiation therapy: flexible treatment planning aligned with clinical preferences.

Head-and-neck simultaneous integrated boost (SIB) treatment planning using intensity modulated radiation therapy is particularly challenging due to th...

Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.

BACKGROUND: Treatment with HER2-targeted therapies is recommended for HER2-positive breast cancer patients with HER2 gene amplification or protein ove...

Fully Automated Online Adaptive Radiation Therapy Decision-Making for Cervical Cancer Using Artificial Intelligence.

BACKGROUND: Interfraction variations during radiation therapy pose a challenge for patients with cervical cancer, highlighting the benefits of online ...

Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods.

Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i....

Development of a GPT-4-Powered Virtual Simulated Patient and Communication Training Platform for Medical Students to Practice Discussing Abnormal Mammogram Results With Patients: Multiphase Study.

BACKGROUND: Standardized patients (SPs) prepare medical students for difficult conversations with patients. Despite their value, SP-based simulation t...

Learning Chemotherapy Drug Action via Universal Physics-Informed Neural Networks.

OBJECTIVE: Quantitative systems pharmacology (QSP) is widely used to assess drug effects and toxicity before the drug goes to clinical trial. However,...

Enhancing security in electromagnetic radiation therapy using fuzzy graph theory.

This research investigates the application of fuzzy graph theory to address critical security challenges in electromagnetic radiation therapy systems....

The role of patient outcomes in shaping moral responsibility in AI-supported decision making.

INTRODUCTION: Integrating decision support mechanisms utilising artificial intelligence (AI) into medical radiation practice introduces unique challen...

Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.

Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Ne...

Integrated multi-omics analysis and machine learning refine molecular subtypes and clinical outcome for hepatocellular carcinoma.

The high morbidity and mortality of hepatocellular carcinoma (HCC) impose a substantial economic burden on patients' families and society, and the maj...

Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity.

Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies, but poor specificity ha...

Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.

Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer...

Smart MXene-based microrobots for targeted drug delivery and synergistic therapies.

MXenes and their composites exhibit remarkable electrical conductivity, mechanical flexibility, and biocompatibility, making them ideal candidates for...

Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: To establish predictive models for radiation-induced hypoglossal neuropathy (RIHN) in patients with nasopharyngeal carcinoma (...

Transforming breast cancer diagnosis and treatment with large language Models: A comprehensive survey.

Breast cancer (BrCa), being one of the most prevalent forms of cancer in women, poses many challenges in the field of treatment and diagnosis due to i...