Cardiovascular

Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models.

BACKGROUND: Peritumoral characteristics demonstrate significant predictive value for neoadjuvant chemotherapy (NAC) response in breast cancer (BC) through tumor-stromal interactions. Radiomics analysis of peritumoral regions has shown robust capability in predicting treatment outcomes; however, the optimal peritumoral thickness for maximizing predictive accuracy remains undefined.

Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics.

OBJECTIVE: This study aims to predict the early efficacy of induction chemotherapy (ICT) in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) through habitat subregion analysis and multimodal MRI radiomics techniques.

Personalized prediction of breast cancer candidates for Anti-HER2 therapy using F-FDG PET/CT parameters and machine learning: a dual-center study.

BACKGROUND: Accurately evaluating human epidermal growth factor receptor (HER2) expression status in breast cancer enables clinicians to develop indiv...

GAIN-BRCA: a graph-based AI-net framework for breast cancer subtype classification using multiomics data.

MOTIVATION: Contextual integration of multiomic datasets from the same patient could improve the accuracy of subtype prediction algorithms to help wit...

Microbiota as diagnostic biomarkers: advancing early cancer detection and personalized therapeutic approaches through microbiome profiling.

The important function of microbiota as therapeutic modulators and diagnostic biomarkers in cancer has been shown by recent developments in microbiome...

A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.

OBJECTIVE: This study aimed to develop an effective model that identifies high-risk breast cancer (BRCA) patients and optimizes clinical treatments.

A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

BACKGROUND: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based s...

TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.

Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in ...

Artificial Intelligence-Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma.

Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and varies in response to treatment, making it one of the...

Artificial intelligence can help individualize Wilms tumor treatment by predicting tumor response to preoperative chemotherapy.

PURPOSE: To create a computer-aided prediction (CAP) system to predict Wilms tumor (WT) responsiveness to preoperative chemotherapy (PC) using pre-the...

Ensemble learning guided survival prediction and chemotherapy benefit analysis in high-grade chondrosarcoma: A study based on the surveillance, epidemiology, and end results (SEER) database.

The chemotherapy benefit for high-grade chondrosarcoma remains controversial. Ensemble learning has better overall performance than single computatio...

Predicting neoadjuvant chemotherapy response in locally advanced gastric cancer using a machine learning model combining radiomics and clinical biomarkers.

RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is a promising therapeutic strategy for managing locally advanced gastric cancer (LAGC), aimi...

Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction

Background: Radiation pneumonitis is a side effect of thoracic radiation therapy. Recently, machine learning models with radiomic features have impr...

SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in Satellite Videos

Vehicle detection and tracking in satellite video is essential in remote sensing (RS) applications. However, upon the statistical analysis of existi...

Analysis of Transferred Pre-Trained Deep Convolution Neural Networks in Breast Masses Recognition

Breast cancer detection based on pre-trained convolution neural network (CNN) has gained much interest among other conventional computer-based syste...

Patherea: Cell Detection and Classification for the 2020s

This paper presents a Patherea, a framework for point-based cell detection and classification that provides a complete solution for developing and e...

Summary of Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images

This study introduces a federated learning-based approach to predict HER2 status from hematoxylin and eosin (HE)-stained whole slide images (WSIs), ...

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 threedimensiona...

Reliable Breast Cancer Molecular Subtype Prediction based on uncertainty-aware Bayesian Deep Learning by Mammography

Breast cancer is a heterogeneous disease with different molecular subtypes, clinical behavior, treatment responses as well as survival outcomes. The...

DLSOM: A Deep learning-based strategy for liver cancer subtyping

Liver cancer is a leading cause of cancer-related mortality worldwide, with its high genetic heterogeneity complicating diagnosis and treatment. Thi...