Cardiovascular

Kinetic-Diffusion-Rotation Algorithm for Dose Estimation in Electron Beam Therapy

Monte Carlo methods are state-of-the-art when it comes to dosimetric computations in radiotherapy. However, the execution time of these methods suffers in high-collisional regimes. We address this problem by introducing a kinetic-diffusion particle tracing scheme. This algorithm, first proposed in the context of neutral transport in fusion energy, relies on explicit simulation of the kinetic mot...

End-to-end Triple-domain PET Enhancement: A Hybrid Denoising-and-reconstruction Framework for Reconstructing Standard-dose PET Images from Low-dose PET Sinograms

As a sensitive functional imaging technique, positron emission tomography (PET) plays a critical role in early disease diagnosis. However, obtaining a high-quality PET image requires injecting a sufficient dose (standard dose) of radionuclides into the body, which inevitably poses radiation hazards to patients. To mitigate radiation hazards, the reconstruction of standard-dose PET (SPET) from lo...

Deep Learning for Longitudinal Gross Tumor Volume Segmentation in MRI-Guided Adaptive Radiotherapy for Head and Neck Cancer

Accurate segmentation of gross tumor volume (GTV) is essential for effective MRI-guided adaptive radiotherapy (MRgART) in head and neck cancer. Howe...

State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.

The implementation of deep neural networks has spurred the creation of deep learning reconstruction (DLR) CT algorithms. DLR CT techniques encompass a...

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

BACKGROUND: Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced ma...

Explainable Machine Learning Predictions for the Benefit From Chemotherapy in Advanced Non-Small Cell Lung Cancer Without Available Targeted Mutations.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a global health challenge. Chemotherapy remains the standard therapy for advanced NSCLC without muta...

Evaluation of machine learning algorithms and computational structural validation of CYP2D6 in predicting the therapeutic response to tamoxifen in breast cancer.

OBJECTIVE: CYP2D6 plays a critical role in metabolizing tamoxifen into its active metabolite, endoxifen, which is crucial for its therapeutic effect i...

Semi-analytical model for the calculation of solar radiation pressure and its effects on a LEO satellite with predicting the change in position vectors using machine learning techniques

The rapid increase in the deployment of Low Earth Orbit (LEO) satellites, catering to diverse applications such as communication, Earth observation,...

NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer

Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires...

Equivariant Graph Attention Networks with Structural Motifs for Predicting Cell Line-Specific Synergistic Drug Combinations

Cancer is the second leading cause of death, with chemotherapy as one of the primary forms of treatment. As a result, researchers are turning to dru...

Leveraging Transfer Learning and Multiple Instance Learning for HER2 Automatic Scoring of H\&E Whole Slide Images

Expression of human epidermal growth factor receptor 2 (HER2) is an important biomarker in breast cancer patients who can benefit from cost-effectiv...

PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images

Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology ...

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. Ho...

Revolutionizing Personalized Cancer Vaccines with NEO: Novel Epitope Optimization Using an Aggregated Feed Forward and Recurrent Neural Network with LSTM Architecture

As cancer cases continue to rise, with a 2023 study from Zhejiang and Harvard predicting a 31 percent increase in cases and a 21 percent increase in...

Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams

In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a pat...

Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Tool

Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype...

Active learning for energy-based antibody optimization and enhanced screening

Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-...

Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

To assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for e...

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features p...

Tailored chemotherapy: Innovative deep-learning model customizing chemotherapy for high-grade serous ovarian carcinoma.