Cardiovascular

Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation

Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases, such as cancer. However, the CT studies can be acquired with low radiation doses, different scanners, and are frequently affected by motion and metal artifacts. Prior approaches have targeted the quality improvement of one specific CT phase (e.g., non-contrast CT). In this work, we...

Synthetic CT image generation from CBCT: A Systematic Review

The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology. This systematic review, following PRISMA guidelines and using the PICO model, comprehensively evaluates the literature from 2014 to 2024 on the generation of sCT images for radiation therapy planning in oncology. A total of 35 rel...

Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction

Measurement quality assurance (QA) practices play a key role in the safe use of Intensity Modulated Radiation Therapies (IMRT) for cancer treatment....

Prediction of Binding Affinity for ErbB Inhibitors Using Deep Neural Network Model with Morgan Fingerprints as Features

The ErbB receptor family, including EGFR and HER2, plays a crucial role in cell growth and survival and is associated with the progression of variou...

End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT

Objective: There exist several X-ray computed tomography (CT) scanning strategies to reduce a radiation dose, such as (1) sparse-view CT, (2) low-do...

ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction

Survival prediction is a crucial task in the medical field and is essential for optimizing treatment options and resource allocation. However, curre...

Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients

This study explores how causal inference models, specifically the Linear Non-Gaussian Acyclic Model (LiNGAM), can extract causal relationships betwe...

Mechanistic modeling and machine learning identifies optimum radiotherapy schedules to prevent treatment-induced metastasis

Lung cancer patients often experience increased metastasis formation after radiotherapy. However, it is incompletely understood whether radiation affe...

Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells

The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multi...

TCUP – An Open Access Tool to Predict Tissue of Origin and Cancer of Unknown Primary (CUP)

Cancer of unknown primary (CUP) remains a major diagnostic hurdle, compromising therapies that depend on accurately identifying tissue of origin. We p...

Characterization of metabolic phenotypes in breast cancer through the integration of genome-scale metabolic models and machine learning

The metabolic heterogeneity of breast cancer represents a significant challenge for the identification of biomarkers and therapeutic targets. To addre...

Integrating Artificial Intelligence-Driven Digital Pathology and Genomics to Establish Patient-Derived Organoids as a Novel Alternative Model for Drug Response in Head and Neck Cancer

Patient-derived organoids (PDOs) are emerging as advanced 3D ex vivo novel alternative method (NAM) preclinical models, offering significant advantage...

Translating clinical gene sequencing into a foundational representation of tumor subtype

While gene sequencing is routine in cancer care, translating sequences into treatment decisions remains a challenge. Here we introduce MutationProject...

An integrative machine learning approach identifies the centrality of ferroptosis, cuproptosis, and immune pathway crosstalk for breast cancer stratification and therapy guidance

Breast cancer (BRCA) is a leading cause of cancer-related mortality in women, characterized by marked heterogeneity in molecular subtypes, immune micr...

Mitosis Detection in the Wild Using Detection Transformers

Identification of mitotic cells and its down-stream analysis, is an important parameter in understanding the pathology of cancer, predicting response ...

Gene-Family Encoding Boosts Domain-Adapted Single-Cell Language Models

Transformer-based single-cell foundation models often rely on ranked-gene (RG) sequences where genes, ranked by expression, are often not functionally...

SpliceSelectNet: A Hierarchical Transformer-Based Deep Learning Model for Splice Site Prediction

Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely ...

Development of an EMT-related exosomal miRNA signature that can predict prognosis in hepatocellular carcinoma

Chemoresistance and epithelial-mesenchymal transition (EMT) are associated with failure of cancer chemotherapy and poor survival of patients. We have ...

Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features

Artificial intelligence (AI) is making notable advances in digital pathology but faces challenges in human interpretability. Here we introduce EXPAND ...

Artificial intelligence-enabled automated analysis of transmission electron micrographs to evaluate chemotherapy impact on mitochondrial morphology in triple negative breast cancer

Advancements in transmission electron microscopy (TEM) have enabled in-depth studies of biological specimens, offering new avenues to large-scale imag...