Breast Cancer

Fine-Tuning Open-Source Large Language Models to Improve Their Performance on Radiation Oncology Tasks: A Feasibility Study to Investigate Their Potential Clinical Applications in Radiation Oncology

Background: The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplored. Purpose: This study aims to investigate whether fine-tuning LLMs with d...

Multivariate Feature Selection and Autoencoder Embeddings of Ovarian Cancer Clinical and Genetic Data

This study explores a data-driven approach to discovering novel clinical and genetic markers in ovarian cancer (OC). Two main analyses were performed: (1) a nonlinear examination of an OC dataset using autoencoders, which compress data into a 3-dimensional latent space to detect potential intrinsic separability between platinum-sensitive and platinum-resistant groups; and (2) an adaptation of th...

Marker Track: Accurate Fiducial Marker Tracking for Evaluation of Residual Motions During Breath-Hold Radiotherapy

Fiducial marker positions in projection image of cone-beam computed tomography (CBCT) scans have been studied to evaluate daily residual motion duri...

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 ...

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 ...

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 ...