Breast Cancer

Spatially distinct chromatin compaction states predict neoadjuvant chemotherapy resistance in Triple Negative Breast Cancer

Organisation and dynamics of chromatin play a key role in regulation of cell state and function. In cancer, chromatin plasticity is known to be important in control of drug resistance, but the relationship between chromatin compaction and chemotherapy response within complex tissue settings remains unclear. Here, we measured single nuclei chromatin compaction using fluorescence lifetime imaging mi...

A Druggable Tumor Suppressor and Leukemic Stem Cell Marker

Acute myeloid leukemia (AML) often enters remission after chemotherapy but frequently relapses due to chemotherapy-resistant leukemic stem cells (LSCs). Relapsed AML remains largely unresponsive to current therapies and carries a poor prognosis. We developed a large-language model (LLM) agent that incorporates multi-modal data to nominate druggable therapeutic targets for AML. We identified that h...

Automated imaging-based tumor burden and pre-treatment circulating tumor DNA in HPV-associated oropharynx cancer

Artificial intelligence (AI)-based imaging analysis has applications for the diagnosis of head and neck malignancies, and serum circulating tumor-asso...

Automated Segmentation of Trunk Musculature with a Deep CNN Trained from Sparse Annotations in Radiation Therapy Patients with Metastatic Spine Disease

Given the high prevalence of vertebral fractures post-radiotherapy in patients with metastatic spine disease, accurate and rapid muscle segmentation c...

Prospective Blinded evaluation of Thermalytix, an artificial intelligence-enhanced breast thermal imaging software, correlated with radiologist-interpreted mammograms: Results of an exploratory study in Zambia

While mammography is commonly used for breast cancer detection, its widespread implementation in resource-constrained nations is challenging. Artifici...

Impact of a machine learning-powered algorithm on pathologist HER2 IHC scoring in breast cancer

HER2 expression level is a key factor in determining the optimal treatment course for breast cancer patients. Roughly 15% of breast cancers are HER2(+...

Performance of an artificial intelligence foundation model for prostate radiotherapy segmentation

Artificial intelligence (AI) foundation models such as Segment Anything Model 2 (SAM 2) offer potential for semi-automated image segmentation with min...

Establishment of in silico prediction of adjuvant chemotherapy response from active mitotic gene signature in non-small cell lung cancer

Conventional chemotherapeutics exploit cancer’s hallmark of active cell cycling, primarily targeting mitotic cells. Consequently, the mitotic index (M...

Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge

The prognostic significance of tumor-infiltrating lymphocytes (TILs) in breast cancer has been recognized for over a decade. Although histology-based ...

Robust cancer crowdfunding predictions: Leveraging large language models and machine learning for success analysis

In the field of medical crowdfunding prediction, traditional statistical methods have long been the standard. Machine learning algorithms are popular ...

Conversational Artificial Intelligence for Translational Precision Medicine: Integrating Social Determinants of Health, Genomics, and Clinical Data with AI-HOPE-PM

Introduction: Achieving equity in translational precision medicine requires the integration of genomic, clinical, and social determinants of health (S...

Corr-A-Net: Interpretable Attention-Based Correlated Feature Learning framework for predicting of HER2 Score in Breast Cancer from H&E Images

Human epidermal growth factor receptor 2 (HER2) expression is a critical biomarker for assessing breast cancer (BC) severity and guiding targeted anti...

Deep Learning on Histopathological Images to Predict Breast Cancer Recurrence Risk and Chemotherapy Benefit

Genomic testing has transformed treatment decisions for hormone receptor-positive, HER2-negative (HR+/HER2-) early breast cancer; however, it remains ...

Transcriptomics-Driven Machine Learning Models Accurately Predict Chemotherapy Response in Muscle-invasive Bladder Cancer

Muscle-invasive bladder cancer (MIBC) is associated with poor predictability of response to cisplatin-based neoadjuvant chemotherapy (NAC). Consequent...

Deep learning NTCP model for late dysphagia after radiotherapy for head and neck cancer patients based on 3D dose, CT and segmentations

Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient’s health and quality of life. Conventional normal t...

Detecting neurodegenerative changes in glaucoma using deep mean kurtosis-curve–corrected tractometry

Glaucoma is increasingly recognized as a neurodegenerative condition involving both retinal and central nervous system structures. Here, we present an...

Precision Oncology Through Dialogue: AI-HOPE-RTK-RAS Integrates Clinical and Genomic Insights into RTK-RAS Alterations in Colorectal Cancer

The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic r...

Modeling the Impact of Social Determinants on Breast Cancer Screening: A Data-Driven Approach

This study addresses the critical implementation science challenge of operationalizing social determinants of health (SDoH) in clinical practice. We d...

Evaluation of Large Language Model-Generated Patient Information for Communicating Radiation Risk

Large language models are increasingly used to generate patient information in healthcare. However, their ability to communicate complex topics, such ...

Prediction of OncotypeDX recurrence score using H&E stained WSI images

The OncotypeDX 21-gene assay is a widely adopted tool for estimating recurrence risk and informing chemotherapy decisions in early-stage, hormone rece...