Area of research
Artificial Intelligence · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, Head and Neck Cancer Studies, and Cancer Genomics and Diagnostics.
Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas
Abstract 2471: Interpretable HPV detection in head and neck cancer using foundation models and synthetic digital pathology
Corrigendum to “Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas”. [Oral Oncol. 163 (2025) 107207]
Slideflow: deep learning for digital histopathology with real-time whole-slide visualization
Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features
Artificial intelligence-based morphologic classification and molecular characterization of neuroblastic tumors from digital histopathology
Acquired resistance to immunotherapy and chemoradiation in MYC amplified head and neck cancer
Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning
Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma
Switching anti-EGFR antibody re-sensitizes head and neck cancer patient following acquired resistance to cetuximab
Deep learning generates synthetic cancer histology for explainability and education
Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence
Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis
Validating a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning
Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology
The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Immune‐related adverse events are associated with improved response, progression‐free survival, and overall survival for patients with head and neck cancer receiving immune checkpoint inhibitors
Nivolumab, nabpaclitaxel, and carboplatin followed by risk/response adaptive de-escalated locoregional therapy for HPV-associated oropharyngeal cancer: OPTIMA II trial.
Risk and response adapted de-intensified treatment for HPV-associated oropharyngeal cancer: Optima paradigm expanded experience
Pan-cancer image-based detection of clinically actionable genetic alterations
Machine Learning–Guided Adjuvant Treatment of Head and Neck Cancer
A randomized phase 2 network trial of tivantinib plus cetuximab versus cetuximab in patients with recurrent/metastatic head and neck squamous cell carcinoma
The Aachen Protocol for Deep Learning Histopathology: A hands-on guide for data preprocessing
A randomized phase 2 study of temsirolimus and cetuximab versus temsirolimus alone in recurrent/metastatic, cetuximab‐resistant head and neck cancer: The MAESTRO study
Machine learning guided adjuvant treatment of head and neck cancer.
Author Correction: Pan-cancer image-based detection of clinically actionable genetic alterations
Deep learning detects virus presence in cancer histology
A pilot study of the pan‐class I PI3K inhibitor buparlisib in combination with cetuximab in patients with recurrent or metastatic head and neck cancer
OPTIMA: a phase II dose and volume de-escalation trial for human papillomavirus-positive oropharyngeal cancer
Association of immune-related adverse events (irAEs) with improved response, progression-free survival, and overall survival for patients with metastatic head and neck cancer receiving anti-PD-1 therapy.