Area of research
Radiology, Nuclear Medicine and Imaging · Genetics
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Glioma Diagnosis and Treatment, Lung Cancer Diagnosis and Treatment, and AI in cancer detection.
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Deep learning-based segmentation of multisite disease in ovarian cancer
Deep learning-based Segmentation of Multi-site Disease in Ovarian Cancer
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation – Analysis of Ranking Scores and Benchmarking Results
Federated Tumor Segmentation
Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis
DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules
DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules
Call for Data Standardization: Lessons Learned and Recommendations in an Imaging Study
Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
The public cancer radiology imaging collections of The Cancer Imaging Archive
Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges
Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group
Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival
Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas–Renal Cell Carcinoma (TCGA–RCC) Imaging Research Group
Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma
Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor
Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients
Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project
MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set
TCIA: An information resource to enable open science
Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers