Area of research
Radiology, Nuclear Medicine and Imaging · Radiation
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Advanced Radiotherapy Techniques, Medical Imaging Techniques and Applications, and AI in cancer detection.
Prognostic impact of pelvic bone magnetic resonance imaging features in patients with cervical cancer undergoing definitive concurrent chemoradiotherapy.
Ductal Carcinoma In Situ Active Monitoring Trials: Do Eligibility Criteria Identify Patients at Low Risk for Upgrade to Invasive Carcinoma?
Effective use of PROs for survival prediction: Transformer-based modelling in NSCLC patients.
Imaging Biomarkers in Radiotherapy.
Fiber-Tip Surface-Micromachined Optical Ultrasound Transducer (SMOUT) Probe for Acoustic Detection Induced by Ultrahigh Dose Rate (UHDR) Electron Beam
Creation of an mHealth Infrastructure to Support Development and Delivery of mHealth Interventions: Protocol and Demonstration Projects Addressing Smoking Cessation in Cancer Care (Preprint)
Creation of an mHealth Infrastructure to Support Development and Delivery of mHealth Interventions: Protocol and Demonstration Projects Addressing Smoking Cessation in Cancer Care (Preprint)
Real-Time Dose-Guided Radiation Therapy
Intricacies of human-AI interaction in dynamic decision-making for precision oncology.
Ionizing radiation acoustic and ultrasound dual-modality imaging for visualization of dose on anatomical structures during radiotherapy
The Biophysics of Flash Radiotherapy: Tools for Measuring Tumor and Normal Tissues Microenvironment.
Artificial Intelligence for Multiscale Spatial Analysis in Oncology: Current Applications and Future Implications.
The Biophysics of Flash Radiotherapy: Tools for Measuring Tumor and Normal Tissues Microenvironment
Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma.
Roadmap: medical physics technologies in brachytherapy.
Learning the Phenotype of Medical Hallucinations
Factors impacting cardiac dose and overall survival in post-operative non-small cell lung cancer patients.
General artificial intelligence for the diagnosis and treatment of cancer: the rise of foundation models.
Using Bayesian Networks to Predict Urgent Care Visits in Patients Receiving Systemic Therapy for Non-Small Cell Lung Cancer.
Easy ensemble classifier-group and intersectional fairness and threshold (EEC-GIFT): a fairness-aware machine learning framework for lung cancer screening eligibility using real-world data.
Supplementary Methods S1 from Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma
Supplementary Figure S6 from Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma
Supplementary Table S1 from Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma
Supplementary Table S3 from Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma
Supplementary Figure S2 from Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma
Supplementary Figure S7 from Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma
Supplementary Figure S4 from Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma
A longitudinal data framework for context-specific genotype-to-phenotype mapping
Supplementary Figure S5 from Integrative Immune Signature of Complementary Circulating and Tumoral Biomarkers Maximizes the Predictive Power of Adjuvant Immunotherapeutic Benefits in High-risk Melanoma