Stylianos Drisis

Institut Jules Bordet — Belgium

Medicine · Health Sciences

13h-index695citations54works0.82yr avg
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Research Topics

Radiomics and Machine Learning in Medical Imaging(24), Medical Imaging Techniques and Applications(19), MRI in cancer diagnosis(17), AI in cancer detection(11), Advanced X-ray and CT Imaging(8)

Publications54 total

Abstract GS2-04: Heating up cold tumors: single-cell mapping of immune and adenosine pathway reprogramming in luminal B breast cancer (Neo-CheckRay trial)
Clinical Cancer Research·2026
94P ML-based pathological and radiological features reveal deeper insights into histological response to neoadjuvant therapies in HER2-positive early breast cancer
ESMO Open·2025Open Access
192MO Primary endpoint of ROSALINE: A phase II neoadjuvant study of endocrine therapy (ET) and entrectinib in invasive lobular breast carcinoma (ILBC)
ESMO Open·2025· 3 citedOpen Access
P348: Carbon Localization for Patients with Breast Cancer Treated by Neoadjuvant Systemic Therapy and Breast-Conserving Surgery
The Breast·2025Open Access
First-in-human study of SBRT and adenosine pathway blockade to potentiate the benefit of immunochemotherapy in early-stage luminal B breast cancer: results of the safety run-in phase of the Neo-CheckRay trial
Journal for ImmunoTherapy of Cancer·2023· 18 citedOpen Access
List of contributors
Elsevier eBooks·2023
Explainable deep learning approach to predict chemotherapy effect on breast tumor’s MRI
Elsevier eBooks·2023
New Explainable Deep CNN Design for Classifying Breast TumorResponse Over Neoadjuvant Chemotherapy
Current Medical Imaging Formerly Current Medical Imaging Reviews·2022· 4 cited
831 Correlation of breast cancer tumor size by magnetic resonance imaging, ultrasonography and mammography according to menopausal status
2021Open Access
Neo-CheckRay: radiation therapy and adenosine pathway blockade to increase benefit of immuno-chemotherapy in early stage luminal B breast cancer, a randomized phase II trial
BMC Cancer·2021· 33 citedOpen Access
The assessment of primary breast cancer tumor size by magnetic resonance imaging, breast ultrasonography and mammography: a comparative study across intrinsic tumor subtype
The Breast·2021· 1 citedOpen Access
Intrinsic tumor subtype and diagnostic performance of conventional breast imaging technique for the detection of unifocal breast cancer
The Breast·2021Open Access
Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images
International Journal of Computer Assisted Radiology and Surgery·2020· 79 cited
Abstract P4-10-13: Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study
Cancer Research·2020· 1 cited
Prognostic value of a three-scale grading system based on combining molecular imaging with 68Ga-DOTATATE and 18F-FDG PET/CT in patients with metastatic gastroenteropancreatic neuroendocrine neoplasias
Oncotarget·2020· 37 citedOpen Access
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study.
Nature Communications·2020· 3 citedOpen Access
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Repository of the University of Namur·2020· 14 citedOpen Access
Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI
Journal of Magnetic Resonance Imaging·2019· 13 cited
Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI.
Journal of Clinical Oncology·2019· 6 cited
Deep Learning approach predicting breast tumor response to neoadjuvant treatment using DCE-MRI volumes acquired before and after chemotherapy
Medical Imaging 2019: Computer-Aided Diagnosis·2019· 14 cited
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Frequent Co-authors

Mohammed El Adoui(15), Mohammed Benjelloun(15), Paul Suetens(11), Guy Marchal(10), Frederik Maes(10), Denis Larsimont(9), Michail Ignatiadis(7), Martine Piccart(6), Christos Sotiriou(6), Isabelle Veys(6), Dirk Loeckx(6), Emiliano D’Agostino(5), Marion Maetens(4), Ahmad Awada(4), Marc Lemort(4), M. Radermeker(4), Nathaniel Braman(4), Manasa Vulchi(4), Paulette Turk(4), Maryam Etesami(4)