Konstantinos Zormpas-Petridis

@gemellihospital.com

Researcher at Direzione Scientifica
Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Konstantinos Zormpas-Petridis


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https://researchid.co/konstantinoszp

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Cancer Research, Artificial Intelligence
22

Scopus Publications

Scopus Publications

  • Generating synthetic CEM from low-energy images using deep learning: A future without contrast media? A proof-of-concept study
    Konstantinos Zormpas-Petridis, Reza Kalantar, Ludovica Iaccarino, Matteo Mancino, Gianluca Franceschini, Paola Clauser, Valentina Longo, Evis Sala, Paolo Belli, Anna D’Angelo
    European Radiology Experimental, 2026
    Objective We used deep learning to generate synthetic, resembling in appearance, iodine-enhanced, mammograms from low-energy contrast-enhanced mammography (CEM) images. Materials and methods We retrospectively selected 140 CEM examinations. We trained a two-dimensional cycle-generative adversarial network on 390 images in 100 patients (195 breasts; 102 positive and 93 negative for lesion detection) using paired low-energy and iodine-enhanced images as input and output, respectively. We validated our model in 40 test patients (63 breasts; 37 positive and 26 negative for lesion detection) by calculating the contrast-to-noise ratio (CNR) for low-energy, synthetic, and clinical iodine-enhanced images and the mean absolute error (MAE) and similarity index metric (SSIM) between clinical and synthetic iodine-enhanced images regarding their changes from low-energy. Three radiologists scored (a-to-d) the test set images for background parenchymal enhancement (BPE) and lesion detection (yes/no) on clinical and synthetic images. The presence of artifacts was reported on all images. Results We observed a high correlation between clinical and synthetic iodine-enhanced images regarding their changes from low-energy: MAE, r = 0.99; SSIM, r = 0.80. CNR was -0.015/-0.16 ± 0.23/0.05 (mean ± standard deviation) for clinical/synthetic, respectively. A “halo” artifact present in above 50% of the clinical iodine-enhanced images was corrected in the synthetic ones. On synthetic images, BPE (scores a–b versus c–d) was 85.8% accurate. Lesion detection accuracy was 89.4% and 79.4%, sensitivity 87.4 and 72.1%, and specificity 92.3% and 90.0% for clinical and synthetic images, respectively. Conclusions Deep learning holds the potential to generate synthetic iodine-enhanced mammograms from low-energy images. Relevance statement Radiologists could perform some clinical tasks, such as lesion detection and BPE estimation on synthetic iodine-enhanced images, without contrast injection. Key Points Our deep learning model generated synthetic iodine-enhanced images that visually resembled the clinical iodine-enhanced images. Radiologists could use the synthetic images to perform clinical tasks, such as lesion detection and BPE evaluation. Our model can improve image quality by removing common artifacts, including the breast-in-breast (halo). Our method is a way to combine the benefits of CEM while sparing the need for contrast media. Graphical Abstract
  • Homologous recombination deficiency in Ovarian cancer: The game-changer for first-line maintenance therapy
    Maria Chiara Cannizzaro, Angelo Minucci, Konstantinos Zormpas-Petridis, Viola Ghizzoni, Anna Fagotti, Domenica Lorusso, Vanda Salutari, Camilla Nero
    Critical Reviews in Oncology Hematology, 2026
    Homologous recombination deficiency (HRD) plays a central role in the pathogenesis and therapeutic vulnerability of epithelial ovarian cancer (EOC), particularly in high-grade serous subtypes. HRD reflects the inability of tumor cells to accurately repair DNA double-strand breaks, rendering them sensitive to platinum-based chemotherapy and poly(ADP-ribose) polymerase (PARP) inhibitors. Several genomic assays have been developed to identify HRD status through the detection of genomic instability patterns, with varying degrees of clinical validation and regulatory approval. Beyond widely adopted assays such as Myriad myChoice® CDx and FoundationFocus™ CDx BRCA LOH, innovative approaches including the Leuven PARPi Benefit Test, circulating tumor DNA-based methods, and artificial intelligence-driven computational pathology are reshaping the diagnostic landscape. Nevertheless, important challenges remain, including the static nature of genomic scar assays, the impact of tumor heterogeneity, and the emergence of resistance through reversion mutations. Functional assays and integrative strategies may provide more dynamic insights into real-time DNA repair capacity, thereby supporting more accurate patient selection and treatment monitoring. Cost-effectiveness analyses further support the integration of HRD testing at diagnosis, highlighting its role in guiding optimal use of PARP inhibitors and improving healthcare resource allocation. This review summarizes the biological rationale, diagnostic methods, therapeutic implications, and economic considerations of HRD in EOC, with a focus on first-line maintenance strategies and future directions in precision oncology.
  • Unraveling Tumor Heterogeneity in Gynecological Cancer Using a Radiogenomics Approach
    Miriam Dolciami, Veronica Celli, Camilla Panico, Konstantinos Zormpas Petridis, Camilla Nero, Maria Chiara Cannizzaro, Alessandra Iacono, Pietro Paolo Maria Azzaro, Giacomo Avesani, Anna Fagotti, Evis Sala
    Rofo Fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren, 2026
    Ovarian cancer (OC) and endometrial cancer (EC) are highly heterogeneous gynecological malignancies with distinct molecular subtypes, therapeutic responses, and clinical outcomes. Traditional biopsy-based profiling often fails to capture the spatial and temporal complexity of these tumors. Radiogenomics, integrating imaging features with genomic and molecular data, has emerged as a promising approach to non-invasively analyze tumor heterogeneity. The purpose of this abstract is to critically examine and synthesize existing research on the application of radiogenomics in OC and EC, focusing on its ability to correlate imaging phenotypes with molecular biomarkers. This narrative review aims to demonstrate how radiogenomics can enhance tumor characterization, support biomarker prediction, and inform prognosis and therapeutic decision-making with non-invasive methods. This narrative review critically synthesizes current literature on radiogenomics applications in OC and EC. Studies using CT, MRI, and PET imaging were evaluated for their ability to correlate imaging phenotypes with molecular biomarkers, gene expression profiles, and clinical outcomes. The analysis emphasizes the role of radiogenomics in enhancing tumor characterization, predicting biomarker status, forecasting treatment response and prognosis. Radiogenomics has successfully identified associations between imaging features and key molecular alterations, such as BRCA mutations, homologous recombination deficiency (HRD), and immune-related biomarkers in OC, as well as POLE mutations, microsatellite instability (MSI), and tumor mutational burden (TMB) in EC. Predictive models incorporating radiomic features have demonstrated notable performance in estimating prognosis, treatment response, and recurrence risk across both cancer types. Radiogenomics has a strong potential to enhance personalized cancer care by analyzing tumor heterogeneity. However, clinical application requires methodological standardization, prospective validation, and integration into precision oncology workflows.
  • Towards quantitative MRI Driving online adaptive MRgRT for lung tumors
    I. Moretti, M. Nardini, C. Mazzarella, A. Romano, G. Chiloiro, G. Panza, M. Galetto, H.E. Tran, K. Zormpas-Petridis, L. Boldrini, M.De Spirito, L. Placidi
    Physica Medica, 2026
    INTRODUCTION: Successful delivery of lung cancer radiotherapy is hindered by respiratory motion, low soft-tissue contrast and anatomical variabilities, often compromising precision. Magnetic Resonance Image-guided Radiotherapy (MRgRT) has emerged as a promising approach, particularly with hybrid MR-Linac systems that offer superior soft-tissue visualization and enable online adaptive radiotherapy (online MRgART). PURPOSE: This review synthesizes current evidence for online MRgART in lung cancer and examines emerging roles for quantitative MRI (qMRI). A PubMed search covering the period from January 2020 to September 2025 identified 19 studies, 3 of which focused specifically on quantitative imaging. MAIN FINDINGS: Online MRgART consistently demonstrated workflow feasibility, frequent online adaptation, improved target coverage while respecting Organs-At-Risk (OARs) constraints and encouraging Local Control (LC) with low high-grade toxicity. qMRI on MR-Linacs, most commonly Diffusion-Weighted Imaging (DWI) and cine-MRI-derived ventilation/perfusion mapping, showed feasibility and early signals for treatment adaptation, toxicity prediction and response assessment. PRINCIPAL CONCLUSIONS: qMRI studies integrated in online MRgART for lungs are, at present, extremely limited; nevertheless, establishing a clear snapshot of the current state-of-the-art is essential, as this topic is expected to become highly prevalent and of particular interest in the near future. To our knowledge, this is the first review centered on online MRgART for lung tumors, with a dedicated subsection summarizing the nascent evidence on qMRI. Looking ahead, integrating AI-driven motion compensation, auto-segmentation and adaptive replanning with qMRI-enabled biomarkers could standardize workflows and accelerate truly personalized online MRgART. Prospective multi-center studies are needed to validate biomarkers and demonstrate clinical benefit.
  • Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools
    Luca Russo, Silvia Bottazzi, Burak Kocak, Konstantinos Zormpas-Petridis, Benedetta Gui, Arnaldo Stanzione, Massimo Imbriaco, Evis Sala, Renato Cuocolo, Andrea Ponsiglione
    European Radiology, 2025
    Objective To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). Methods We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. Results Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9–14) and METRICS score of 67.6% (IQR, 58.8–76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. Conclusions Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. Clinical relevance statement Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. Key Points The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.
  • Radiology and multi-scale data integration for precision oncology
    Hania Paverd, Konstantinos Zormpas-Petridis, Hannah Clayton, Sarah Burge, Mireia Crispin-Ortuzar
    Npj Precision Oncology, 2024
    In this Perspective paper we explore the potential of integrating radiological imaging with other data types, a critical yet underdeveloped area in comparison to the fusion of other multi-omic data. Radiological images provide a comprehensive, three-dimensional view of cancer, capturing features that would be missed by biopsies or other data modalities. This paper explores the complexities and challenges of incorporating medical imaging into data integration models, in the context of precision oncology. We present the different categories of imaging-omics integration and discuss recent progress, highlighting the opportunities that arise from bringing together spatial data on different scales.
  • Editorial Comment: Integrating Morphomics in Clinical Practice for Personalized Medicine: A Paradigm Shift Toward Holistic Care
    Konstantinos Zormpas-Petridis, Marica Vagni, Matteo Mancino, Evis Sala
    Canadian Association of Radiologists Journal, 2024
  • Longitudinal Assessment of Tumor-Infiltrating Lymphocytes in Primary Breast Cancer Following Neoadjuvant Radiation Therapy
    Miki Yoneyama, Konstantinos Zormpas-Petridis, Ruth Robinson, Faranak Sobhani, Elena Provenzano, Harriet Steel, Sara Lightowlers, Catherine Towns, Simon P. Castillo, Selvakumar Anbalagan, Tom Lund, Erik Wennerberg, Alan Melcher, Charlotte E. Coles, Ioannis Roxanis, Yinyin Yuan, Navita Somaiah
    International Journal of Radiation Oncology Biology Physics, 2024
  • Auto-segmentation of pelvic organs at risk on 0.35T MRI using 2D and 3D Generative Adversarial Network models
    Marica Vagni, Huong Elena Tran, Angela Romano, Giuditta Chiloiro, Luca Boldrini, Konstantinos Zormpas-Petridis, Maria Kawula, Guillaume Landry, Christopher Kurz, Stefanie Corradini, Claus Belka, Luca Indovina, Maria Antonietta Gambacorta, Lorenzo Placidi, Davide Cusumano
    Physica Medica, 2024
    PurposeManual recontouring of targets and Organs At Risk (OARs) is a time-consuming and operator-dependent task. We explored the potential of Generative Adversarial Networks (GAN) to auto-segment the rectum, bladder and femoral heads on 0.35T MRIs to accelerate the online MRI-guided-Radiotherapy (MRIgRT) workflow.Methods3D planning MRIs from 60 prostate cancer patients treated with 0.35T MR-Linac were collected. A 3D GAN architecture and its equivalent 2D version were trained, validated and tested on 40, 10 and 10 patients respectively. The volumetric Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95th) were computed against expert drawn ground-truth delineations. The networks were also validated on an independent external dataset of 16 patients.ResultsIn the internal test set, the 3D and 2D GANs showed DSC/HD95th of 0.83/9.72 mm and 0.81/10.65 mm for the rectum, 0.92/5.91 mm and 0.85/15.72 mm for the bladder, and 0.94/3.62 mm and 0.90/9.49 mm for the femoral heads. In the external test set, the performance was 0.74/31.13 mm and 0.72/25.07 mm for the rectum, 0.92/9.46 mm and 0.88/11.28 mm for the bladder, and 0.89/7.00 mm and 0.88/10.06 mm for the femoral heads. The 3D and 2D GANs required on average 1.44 s and 6.59 s respectively to generate the OARs’ volumetric segmentation for a single patient.ConclusionsThe proposed 3D GAN auto-segments pelvic OARs with high accuracy on 0.35T, in both the internal and the external test sets, outperforming its 2D equivalent in both segmentation robustness and volume generation time.
  • Enhanced-QuickDWI: Achieving Equivalent Clinical Quality by Denoising Heavily Sub-sampled Diffusion-Weighted Imaging Data
    Konstantinos Zormpas-Petridis, Antonio Candito, Christina Messiou, Dow-Mu Koh, Matthew D. Blackledge
    Lecture Notes in Computer Science, 2024
  • Radiomics systematic review in cervical cancer: Gynecological oncologists' perspective
    Nicolò Bizzarri, Luca Russo, Miriam Dolciami, Konstantinos Zormpas-Petridis, Luca Boldrini, Denis Querleu, Gabriella Ferrandina, Luigi Pedone Anchora, Benedetta Gui, Evis Sala, Giovanni Scambia
    International Journal of Gynecological Cancer, 2023
  • Radiomics and Radiogenomics of Ovarian Cancer: Implications for Treatment Monitoring and Clinical Management
    Camilla Panico, Giacomo Avesani, Konstantinos Zormpas-Petridis, Leonardo Rundo, Camilla Nero, Evis Sala
    Radiologic Clinics of North America, 2023
  • Investigating the contribution of hyaluronan to the breast tumour microenvironment using multiparametric MRI and MR elastography
    Emma L. Reeves, Jin Li, Konstantinos Zormpas‐Petridis, Jessica K. R. Boult, James Sullivan, Craig Cummings, Barbara Blouw, David Kang, Ralph Sinkus, Jeffrey C. Bamber, Yann Jamin, Simon P. Robinson
    Molecular Oncology, 2023
  • Deep-learned estimation of uncertainty in measurements of apparent diffusion coefficient from whole-body diffusion-weighted MRI
    Konstantinos Zormpas-Petridis, Nina Tunariu, David J. Collins, Christina Messiou, Dow-Mu Koh, Matthew D. Blackledge
    Computers in Biology and Medicine, 2022
  • Accelerating whole-body diffusion-weighted mri with deep learning-based denoising image filters
    Konstantinos Zormpas-Petridis, Nina Tunariu, Andra Curcean, Christina Messiou, Sebastian Curcean, David J. Collins, Julie C. Hughes, Yann Jamin, Dow-Mu Koh, Matthew D. Blackledge
    Radiology Artificial Intelligence, 2021
  • Noninvasive MRI native T1 mapping detects response to MYCN-targeted therapies in the Th-MYCN model of neuroblastoma
    Konstantinos Zormpas-Petridis, Evon Poon, Matthew Clarke, Neil P. Jerome, Jessica K.R. Boult, Matthew D. Blackledge, Fernando Carceller, Alexander Koers, Giuseppe Barone, Andrew D.J. Pearson, Lucas Moreno, John Anderson, Neil Sebire, Kieran McHugh, Dow-Mu Koh, Louis Chesler, Yinyin Yuan, Simon P. Robinson, Yann Jamin
    Cancer Research, 2021
  • SuperHistopath: A Deep Learning Pipeline for Mapping Tumor Heterogeneity on Low-Resolution Whole-Slide Digital Histopathology Images
    Konstantinos Zormpas-Petridis, Rosa Noguera, Daniela Kolarevic Ivankovic, Ioannis Roxanis, Yann Jamin, Yinyin Yuan
    Frontiers in Oncology, 2021
  • Investigating the contribution of collagen to the tumor biomechanical phenotype with noninvasive magnetic resonance elastography
    Jin Li, Konstantinos Zormpas-Petridis, Jessica K.R. Boult, Emma L. Reeves, Andreas Heindl, Maria Vinci, Filipa Lopes, Craig Cummings, Caroline J. Springer, Louis Chesler, Chris Jones, Jeffrey C. Bamber, Yinyin Yuan, Ralph Sinkus, Yann Jamin, Simon P. Robinson
    Cancer Research, 2019
  • Superpixel-Based Conditional Random Fields (SuperCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma Histopathology
    Konstantinos Zormpas-Petridis, Henrik Failmezger, Shan E Ahmed Raza, Ioannis Roxanis, Yann Jamin, Yinyin Yuan
    Frontiers in Oncology, 2019
  • MRI imaging of the hemodynamic vasculature of neuroblastoma predicts response to antiangiogenic treatment
    Konstantinos Zormpas-Petridis, Neil P. Jerome, Matthew D. Blackledge, Fernando Carceller, Evon Poon, Matthew Clarke, Ciara M. McErlean, Giuseppe Barone, Alexander Koers, Sucheta J. Vaidya, Lynley V. Marshall, Andrew D.J. Pearson, Lucas Moreno, John Anderson, Neil Sebire, Kieran McHugh, Dow-Mu Koh, Yinyin Yuan, Louis Chesler, Simon P. Robinson, Yann Jamin
    Cancer Research, 2019
  • Capturing Global Spatial Context for Accurate Cell Classification in Skin Cancer Histology
    Konstantinos Zormpas-Petridis, Henrik Failmezger, Ioannis Roxanis, Matthew Blackledge, Yann Jamin, Yinyin Yuan
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
  • Non-invasive prostate cancer characterization with diffusion-weighted MRI: Insight from in silico studies of a transgenic mouse model
    Deborah K. Hill, Andreas Heindl, Konstantinos Zormpas-Petridis, David J. Collins, Leslie R. Euceda, Daniel N. Rodrigues, Siver A. Moestue, Yann Jamin, Dow-Mu Koh, Yinyin Yuan, Tone F. Bathen, Martin O. Leach, Matthew D. Blackledge
    Frontiers in Oncology, 2017