Konstantinos Zormpas-Petridis
@gemellihospital.com
Researcher at Direzione Scientifica
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
RESEARCH, TEACHING, or OTHER INTERESTS
Multidisciplinary, Cancer Research, Artificial Intelligence
Scopus Publications
Scopus Publications
Marica Vagni, Huong Elena Tran, Angela Romano, Giuditta Chiloiro, Luca Boldrini, Konstantinos Zormpas-Petridis, Maria Kawula, Guillaume Landry, Christopher Kurz, Stefanie Corradini,et al.
Elsevier BV
Nicolò Bizzarri, Luca Russo, Miriam Dolciami, Konstantinos Zormpas-Petridis, Luca Boldrini, Denis Querleu, Gabriella Ferrandina, Luigi Pedone Anchora, Benedetta Gui, Evis Sala,et al.
BMJ
ObjectiveRadiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer.MethodsA systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model.ResultsA total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease.ConclusionRadiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.
Camilla Panico, Giacomo Avesani, Konstantinos Zormpas-Petridis, Leonardo Rundo, Camilla Nero, and Evis Sala
Elsevier BV
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,et al.
Wiley
Hyaluronan (HA) is a key component of the dense extracellular matrix in breast cancer, and its accumulation is associated with poor prognosis and metastasis. Pegvorhyaluronidase alfa (PEGPH20) enzymatically degrades HA and can enhance drug delivery and treatment response in preclinical tumour models. Clinical development of stromal‐targeted therapies would be accelerated by imaging biomarkers that inform on therapeutic efficacy in vivo. Here, PEGPH20 response was assessed by multiparametric magnetic resonance imaging (MRI) in three orthotopic breast tumour models. Treatment of 4T1/HAS3 tumours, the model with the highest HA accumulation, reduced T1 and T2 relaxation times and the apparent diffusion coefficient (ADC), and increased the magnetisation transfer ratio, consistent with lower tissue water content and collapse of the extracellular space. The transverse relaxation rate R2* increased, consistent with greater erythrocyte accessibility following vascular decompression. Treatment of MDA‐MB‐231 LM2‐4 tumours reduced ADC and dramatically increased tumour viscoelasticity measured by MR elastography. Correlation matrix analyses of data from all models identified ADC as having the strongest correlation with HA accumulation, suggesting that ADC is the most sensitive imaging biomarker of tumour response to PEGPH20.
Konstantinos Zormpas-Petridis, Nina Tunariu, David J. Collins, Christina Messiou, Dow-Mu Koh, and Matthew D. Blackledge
Elsevier BV
Konstantinos Zormpas-Petridis, Nina Tunariu, Andra Curcean, Christina Messiou, Sebastian Curcean, David J. Collins, Julie C. Hughes, Yann Jamin, Dow-Mu Koh, and Matthew D. Blackledge
Radiological Society of North America (RSNA)
Purpose To use deep learning to improve the image quality of subsampled images (number of acquisitions = 1 [NOA1]) to reduce whole-body diffusion-weighted MRI (WBDWI) acquisition times. Materials and Methods Both retrospective and prospective patient groups were used to develop a deep learning–based denoising image filter (DNIF) model. For initial model training and validation, 17 patients with metastatic prostate cancer with acquired WBDWI NOA1 and NOA9 images (acquisition period, 2015–2017) were retrospectively included. An additional 22 prospective patients with advanced prostate cancer, myeloma, and advanced breast cancer were used for model testing (2019), and the radiologic quality of DNIF-processed NOA1 (NOA1-DNIF) images were compared with NOA1 images and clinical NOA16 images by using a three-point Likert scale (good, average, or poor; statistical significance was calculated by using a Wilcoxon signed ranked test). The model was also retrained and tested in 28 patients with malignant pleural mesothelioma (MPM) who underwent lung MRI (2015–2017) to demonstrate feasibility in other body regions. Results The model visually improved the quality of NOA1 images in all test patients, with the majority of NOA1-DNIF and NOA16 images being graded as either “average” or “good” across all image-quality criteria. From validation data, the mean apparent diffusion coefficient (ADC) values within NOA1-DNIF images of bone disease deviated from those within NOA9 images by an average of 1.9% (range, 1.1%–2.6%). The model was also successfully applied in the context of MPM; the mean ADCs from NOA1-DNIF images of MPM deviated from those measured by using clinical-standard images (NOA12) by 3.7% (range, 0.2%–10.6%). Conclusion Clinical-standard images were generated from subsampled images by using a DNIF. Keywords: Image Postprocessing, MR-Diffusion-weighted Imaging, Neural Networks, Oncology, Whole-Body Imaging, Supervised Learning, MR-Functional Imaging, Metastases, Prostate, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license.
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,et al.
American Association for Cancer Research (AACR)
Abstract Noninvasive early indicators of treatment response are crucial to the successful delivery of precision medicine in children with cancer. Neuroblastoma is a common solid tumor of young children that arises from anomalies in neural crest development. Therapeutic approaches aiming to destabilize MYCN protein, such as small-molecule inhibitors of Aurora A and mTOR, are currently being evaluated in early phase clinical trials in children with high-risk MYCN-driven disease, with limited ability to evaluate conventional pharmacodynamic biomarkers of response. T1 mapping is an MRI scan that measures the proton spin-lattice relaxation time T1. Using a multiparametric MRI-pathologic cross-correlative approach and computational pathology methodologies including a machine learning–based algorithm for the automatic detection and classification of neuroblasts, we show here that T1 mapping is sensitive to the rich histopathologic heterogeneity of neuroblastoma in the Th-MYCN transgenic model. Regions with high native T1 corresponded to regions dense in proliferative undifferentiated neuroblasts, whereas regions characterized by low T1 were rich in apoptotic or differentiating neuroblasts. Reductions in tumor-native T1 represented a sensitive biomarker of response to treatment-induced apoptosis with two MYCN-targeted small-molecule inhibitors, Aurora A kinase inhibitor alisertib (MLN8237) and mTOR inhibitor vistusertib (AZD2014). Overall, we demonstrate the potential of T1 mapping, a scan readily available on most clinical MRI scanners, to assess response to therapy and guide clinical trials for children with neuroblastoma. The study reinforces the potential role of MRI-based functional imaging in delivering precision medicine to children with neuroblastoma. Significance: This study shows that MRI-based functional imaging can detect apoptotic responses to MYCN-targeted small-molecule inhibitors in a genetically engineered murine model of MYCN-driven neuroblastoma.
Konstantinos Zormpas-Petridis, Rosa Noguera, Daniela Kolarevic Ivankovic, Ioannis Roxanis, Yann Jamin, and Yinyin Yuan
Frontiers Media SA
High computational cost associated with digital pathology image analysis approaches is a challenge towards their translation in routine pathology clinic. Here, we propose a computationally efficient framework (SuperHistopath), designed to map global context features reflecting the rich tumor morphological heterogeneity. SuperHistopath efficiently combines i) a segmentation approach using the linear iterative clustering (SLIC) superpixels algorithm applied directly on the whole-slide images at low resolution (5x magnification) to adhere to region boundaries and form homogeneous spatial units at tissue-level, followed by ii) classification of superpixels using a convolution neural network (CNN). To demonstrate how versatile SuperHistopath was in accomplishing histopathology tasks, we classified tumor tissue, stroma, necrosis, lymphocytes clusters, differentiating regions, fat, hemorrhage and normal tissue, in 127 melanomas, 23 triple-negative breast cancers, and 73 samples from transgenic mouse models of high-risk childhood neuroblastoma with high accuracy (98.8%, 93.1% and 98.3% respectively). Furthermore, SuperHistopath enabled discovery of significant differences in tumor phenotype of neuroblastoma mouse models emulating genomic variants of high-risk disease, and stratification of melanoma patients (high ratio of lymphocyte-to-tumor superpixels (p = 0.015) and low stroma-to-tumor ratio (p = 0.028) were associated with a favorable prognosis). Finally, SuperHistopath is efficient for annotation of ground-truth datasets (as there is no need of boundary delineation), training and application (~5 min for classifying a whole-slide image and as low as ~30 min for network training). These attributes make SuperHistopath particularly attractive for research in rich datasets and could also facilitate its adoption in the clinic to accelerate pathologist workflow with the quantification of phenotypes, predictive/prognosis markers.
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,et al.
American Association for Cancer Research (AACR)
AbstractIncreased stiffness in the extracellular matrix (ECM) contributes to tumor progression and metastasis. Therefore, stromal modulating therapies and accompanying biomarkers are being developed to target ECM stiffness. Magnetic resonance (MR) elastography can noninvasively and quantitatively map the viscoelastic properties of tumors in vivo and thus has clear clinical applications. Herein, we used MR elastography, coupled with computational histopathology, to interrogate the contribution of collagen to the tumor biomechanical phenotype and to evaluate its sensitivity to collagenase-induced stromal modulation. Elasticity (Gd) and viscosity (Gl) were significantly greater for orthotopic BT-474 (Gd = 5.9 ± 0.2 kPa, Gl = 4.7 ± 0.2 kPa, n = 7) and luc-MDA-MB-231-LM2-4 (Gd = 7.9 ± 0.4 kPa, Gl = 6.0 ± 0.2 kPa, n = 6) breast cancer xenografts, and luc-PANC1 (Gd = 6.9 ± 0.3 kPa, Gl = 6.2 ± 0.2 kPa, n = 7) pancreatic cancer xenografts, compared with tumors associated with the nervous system, including GTML/Trp53KI/KI medulloblastoma (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 7), orthotopic luc-D-212-MG (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 7), luc-RG2 (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 5), and luc-U-87-MG (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 8) glioblastoma xenografts, intracranially propagated luc-MDA-MB-231-LM2-4 (Gd = 3.7 ± 0.2 kPa, Gl = 2.2 ± 0.1 kPa, n = 7) breast cancer xenografts, and Th-MYCN neuroblastomas (Gd = 3.5 ± 0.2 kPa, Gl = 2.3 ± 0.2 kPa, n = 5). Positive correlations between both elasticity (r = 0.72, P < 0.0001) and viscosity (r = 0.78, P < 0.0001) were determined with collagen fraction, but not with cellular or vascular density. Treatment with collagenase significantly reduced Gd (P = 0.002) and Gl (P = 0.0006) in orthotopic breast tumors. Texture analysis of extracted images of picrosirius red staining revealed significant negative correlations of entropy with Gd (r = −0.69, P < 0.0001) and Gl (r = −0.76, P < 0.0001), and positive correlations of fractal dimension with Gd (r = 0.75, P < 0.0001) and Gl (r = 0.78, P < 0.0001). MR elastography can thus provide sensitive imaging biomarkers of tumor collagen deposition and its therapeutic modulation.Significance:MR elastography enables noninvasive detection of tumor stiffness and will aid in the development of ECM-targeting therapies.
Konstantinos Zormpas-Petridis, Henrik Failmezger, Shan E Ahmed Raza, Ioannis Roxanis, Yann Jamin, and Yinyin Yuan
Frontiers Media SA
Computational pathology-based cell classification algorithms are revolutionizing the study of the tumor microenvironment and can provide novel predictive/prognosis biomarkers crucial for the delivery of precision oncology. Current algorithms used on hematoxylin and eosin slides are based on individual cell nuclei morphology with limited local context features. Here, we propose a novel multi-resolution hierarchical framework (SuperCRF) inspired by the way pathologists perceive regional tissue architecture to improve cell classification and demonstrate its clinical applications. We develop SuperCRF by training a state-of-art deep learning spatially constrained- convolution neural network (SC-CNN) to detect and classify cells from 105 high-resolution (20×) H&E-stained slides of The Cancer Genome Atlas melanoma dataset and subsequently, a conditional random field (CRF) by combining cellular neighborhood with tumor regional classification from lower resolution images (5, 1.25×) given by a superpixel-based machine learning framework. SuperCRF led to an 11.85% overall improvement in the accuracy of the state-of-art deep learning SC-CNN cell classifier. Consistent with a stroma-mediated immune suppressive microenvironment, SuperCRF demonstrated that (i) a high ratio of lymphocytes to all lymphocytes within the stromal compartment (p = 0.026) and (ii) a high ratio of stromal cells to all cells (p < 0.0001 compared to p = 0.039 for SC-CNN only) are associated with poor survival in patients with melanoma. SuperCRF improves cell classification by introducing global and local context-based information and can be implemented in combination with any single-cell classifier. SuperCRF provides valuable tools to study the tumor microenvironment and identify predictors of survival and response to therapy.
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,et al.
American Association for Cancer Research (AACR)
Abstract Childhood neuroblastoma is a hypervascular tumor of neural origin, for which antiangiogenic drugs are currently being evaluated; however, predictive biomarkers of treatment response, crucial for successful delivery of precision therapeutics, are lacking. We describe an MRI-pathologic cross-correlative approach using intrinsic susceptibility (IS) and susceptibility contrast (SC) MRI to noninvasively map the vascular phenotype in neuroblastoma Th-MYCN transgenic mice treated with the vascular endothelial growth factor receptor inhibitor cediranib. We showed that the transverse MRI relaxation rate R2* (second−1) and fractional blood volume (fBV, %) were sensitive imaging biomarkers of hemorrhage and vascular density, respectively, and were also predictive biomarkers of response to cediranib. Comparison with MRI and pathology from patients with MYCN-amplified neuroblastoma confirmed the high degree to which the Th-MYCN model vascular phenotype recapitulated that of the clinical phenotype, thereby supporting further evaluation of IS- and SC-MRI in the clinic. This study reinforces the potential role of functional MRI in delivering precision medicine to children with neuroblastoma. Significance: This study shows that functional MRI predicts response to vascular-targeted therapy in a genetically engineered murine model of neuroblastoma.
Konstantinos Zormpas-Petridis, Henrik Failmezger, Ioannis Roxanis, Matthew Blackledge, Yann Jamin, and Yinyin Yuan
Springer International Publishing
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,et al.
Frontiers Media SA
Diffusion-weighted magnetic resonance imaging (DWI) enables non-invasive, quantitative staging of prostate cancer via measurement of the apparent diffusion coefficient (ADC) of water within tissues. In cancer, more advanced disease is often characterized by higher cellular density (cellularity), which is generally accepted to correspond to a lower measured ADC. A quantitative relationship between tissue structure and in vivo measurements of ADC has yet to be determined for prostate cancer. In this study, we establish a theoretical framework for relating ADC measurements with tissue cellularity and the proportion of space occupied by prostate lumina, both of which are estimated through automatic image processing of whole-slide digital histology samples taken from a cohort of six healthy mice and nine transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. We demonstrate that a significant inverse relationship exists between ADC and tissue cellularity that is well characterized by our model, and that a decrease of the luminal space within the prostate is associated with a decrease in ADC and more aggressive tumor subtype. The parameters estimated from our model in this mouse cohort predict the diffusion coefficient of water within the prostate-tissue to be 2.18 × 10−3 mm2/s (95% CI: 1.90, 2.55). This value is significantly lower than the diffusion coefficient of free water at body temperature suggesting that the presence of organelles and macromolecules within tissues can drastically hinder the random motion of water molecules within prostate tissue. We validate the assumptions made by our model using novel in silico analysis of whole-slide histology to provide the simulated ADC (sADC); this is demonstrated to have a significant positive correlation with in vivo measured ADC (r2 = 0.55) in our mouse population. The estimation of the structural properties of prostate tissue is vital for predicting and staging cancer aggressiveness, but prostate tissue biopsies are painful, invasive, and are prone to complications such as sepsis. The developments made in this study provide the possibility of estimating the structural properties of prostate tissue via non-invasive virtual biopsies from MRI, minimizing the need for multiple tissue biopsies and allowing sequential measurements to be made for prostate cancer monitoring.