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Zhang S, Yuan Z, Zhou X, Wang H, Chen B, Wang Y. VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108178. [PMID: 38652995 DOI: 10.1016/j.cmpb.2024.108178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/08/2024] [Accepted: 04/13/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND AND OBJECTIVE Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis. METHODS To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features. RESULTS The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs. CONCLUSIONS The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.
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Affiliation(s)
- Shuchang Zhang
- Department of Mathematics, National University of Defense Technology, Changsha, China.
| | - Ziyang Yuan
- Academy of Military Sciences of the People's Liberation Army, Beijing, China.
| | - Xianchen Zhou
- Department of Mathematics, National University of Defense Technology, Changsha, China
| | - Hongxia Wang
- Department of Mathematics, National University of Defense Technology, Changsha, China.
| | - Bo Chen
- Suzhou Research Center, Institute of Automation, Chinese Academy of Sciences, Suzhou, China
| | - Yadong Wang
- Department of Laboratory Pathology, Baiyun Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Leal JFDC, Barroso DH, Trindade NS, de Miranda VL, Gurgel-Gonçalves R. Automated Identification of Cutaneous Leishmaniasis Lesions Using Deep-Learning-Based Artificial Intelligence. Biomedicines 2023; 12:12. [PMID: 38275373 PMCID: PMC10813291 DOI: 10.3390/biomedicines12010012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
The polymorphism of cutaneous leishmaniasis (CL) complicates diagnosis in health care services because lesions may be confused with other dermatoses such as sporotrichosis, paracocidiocomycosis, and venous insufficiency. Automated identification of skin diseases based on deep learning (DL) has been applied to assist diagnosis. In this study, we evaluated the performance of AlexNet, a DL algorithm, to identify pictures of CL lesions in patients from Midwest Brazil. We used a set of 2458 pictures (up to 10 of each lesion) obtained from patients treated between 2015 and 2022 in the Leishmaniasis Clinic at the University Hospital of Brasilia. We divided the picture database into training (80%), internal validation (10%), and testing sets (10%), and trained and tested AlexNet to identify pictures of CL lesions. We performed three simulations and trained AlexNet to differentiate CL from 26 other dermatoses (e.g., chromomycosis, ecthyma, venous insufficiency). We obtained an average accuracy of 95.04% (Confidence Interval 95%: 93.81-96.04), indicating an excellent performance of AlexNet in identifying pictures of CL lesions. We conclude that automated CL identification using AlexNet has the potential to assist clinicians in diagnosing skin lesions. These results contribute to the development of a mobile application to assist in the diagnosis of CL in health care services.
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Affiliation(s)
- José Fabrício de Carvalho Leal
- Graduate Program in Tropical Medicine, Center for Tropical Medicine, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil;
- Laboratory of Medical Parasitology and Vector Biology, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil; (N.S.T.); (V.L.d.M.)
| | - Daniel Holanda Barroso
- Postgraduate Program in Medical Sciences, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil;
| | - Natália Santos Trindade
- Laboratory of Medical Parasitology and Vector Biology, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil; (N.S.T.); (V.L.d.M.)
| | - Vinícius Lima de Miranda
- Laboratory of Medical Parasitology and Vector Biology, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil; (N.S.T.); (V.L.d.M.)
| | - Rodrigo Gurgel-Gonçalves
- Graduate Program in Tropical Medicine, Center for Tropical Medicine, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil;
- Laboratory of Medical Parasitology and Vector Biology, Faculty of Medicine, University of Brasília–UnB, Brasília 70904-970, Brazil; (N.S.T.); (V.L.d.M.)
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Cazzaniga G, Eccher A, Munari E, Marletta S, Bonoldi E, Della Mea V, Cadei M, Sbaraglia M, Guerriero A, Dei Tos AP, Pagni F, L’Imperio V. Natural Language Processing to extract SNOMED-CT codes from pathological reports. Pathologica 2023; 115:318-324. [PMID: 38180139 PMCID: PMC10767798 DOI: 10.32074/1591-951x-952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024] Open
Abstract
Objective The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department. Methods Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports. Results The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance. Conclusions AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports.
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Affiliation(s)
- Giorgio Cazzaniga
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
| | - Albino Eccher
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, Modena, Italy
| | - Enrico Munari
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Stefano Marletta
- Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy
| | - Emanuela Bonoldi
- Unit of Surgical Pathology and Cytogenetics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Vincenzo Della Mea
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Moris Cadei
- Pathology Unit, ASST Spedali Civili di Brescia, Brescia, Italy
| | - Marta Sbaraglia
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Angela Guerriero
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Angelo Paolo Dei Tos
- Surgical Pathology and Cytopathology Unit, Department of Medicine-DIMED, University of Padua School of Medicine, Padua, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
| | - Vincenzo L’Imperio
- Department of Medicine and Surgery, Pathology, IRCCS Fondazione San Gerardo dei Tintori, University of Milano-Bicocca, Italy
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Meehan GR, Herder V, Allan J, Huang X, Kerr K, Mendonca DC, Ilia G, Wright DW, Nomikou K, Gu Q, Molina Arias S, Hansmann F, Hardas A, Attipa C, De Lorenzo G, Cowton V, Upfold N, Palmalux N, Brown JC, Barclay WS, Filipe ADS, Furnon W, Patel AH, Palmarini M. Phenotyping the virulence of SARS-CoV-2 variants in hamsters by digital pathology and machine learning. PLoS Pathog 2023; 19:e1011589. [PMID: 37934791 PMCID: PMC10656012 DOI: 10.1371/journal.ppat.1011589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/17/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to evolve throughout the coronavirus disease-19 (COVID-19) pandemic, giving rise to multiple variants of concern (VOCs) with different biological properties. As the pandemic progresses, it will be essential to test in near real time the potential of any new emerging variant to cause severe disease. BA.1 (Omicron) was shown to be attenuated compared to the previous VOCs like Delta, but it is possible that newly emerging variants may regain a virulent phenotype. Hamsters have been proven to be an exceedingly good model for SARS-CoV-2 pathogenesis. Here, we aimed to develop robust quantitative pipelines to assess the virulence of SARS-CoV-2 variants in hamsters. We used various approaches including RNAseq, RNA in situ hybridization, immunohistochemistry, and digital pathology, including software assisted whole section imaging and downstream automatic analyses enhanced by machine learning, to develop methods to assess and quantify virus-induced pulmonary lesions in an unbiased manner. Initially, we used Delta and Omicron to develop our experimental pipelines. We then assessed the virulence of recent Omicron sub-lineages including BA.5, XBB, BQ.1.18, BA.2, BA.2.75 and EG.5.1. We show that in experimentally infected hamsters, accurate quantification of alveolar epithelial hyperplasia and macrophage infiltrates represent robust markers for assessing the extent of virus-induced pulmonary pathology, and hence virus virulence. In addition, using these pipelines, we could reveal how some Omicron sub-lineages (e.g., BA.2.75 and EG.5.1) have regained virulence compared to the original BA.1. Finally, to maximise the utility of the digital pathology pipelines reported in our study, we developed an online repository containing representative whole organ histopathology sections that can be visualised at variable magnifications (https://covid-atlas.cvr.gla.ac.uk). Overall, this pipeline can provide unbiased and invaluable data for rapidly assessing newly emerging variants and their potential to cause severe disease.
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Affiliation(s)
- Gavin R. Meehan
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
- CVR-CRUSH, MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Vanessa Herder
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
- CVR-CRUSH, MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Jay Allan
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Xinyi Huang
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Karen Kerr
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
- CVR-CRUSH, MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Diogo Correa Mendonca
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
- CVR-CRUSH, MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Georgios Ilia
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Derek W. Wright
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Kyriaki Nomikou
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Quan Gu
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Sergi Molina Arias
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
- CVR-CRUSH, MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Florian Hansmann
- Institute of Veterinary Pathology, Faculty of Veterinary Medicine, Leipzig University, Germany
| | - Alexandros Hardas
- Department of Pathobiology & Population Sciences, The Royal Veterinary College, North Mymms, United Kingdom
| | - Charalampos Attipa
- The Royal (Dick) School of Veterinary Studies, The University of Edinburgh, United Kingdom
| | | | - Vanessa Cowton
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Nicole Upfold
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
- CVR-CRUSH, MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Natasha Palmalux
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Jonathan C. Brown
- Department of Infectious Disease, Imperial College London, United Kingdom
| | - Wendy S. Barclay
- Department of Infectious Disease, Imperial College London, United Kingdom
| | | | - Wilhelm Furnon
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
| | - Arvind H. Patel
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
- CVR-CRUSH, MRC-University of Glasgow Centre for Virus Research, United Kingdom
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Fusco N, Ivanova M, Frascarelli C, Criscitiello C, Cerbelli B, Pignataro MG, Pernazza A, Sajjadi E, Venetis K, Cursano G, Pagni F, Di Bella C, Accardo M, Amato M, Amico P, Bartoli C, Bogina G, Bortesi L, Boldorini R, Bruno S, Cabibi D, Caruana P, Dainese E, De Camilli E, Dell'Anna V, Duda L, Emmanuele C, Fanelli GN, Fernandes B, Ferrara G, Gnetti L, Gurrera A, Leone G, Lucci R, Mancini C, Marangi G, Mastropasqua MG, Nibid L, Orrù S, Pastena M, Peresi M, Perracchio L, Santoro A, Vezzosi V, Zambelli C, Zuccalà V, Rizzo A, Costarelli L, Pietribiasi F, Santinelli A, Scatena C, Curigliano G, Guerini-Rocco E, Martini M, Graziano P, Castellano I, d'Amati G. Advancing the PD-L1 CPS test in metastatic TNBC: Insights from pathologists and findings from a nationwide survey. Crit Rev Oncol Hematol 2023; 190:104103. [PMID: 37595344 DOI: 10.1016/j.critrevonc.2023.104103] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 08/20/2023] Open
Abstract
Pembrolizumab has received approval as a first-line treatment for unresectable/metastatic triple-negative breast cancer (mTNBC) with a PD-L1 combined positive score (CPS) of ≥ 10. However, assessing CPS in mTNBC poses challenges. Firstly, it represents a novel analysis for breast pathologists. Secondly, the heterogeneity of PD-L1 expression in mTNBC further complicates the assessment. Lastly, the lack of standardized assays and staining platforms adds to the complexity. In KEYNOTE trials, PD-L1 expression was evaluated using the IHC 22C3 pharmDx kit as a companion diagnostic test. However, both the 22C3 pharmDx and VENTANA PD-L1 (SP263) assays are validated for CPS assessment. Consequently, assay-platform choice, staining conditions, and scoring methods can significantly impact the testing outcomes. This consensus paper aims to discuss the intricacies of PD-L1 CPS testing in mTNBC and provide practical recommendations for pathologists. Additionally, we present findings from a nationwide Italian survey elucidating the state-of-the-art in PD-L1 CPS testing in mTNBC.
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Affiliation(s)
- Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
| | - Mariia Ivanova
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Chiara Frascarelli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Carmen Criscitiello
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Division of New Drugs and Early Drug Development for Innovative Therapies, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Bruna Cerbelli
- Department of Medical-Surgical Sciences and Biotechnologies Sapienza University of Rome, Rome, Italy
| | - Maria Gemma Pignataro
- Department of Medical-Surgical Sciences and Biotechnologies Sapienza University of Rome, Rome, Italy
| | - Angelina Pernazza
- Department of Medical-Surgical Sciences and Biotechnologies Sapienza University of Rome, Rome, Italy
| | - Elham Sajjadi
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Giulia Cursano
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, University Milan Bicocca, Monza (MB), Italy; Department of Pathology, IRCCS San Gerardo Hospital, Monza (MB), Italy
| | - Camillo Di Bella
- Department of Pathology, IRCCS San Gerardo Hospital, Monza (MB), Italy
| | - Marina Accardo
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "L. Vanvitelli", Naples, Italy
| | - Michelina Amato
- Department of Pathology, San Giovanni-Addolorata Hospital, Rome Italy
| | - Paolo Amico
- Department of Pathology, Ospedale Maria Paternò Arezzo, Ragusa, Italy
| | - Caterina Bartoli
- Morphological Diagnostic and Biomolecular Characterization Area, Complex Unit of Pathological Anatomy Empoli-Prato, Oncological Department Azienda USL Toscana Centro, Italy
| | - Giuseppe Bogina
- Pathology Unit, IRCCS Ospedale Sacro Cuore Don Calabria, Negrar di Valpolicella, Italy
| | - Laura Bortesi
- Pathology Unit, IRCCS Ospedale Sacro Cuore Don Calabria, Negrar di Valpolicella, Italy
| | - Renzo Boldorini
- Pathology Unit, University of Eastern Piedmont, Novara, Italy
| | - Sara Bruno
- Division of Pathology, ASL2 Savona, Liguria, Italy
| | - Daniela Cabibi
- Department of Sciences for the Promotion of Health and Mother and Child Care, Anatomic Pathology, University of Palermo, Palermo, Italy
| | - Pietro Caruana
- Pathology Unit, Department of Medicine and Surgery, University Hospital of Parma, Parma, Italy
| | - Emanuele Dainese
- Surgical Pathology Division, Department of Oncology, ASST Lecco, "A. Manzoni" Hospital, Lecco, Italy
| | - Elisa De Camilli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | | | - Loren Duda
- Department of Clinical and Experimental Medicine, Pathology Unit, University of Foggia, Foggia, Italy
| | - Carmela Emmanuele
- Division of Pathology, Umberto I Hospital Presidium, Enna Provincial Health Department (ASP), Enna, Italy
| | - Giuseppe Nicolò Fanelli
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | | | - Gerardo Ferrara
- Department of Anatomic Pathology and Cytopathology, G. Pascale National Cancer Institute Foundation (IRCCS) Naples, Italy
| | - Letizia Gnetti
- Division of Pathology, Umberto I Hospital Presidium, Enna Provincial Health Department (ASP), Enna, Italy
| | | | - Giorgia Leone
- Division of Pathology, Clinical Institute Humanitas Catania Cubba, Misterbianco (Catania), Italy
| | - Raffaella Lucci
- Pathology Unit, Monaldi Hospital, A.O. dei Colli of Naples, Naples, Italy
| | - Cristina Mancini
- Division of Pathology, Umberto I Hospital Presidium, Enna Provincial Health Department (ASP), Enna, Italy
| | - Grazia Marangi
- Anatomic Pathology Unit, SS. Annunziata Hospital, Taranto, Italy
| | - Mauro G Mastropasqua
- Department of Precision and Regenerative Medicine and Jonian Area, School of Medicine, University of Bari "Aldo Moro", Bari, Italy
| | - Lorenzo Nibid
- Research Unit of Anatomical Pathology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy; Anatomical Pathology Operative Research Unit, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy
| | - Sandra Orrù
- Businco Oncologic Hospital, ARNAS Brotzu, Cagliari, Italy
| | - Maria Pastena
- IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Monica Peresi
- Pathology and Cytopathology Diagnostic Unit, Ospedale Villa Scassi di Genova, Genoa, Italy
| | - Letizia Perracchio
- Department of Pathology, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Angela Santoro
- General Pathology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Vania Vezzosi
- Histopathology and Molecular Diagnostics Unit, Careggi Hospital, Firenze, Italy
| | | | - Valeria Zuccalà
- Pathology Unit, Pugliese-Ciaccio Hospital Catanzaro, Catanzaro, Italy
| | - Antonio Rizzo
- Division of Pathology, Clinical Institute Humanitas Catania Cubba, Misterbianco (Catania), Italy
| | | | | | - Alfredo Santinelli
- Anatomic Pathology, Azienda Sanitaria Territoriale di Pesaro-Urbino, Pesaro, Italy
| | - Cristian Scatena
- Division of Pathology, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Giuseppe Curigliano
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy; Division of New Drugs and Early Drug Development for Innovative Therapies, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Elena Guerini-Rocco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, Milan, Italy; Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Maurizio Martini
- Department of Human and Developmental Pathology, University of Messina, Messina, Italy
| | - Paolo Graziano
- Pathology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), Italy
| | | | - Giulia d'Amati
- Department of Medical-Surgical Sciences and Biotechnologies Sapienza University of Rome, Rome, Italy
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