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Ledda RE, Schirò S, Leo L, Milanese G, Branchi C, Commisso C, Borgia E, Mura R, Zilioli C, Sverzellati N. Diagnostic performance of chest CT average intensity projection (AIP) reconstruction for the assessment of pleuro-parenchymal abnormalities. Clin Radiol 2024:S0009-9260(24)00197-1. [PMID: 38693034 DOI: 10.1016/j.crad.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 04/03/2024] [Accepted: 04/09/2024] [Indexed: 05/03/2024]
Abstract
AIM The comparison between chest x-ray (CXR) and computed tomography (CT) images is commonly required in clinical practice to assess the evolution of chest pathological manifestations. Intrinsic differences between the two techniques, however, limit reader confidence in such a comparison. CT average intensity projection (AIP) reconstruction allows obtaining "synthetic" CXR (s-CXR) images, which are thought to have the potential to increase the accuracy of comparison between CXR and CT imaging. We aim at assessing the diagnostic performance of s-CXR imaging in detecting common pleuro-parenchymal abnormalities. MATERIALS AND METHODS 142 patients who underwent chest CT examination and CXR within 24 hours were enrolled. CT was the standard of reference. Both conventional CXR (c-CXR) and s-CXR images were retrospectively reviewed for the presence of consolidation, nodule/mass, linear opacities, reticular opacities, and pleural effusion by 3 readers in two separate sessions. Sensitivity, specificity, accuracy and their 95% confidence interval were calculated for each reader and setting and tested by McNemar test. Inter-observer agreement was tested by Cohen's K test and its 95%CI. RESULTS Overall, s-CXR sensitivity ranged 45-67% for consolidation, 12-28% for nodule/mass, 17-33% for linear opacities, 2-61% for reticular opacities, and 33-58% for pleural effusion; specificity 65-83%, 83-94%, 94-98%, 93-100% and 79-86%; accuracy 66-68%, 74-79%, 89-91%, 61-65% and 68-72%, respectively. K values ranged 0.38-0.50, 0.05-0.25, -0.05-0.11, -0.01-0.15, and 0.40-0.66 for consolidation, nodule/mass, linear opacities, reticular opacities, and pleural effusion, respectively. CONCLUSION S-CXR images, reconstructed with AIP technique, can be compared with conventional images in clinical practice and for educational purposes.
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Affiliation(s)
- R E Ledda
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
| | - S Schirò
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
| | - L Leo
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
| | - G Milanese
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
| | - C Branchi
- Radiological Sciences Unit, Diagnostic Department, University Hospital of Parma, Parma, Italy.
| | - C Commisso
- Radiology Unit, Diagnostic Department, University Hospital of Parma, Parma, Italy.
| | - E Borgia
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
| | - R Mura
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
| | - C Zilioli
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
| | - N Sverzellati
- Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
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Milanese G, Silva M, Ledda RE, Iezzi E, Bortolotto C, Mauro LA, Valentini A, Reali L, Bottinelli OM, Ilardi A, Basile A, Palmucci S, Preda L, Sverzellati N. Study rationale and design of the PEOPLHE trial. Radiol Med 2024; 129:411-419. [PMID: 38319494 PMCID: PMC10943160 DOI: 10.1007/s11547-024-01764-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
PURPOSE Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. METHODS PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. CONCLUSION The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.
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Affiliation(s)
- Gianluca Milanese
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | - Mario Silva
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | | | - Chandra Bortolotto
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Letizia Antonella Mauro
- Radiology Unit 1, University Hospital Policlinico G. Rodolico-San Marco, Catania, Catania, Italy
| | - Adele Valentini
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Linda Reali
- Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Olivia Maria Bottinelli
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
| | - Adriana Ilardi
- Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Antonio Basile
- Radiology Unit 1-Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Stefano Palmucci
- UOSD I.P.T.R.A.-Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Lorenzo Preda
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Nicola Sverzellati
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy.
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Rea G, Bocchino M, Lieto R, Ledda RE, D’Alto M, Sperandeo M, Lucci R, Pasquinelli P, Sanduzzi Zamparelli S, Bocchini G, Valente T, Sica G. The Unveiled Triad: Clinical, Radiological and Pathological Insights into Hypersensitivity Pneumonitis. J Clin Med 2024; 13:797. [PMID: 38337490 PMCID: PMC10856167 DOI: 10.3390/jcm13030797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Hypersensitivity pneumonitis (HP) is a diffuse parenchymal lung disease (DLPD) characterized by complex interstitial lung damage with polymorphic and protean inflammatory aspects affecting lung tissue targets including small airways, the interstitium, alveolar compartments and vascular structures. HP shares clinical and often radiological features with other lung diseases in acute or chronic forms. In its natural temporal evolution, if specific therapy is not initiated promptly, HP leads to progressive fibrotic damage with reduced lung volumes and impaired gas exchange. The prevalence of HP varies considerably worldwide, influenced by factors like imprecise disease classification, diagnostic method limitations for obtaining a confident diagnosis, diagnostic limitations in the correct processing of high-resolution computed tomography (HRCT) radiological parameters, unreliable medical history, diverse geographical conditions, heterogeneous agricultural and industrial practices and occasionally ineffective individual protections regarding occupational exposures and host risk factors. The aim of this review is to present an accurate and detailed 360-degree analysis of HP considering HRCT patterns and the role of the broncho-alveolar lavage (BAL), without neglecting biopsy and anatomopathological aspects and future technological developments that could make the diagnosis of this disease less challenging.
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Affiliation(s)
- Gaetano Rea
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Marialuisa Bocchino
- Department of Clinical Medicine and Surgery, Section of Respiratory Diseases, University Federico II, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy;
| | - Roberta Lieto
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Roberta Eufrasia Ledda
- Section of Radiology, Unit of Surgical Science, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy;
| | - Michele D’Alto
- Department of Cardiology, University “L. Vanvitelli”, Monaldi Hospital, 80131 Naples, Italy;
| | - Marco Sperandeo
- Interventional Ultrasound Unit, Department of Internal Medicine, IRCCS “Casa Sollievo Della Sofferenza” Hospital, San Giovanni Rotondo, 71013 Foggia, Italy;
| | - Raffaella Lucci
- Department of Pathology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy;
| | - Patrizio Pasquinelli
- Italian Federation of Pulmonary Fibrosis and Rare Pulmonary Diseases “FIMARP”, 00185 Rome, Italy;
- Department of Pulmonary Diseases, San Camillo-Forlanini Hospital, 00152 Rome, Italy
| | | | - Giorgio Bocchini
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Tullio Valente
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
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Paladini I, Schirò S, Ledda RE, Leo L, Milanese G, Epifani E, Andreone A, Capurri G, Fantoni M, Gemignani A, Gritti A, Sesenna E, Menozzi R. Percutaneous injection of sclerosant agents as an effective treatment for cystic malformations of the head and neck. Oral Maxillofac Surg 2024:10.1007/s10006-024-01210-9. [PMID: 38261079 DOI: 10.1007/s10006-024-01210-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 01/14/2024] [Indexed: 01/24/2024]
Abstract
PURPOSE To evaluate the clinical and aesthetic outcome of percutaneous injection of sclerosant agents to treat head and neck cystic malformations (HNCM) and to assess their recurrence rate based on histology and site. METHODS Fifty-four subjects (mean age 46 years) with HNCM treated by percutaneous injection of sclerosant agents between January and December 2017 were included. Imaging and clinical data before and after the procedure were collected. Quality of Life Index, Pain Visual Analogue Scale, and Aesthetic Scale scores were measured to assess clinical and aesthetic outcomes. A size reduction of ≥ 70% assessed through the visual scale was considered significant. RESULTS Of the 54 HNCM, there were 26 (48%) lymphatic malformations (LM), 13 (24%) salivary epithelial duct cysts of the parotid gland, 12 (22%) salivary mucoceles, and 3 (5%) branchial cysts. A significant size reduction and a satisfactory clinical-aesthetic outcome were observed in all types of LM. The number of reinterventions was significantly associated with the number of lesions (p < 0.001). The lowest number of interventions was observed in macrocystic lymphatic malformations (average of 1.2 interventions). All salivary epithelial duct cysts showed a significant reduction in size, a satisfactory clinical-aesthetic outcome, and an average of 1.16 interventions per patient. Mucoceles had a worse response, with only 3/14 patients showing a satisfactory and long-lasting clinical outcome (average of 1.16 interventions). Treatment of branchial cysts showed the worst outcome with a limited clinical response (3/3). CONCLUSION Percutaneous injection of sclerosant agents may be considered as a first-line treatment for LM and salivary epithelial duct cysts.
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Affiliation(s)
- Ilaria Paladini
- Unit of Interventional Radiology, Diagnostic Department, Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy
| | - Silvia Schirò
- Department of Medicine and Surgery (DiMec), University of Parma, Via gramsci 14 (43126), Parma, Italy.
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery (DiMec), University of Parma, Via gramsci 14 (43126), Parma, Italy
| | - Ludovica Leo
- Department of Medicine and Surgery (DiMec), University of Parma, Via gramsci 14 (43126), Parma, Italy
| | - Gianluca Milanese
- Unit of "Scienze Radiologiche", Department of Medicine and Surgery (DiMeC), University Hospital of Parma, Parma, Italy
| | - Enrico Epifani
- Unit of Interventional Radiology, Diagnostic Department, Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy
| | - Andrea Andreone
- Unit of Interventional Radiology, Diagnostic Department, Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy
| | - Giulia Capurri
- Unit of Interventional Radiology, Diagnostic Department, Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy
| | - Matteo Fantoni
- Neuroradiology Unit, Diagnostic Department, University Hospital of Parma, Via Volturno 39, 43125, Parma, Italy
| | - Andrea Gemignani
- Department of Medicine and Surgery (DiMec), University of Parma, Via gramsci 14 (43126), Parma, Italy
| | - Alessandro Gritti
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma,, Parma, Italy
| | - Enrico Sesenna
- Maxillo-Facial Surgery Division, Head and Neck Department, University Hospital of Parma, Parma, Italy
| | - Roberto Menozzi
- Interventional Neuroradiology Unit, Diagnostic Department, University Hospital of Parma, Via Volturno 39, 43125, Parma, Italy
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Henao JAG, Depotter A, Bower DV, Bajercius H, Todorova PT, Saint-James H, de Mortanges AP, Barroso MC, He J, Yang J, You C, Staib LH, Gange C, Ledda RE, Caminiti C, Silva M, Cortopassi IO, Dela Cruz CS, Hautz W, Bonel HM, Sverzellati N, Duncan JS, Reyes M, Poellinger A. A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans. Invest Radiol 2023; 58:882-893. [PMID: 37493348 PMCID: PMC10662611 DOI: 10.1097/rli.0000000000001005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 05/26/2023] [Indexed: 07/27/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans. MATERIALS AND METHODS The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19-induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19-positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19-positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion. RESULTS AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95. CONCLUSIONS A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.
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Ruggirello M, Valsecchi C, Ledda RE, Sabia F, Vigorito R, Sozzi G, Pastorino U. Long-term outcomes of lung cancer screening in males and females. Lung Cancer 2023; 185:107387. [PMID: 37801898 PMCID: PMC10788694 DOI: 10.1016/j.lungcan.2023.107387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/08/2023]
Abstract
BACKGROUND This study explored female and male overall mortality and lung cancer (LC) survival in two LC screening (LCS) populations, focusing on the predictive value of coronary artery calcification (CAC) at baseline low-dose computed tomography (LDCT). METHODS This retrospective study analysed data of 6495 heavy smokers enrolled in the MILD and BioMILD LCS trials between 2005 and 2016. The primary objective of the study was to assess sex differences in all-cause mortality and LC survival. CAC scores were automatically calculated on LDCT images by a validated artificial intelligence (AI) software. Sex differences in 12-year cause-specific mortality rates were stratified by age, pack-years and CAC score. RESULTS The study included 2368 females and 4127 males. The 12-year all-cause mortality rates were 4.1 % in females and 7.7 % in males (p < 0.0001), and median CAC score was 8.7 vs. 41 respectively (p < 0.0001). All-cause mortality increased with rising CAC scores (log-rank test, p < 0.0001) for both sexes. Although LC incidence was not different between the two sexes, females had lower rates of 12-year LC mortality (1.0 % vs. 1.9 %, p = 0.0052), and better LC survival from diagnosis (72.3 % vs. 51.7 %; p = 0.0005), with a similar proportion of stage I (58.1 % vs. 51.2 %, p = 0.2782). CONCLUSIONS Our findings demonstrate that female LCS participants had lower rates of all-cause mortality at 12 years and better LC survival than their male counterparts, with similar LC incidence rates and stage at diagnosis. The lower CAC burden observed in women at all ages might contribute to explain their lower rates of all-cause mortality and better LC survival.
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Affiliation(s)
- Margherita Ruggirello
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Camilla Valsecchi
- Division of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Roberta Eufrasia Ledda
- Division of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Federica Sabia
- Division of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Raffaella Vigorito
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Gabriella Sozzi
- Tumour Genomics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Ugo Pastorino
- Division of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
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Buccardi M, Ferrini E, Pennati F, Vincenzi E, Ledda RE, Grandi A, Buseghin D, Villetti G, Sverzellati N, Aliverti A, Stellari FF. A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy. Respir Res 2023; 24:126. [PMID: 37161569 PMCID: PMC10170869 DOI: 10.1186/s12931-023-02432-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 04/24/2023] [Indexed: 05/11/2023] Open
Abstract
Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and to longitudinally follow fibrosis evolution and response to the human-use-approved drug Nintedanib, using both inspiratory and expiratory μCT. Particularly significant advantages of this µCT-DL approach, are: (i) its reduced experimental variability, due to the fact that each animal acts as its own control and the measured, operator bias-free biomarkers can be quantitatively compared across experiments; (ii) its ability to monitor longitudinally the spatial distribution of fibrotic lesions, thus eliminating potential confounding effects associated with the more severe fibrosis observed in the apical region of the left lung and the compensatory effects taking place in the right lung; (iii) the animal sparing afforded by its non-invasive nature and high reliability; and (iv) the fact that it can be integrated into different drug discovery pipelines with a substantial increase in both the speed and robustness of the evaluation of new candidate drugs. The µCT-DL approach thus lends itself as a powerful new tool for the precision preclinical monitoring of BLM-induced lung fibrosis and other disease models as well. Its ease of operation and use of standard imaging instrumentation make it easily transferable to other laboratories and to other experimental settings, including clinical diagnostic applications.
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Affiliation(s)
- Martina Buccardi
- Department of Mathematical, Physical and Computer Sciences, University of Parma, Parma, Italy
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | - Erica Ferrini
- Department of Veterinary Science, University of Parma, Parma, Italy
| | - Francesca Pennati
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico Di Milano, Milan, Italy
| | - Elena Vincenzi
- Department of Computer Science, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
- Camelot Biomedical System S.R.L, Via Al Ponte Reale 2/20, 16124, Genoa, Italy
| | | | - Andrea Grandi
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | - Davide Buseghin
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Gino Villetti
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy
| | | | - Andrea Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico Di Milano, Milan, Italy
| | - Franco Fabio Stellari
- Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy.
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Marrocchio C, Ledda RE. Predicting Future Lung Cancer Risk from a Single Low-Dose CT Using Deep Learning. Radiol Imaging Cancer 2023; 5:e239013. [PMID: 37233206 DOI: 10.1148/rycan.239013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
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Milanese G, Ledda RE, Sabia F, Ruggirello M, Sestini S, Silva M, Sverzellati N, Marchianò AV, Pastorino U. Ultra-low dose computed tomography protocols using spectral shaping for lung cancer screening: Comparison with low-dose for volumetric LungRADS classification. Eur J Radiol 2023; 161:110760. [PMID: 36878153 DOI: 10.1016/j.ejrad.2023.110760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023]
Abstract
PURPOSE To compare Low-Dose Computed Tomography (LDCT) with four different Ultra-Low-Dose Computed Tomography (ULDCT) protocols for PN classification according to the Lung Reporting and Data System (LungRADS). METHODS Three hundred sixty-one participants of an ongoing lung cancer screening (LCS) underwent single-breath-hold double chest Computed Tomography (CT), including LDCT (120kVp, 25mAs; CTDIvol 1,62 mGy) and one ULDCT among: fully automated exposure control ("ULDCT1"); fixed tube-voltage and current according to patient size ("ULDCT2"); hybrid approach with fixed tube-voltage ("ULDCT3") and tube current automated exposure control ("ULDCT4"). Two radiologists (R1, R2) assessed LungRADS 2022 categories on LDCT, and then after 2 weeks on ULDCT using two different kernels (R1: Qr49ADMIRE 4; R2: Br49ADMIRE 3). Intra-subject agreement for LungRADS categories between LDCT and ULDCT was measured by the k-Cohen Index with Fleiss-Cohen weights. RESULTS LDCT-dominant PNs were detected in ULDCT in 87 % of cases on Qr49ADMIRE 4 and 88 % on Br49ADMIRE 3. The intra-subject agreement was: κULDCT1 = 0.89 [95 %CI 0.82-0.96]; κULDCT2 = 0.90 [0.81-0.98]; κULDCT3 = 0.91 [0.84-0.99]; κULDCT4 = 0.88 [0.78-0.97] on Qr49ADMIRE 4, and κULDCT1 = 0.88 [0.80-0.95]; κULDCT2 = 0.91 [0.86-0.96]; κULDCT3 = 0.87 [0.78-0.95]; and κULDCT4 = 0.88 [0.82-0.94] on Br49ADMIRE 3. LDCT classified as LungRADS 4B were correctly identified as LungRADS 4B at ULDCT3, with the lowest radiation exposure among the tested protocols (median effective doses were 0.31, 0.36, 0.27 and 0.37 mSv for ULDCT1, ULDCT2, ULDCT3, and ULDCT4, respectively). CONCLUSIONS ULDCT by spectral shaping allows the detection and characterization of PNs with an excellent agreement with LDCT and can be proposed as a feasible approach in LCS.
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Affiliation(s)
- Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy; Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
| | - Roberta Eufrasia Ledda
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy; Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
| | - Federica Sabia
- Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
| | - Margherita Ruggirello
- Fondazione IRCCS Istituto Nazionale dei Tumori, Department of Diagnostic Imaging and Radiotherapy, Milan, Italy.
| | - Stefano Sestini
- Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Alfonso Vittorio Marchianò
- Fondazione IRCCS Istituto Nazionale dei Tumori, Department of Diagnostic Imaging and Radiotherapy, Milan, Italy.
| | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale dei Tumori, Thoracic Surgery, Milan, Lombardia, Italy.
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10
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Milanese G, Mazzaschi G, Ledda RE, Balbi M, Lamorte S, Caminiti C, Colombi D, Tiseo M, Silva M, Sverzellati N. The radiological appearances of lung cancer treated with immunotherapy. Br J Radiol 2023; 96:20210270. [PMID: 36367539 PMCID: PMC10078868 DOI: 10.1259/bjr.20210270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 09/29/2022] [Accepted: 10/06/2022] [Indexed: 11/13/2022] Open
Abstract
Therapy and prognosis of several solid and hematologic malignancies, including non-small cell lung cancer (NSCLC), have been favourably impacted by the introduction of immune checkpoint inhibitors (ICIs). Their mechanism of action relies on the principle that some cancers can evade immune surveillance by expressing surface inhibitor molecules, known as "immune checkpoints". ICIs aim to conceal tumoural checkpoints on the cell surface and reinvigorate the ability of the host immune system to recognize tumour cells, triggering an antitumoural immune response.In this review, we will focus on the imaging patterns of different responses occurring in patients treated by ICIs. We will also discuss imaging findings of immune-related adverse events (irAEs), along with current and future perspectives of metabolic imaging. Finally, we will explore the role of radiomics in the setting of ICI-treated patients.
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Affiliation(s)
- Gianluca Milanese
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Giulia Mazzaschi
- Department of Medicine and Surgery, Unit of Medical Oncology, University of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Maurizio Balbi
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Sveva Lamorte
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Caterina Caminiti
- Unit of Research and Innovation, University Hospital of Parma, Parma, Italy
| | - Davide Colombi
- Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy
| | - Marcello Tiseo
- Department of Medicine and Surgery, Unit of Medical Oncology, University of Parma, Parma, Italy
| | - Mario Silva
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
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11
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Evangelista L, Bianchi A, Annovazzi A, Sciuto R, Di Traglia S, Bauckneht M, Lanfranchi F, Morbelli S, Nappi AG, Ferrari C, Rubini G, Panareo S, Urso L, Bartolomei M, D’Arienzo D, Valente T, Rossetti V, Caroli P, Matteucci F, Aricò D, Bombaci M, Caponnetto D, Bertagna F, Albano D, Dondi F, Gusella S, Spimpolo A, Carriere C, Balma M, Buschiazzo A, Gallicchio R, Storto G, Ruffini L, Cervati V, Ledda RE, Cervino AR, Cuppari L, Burei M, Trifirò G, Brugola E, Zanini CA, Alessi A, Fuoco V, Seregni E, Deandreis D, Liberini V, Moreci AM, Ialuna S, Pulizzi S, De Rimini ML. ITA-IMMUNO-PET: The Role of [18F]FDG PET/CT for Assessing Response to Immunotherapy in Patients with Some Solid Tumors. Cancers (Basel) 2023; 15:cancers15030878. [PMID: 36765835 PMCID: PMC9913289 DOI: 10.3390/cancers15030878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/24/2023] [Accepted: 01/27/2023] [Indexed: 02/04/2023] Open
Abstract
AIM To examine the role of [18F]FDG PET/CT for assessing response to immunotherapy in patients with some solid tumors. METHODS Data recorded in a multicenter (n = 17), retrospective database between March and November 2021 were analyzed. The sample included patients with a confirmed diagnosis of a solid tumor who underwent serial [18F]FDG PET/CT (before and after one or more cycles of immunotherapy), who were >18 years of age, and had a follow-up of at least 12 months after their first PET/CT scan. Patients enrolled in clinical trials or without a confirmed diagnosis of cancer were excluded. The authors classified cases as having a complete or partial metabolic response to immunotherapy, or stable or progressive metabolic disease, based on a visual and semiquantitative analysis according to the EORTC criteria. Clinical response to immunotherapy was assessed at much the same time points as the serial PET scans, and both the obtained responses were compared. RESULTS The study concerned 311 patients (median age: 67; range: 31-89 years) in all. The most common neoplasm was lung cancer (56.9%), followed by malignant melanoma (32.5%). Nivolumab was administered in 46.3%, and pembrolizumab in 40.5% of patients. Baseline PET and a first PET scan performed at a median 3 months after starting immunotherapy were available for all 311 patients, while subsequent PET scans were obtained after a median 6, 12, 16, and 21 months for 199 (64%), 102 (33%), 46 (15%), and 23 (7%) patients, respectively. Clinical response to therapy was recorded at around the same time points after starting immunotherapy for 252 (81%), 173 (56%), 85 (27%), 40 (13%), and 22 (7%) patients, respectively. After a median 18 (1-137) months, 113 (36.3%) patients had died. On Kaplan-Meier analysis, metabolic responders on the first two serial PET scans showed a better prognosis than non-responders, while clinical response became prognostically informative from the second assessment after starting immunotherapy onwards. CONCLUSIONS [18F]FDG PET/CT could have a role in the assessment of response to immunotherapy in patients with some solid tumors. It can provide prognostic information and thus contribute to a patient's appropriate treatment. Prospective randomized controlled trials are mandatory.
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Affiliation(s)
- Laura Evangelista
- Nuclear Medicine Unit, Department of Medicine DIMED, University of Padua, 35129 Padua, Italy
- Correspondence:
| | - Andrea Bianchi
- Nuclear Medicine Unit, ASO S.Croce e Carle Cuneo, 12100 Cuneo, Italy
| | - Alessio Annovazzi
- Nuclear Medicine Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Rosa Sciuto
- Nuclear Medicine Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Silvia Di Traglia
- Nuclear Medicine Unit, IRCCS Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Matteo Bauckneht
- Department of Health Sciences (DISSAL), University of Genova, 16126 Genova, Italy
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Francesco Lanfranchi
- Department of Health Sciences (DISSAL), University of Genova, 16126 Genova, Italy
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Silvia Morbelli
- Department of Health Sciences (DISSAL), University of Genova, 16126 Genova, Italy
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, 16132 Genoa, Italy
| | - Anna Giulia Nappi
- Section of Nuclear Medicine, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, 70121 Bari, Italy
| | - Cristina Ferrari
- Section of Nuclear Medicine, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, 70121 Bari, Italy
| | - Giuseppe Rubini
- Section of Nuclear Medicine, Interdisciplinary Department of Medicine, University of Bari “Aldo Moro”, 70121 Bari, Italy
| | - Stefano Panareo
- Nuclear Medicine Unit, Azienda Ospedaliero Universitaria di Modena, 41124 Modena, Italy
| | - Luca Urso
- Nuclear Medicine Unit, University of Ferrara, 44121 Ferrara, Italy
| | - Mirco Bartolomei
- Nuclear Medicine Unit, University of Ferrara, 44121 Ferrara, Italy
| | - Davide D’Arienzo
- Nuclear Medicine Unit, Dept Servizi Sanitari, AORN Ospedali dei Colli, 80131 Naples, Italy
| | - Tullio Valente
- Radiology Department, AORN Ospedali dei Colli, 80131 Naples, Italy
| | - Virginia Rossetti
- Nuclear Medicine Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST), 47014 Meldola, Italy
| | - Paola Caroli
- Nuclear Medicine Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST), 47014 Meldola, Italy
| | - Federica Matteucci
- Nuclear Medicine Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST), 47014 Meldola, Italy
| | - Demetrio Aricò
- Nuclear Medicine Unit, Humanitas Istituto Clinico Catanese, 95045 Misterbianco, Italy
| | - Michelangelo Bombaci
- Nuclear Medicine Unit, Humanitas Istituto Clinico Catanese, 95045 Misterbianco, Italy
| | - Domenica Caponnetto
- Nuclear Medicine Unit, Humanitas Istituto Clinico Catanese, 95045 Misterbianco, Italy
| | | | - Domenico Albano
- Nuclear Medicine Unit, University of Brescia, 25123 Brescia, Italy
| | - Francesco Dondi
- Nuclear Medicine Unit, University of Brescia, 25123 Brescia, Italy
| | - Sara Gusella
- Nuclear Medicine Department, Central Hospital Bolzano (SABES-ASDAA), 39100 Bolzano-Bozen, Italy
| | - Alessandro Spimpolo
- Nuclear Medicine Department, Central Hospital Bolzano (SABES-ASDAA), 39100 Bolzano-Bozen, Italy
| | - Cinzia Carriere
- Dermatology Department, Central Hospital Bolzano (SABES-ASDAA), 39100 Bolzano-Bozen, Italy
| | - Michele Balma
- Nuclear Medicine Unit, ASO S.Croce e Carle Cuneo, 12100 Cuneo, Italy
| | - Ambra Buschiazzo
- Nuclear Medicine Unit, ASO S.Croce e Carle Cuneo, 12100 Cuneo, Italy
| | - Rosj Gallicchio
- Nuclear Medicine Unit, IRCCS CROB Referral Cancer Center of Basilicata, 85028 Rionero in Vulture, Italy
| | - Giovanni Storto
- Nuclear Medicine Unit, IRCCS CROB Referral Cancer Center of Basilicata, 85028 Rionero in Vulture, Italy
| | - Livia Ruffini
- Nuclear Medicine Division, Azienda Ospedaliero-Universitaria of Parma, 43126 Parma, Italy
| | - Veronica Cervati
- Nuclear Medicine Division, Azienda Ospedaliero-Universitaria of Parma, 43126 Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, 43126 Parma, Italy
| | - Anna Rita Cervino
- Nuclear Medicine Unit, Veneto Institute Of Oncology IOV—IRCSS, 35128 Padua, Italy
| | - Lea Cuppari
- Nuclear Medicine Unit, Veneto Institute Of Oncology IOV—IRCSS, 35128 Padua, Italy
| | - Marta Burei
- Nuclear Medicine Unit, Veneto Institute Of Oncology IOV—IRCSS, 35128 Padua, Italy
| | - Giuseppe Trifirò
- Nuclear Medicine Unit, ICS MAUGERI SPA SB—IRCCS, 35128 Padua, Italy
| | | | | | - Alessandra Alessi
- Nuclear Medicine Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Valentina Fuoco
- Nuclear Medicine Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Ettore Seregni
- Nuclear Medicine Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milan, Italy
| | - Désirée Deandreis
- Nuclear Medicine Division, Department of Medical Sciences, University of Turin, 10124 Turin, Italy
| | - Virginia Liberini
- Nuclear Medicine Unit, ASO S.Croce e Carle Cuneo, 12100 Cuneo, Italy
- Nuclear Medicine Division, Department of Medical Sciences, University of Turin, 10124 Turin, Italy
| | - Antonino Maria Moreci
- Nuclear Medicine Unit, Az. Ospedaliera Ospedali Riuniti Villa Sofia-Cervello di Palermo, 90100 Palermo, Italy
| | - Salvatore Ialuna
- Nuclear Medicine Unit, Az. Ospedaliera Ospedali Riuniti Villa Sofia-Cervello di Palermo, 90100 Palermo, Italy
| | - Sabina Pulizzi
- Nuclear Medicine Unit, Az. Ospedaliera Ospedali Riuniti Villa Sofia-Cervello di Palermo, 90100 Palermo, Italy
| | - Maria Luisa De Rimini
- Nuclear Medicine Unit, Dept Servizi Sanitari, AORN Ospedali dei Colli, 80131 Naples, Italy
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12
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Milanese G, Sabia F, Ledda RE, Sestini S, Marchianò AV, Sverzellati N, Pastorino U. Volumetric Measurements in Lung Cancer Screening Reduces Unnecessary Low-Dose Computed Tomography Scans: Results from a Single-Center Prospective Trial on 4119 Subjects. Diagnostics (Basel) 2022; 12:diagnostics12020229. [PMID: 35204320 PMCID: PMC8871316 DOI: 10.3390/diagnostics12020229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/07/2022] [Accepted: 01/09/2022] [Indexed: 02/05/2023] Open
Abstract
This study aims to compare the low-dose computed tomography (LDCT) outcome and volume-doubling time (VDT) derived from the measured volume (MV) and estimated volume (EV) of pulmonary nodules (PNs) detected in a single-center lung cancer screening trial. MV, EV and VDT were obtained for prevalent pulmonary nodules detected at the baseline round of the bioMILD trial. The LDCT outcome (based on bioMILD thresholds) and VDT categories were simulated on PN- and screenee-based analyses. A weighted Cohen’s kappa test was used to assess the agreement between diagnostic categories as per MV and EV, and 1583 screenees displayed 2715 pulmonary nodules. In the PN-based analysis, 40.1% PNs were included in different LDCT categories when measured by MV or EV. The agreements between MV and EV were moderate (κ = 0.49) and fair (κ = 0.37) for the LDCT outcome and VDT categories, respectively. In the screenee-based analysis, 46% pulmonary nodules were included in different LDCT categories when measured by MV or EV. The agreements between MV and EV were moderate (κ = 0.52) and fair (κ = 0.34) for the LDCT outcome and VDT categories, respectively. Within a simulated lung cancer screening based on a recommendation by estimated volumetry, the number of LDCTs performed for the evaluation of pulmonary nodules was higher compared with in prospective volumetric management.
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Affiliation(s)
- Gianluca Milanese
- Radiological Sciences, Department of Medicine and Surgery (DiMeC), University Hospital of Parma, 43126 Parma, Italy; (G.M.); (R.E.L.); (N.S.)
| | - Federica Sabia
- Fondazione IRCCS Istituto Nazionale Tumori of Milan, 20133 Milan, Italy; (F.S.); (S.S.); (A.V.M.)
| | - Roberta Eufrasia Ledda
- Radiological Sciences, Department of Medicine and Surgery (DiMeC), University Hospital of Parma, 43126 Parma, Italy; (G.M.); (R.E.L.); (N.S.)
- Fondazione IRCCS Istituto Nazionale Tumori of Milan, 20133 Milan, Italy; (F.S.); (S.S.); (A.V.M.)
| | - Stefano Sestini
- Fondazione IRCCS Istituto Nazionale Tumori of Milan, 20133 Milan, Italy; (F.S.); (S.S.); (A.V.M.)
| | | | - Nicola Sverzellati
- Radiological Sciences, Department of Medicine and Surgery (DiMeC), University Hospital of Parma, 43126 Parma, Italy; (G.M.); (R.E.L.); (N.S.)
| | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale Tumori of Milan, 20133 Milan, Italy; (F.S.); (S.S.); (A.V.M.)
- Correspondence:
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13
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Ledda RE, Silva M, McMichael N, Sartorio C, Branchi C, Milanese G, Nayak SM, Sverzellati N. The diagnostic value of grey-scale inversion technique in chest radiography. Radiol Med 2022; 127:294-304. [PMID: 35041136 PMCID: PMC8960630 DOI: 10.1007/s11547-022-01453-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/03/2022] [Indexed: 12/01/2022]
Abstract
Purpose We investigated whether the additional use of grey-scale inversion technique improves the interpretation of eight chest abnormalities, in terms of diagnostic performance and interobserver variability. Material and methods A total of 507 patients who underwent a chest computed tomography (CT) examination and a chest radiography (CXR) within 24 h were enrolled. CT was the standard of reference. Images were retrospectively reviewed for the presence of atelectasis, consolidation, interstitial abnormality, nodule, mass, pleural effusion, pneumothorax and rib fractures. Four CXR reading settings, involving 3 readers were organized: only standard; only inverted; standard followed by inverted; and inverted followed by standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy, assessed with the area under the curve (AUC), and their 95% confidence interval were calculated for each reader and setting. Interobserver agreement was tested by Cohen’s K test with quadratic weights (Kw) and its 95%CI.
Results CXR sensitivity % for any finding was 35.1 (95% CI: 33 to 37) for setting 1, 35.9 (95% CI: 33 to 37), for setting 2, 32.59 (95% CI: 30 to 34) for setting 3, and 35.56 (95% CI: 33 to 37) for setting 4; specificity % 93.78 (95% CI: 91 to 95), 93.92 (95% CI: 91 to 95), 94.43 (95% CI: 92 to 96), 93.86 (95% CI: 91 to 95); PPV % 56.22 (95% CI: 54.2 to 58.2), 56.49 (95% CI: 54.5 to 58.5), 57.15 (95% CI: 55 to 59), 56.75 (95% CI: 54 to 58); NPV % 85.66 (95% CI: 83 to 87), 85.74 (95% CI: 83 to 87), 85.29 (95% CI: 83 to 87), 85.73 (95% CI: 83 to 87); AUC values 0.64 (95% CI: 0.62 to 0.66), 0.65 (95% CI: 0.63 to 0.67), 0.64 (95% CI: 0.62 to 0.66), 0.65 (95% CI: 0.63 to 0.67); Kw values 0.42 (95% CI: 0.4 to 0.44), 0.40 (95% CI: 0.38 to 0.42), 0.42 (95% CI: 0.4 to 0.44), 0.41 (95% CI: 0.39 to 0.43) for settings 1, 2, 3 and 4, respectively.
Conclusions No significant advantages were observed in the use of grey-scale inversion technique neither over standard display mode nor in combination at the detection of eight chest abnormalities. Supplementary Information The online version contains supplementary material available at 10.1007/s11547-022-01453-0.
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Affiliation(s)
- Roberta Eufrasia Ledda
- Department of Medicine and Surgery, University of Parma, Scienze Radiologiche, University Hospital of Parma, Padiglione Barbieri, Via Gramsci 14, 43126, Parma, Italy
| | - Mario Silva
- Department of Medicine and Surgery, University of Parma, Scienze Radiologiche, University Hospital of Parma, Padiglione Barbieri, Via Gramsci 14, 43126, Parma, Italy
| | - Nicole McMichael
- Department of Radiology Diagnostics, Skåne University Hospital of Malmö, Malmö, Sweden
| | - Carlotta Sartorio
- Department of Medicine and Surgery, University of Parma, Scienze Radiologiche, University Hospital of Parma, Padiglione Barbieri, Via Gramsci 14, 43126, Parma, Italy
| | - Cristina Branchi
- Department of Medicine and Surgery, University of Parma, Scienze Radiologiche, University Hospital of Parma, Padiglione Barbieri, Via Gramsci 14, 43126, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery, University of Parma, Scienze Radiologiche, University Hospital of Parma, Padiglione Barbieri, Via Gramsci 14, 43126, Parma, Italy.
| | - Sundeep M Nayak
- Department of Radiology, Kaiser Permanente Northern California, San Leandro, CA, USA
| | - Nicola Sverzellati
- Department of Medicine and Surgery, University of Parma, Scienze Radiologiche, University Hospital of Parma, Padiglione Barbieri, Via Gramsci 14, 43126, Parma, Italy
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14
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Buti S, Perrone F, Zielli T, Mazzaschi G, Casartelli C, Leonetti A, Milanese G, Silva M, Eufrasia Ledda R, Musolino A, Pucci F, Bersanelli M, Tiseo M. Clinical Impact of COVID-19 Outbreak on Cancer Patients: A Retrospective Study. Clin Med Insights Oncol 2021; 15:11795549211043427. [PMID: 34526833 PMCID: PMC8436296 DOI: 10.1177/11795549211043427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 08/08/2021] [Indexed: 12/15/2022]
Abstract
Background Coronavirus disease (COVID-19), an acute respiratory syndrome caused by a novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has rapidly spread worldwide, significantly affecting the outcome of a highly vulnerable group such as cancer patients. The aim of the present study was to evaluate the clinical impact of COVID-19 infection on outcome and oncologic treatment of cancer patients. Patient and methods We retrospectively enrolled cancer patients with laboratory and/or radiologic confirmed SARS-CoV-2 infection, admitted to our center from February to April 2020. Descriptive statistics were used to summarize the clinical data and univariate analyses were performed to investigate the impact of anticancer treatment modifications due to COVID-19 outbreak on the short-term overall survival (OS). Results Among 61 patients enrolled, 49 (80%) were undergoing anticancer treatment and 41 (67%) had metastatic disease. Most patients were men; median age was 68 years. Median OS was 46.6 days (40% of deaths occurred within 20 days from COVID-19 diagnosis). Among 59 patients with available data on therapeutic course, 46 experienced consequences on their anticancer treatment schedule. Interruption or a starting failure of the oncologic therapy correlated with significant shorter OS. Anticancer treatment delays did not negatively affect the OS. Lymphocytopenia development after COVID was significantly associated with worst outcome. Conclusions COVID-19 diagnosis in cancer patients may affect their short-term OS, especially in case of interruption/starting failure of cancer therapy. Maintaining/delaying cancer therapy seems not to influence the outcome in selected patients with recent COVID-19 diagnosis.
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Affiliation(s)
- Sebastiano Buti
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Fabiana Perrone
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Teresa Zielli
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Giulia Mazzaschi
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | | | - Alessandro Leonetti
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy.,Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery, University of Parma, Parma, Italy.,Radiology Unit, University Hospital of Parma, Parma, Italy
| | - Mario Silva
- Department of Medicine and Surgery, University of Parma, Parma, Italy.,Radiology Unit, University Hospital of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery, University of Parma, Parma, Italy.,Radiology Unit, University Hospital of Parma, Parma, Italy
| | - Antonino Musolino
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy.,Department of Medicine and Surgery, University of Parma, Parma, Italy.,Breast Unit, University Hospital of Parma, Parma, Italy
| | - Francesca Pucci
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy.,Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Melissa Bersanelli
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy.,Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Marcello Tiseo
- Medical Oncology Unit, University Hospital of Parma, Parma, Italy.,Department of Medicine and Surgery, University of Parma, Parma, Italy
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15
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Rundo L, Ledda RE, di Noia C, Sala E, Mauri G, Milanese G, Sverzellati N, Apolone G, Gilardi MC, Messa MC, Castiglioni I, Pastorino U. A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules. Diagnostics (Basel) 2021; 11:1610. [PMID: 34573951 PMCID: PMC8471292 DOI: 10.3390/diagnostics11091610] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/25/2021] [Accepted: 08/30/2021] [Indexed: 12/25/2022] Open
Abstract
Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.
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Affiliation(s)
- Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Roberta Eufrasia Ledda
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
| | - Christian di Noia
- Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Gianluca Milanese
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
| | - Nicola Sverzellati
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
| | - Giovanni Apolone
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
| | - Maria Carla Gilardi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (M.C.G.); (M.C.M.)
| | - Maria Cristina Messa
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (M.C.G.); (M.C.M.)
- Institute of Biomedical Imaging and Physiology, Italian National Research Council (IBFM-CNR), Segrate, 20090 Milan, Italy
- Fondazione Tecnomed, University of Milano-Bicocca, 20900 Monza, Italy
| | - Isabella Castiglioni
- Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy;
- Institute of Biomedical Imaging and Physiology, Italian National Research Council (IBFM-CNR), Segrate, 20090 Milan, Italy
| | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
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Ledda RE, Balbi M, Milone F, Ciuni A, Silva M, Sverzellati N, Milanese G. Imaging in non-cystic fibrosis bronchiectasis and current limitations. BJR Open 2021; 3:20210026. [PMID: 34381953 PMCID: PMC8328081 DOI: 10.1259/bjro.20210026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 01/21/2023] Open
Abstract
Non-cystic fibrosis bronchiectasis represents a heterogenous spectrum of disorders characterised by an abnormal and permanent dilatation of the bronchial tree associated with respiratory symptoms. To date, diagnosis relies on computed tomography (CT) evidence of dilated airways. Nevertheless, definite radiological criteria and standardised CT protocols are still to be defined. Although largely used, current radiological scoring systems have shown substantial drawbacks, mostly failing to correlate morphological abnormalities with clinical and prognostic data. In limited cases, bronchiectasis morphology and distribution, along with associated CT features, enable radiologists to confidently suggest an underlying cause. Quantitative imaging analyses have shown a potential to overcome the limitations of the current radiological criteria, but their application is still limited to a research setting. In the present review, we discuss the role of imaging and its current limitations in non-cystic fibrosis bronchiectasis. The potential of automatic quantitative approaches and artificial intelligence in such a context will be also mentioned.
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Affiliation(s)
- Roberta Eufrasia Ledda
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Maurizio Balbi
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Francesca Milone
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Andrea Ciuni
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
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17
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Tringali G, Milanese G, Ledda RE, Pastorino U, Sverzellati N, Silva M. Lung Cancer Screening: Evidence, Risks, and Opportunities for Implementation. ROFO-FORTSCHR RONTG 2021; 193:1153-1161. [PMID: 33772489 DOI: 10.1055/a-1382-8648] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Lung cancer is the most common cause of cancer death worldwide. Several trials with different screening approaches have recognized the role of lung cancer screening with low-dose CT for reducing lung cancer mortality. The efficacy of lung cancer screening depends on many factors and implementation is still pending in most European countries. METHODS This review aims to portray current evidence on lung cancer screening with a focus on the potential for opportunities for implementation strategies. Pillars of lung cancer screening practice will be discussed according to the most updated literature (PubMed search until November 16, 2020). RESULTS AND CONCLUSION The NELSON trial showed reduction of lung cancer mortality, thus confirming previous results of independent European studies, notably by volume of lung nodules. Heterogeneity in patient recruitment could influence screening efficacy, hence the importance of risk models and community-based screening. Recruitment strategies develop and adapt continuously to address the specific needs of the heterogeneous population of potential participants, the most updated evidence comes from the UK. The future of lung cancer screening is a tailored approach with personalized continuous stratification of risk, aimed at reducing costs and risks. KEY POINTS · Secondary prevention of lung cancer by low-dose computed tomography showed a reduction of lung cancer mortality.. · Semi-automated volume measurement and use of volume doubling time should be the reference method for optimization of risks, namely controlling measurement variability and the false-positive rate.. · A conservative approach with surveillance of subsolid nodules can be one of the strategies to reduce the risk of overdiagnosis and overtreatment.. · The goal of a tailored approach with personalized risk stratification aims to reduce costs and risks. A longer interval between rounds is one option for participants at lower risk.. CITATION FORMAT · Tringali G, Milanese G, Ledda RE et al. Lung Cancer Screening: Evidence, Risks, and Opportunities for Implementation. Fortschr Röntgenstr 2021; 193: 1153 - 1161.
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Affiliation(s)
- Giulia Tringali
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
| | - Ugo Pastorino
- Department of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
| | - Mario Silva
- Department of Medicine and Surgery (DiMeC - Scienze Radiologiche), University of Parma, Italy
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18
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Milanese G, Sabia F, Sestini S, Ledda RE, Rolli L, Suatoni P, Sverzellati N, Sozzi G, Apolone G, Marchianò AV, Pastorino U. Feasibility and Safety of Lung Cancer Screening and Prevention Program During the COVID-19 Pandemic. Chest 2021; 160:e5-e7. [PMID: 33745992 PMCID: PMC7970789 DOI: 10.1016/j.chest.2021.02.072] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 02/26/2021] [Indexed: 11/25/2022] Open
Affiliation(s)
- Gianluca Milanese
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy; Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Federica Sabia
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | | | - Roberta Eufrasia Ledda
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy; Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Luigi Rolli
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Paola Suatoni
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Nicola Sverzellati
- Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | | | | | | | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy.
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Trentini F, Mazzaschi G, Milanese G, Pavone C, Madeddu D, Gnetti L, Frati C, Lorusso B, Lagrasta CAM, Minari R, Ampollini L, Ledda RE, Silva M, Sverzellati N, Quaini F, Roti G, Tiseo M. Validation of a radiomic approach to decipher NSCLC immune microenvironment in surgically resected patients. Tumori 2021; 108:86-92. [PMID: 33730957 DOI: 10.1177/03008916211000808] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Radiomics has emerged as a noninvasive tool endowed with the potential to intercept tumor characteristics thereby predicting clinical outcome. In a recent study on resected non-small cell lung cancer (NSCLC), we identified highly prognostic computed tomography (CT)-derived radiomic features (RFs), which in turn were able to discriminate hot from cold tumor immune microenvironment (TIME). We aimed at validating a radiomic model capable of dissecting specific TIME profiles bearing prognostic power in resected NSCLC. METHODS The validation cohort included 31 radically resected NSCLCs clinicopathologically matched with the training set (n = 69). TIME was classified in hot and cold according to a multiparametric immunohistochemical analysis involving PD-L1 score and incidence of immune effector phenotypes among tumor infiltrating lymphocytes (TILs). High-throughput radiomic features (n = 841) extracted from CT images were correlated to TIME parameters to ultimately define prognostic classes. RESULTS We confirmed PD-1 to CD8 ratio as best predictor of clinical outcome among TIME characteristics. Significantly prolonged overall survival (OS) was observed in patients carrying hot (median OS not reached) vs cold (median OS 22 months; hazard ratio 0.28, 95% confidence interval 0.09-0.82; p = 0.015) immune background, thus validating the prognostic impact of these two TIME categories in resected NSCLC. Importantly, in the validation setting, three out of eight previously identified RFs sharply distinguishing hot from cold TIME were endorsed. Among signature-related RFs, Wavelet-HHH_gldm_HighGrayLevelEmphasis highly performed as descriptor of hot immune contexture (area under the receiver operating characteristic curve 0.94, 95% confidence interval 0.81-1.00; p = 0.01). CONCLUSION Radiomics may decipher specific TIME profiles providing a noninvasive prognostic approach in resected NSCLC and an exploitable predictive strategy in advanced cases.
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Affiliation(s)
- Francesca Trentini
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Giulia Mazzaschi
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Claudio Pavone
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Denise Madeddu
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Letizia Gnetti
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Caterina Frati
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Bruno Lorusso
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Costanza Anna Maria Lagrasta
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Roberta Minari
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Luca Ampollini
- Department of Medicine and Surgery, University of Parma, Thoracic Surgery, University Hospital of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Mario Silva
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Federico Quaini
- Department of Medicine and Surgery, Hematology and Bone Marrow Transplantation, University Hospital of Parma, Parma, Italy
| | - Giovanni Roti
- Department of Medicine and Surgery, Hematology and Bone Marrow Transplantation, University Hospital of Parma, Parma, Italy
| | - Marcello Tiseo
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Parma, Italy
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20
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Silva M, Ledda RE, Schiebler M, Balbi M, Sironi S, Milone F, Affanni P, Milanese G, Sverzellati N. Frequency and characterization of ancillary chest CT findings in COVID-19 pneumonia. Br J Radiol 2021; 94:20200716. [PMID: 33471553 PMCID: PMC7934290 DOI: 10.1259/bjr.20200716] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES Ground-glass opacity and consolidation are recognized typical features of Coronavirus disease-19 (COVID-19) pneumonia on Chest CT, yet ancillary findings have not been fully described. We aimed to describe ancillary findings of COVID-19 pneumonia on CT, to define their prevalence, and investigate their association with clinical data. METHODS We retrospectively reviewed our CT chest cases with coupled reverse transcriptase polymerase chain reaction (rt-PCR). Patients with negative rt-PCR or without admission chest CT were excluded. Ancillary findings included: vessel enlargement, subpleural curvilinear lines, dependent subpleural atelectasis, centrilobular solid nodules, pleural and/or pericardial effusions, enlarged mediastinal lymph nodes. Continuous data were expressed as median and 95% confidence interval (95% CI) and tested by Mann-Whitney U test. RESULTS Ancillary findings were represented by 106/252 (42.1%, 36.1 to 48.2) vessel enlargement, 50/252 (19.8%, 15.4 to 25.2) subpleural curvilinear lines, 26/252 (10.1%, 7.1 to 14.7) dependent subpleural atelectasis, 15/252 (5.9%, 3.6 to 9.6) pleural effusion, 15/252 (5.9%, 3.6 to 9.6) mediastinal lymph nodes enlargement, 13/252 (5.2%, 3 to 8.6) centrilobular solid nodules, and 6/252 (2.4%, 1.1 to 5.1) pericardial effusion. Air space disease was more extensive in patients with vessel enlargement or centrilobular solid nodules (p < 0.001). Vessel enlargement was associated with longer history of fever (p = 0.035) and lower admission oxygen saturation (p = 0.014); dependent subpleural atelectasis with lower oxygen saturation (p < 0.001) and higher respiratory rate (p < 0.001); mediastinal lymph nodes with shorter history of cough (p = 0.046); centrilobular solid nodules with lower prevalence of cough (p = 0.023), lower oxygen saturation (p < 0.001), and higher respiratory rate (p = 0.032), and pericardial effusion with shorter history of cough (p = 0.015). Ancillary findings associated with longer hospital stay were subpleural curvilinear lines (p = 0.02), whereas centrilobular solid nodules were associated with higher rate of intensive care unit admission (p = 0.01). CONCLUSION Typical high-resolution CT findings of COVID-19 pneumonia are frequently associated with ancillary findings that variably associate with disease extent, clinical parameters, and disease severity. ADVANCES IN KNOWLEDGE Ancillary findings might reflect the broad range of heterogeneous mechanisms in severe acute respiratory syndrome from viral pneumonia, and potentially help disease phenotyping.
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Affiliation(s)
- Mario Silva
- Department of Medicine and Surgery, Unit of "Scienze Radiologiche", University of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery, Unit of "Scienze Radiologiche", University of Parma, Parma, Italy
| | - Mark Schiebler
- Department of Radiology, UW-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Maurizio Balbi
- Department of Radiology, ASST Papa Giovanni XXIII, University of Milano-Bicocca, Milan, Italy
| | - Sandro Sironi
- Department of Radiology, ASST Papa Giovanni XXIII, University of Milano-Bicocca, Milan, Italy
| | - Francesca Milone
- Department of Medicine and Surgery, Unit of "Scienze Radiologiche", University of Parma, Parma, Italy
| | - Paola Affanni
- Laboratorio di Igiene e Sanità Pubblica, Dipartimento di Medicina e Chirurgia, Università di Parma, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery, Unit of "Scienze Radiologiche", University of Parma, Parma, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery, Unit of "Scienze Radiologiche", University of Parma, Parma, Italy
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21
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Ledda RE, Milanese G, Cademartiri F, Maffei E, Benedetti G, Goldoni M, Silva M, Sverzellati N. Association of hepatic steatosis with epicardial fat volume and coronary artery disease in symptomatic patients. Radiol Med 2021; 126:652-660. [PMID: 33389661 DOI: 10.1007/s11547-020-01321-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 11/26/2020] [Indexed: 12/12/2022]
Abstract
AIMS This study aims to investigate whether HS-when associated with an excessive amount of epicardial adipose tissue-correlates with CAD in subjects with symptoms suggestive of CVD. METHODS AND RESULTS CCTA images, demographic and clinical variables of 1.182 individuals were retrieved: semi-automated measurements for EFV, CAC, and MLD were obtained. Individuals were grouped into three categories according to the presence of CAD, resulting in absent (CAD0), non-obstructive (CAD1) or obstructive (CAD2) disease-groups, and into two categories based on the presence of HS (with no HS, named HS-, and with HS, named HS+). EFV was significantly higher in HS+ than in HS- group (p < 0.001), whereas MLD was lower in CAD+ than in CAD- subjects (p < 0.001). Two predictive models for CAD were tested: the former included clinical risk factors for CAD along with age, gender, EFV and MLD, whereas the latter did not include clinical variables. The logistic regression analysis of the second proposed model reliably discriminated CAD0 from CAD1 and CAD2 (AUC of 0.712, range 0.682-0.742). CONCLUSION Lower MLD was associated with increased EFV, and MLD-as a marker of HS-discriminate symptomatic patients with CAD from whom without.
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Affiliation(s)
- Roberta Eufrasia Ledda
- Division of Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Gianluca Milanese
- Division of Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | | | - Erica Maffei
- Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy
| | - Giorgio Benedetti
- Division of Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Matteo Goldoni
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Mario Silva
- Division of Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Nicola Sverzellati
- Division of Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
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22
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Milanese G, Silva M, Ledda RE, Goldoni M, Nayak S, Bruno L, Rossi E, Maffei E, Cademartiri F, Sverzellati N. Validity of epicardial fat volume as biomarker of coronary artery disease in symptomatic individuals: Results from the ALTER-BIO registry. Int J Cardiol 2020; 314:20-24. [DOI: 10.1016/j.ijcard.2020.04.031] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/17/2020] [Accepted: 04/09/2020] [Indexed: 01/05/2023]
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Balbi M, Ristani A, Milanese G, Silva M, Ledda RE, Milone F, Sartorio C, Tringali G, Sverzellati N. The role of the radiologist in diagnosing the COVID-19 infection. Parma experiences. Acta Biomed 2020; 91:169-171. [PMID: 32420940 PMCID: PMC7569662 DOI: 10.23750/abm.v91i2.9564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 04/17/2020] [Indexed: 11/29/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new virus responsible for the coronavirus disease 2019 (COVID-19), a respiratory disease that ranges from an asymptomatic or mild flu-like illness to severe pneumonia, multiorgan failure, and death. Imaging might play an important role in clinical decision making by supporting rapid triage of patients with suspected COVID-19 and assessing supervening complications, such as super-added bacterial infection and thrombosis. Further studies will clarify the real impact of imaging on COVID-19 patients' management and the potential role of radiology in future outbreaks.
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Affiliation(s)
- Maurizio Balbi
- University of Milano-Bicocca, School of Medicine and Surgery, Monza, Italy; Department of Radiology, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy.
| | - Adela Ristani
- Division of Radiology, University of Parma, Parma, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Gianluca Milanese
- Division of Radiology, University of Parma, Parma, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Mario Silva
- Division of Radiology, University of Parma, Parma, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Roberta Eufrasia Ledda
- Division of Radiology, University of Parma, Parma, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Francesca Milone
- Division of Radiology, University of Parma, Parma, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Carlotta Sartorio
- Division of Radiology, University of Parma, Parma, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Giulia Tringali
- Division of Radiology, University of Parma, Parma, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy.
| | - Nicola Sverzellati
- Division of Radiology, University of Parma, Parma, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy.
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Crivelli P, Ledda RE, Carboni M, Balestrieri A, Sotgiu MA, Saba L, Conti M. Erdheim-Chester disease presenting with cough, abdominal pain, and headache. Radiol Case Rep 2020; 15:745-748. [PMID: 32300470 PMCID: PMC7152688 DOI: 10.1016/j.radcr.2020.03.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 03/05/2020] [Accepted: 03/06/2020] [Indexed: 11/28/2022] Open
Abstract
Erdheim-Chester disease (ECD) is a rare non-Langerhans cell histiocytic disorder. The diagnosis was based on the relationship between radiologic findings, clinical manifestations, and pathologic features of the bone biopsy. We report a case of ECD with unusual presenting symptoms: a 56 year-old man presented with cough, abdominal pain, and recurrent episodes of headache associated without any seizures. Peculiar computer tomography (CT) findings were key for the diagnostic suspicion. Bone biopsy and other radiological investigations confirmed the diagnosis. CT findings can help raise the suspicion of ECD. CT is easy to perform and widely available in comparison with kinetic cardiac magnetic resonance imaging and nuclear imaging. Therefore, CT findings of ECD can reduce the therapeutic delay between diagnosis and therapy prescription.
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Affiliation(s)
| | - Roberta Eufrasia Ledda
- Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | | | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy
| | | | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy
| | - Maurizio Conti
- Department of Clinical and Experimental Medicine, Institute of Diagnostic Imaging 2, University of Sassari, Sassari, Italy
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Ledda RE, Raikes J, Crivelli P, Weichert I. Concurrent pulmonary embolism in female monozygotic twins affected by Dercum's disease. Oxf Med Case Reports 2019; 2019:omy123. [PMID: 30697439 PMCID: PMC6345092 DOI: 10.1093/omcr/omy123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/10/2018] [Accepted: 11/09/2018] [Indexed: 02/06/2023] Open
Abstract
We describe a pair of female monozygotic twins with Dercum’s disease (DD) who presented simultaneously with unprovoked pulmonary emboli. Several genetic determinants have been associated with venous thromboembolism (VTE) but the overall influence of genetic factors is unknown. As yet there is no published evidence to support an increase in the risk of VTE in female monozygotic twins. DD is a rare condition characterized by multiple, painful lipomas. The underlying pathology of it is poorly understood. To date, there has been no recorded association with an increased risk of VTE but there have been reports of stroke-like events. It is unclear if these are caused by the condition itself or are co-incidental. We acknowledge the possibility of a coincidence but the two cases raise the question of an association between VTE and DD. This report should encourage further studies into the risk of VTE in female monozygotic twins and DD.
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Affiliation(s)
- Roberta Eufrasia Ledda
- Department of Clinical and Experimental Medicine, Institute of Diagnostic Imaging 2, University of Sassari, Sassari, Italy
| | | | - Paola Crivelli
- Biomedical Sciences Department, Institute of Diagnostic Imaging 2, University of Sassari, Sassari, Italy
| | - Immo Weichert
- Department of Acute Medicine, Ipswich Hospital, East Suffolk and North Essex NHS Foundation Trust, Ipswich, Suffolk, UK
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Crivelli P, Ledda RE, Parascandolo N, Fara A, Soro D, Conti M. A New Challenge for Radiologists: Radiomics in Breast Cancer. Biomed Res Int 2018; 2018:6120703. [PMID: 30402486 PMCID: PMC6196984 DOI: 10.1155/2018/6120703] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 08/24/2018] [Accepted: 09/09/2018] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Over the last decade, the field of medical imaging experienced an exponential growth, leading to the development of radiomics, with which innumerable quantitative features are obtained from digital medical images, providing a comprehensive characterization of the tumor. This review aims to assess the role of this emerging diagnostic tool in breast cancer, focusing on the ability of radiomics to predict malignancy, response to neoadjuvant chemotherapy, prognostic factors, molecular subtypes, and risk of recurrence. EVIDENCE ACQUISITION A literature search on PubMed and on Cochrane database websites to retrieve English-written systematic reviews, review articles, meta-analyses, and randomized clinical trials published from August 2013 up to July 2018 was carried out. RESULTS Twenty papers (19 retrospective and 1 prospective studies) conducted with different conventional imaging modalities were included. DISCUSSION The integration of quantitative information with clinical, histological, and genomic data could enable clinicians to provide personalized treatments for breast cancer patients. Current limitations of a routinely application of radiomics are represented by the limited knowledge of its basics concepts among radiologists and by the lack of efficient and standardized systems of feature extraction and data sharing.
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Affiliation(s)
- Paola Crivelli
- Department of Biomedical Sciences, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Roberta Eufrasia Ledda
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Nicola Parascandolo
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Alberto Fara
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Daniela Soro
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Maurizio Conti
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
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