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Zhang Y, Xu Z, Wu S, Zhu T, Hong X, Chi Z, Malla R, Jiang J, Huang Y, Xu Q, Wang Z, Zhang Y. Construction of 3D and 2D contrast-enhanced CT radiomics for prediction of CGB3 expression level and clinical prognosis in bladder cancer. Heliyon 2023; 9:e20335. [PMID: 37809854 PMCID: PMC10560067 DOI: 10.1016/j.heliyon.2023.e20335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/12/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Objective The purpose of this study was to construct a 3D and 2D contrast-enhanced computed tomography (CECT) radiomics model to predict CGB3 levels and assess its prognostic abilities in bladder cancer (Bca) patients. Methods Transcriptome data and CECT images of Bca patients were downloaded from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database. Clinical data of 43 cases from TCGA and TCIA were used for radiomics model evaluation. The Volume of interest (VOI) (3D) and region of interest (ROI) (2D) radiomics features were extracted. For the construction of predicting radiomics models, least absolute shrinkage and selection operator regression were used, and the filtered radiomics features were fitted using the logistic regression algorithm (LR). The model's effectiveness was measured using 10-fold cross-validation and the area under the receiver operating characteristic curve (AUC of ROC). Result CGB3 was a differential expressed prognosis-related gene and involved in the immune response process of plasma cells and T cell gamma delta. The high levels of CGB3 are a risk element for overall survival (OS). The AUCs of VOI and ROI radiomics models in the training set were 0.841 and 0.776, while in the validation set were 0.815 and 0.754, respectively. The Delong test revealed that the AUCs of the two models were not statistically different, and both models had good predictive performance. Conclusion The CGB3 expression level is an important prognosis factor for Bca patients. Both 3D and 2D CECT radiomics are effective in predicting CGB3 expression levels.
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Affiliation(s)
- Yuanfeng Zhang
- Department of Urology, Shantou Central Hospital, Shantou, PR China
- Department of Urology, Lanzhou University Second Hospital, Key Laboratory of Urological Disease of Gansu Province, Clinical Center of Gansu Province for Nephron-Urology, Lanzhou, PR China
| | - Zhuangyong Xu
- Department of Radiology,Shantou Central Hospital, Shantou, PR China
| | - Shaoxu Wu
- Department of Urology, Sun Yat-sen Memorial Hospital, Guangzhou, PR China
| | - Tianxiang Zhu
- Department of Cardiothoracic Surgery, Shantou Central Hospital, Shantou, PR China
| | - Xuwei Hong
- Department of Urology, Shantou Central Hospital, Shantou, PR China
| | - Zepai Chi
- Department of Urology, Shantou Central Hospital, Shantou, PR China
| | - Rujan Malla
- Department of Radiology, Nepal Medical Collage Teaching Hospital, Kathmandu, Nepal
| | - Jingqi Jiang
- Department of Urology, Lanzhou University Second Hospital, Key Laboratory of Urological Disease of Gansu Province, Clinical Center of Gansu Province for Nephron-Urology, Lanzhou, PR China
| | - Yi Huang
- Department of Urology, Sun Yat-sen Memorial Hospital, Guangzhou, PR China
| | - Qingchun Xu
- Department of Urology, Shantou Central Hospital, Shantou, PR China
| | - Zhiping Wang
- Department of Urology, Lanzhou University Second Hospital, Key Laboratory of Urological Disease of Gansu Province, Clinical Center of Gansu Province for Nephron-Urology, Lanzhou, PR China
| | - Yonghai Zhang
- Department of Urology, Shantou Central Hospital, Shantou, PR China
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Liu S, Chen H, Zheng Z, He Y, Yao X. Development of a Molecular-Subtype-Associated Immune Prognostic Signature That Can Be Recognized by MRI Radiomics Features in Bladder Cancer. Bioengineering (Basel) 2023; 10:bioengineering10030318. [PMID: 36978709 PMCID: PMC10045524 DOI: 10.3390/bioengineering10030318] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/04/2023] [Accepted: 02/24/2023] [Indexed: 03/06/2023] Open
Abstract
Background: Bladder cancer (BLCA) is highly heterogeneous with distinct molecular subtypes. This research aimed to investigate the heterogeneity of different molecular subtypes from a tumor microenvironment perspective and develop a molecular-subtype-associated immune prognostic signature that can be recognized by MRI radiomics features. Methods: Individuals with BLCA in The Cancer Genome Atlas (TCGA) and IMvigor210 were classified into luminal and basal subtypes according to the UNC classification. The proportions of tumor-infiltrating immune cells (TIICs) were examined using The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm. Immune-linked genes that were expressed differentially between luminal and basal subtypes and associated with prognosis were selected to develop the immune prognostic signature (IPS) and utilized for the classification of the selected individuals into low- and high-risk groups. Functional enrichment analysis (GSEA) was performed on the IPS. The data from RNA-sequencing and MRI images of 111 BLCA samples in our center were utilized to construct a least absolute shrinkage and selection operator (LASSO) model for the prediction of patients’ IPSs. Results: Half of the TIICs showed differential distributions between the luminal and basal subtypes. IPS was highly associated with molecular subtypes, critical immune checkpoint gene expression, prognoses, and immunotherapy response. The prognostic value of the IPS was further verified through several validation data sets (GSE32894, GSE31684, GSE13507, and GSE48277) and meta-analysis. GSEA revealed that some oncogenic pathways were co-enriched in the group at high risk. A novel performance of a LASSO model developed as per ten radiomics features was achieved in terms of IPS prediction in both the validation (area under the curve (AUC): 0.810) and the training (AUC: 0.839) sets. Conclusions: Dysregulation of TIICs contributed to the heterogeneity between the luminal and basal subtypes. The IPS can facilitate molecular subtyping, prognostic evaluation, and personalized immunotherapy. A LASSO model developed as per the MRI radiomics features can predict the IPSs of affected individuals.
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Affiliation(s)
- Shenghua Liu
- Department of Urology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai 200072, China
| | - Haotian Chen
- Department of Urology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai 200072, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai 200072, China
| | - Zongtai Zheng
- Department of Urology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai 200072, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai 200072, China
| | - Yanyan He
- Department of Pathology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai 200072, China
- Correspondence: (Y.H.); (X.Y.)
| | - Xudong Yao
- Department of Urology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai 200072, China
- Urologic Cancer Institute, School of Medicine, Tongji University, Shanghai 200072, China
- Correspondence: (Y.H.); (X.Y.)
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Huang Q, Yan J, Jiang Q, Guo F, Mo L, Deng T. Construction of a pyroptosis-related lncRNAs signature for predicting prognosis and immunotherapy response in glioma. Medicine (Baltimore) 2023; 102:e32793. [PMID: 36820554 PMCID: PMC9907962 DOI: 10.1097/md.0000000000032793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Abstract
Recent studies have proved that pyroptosis-related long non-coding RNAs (PRlncRNAs) are closely linked to tumor progression, prognosis, and immunity. Here, we systematically evaluated the correlation of PRlncRNAs with glioma prognosis. This study included 3 glioma cohorts (The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gravendeel). Through Pearson correlation analysis, PRlncRNAs were screened from these 3 cohorts. Univariate Cox regression analysis was then carried out to determine the prognostic PRlncRNAs. A pyroptosis-related lncRNAs signature (PRLS) was then built by least absolute shrinkage and selection operator and multivariate Cox analyses. We systematically evaluated the correlation of the PRLS with the prognosis, immune features, and tumor mutation burden in glioma. A total of 14 prognostic PRlncRNAs overlapped in all cohorts and were selected as candidate lncRNAs. Based on The Cancer Genome Atlas cohort, a PRLS containing 7 PRlncRNAs was built. In all cohorts, the PRLS was proved to be a good predictor of glioma prognosis, with a higher risk score related to a poorer prognosis. We observed obvious differences in the immune microenvironment, immune cell infiltration level, and immune checkpoint expression in low- and high-risk subgroups. Compared with low-risk cases, high-risk cases had lower Tumor Immune Dysfunction and Exclusion scores and greater tumor mutation burden, indicating that high-risk cases can be more sensitive to immunotherapy. A nomogram combining PRLS and clinical parameters was constructed, which showed more robust and accurate predictive power. In conclusion, the PRLS is a potentially useful indicator for predicting prognosis and response to immunotherapy in glioma. Our findings may provide a useful insight into clinically individualized treatment strategies for patients.
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Affiliation(s)
- Qianrong Huang
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, PR China
| | - Jun Yan
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, PR China
| | - Qian Jiang
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, PR China
| | - Fangzhou Guo
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, PR China
| | - Ligen Mo
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, PR China
| | - Teng Deng
- Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, PR China
- * Correspondence: Teng Deng, Department of Neurosurgery, Guangxi Medical University Cancer Hospital, No. 71 Hedi Road, Qingxiu District, Nanning, Guangxi 530021, PR China (e-mail: )
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Cui H, Zeng L, Li R, Li Q, Hong C, Zhu H, Chen L, Liu L, Zou X, Xiao L. Radiomics signature based on CECT for non-invasive prediction of response to anti-PD-1 therapy in patients with hepatocellular carcinoma. Clin Radiol 2023; 78:e37-e44. [PMID: 36257868 DOI: 10.1016/j.crad.2022.09.113] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/07/2022] [Accepted: 09/02/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE This study aimed to develop a radiomics signature (RS) based on contrast-enhanced computed tomography (CECT) and evaluate its potential predictive value in hepatocellular carcinoma (HCC) patients receiving anti-PD-1 therapy. METHOD CECT scans of 76 HCC patients who received anti-PD-1 therapy were obtained in this study (training group = 53 and validation group = 23). The least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics features of primary and metastatic lesions and establish a RS to predict lesion-level response. Then, a nomogram combined the mean RS (MRS) and clinical variables with patient-level response as the end point. RESULTS In the lesion-level analysis, the area under the curves (AUCs) of RS in the training and validation groups were 0.751 (95% CI, 0.668-0.835) and 0.734 (95% CI, 0.604-0.864), respectively. In the patient-level analysis, the AUCs of the nomogram in the training and validation groups were 0.897 (95% CI, 0.798-0.996) and 0.889 (95% CI, 0.748-1.000), respectively. The nomogram stratified patients into low- and high-risk groups, which showed a significant difference in progression-free survival (PFS) (p<0.05). CONCLUSIONS The RS is a noninvasive biomarker for predicting anti-PD-1 therapy response in patients with HCC. The nomogram may be of clinical use for identifying high-risk patients and formulating individualised treatments.
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Affiliation(s)
- H Cui
- Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - L Zeng
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - R Li
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Q Li
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - C Hong
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - H Zhu
- Department of Medical Oncology, the First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China
| | - L Chen
- Department of Medical Quality Management, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - L Liu
- Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - X Zou
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - L Xiao
- Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
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Li Q, Luo Y, Liu D, Li B, Liu Y, Wang T. Construction and prognostic value of enhanced CT image omics model for noninvasive prediction of HRG in bladder cancer based on logistic regression and support vector machine algorithm. Front Oncol 2023; 12:966506. [PMID: 36727079 PMCID: PMC9884970 DOI: 10.3389/fonc.2022.966506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 12/29/2022] [Indexed: 01/18/2023] Open
Abstract
Background Urothelial Carcinoma of the bladder (BLCA) is the most prevalent cancer of the urinary system. In cancer patients, HRG fusion is linked to a poor prognosis. The prediction of HRG expression by imaging omics in BLCA has not yet been fully investigated. Methods HRG expression in BLCA and healthy adjoining tissues was primarily identified utilizing data sourced from The Cancer Genome Atlas (TCGA). Using Kaplan-Meier survival curves and Landmark analysis, the relationship between HRG expression, clinicopathological parameters, and overall survival (OS) was investigated. Additionally, gene set variation analysis (GSVA) was conducted and CIBERSORTx was used to investigate the relationship between HRG expression and immune cell infiltration. The Cancer Imaging Archive (TCIA) provided CT images that were used for prognostic analysis, radiomic feature extraction, and construction of the model, respectively. The HRG expression levels were predicted using the constructed and evaluated LR and SMV models. Results HRG expression was shown to be substantially lower in BLCA tumors as opposed to that observed in normal tissues (p < 0.05). HRG expression had a close positive relationship with Eosinophils and a close negative relationship with B cells naive. The findings of the Landmark analysis illustrated that higher HRG was associated with improved patient survival at an early stage (P=0.048). The predictive performance of the two models, based on logistic regression analysis and support vector machine, was outstanding in the training and validation sets, yielding AUCs of 0.722 and 0.708, respectively, in the SVM model, and 0.727 and 0.662, respectively, in the LR.The models have good predictive efficiency. Conclusion HRG expression levels can have a significant impact on BLCA patients' prognoses. The radiomic characteristics can successfully predict the pre-surgical HRG expression levels, based on CT- Image omics.
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Lam LHT, Chu NT, Tran TO, Do DT, Le NQK. A Radiomics-Based Machine Learning Model for Prediction of Tumor Mutational Burden in Lower-Grade Gliomas. Cancers (Basel) 2022; 14:cancers14143492. [PMID: 35884551 PMCID: PMC9324877 DOI: 10.3390/cancers14143492] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/03/2022] [Accepted: 07/12/2022] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Lower-grade glioma (LGG) is a kind of center nervous system neoplasm that arises from the glial cells. Lower-grade glioma patients have a median survival time in the range of 1.5–8 years based on the tumor genotypes. In term of epidemiology, most of the lower-grade glioma patients are diagnosed at young adult of age, which led to an early age of death. For exact diagnosis and effective treatment, a pathological result from biopsy sample is required. However, it is long turnaround time. In this study, using pre-operative magnetic resonance images, we developed a non-invasive model to classify tumor mutational burden (TMB), a prognostic factor of treatment response in lower-grade glioma patients, with an accuracy of 0.7936. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present. Abstract Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present.
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Affiliation(s)
- Luu Ho Thanh Lam
- International Master/Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan;
- Children’s Hospital 2, Ho Chi Minh City 70000, Vietnam
| | - Ngan Thy Chu
- City Children’s Hospital, Ho Chi Minh City 70000, Vietnam;
| | - Thi-Oanh Tran
- International Ph.D. Program for Cell Therapy and Regeneration Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan;
- Hematology and Blood Transfusion Center, Bach Mai Hospital, Hanoi 115-19, Vietnam
| | - Duyen Thi Do
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan;
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei 106, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan
- Neuroscience Research Center, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-2-66382736 (ext. 1992)
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Barone B, Calogero A, Scafuri L, Ferro M, Lucarelli G, Di Zazzo E, Sicignano E, Falcone A, Romano L, De Luca L, Oliva F, Mirto BF, Capone F, Imbimbo C, Crocetto F. Immune Checkpoint Inhibitors as a Neoadjuvant/Adjuvant Treatment of Muscle-Invasive Bladder Cancer: A Systematic Review. Cancers (Basel) 2022; 14:2545. [PMID: 35626149 PMCID: PMC9139497 DOI: 10.3390/cancers14102545] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/30/2022] [Accepted: 05/20/2022] [Indexed: 11/16/2022] Open
Abstract
Bladder cancer is the ninth most common cancer worldwide. Over 75% of non-muscle invasive cancer patients require conservative local treatment, while the remaining 25% of patients undergo radical cystectomy or radiotherapy. Immune checkpoint inhibitors represent a novel class of immunotherapy drugs that restore natural antitumoral immune activity via the blockage of inhibitory receptors and ligands expressed on antigen-presenting cells, T lymphocytes and tumour cells. The use of immune checkpoint inhibitors in bladder cancer has been expanded from the neoadjuvant setting, i.e., after radical cystectomy, to the adjuvant setting, i.e., before the operative time or chemotherapy, in order to improve the overall survival and to reduce the morbidity and mortality of both the disease and its treatment. However, some patients do not respond to checkpoint inhibitors. As result, the capability for identifying patients that are eligible for this immunotherapy represent one of the efforts of ongoing studies. The aim of this systematic review is to summarize the most recent evidence regarding the use of immune checkpoint inhibitors, in a neoadjuvant and adjuvant setting, in the treatment of muscle-invasive bladder cancer.
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Affiliation(s)
- Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
| | - Armando Calogero
- Department of Advanced Biomedical Sciences, Federico II University, 80131 Naples, Italy;
- Servicio de Cirugía General, Xerencia de Xestión Integrada de Santiago (XXIS/SERGAS), 15706 Santiago de Compostela, Spain
| | - Luca Scafuri
- Oncology Unit, Hospital ‘Andrea Tortora,’ ASL Salerno, 84016 Pagani, Italy;
| | - Matteo Ferro
- Division of Urology, European Institute of Oncology, IRCSS, Milan, Via Ripamonti 435, 20141 Milan, Italy;
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy;
| | - Erika Di Zazzo
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Enrico Sicignano
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
| | - Alfonso Falcone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
| | - Lorenzo Romano
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
| | - Luigi De Luca
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
| | - Francesco Oliva
- Department of Urology, Policlinico di Abano, 35031 Abano Terme, Italy;
| | - Benito Fabio Mirto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
| | - Federico Capone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (B.B.); (E.S.); (A.F.); (L.R.); (L.D.L.); (B.F.M.); (F.C.); (C.I.)
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