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555 results on '"Giuliante, F."'

1. Predicting early recurrence after resection of initially unresectable colorectal liver metastases: the role of baseline and pre-surgery clinical, radiological and molecular factors in a real-life multicentre experience

Author

Moretto, R., Germani, M.M., Borelli, B., Conca, V., Rossini, D., Boraschi, P., Donati, F., Urbani, L., Lonardi, S., Bergamo, F., Cerma, K., Ramondo, G., D’Amico, F.E., Salvatore, L., Valente, G., Barbaro, B., Giuliante, F., Di Maio, M., Masi, G., and Cremolini, C.

Published
2024
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2. Heterogeneity of management practices surrounding operable gallbladder cancer – results of the OMEGA-S international HPB surgical survey

Author

Abe, T., Achalandabaso, M., Adham, M., Ahmet, A., Al-Sarireh, B., Albiol Quer, M., Alconchel, F., Alsammani, M., Alseidi, A., Anand, A., Anselmo, A., Antonakis, P., Arabadzhieva, E., de Aretxabala, X., Aroori, S., Ashley, S., Ausania, F., Banerjee, A., Barabino, M., Bartlett, A., Bartsch, F., Belli, A., Beristain-Hernandez, J., Berrevoet, F., Bhatti, A.B.H., Bhojwani, R., Bjornsson, B., Blaz, T., Byrne, M., Calvo, M.P., Castellanos, J., Castro, M.J., Cavallucci, D., Chang, D., Christodoulis, G., Ciacio, O., Clavien, P.A., Coker, A., Conde-Rodriguez, M., D'Amico, F.E., D'Hondt, M., Daams, F., Dasari, B.V.M., De Bellis, M., de Meijer, V.E., Dede, K., Deiro, G., Delgado, F.J.B., Desai, G., Di Gioia, A., Di Martino, M., Dixon, M., Dorovinis, P., Dumitrascu, T., Ebata, T., Eilard, M.S., Erdmann, J., Erkan, M., Famularo, S., Felli, E., Fergadi, M., Fernandez, G.B., Fox, A., Galodha, S., Galun, D., Ganandha, S., Garcia, R.J.R., Gemenetzis, G., Giannone, F., Gil, L., Giorgakis, E., Giovinazzo, F., Giuffrida, M., Giuliani, T., Giuliante, F., Gkekas, I., Goel, M., Goh, B.K., Gomes, A., Gruenberger, T., Guevara, O., Gulla, A., Gupta, A., Gupta, R., Hakeem, A.R., Hamid, H.K.S., Heinrich, S., Helton, S., Hernandez-Alejandro, R., Heumann, A., Higuchi, R., Hughes, D., Inarejos, B.C., Ivanecz, A., Iwao, Y., Iype, S., Jaen, I., Jie, M.J., Jones, R., Kacirek, K., Kalayarasan, R., Kaldarov, A., Kaman, L., Kanhere, H., Kapoor, V.K., Karanicolas, P., Karayiannakis, A., Kausar, A., Khan, Z.A., Kim, D.-S., Klose, J., Knowles, B., Koh, P.S., Kolodziejczyk, P., Komorowski, A.L., Koong, J.K., Kozyrin, I., Krishna, A., Kron, P., Kumar, N., van Laarhoven, S., Lakhey, P.J., Lanari, J., Laurenzi, A., Leow, V.M., Limbu, Y., Liu, Y.-B., Lob, S., Lolis, E., Lopez-Lopez, V., Lozano, R.C., Lundgren, L., Machairas, N., Magouliotis, D., Mahamid, A., Malde, D., Malek, A., Malik, H., Malleo, G., Marino, M.V., Mayo, S.C., Mazzola, M., Memeo, R., Menon, K., Menzulin, R., Mohan, R., Morgul, H., Moris, D., Mulita, F., Muttillo, E.M., Nahm, C., Nandasena, M., Nashidengo, P.R., Nickkholgh, A., Nikov, A., Noel, C., O'Reilly, D., O'Rourke, T., Ohtsuka, M., Omoshoro-Jones, J.A.O., Pandanaboyana, S., Pararas, N., Patel, R., Patkar, S., Peng, J.S., Perfecto, A., Perinel, J., Perivoliotis, K., Perra, T., Phan, M.T., Piccolo, G., Porcu, A., Primavesi, F., Primrose, J., Pueyo-Periz, E., Radenkovic, D., Rammohan, A., Rowcroft, A., Sakata, J., Saladino, E., Schena, C.A., Scholer, A., Schwarz, C., Serrano, P., Silva, M., Soreide, K., Sparrelid, E., Stattner, S., Sturesson, C., Sugiura, T., Sumo, M., Sutcliffe, R., Teh, C., Teo, J.Y., Tepetes, K., Thapa, P.B., Thepbunchonchai, A., Torres, J.B.P., Torres, O.J.M., Torzili, G., Tovikkai, C., Troncoso, A., Tsoulfas, G., Tuzuher, A., Tzimas, G., Umar, G.I., Urbani, L., Vanagas, T., Varga, Velayutham, V., Vigano, L., Wakai, T., Yang, Z., Yip, V., Zacharoulis, D., Zakharov, E.A., Zimmitti, G., Balakrishnan, Anita, Jah, Asif, Lesurtel, Mickael, Andersson, Bodil, Gibbs, Paul, Harper, Simon J.F., Huguet, Emmanuel L., Kosmoliaptsis, Vasilis, Liau, Siong S., Praseedom, Raaj K., Ramia, Jose M., Branes, Alejandro, Lendoire, Javier, Maithel, Shishir, and Serrablo, Alejandro

Published
2022
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3. Dragon 1 Protocol Manuscript: Training, Accreditation, Implementation and Safety Evaluation of Portal and Hepatic Vein Embolization (PVE/HVE) to Accelerate Future Liver Remnant (FLR) Hypertrophy

Author

Korenblik, R., Olij, B., Aldrighetti, L. A., Hilal, M. Abu, Ahle, M., Arslan, B., van Baardewijk, L. J., Baclija, I., Bent, C., Bertrand, C. L., Björnsson, B., de Boer, M. T., de Boer, S. W., Bokkers, R. P. H., Rinkes, I. H. M. Borel, Breitenstein, S., Bruijnen, R. C. G., Bruners, P., Büchler, M. W., Camacho, J. C., Cappelli, A., Carling, U., Chan, B. K. Y., Chang, D. H., choi, J., Font, J. Codina, Crawford, M., Croagh, D., Cugat, E., Davis, R., De Boo, D. W., De Cobelli, F., De Wispelaere, J. F., van Delden, O. M., Delle, M., Detry, O., Díaz-Nieto, R., Dili, A., Erdmann, J. I., Fisher, O., Fondevila, C., Fretland, Å., Borobia, F. Garcia, Gelabert, A., Gérard, L., Giuliante, F., Gobardhan, P. D., Gómez, F., Grünberger, T., Grünhagen, D. J., Guitart, J., Hagendoorn, J., Heil, J., Heise, D., Herrero, E., Hess, G. F., Hoffmann, M. H., Iezzi, R., Imani, F., Nguyen, J., Jovine, E., Kalff, J. C., Kazemier, G., Kingham, T. P., Kleeff, J., Kollmar, O., Leclercq, W. K. G., Ben, S. Lopez, Lucidi, V., MacDonald, A., Madoff, D. C., Manekeller, S., Martel, G., Mehrabi, A., Mehrzad, H., Meijerink, M. R., Menon, K., Metrakos, P., Meyer, C., Moelker, A., Modi, S., Montanari, N., Navines, J., Neumann, U. P., Peddu, P., Primrose, J. N., Qu, X., Raptis, D., Ratti, F., Ridouani, F., Rogan, C., Ronellenfitsch, U., Ryan, S., Sallemi, C., Moragues, J. Sampere, Sandström, P., Sarriá, L., Schnitzbauer, A., Serenari, M., Serrablo, A., Smits, M. L. J., Sparrelid, E., Spüntrup, E., Stavrou, G. A., Sutcliffe, R. P., Tancredi, I., Tasse, J. C., Udupa, V., Valenti, D., Fundora, Y., Vogl, T. J., Wang, X., White, S. A., Wohlgemuth, W. A., Yu, D., Zijlstra, I. A. J., Binkert, C. A., Bemelmans, M. H. A., van der Leij, C., Schadde, E., and van Dam, R. M.

Published
2022
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4. Post cholecystectomy bile duct injury: early, intermediate or late repair with hepaticojejunostomy – an E-AHPBA multi-center study

Author

Rystedt, Jenny M.L., Kleeff, Jörg, Salvia, Roberto, Besselink, Mark G., Prasad, Raj, Lesurtel, Mickael, Sturesson, Christian, Abu Hilal, M., Aljaiuossi, A., Antonucci, A., Ardito, F., Ausania, F., Bernon, M., Berrevoet, F., Björnsson, B., Bonsing, B.A., Boonstra, E.A., Bracke, B., Brusadin, R., Burda, L., Caraballo, M., Casellas-Robert, M., Çoker, A., Davide, J., De Gelder, A., De Rose, A.M., Djokic, M., Dudek, K., Ekmekçigil, E., Filauro, M., Fülöp, A., Gallagher, T., Gastaca, M., Gefen, R., Giuliante, F., Habibeh, H., Halle-Smith, J., Haraldsdottir, K.H., Hartman, V., Hauer, A., Hemmingsson, O., Hoskovec, D., Isaksson, B., Jonas, E., Khalaileh, A., Klug, R., Krige, J., Lignier, D., Lindemann, J., López-López, V., Lucidi, V., Mabrut, J.-Y., Månsson, C., Mieog, S., Mirza, D.F., Oldhafer, K.J., Omoshoro-Jones, J.A.O., Ortega-Torrecilla, N., Otto, W., Panaro, F., Pando, E., Paterna-López, S., Pekmezci, S., Pesce, A., Porte, R.J., Poves, I., Prieto Calvo, M., Primavesi, F., Puleo, S., Recordare, A., Rizell, M., Roberts, K., Robles-Campos, R., Sanchiz-Cardenas, E., Sandström, P., Saribeyoglu, K., Schauer, M., Schreuder, M., Siriwardena, A.K., Smith, M.D., Sousa Silva, D., Sparrelid, E., Stättner, S., Stavrou, G.A., Straka, M., Strömberg, C., Sutcliffe, R.P., Szijártó, A., Taflin, H., Trotovšek, B., van Gulik, T., Wallach, N., and Zieniewicz, K.

Published
2019
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7. Development and validation of a comprehensive model to predict complications after hepatectomy.

Author

GASPARI, R., ARDITO, F., PAFUNDI, P. C., AVOLIO, A. W., ACETO, P., ADDUCCI, E., PALLOCCHI, M., PARENTE, E., SOLLAZZI, L., ANTONELLI, M., and GIULIANTE, F.

Abstract

OBJECTIVE: Despite advances in perioperative care, hepatectomy remains associated with morbidity rates of up to 40%. Currently, available nomograms for predicting severe post-hepatectomy complications do not include early postoperative data. This retrospective observational study aimed to determine whether the parameters routinely measured in patients admitted to the Intensive Care Unit (ICU) after hepatectomy could represent risk factors for severe morbidity and to propose a nomogram scoring system to predict severe postoperative complications. PATIENTS AND METHODS: 411 adult patients who underwent elective hepatectomy at a high-volume tertiary care center for hepatic surgery from December 2016 to June 2022 were enrolled. The primary outcome was the assessment of predictors of 30-day severe postoperative complications following hepatectomy, defined as Clavien-Dindo grade 3a or higher. As a secondary outcome, we aimed to develop an easy-to-use scoring system to estimate the risk of severe postoperative complications. RESULTS: Severe complications occurred in 78 patients (19%). The final model included body mass index, preoperative bilirubin level, and ICU data (i.e., pH, lactate clearance, arterial lactate concentration 12 hours after ICU admission, need for packed red blood cell transfusions, and length of stay). Notably, the latter three variables were proven to be independent predictors of the outcomes. The model showed an overall good fit (C-index=0.754, corrected Dxy=0.692). A calibration plot using bootstrap internal validity resampling confirmed the stability of the model (mean absolute error=0.017, root mean square error of approximation=0.00051). CONCLUSIONS: We developed an accurate and practical scoring system based on preoperative and early postoperative data to predict poor outcomes after hepatectomy. Further external validation on larger series could lead to the integration of such a tool in the routine clinical practice to support patients' management and early warning during ICU stay. [ABSTRACT FROM AUTHOR]

Published
2024

8. Mri-based clinical-radiomics analyses of colorectal liver metastases to predict tumor growth patterns by machine learning approach.

Author

Famularo, S., Tran, E.H., Ammirabile, A., Boldrini, L., Bonifacio, C., Mele, C., Boccia, E., Genco, E., Costa, G., Giuliante, F., Torzilli, G., Viganò, L., and Ardito, F.

Abstract

Tumor growth patterns in colorectal liver metastases (CRLM) were associated with different recurrence risk after liver resection, but this information is not available before therapy decision. This study explores a combined clinical-radiomic approach to perform MRI analysis of hepatic metastases with the aim to predict tumor growth patterns by different machine learning algorithms, specifically distinguishing between replacement and desmoplastic/pushing types. 3-phases MRI scans were retrospectively collected among 2 Italian centers. DICOM files were manually segmented to detect the tumor (core) and the peritumoral area (ring). Clinical variables association with the outcome (replacement vs. desmoplastic/pushing pattern) was assessed via statistical analysis. Radiomics features were extracted in the hepatobiliary phase using Pyradiomics and ComBat harmonization was applied to features to reduce inter-centers variability effects. Features selection was performed via minimum redundancy maximum relevance (MRMR) approach. Models of different complexity, including logistic regression, Random Forest, XGBoost and SVM were evaluated. The area under the curve (AUC) of the receiver operating characteristics (ROC), together with sensitivity and specificity were computed to assess model performance. Two hundred-twelve patients were enrolled, dividing them into training (148 patients, 70%) and test (64 patients, 30%) cohorts. Harmonization via ComBat reduced feature instability related to imaging protocol variances, enhancing the overall reliability of the feature set. Feature selection on the training set yielded 1 clinical variable (tumor size) and 5 radiomics features. The Random Forest model showed the best performance with the combined clinical-radiomic approach, yielding an AUC of 0.91, sensitivity of 0.87, and specificity of 0.79 on the training set. On the test set, these metrics were lower (AUC 0.70, sensitivity 0.73, specificity 0.60, figure 1), demonstrating a good calibration. The clinical-radiomic modeling approach shows promising potential for accurately predicting growth patterns of hepatic metastases in MRI. [ABSTRACT FROM AUTHOR]

Published
2025
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12. Single-step multimodal locoregional treatment for unresectable hepatocellular carcinoma: balloon-occluded percutaneous radiofrequency thermal ablation (BO-RFA) plus transcatheter arterial chemoembolization (TACE)

Author

Iezzi, R., Cesario, V., Siciliani, L., Campanale, M., De Gaetano, A. M., Siciliano, M., Agnes, S., Giuliante, F., Grieco, A., Pompili, M., Rapaccini, G. L., Gasbarrini, A., Bonomo, L., and HepatoCATT Group for the Multidisciplinary Management of HCC

Published
2013
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14. 124P Clinicopathological nomogram for predicting fibroblast growth factor receptor 2 (FGFR2) gene fusion/rearrangement (F/R) in intrahepatic cholangiocarcinoma (iCCA)

Author

Bensi, M., Giannarelli, D., Bagalà, C., Niger, M., Bergamo, F., Barone, D., Rizzato, M.D., Nichetti, F., Spring, A., De Rosa, A., Beccia, V., Ricagno, G., Pircher, C.C., Ardito, F., Barbaro, B., Giuliante, F., Martinelli, E., De Braud, F.G.M., Lonardi, S., Tortora, G., and Salvatore, L.

Published
2023
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22. Single Large Hepatocellular Carcinoma > 5 cm with Surgical Indication: Is it Mandatory a Major Hepatectomy? A Propensity-score Weighted Analysis

Author

Romano, Fabrizio, Donadon, Matteo, Cipriani, Federica, Serenari, Matteo, Ardito, Francesco, Lauterio, Andrea, Vitale, Alessandro, Fazio, Federico, Nicolini, Daniele, Perri, Pasquale, Iaria, Giuseppe, Famularo, Simone, Dominioni, Tommaso, Zanello, Matteo, Lai, Quirino, Conci, Simone, Molfino, Sarah, Ferrari, Cecilia, Germani, Paola, Scotti, Mauro Alessandro, Zago, Mauro, Zimmitti, Giuseppe, La Barba Maurizio Romano, Giuliano, Sciannamea, Ivano, Fumagalli, Luca, Troci, Albert, Ferraro, Valentina, Memeo, Riccardo, Crespi, Michele, Chiarelli, Marco, Antonucci, Adelmo, Zanus, Giacomo, Ercolani, Giorgio, Hilal, Moh’d Abu, Pinotti, Enrico, Tarchi, Paola, Griseri, Guido, Baiocchi, Gian Luca, Ruzzenente, Andrea, Rossi, Massimo, Jovine, Elio, Maestri, Marcello, Garancini, M., Ferrero, A., De Carlis, L., Grazi, G.L., Giuliante, F., Cillo, U., Cescon, M., Valle, R. Dalla, Vivarelli, M., Torzilli, G., and Aldrighetti, L.

Published
2023
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42. Bile Leakage after Laparoscopic and Open Liver Resection; Incidence and Clinical Impact: An International Multicenter Propensity Score-Matched Study of 13,379 Patients

Author

Gorgec, B., Benedetti Cacciaguerra, A., Cipriani, F., Ratti, F., Russolillo, N., Lanari, J., Al-Jarrah, R., Aghayan, D., Primrose, J.N., Giuliante, F., D’Hondt, M., Rotellar, F., Fuks, D., Lopez-Ben, S., Vivarelli, M., Edwin, B., Cillo, U., Ferrero, A., Aldrighetti, L.A., Besselink, M.G., and Abu Hilal, M.

Published
2022
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43. Contour prognostic model for predicting survival after resection of colorectal liver metastases: development and multicentre validation study using largest diameter and number of metastases with RAS mutation status.

Author

Kawaguchi, Y., Kopetz, S., Cao, H. S. Tran, Panettieri, E., De Bellis, M., Nishioka, Y., Hwang, H., Wang, X., Tzeng, C.-W. D., Chun, Y. S., Aloia, T. A., Hasegawa, K., Guglielmi, A., Giuliante, F., and Vauthey, J.-N.

Subjects
LIVER metastasis, PROGNOSTIC models, OVERALL survival, METASTATIC breast cancer, SURVIVAL analysis (Biometry), LYMPHATIC metastasis
Abstract

Background: Most current models for predicting survival after resection of colorectal liver metastasis include largest diameter and number of colorectal liver metastases as dichotomous variables, resulting in underestimation of the extent of risk variation and substantial loss of statistical power. The aim of this study was to develop and validate a new prognostic model for patients undergoing liver resection including largest diameter and number of colorectal liver metastases as continuous variables. Methods: A prognostic model was developed using data from patients who underwent liver resection for colorectal liver metastases at MD Anderson Cancer Center and had RAS mutational data. A Cox proportional hazards model analysis was used to develop a model based on largest colorectal liver metastasis diameter and number of metastases as continuous variables. The model results were shown using contour plots, and validated externally in an international multi-institutional cohort. Results: A total of 810 patients met the inclusion criteria. Largest colorectal liver metastasis diameter (hazard ratio (HR) 1.11, 95 per cent confidence interval 1.06 to 1.16; P<0.001), number of colorectal liver metastases (HR 1.06, 1.03 to 1.09; P<0.001), and RAS mutation status (HR 1.76, 1.42 to 2.18; P<0.001) were significantly associated with overall survival, together with age, primary lymph node metastasis, and prehepatectomy chemotherapy. The model performed well in the external validation cohort, with predicted overall survival values almost lying within 10 per cent of observed values. Wild-type RAS was associated with better overall survival than RAS mutation even when liver resection was performed for larger and/or multiple colorectal liver metastases. Conclusion: The contour prognostic model, based on diameter and number of lesions considered as continuous variables along with RAS mutation, predicts overall survival after resection of colorectal liver metastasis. [ABSTRACT FROM AUTHOR]

Published
2021
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44. Splenic artery transposition for hepatic arterial reconstruction in a locally advanced pancreatic cancer: a case report and literature review.

Author

TINELLI, G., MONTANARI, F., SICA, S., RAZIONALE, F., ARDITO, F., MINELLI, F., DE NIGRIS, F., TSHOMBA, Y., and GIULIANTE, F.

Abstract

OBJECTIVE: During pancreatic surgery for malignancies, hepatic revascularization is needed in case of en bloc resection with hepatic artery involvement. In these cases, the use of the splenic artery is described in the literature, including transposition and interposition techniques. PATIENTS AND METHODS: We report the case of pancreatic cancer resection with involvement of the right hepatic artery, anomalous arising from the superior mesenteric artery, and hepatic revascularization with splenic artery reconstruction. A literature review to analyze the use of splenic artery in hepatic revascularization during pancreatic cancer surgery was performed. RESULTS: A 61-year-old man with a 55-mm hypovascular tumor in the pancreatic head, in wide contact with the right hepatic artery, underwent total pancreatectomy and splenectomy. Right hepatic artery was resected, and the distal part of the splenic artery was transposed to the right hepatic artery with a termino-terminal anastomosis. Histopathological examination revealed R0 resection. CONCLUSIONS: Hepatic revascularization with splenic artery should be considered in patients suitable to extend resectability in pancreatic cancer surgery. A multidisciplinary approach and careful pre-operative planning are essential. [ABSTRACT FROM AUTHOR]

Published
2021

47. Risk‐adjusted benchmarks in laparoscopic liver surgery in a national cohort.

Author

Russolillo, N., Aldrighetti, L., Cillo, U., Guglielmi, A., Ettorre, G. M., Giuliante, F., Mazzaferro, V., Dalla Valle, R., De Carlis, L., Jovine, E., Ferrero, A., Ratti, F., Lo Tesoriere, R., Gringeri, E., Ruzzenente, A., Levi Sandri, G. B., Ardito, F., Virdis, M., Iaria, M., and Ferla, F.

Subjects
LAPAROSCOPIC surgery, MINIMALLY invasive procedures, LIVER surgery, COMORBIDITY, ODDS ratio
Abstract

Copyright of British Journal of Surgery is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2020
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48. Comparing surgery versus sorafenib in advanced hepatocellular carcinoma: An Italian weighted study

Author

Famularo, S., Donadon, M., Cipriani, F., Giuliante, F., Ferrero, A., Molfino, S., Dalla Valle, R., Chiarelli, M., Maestri, M., Jovine, E., Grazi, G.L., Ruzzenente, A., Memeo, R., Crespi, M., Antonucci, A., Bernasconi, D.P., Romano, F., Griseri, G., Aldrighetti, L., Torzilli, G., Trevisani, F., and ITA.LI.CA. Group and HE.RC.O.LE.S. Group, on behalf of

Published
2021
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49. The best potential treatment for recurrent hepatocellular carcinoma after surgery: a machine learning predictive model for treatment allocation based on an Italian multicentric database

Author

Famularo, S., Donadon, M., Cipriani, F., Fazio, F., Ardito, F., Carissimi, F., Iaria, M., Perri, P., Conci, S., Pontarolo, N., Lai, Q., La Barba, G., Patauner, S., Molfino, S., Germani, P., Manzoni, A., Pinotti, E., Zanello, M., Fumagalli, L., Ferrari, C., Romano, M., Delvecchio, A., Bernasconi, D.P., Valsecchi, M.G., Antonucci, A., Memeo, R., Zanus, G., Griseri, G., Chiarelli, M., Jovine, E., Zago, M., Zimmitti, G., Tarchi, P., Baiocchi, G.L., Frena, A., Ercolani, G., Rossi, M., Maestri, M., Ruzzenente, A., Grazi, G.L., Valle, R. Dalla, Romano, F., Giuliante, F., Ferrero, A., Aldrighetti, L., Torzilli, G., and Group, HE.RC.O.LE.S.

Published
2021
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50. Benefits of Laparoscopy for Major Liver Resection in Cirrhotic Patients: Time for Pushing Indications Beyond the Limits? A Propensity Score Matched Analysis

Author

Benedetti Cacciaguerra, A., Görgec, B., Lanari, J., Cipriani, F., Russolillo, N., Mocchegiani, F., Zimmitti, G., Alseidi, A., Ruzzenente, A., Edwin, B., D’Hondt, M., Besselink, M., Giuliante, F., Fuks, D., Rotellar, F., López-Ben, S., Vivarelli, M., Ferrero, A., Aldrighetti, L., Cillo, U., and Abu Hilal, M.

Published
2021
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