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Predictive Value of the Mayo Adhesive Probability (MAP) Score in Laparoscopic Partial Nephrectomies: A Systematic Review from the EAU Section of Uro-Technology (ESUT)

Title: Predictive Value of the Mayo Adhesive Probability (MAP) Score in Laparoscopic Partial Nephrectomies: A Systematic Review from the EAU Section of Uro-Technology (ESUT)
Authors: Panagiotis Kallidonis; Theodoros Spinos; Patricia Zondervan; Peter Nyirády; Miguel Ramírez Backhaus; Salvatore Micali; Stephan Hruby; Mario Alvarez-Maestro; Vasileios Tatanis; Evangelos Liatsikos; Ali Serdar Gözen
Source: Cancers, Vol 16, Iss 8, p 1455 (2024)
Publisher Information: MDPI AG
Publication Year: 2024
Collection: Directory of Open Access Journals: DOAJ Articles
Subject Terms: Mayo adhesive probability; MAP; adherent perinephric fat; APF; laparoscopic partial nephrectomy; LPN; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; RC254-282
Description: The Mayo Adhesive Probability (MAP) score is a radiographic scoring system that predicts the presence of adherent perinephric fat (APF) during partial nephrectomies (PNs). The purpose of this systematic review is to summarize the current literature on the application of the MAP score for predicting intraoperative difficulties related to APF and complications in laparoscopic PNs. Three databases, PubMed, Scopus and Cochrane, were screened, from inception to 29 October 2023, taking into consideration the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. All the inclusion criteria were met by eight studies. The total operative time was around two hours in most studies, while the warm ischemia time was
Document Type: article in journal/newspaper
Language: English
Relation: https://www.mdpi.com/2072-6694/16/8/1455; https://doaj.org/toc/2072-6694; https://doaj.org/article/a0885717070b4bca9030739958d45ae4
DOI: 10.3390/cancers16081455
Availability: https://doi.org/10.3390/cancers16081455; https://doaj.org/article/a0885717070b4bca9030739958d45ae4
Accession Number: edsbas.B0B2DAFB
Database: BASE