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Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy
Accurate prediction of new baseline GFR (NBGFR) after radical nephrectomy (RN) can inform clinical management and patient counseling whenever RN is a strong consideration. Preoperative global GFR, split renal function (SRF), and renal functional compensation (RFC) are fundamentally important for the...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10110585/ https://www.ncbi.nlm.nih.gov/pubmed/37069196 http://dx.doi.org/10.1038/s41598-023-33236-6 |
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author | Rathi, Nityam Attawettayanon, Worapat Yasuda, Yosuke Lewis, Kieran Roversi, Gustavo Shah, Snehi Wood, Andrew Munoz-Lopez, Carlos Palacios, Diego A. Li, Jianbo Abdallah, Nour Schober, Jared P. Strother, Marshall Kutikov, Alexander Uzzo, Robert Weight, Christopher J. Eltemamy, Mohamed Krishnamurthi, Venkatesh Abouassaly, Robert Campbell, Steven C. |
author_facet | Rathi, Nityam Attawettayanon, Worapat Yasuda, Yosuke Lewis, Kieran Roversi, Gustavo Shah, Snehi Wood, Andrew Munoz-Lopez, Carlos Palacios, Diego A. Li, Jianbo Abdallah, Nour Schober, Jared P. Strother, Marshall Kutikov, Alexander Uzzo, Robert Weight, Christopher J. Eltemamy, Mohamed Krishnamurthi, Venkatesh Abouassaly, Robert Campbell, Steven C. |
author_sort | Rathi, Nityam |
collection | PubMed |
description | Accurate prediction of new baseline GFR (NBGFR) after radical nephrectomy (RN) can inform clinical management and patient counseling whenever RN is a strong consideration. Preoperative global GFR, split renal function (SRF), and renal functional compensation (RFC) are fundamentally important for the accurate prediction of NBGFR post-RN. While SRF has traditionally been obtained from nuclear renal scans (NRS), differential parenchymal volume analysis (PVA) via software analysis may be more accurate. A simplified approach to estimate parenchymal volumes and SRF based on length/width/height measurements (LWH) has also been proposed. We compare the accuracies of these three methods for determining SRF, and, by extension, predicting NBGFR after RN. All 235 renal cancer patients managed with RN (2006–2021) with available preoperative CT/MRI and NRS, and relevant functional data were analyzed. PVA was performed on CT/MRI using semi-automated software, and LWH measurements were obtained from CT/MRI images. RFC was presumed to be 25%, and thus: Predicted NBGFR = 1.25 × Global GFR(Pre-RN) × SRF(Contralateral). Predictive accuracies were assessed by mean squared error (MSE) and correlation coefficients (r). The r values for the LWH/NRS/software-derived PVA approaches were 0.72/0.71/0.86, respectively (p < 0.05). The PVA-based approach also had the most favorable MSE, which were 120/126/65, respectively (p < 0.05). Our data show that software-derived PVA provides more accurate and precise SRF estimations and predictions of NBGFR post-RN than NRS/LWH methods. Furthermore, the LWH approach is equivalent to NRS, precluding the need for NRS in most patients. |
format | Online Article Text |
id | pubmed-10110585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101105852023-04-19 Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy Rathi, Nityam Attawettayanon, Worapat Yasuda, Yosuke Lewis, Kieran Roversi, Gustavo Shah, Snehi Wood, Andrew Munoz-Lopez, Carlos Palacios, Diego A. Li, Jianbo Abdallah, Nour Schober, Jared P. Strother, Marshall Kutikov, Alexander Uzzo, Robert Weight, Christopher J. Eltemamy, Mohamed Krishnamurthi, Venkatesh Abouassaly, Robert Campbell, Steven C. Sci Rep Article Accurate prediction of new baseline GFR (NBGFR) after radical nephrectomy (RN) can inform clinical management and patient counseling whenever RN is a strong consideration. Preoperative global GFR, split renal function (SRF), and renal functional compensation (RFC) are fundamentally important for the accurate prediction of NBGFR post-RN. While SRF has traditionally been obtained from nuclear renal scans (NRS), differential parenchymal volume analysis (PVA) via software analysis may be more accurate. A simplified approach to estimate parenchymal volumes and SRF based on length/width/height measurements (LWH) has also been proposed. We compare the accuracies of these three methods for determining SRF, and, by extension, predicting NBGFR after RN. All 235 renal cancer patients managed with RN (2006–2021) with available preoperative CT/MRI and NRS, and relevant functional data were analyzed. PVA was performed on CT/MRI using semi-automated software, and LWH measurements were obtained from CT/MRI images. RFC was presumed to be 25%, and thus: Predicted NBGFR = 1.25 × Global GFR(Pre-RN) × SRF(Contralateral). Predictive accuracies were assessed by mean squared error (MSE) and correlation coefficients (r). The r values for the LWH/NRS/software-derived PVA approaches were 0.72/0.71/0.86, respectively (p < 0.05). The PVA-based approach also had the most favorable MSE, which were 120/126/65, respectively (p < 0.05). Our data show that software-derived PVA provides more accurate and precise SRF estimations and predictions of NBGFR post-RN than NRS/LWH methods. Furthermore, the LWH approach is equivalent to NRS, precluding the need for NRS in most patients. Nature Publishing Group UK 2023-04-17 /pmc/articles/PMC10110585/ /pubmed/37069196 http://dx.doi.org/10.1038/s41598-023-33236-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Rathi, Nityam Attawettayanon, Worapat Yasuda, Yosuke Lewis, Kieran Roversi, Gustavo Shah, Snehi Wood, Andrew Munoz-Lopez, Carlos Palacios, Diego A. Li, Jianbo Abdallah, Nour Schober, Jared P. Strother, Marshall Kutikov, Alexander Uzzo, Robert Weight, Christopher J. Eltemamy, Mohamed Krishnamurthi, Venkatesh Abouassaly, Robert Campbell, Steven C. Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy |
title | Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy |
title_full | Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy |
title_fullStr | Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy |
title_full_unstemmed | Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy |
title_short | Point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy |
title_sort | point of care parenchymal volume analyses to estimate split renal function and predict functional outcomes after radical nephrectomy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10110585/ https://www.ncbi.nlm.nih.gov/pubmed/37069196 http://dx.doi.org/10.1038/s41598-023-33236-6 |
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