A New Nephrometry Score for Predicting Positive Surgical Margin After Laparoscopic Partial Nephrectomy
Purpose To introduce the KESKIN ratio as a novel predictor of positive surgical margin (PSM) after laparoscopic partial nephrectomy (PN) and to evaluate other clinical characteristics and nephrometry scores (including RENAL, PADUA, and C-index) for predicting PSM. Methods We retrospectively analyzed...
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Veröffentlicht in: | Annals of surgical oncology 2024-05, Vol.31 (5), p.3523-3530 |
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Sprache: | eng |
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Zusammenfassung: | Purpose
To introduce the KESKIN ratio as a novel predictor of positive surgical margin (PSM) after laparoscopic partial nephrectomy (PN) and to evaluate other clinical characteristics and nephrometry scores (including RENAL, PADUA, and C-index) for predicting PSM.
Methods
We retrospectively analyzed 95 patients who underwent laparoscopic PN between June 2020 and April 2023. The KESKIN ratio was defined for all patients. The KESKIN ratio, tumor and patient-related paramaters, and nephrometry scores were analyzed to predict PSM.
Results
Positive surgical margin was found in 12 of 95 patients (12.6%). There was no statistical difference between the PSM and negative surgical margin (NSM) groups in RENAL, PADUA, and C-index scores. Only the KESKIN ratio was found to be a statistically significant predictor of PSM in both univariate and multivariate regression analysis (
p
= 0.007 and
p
= 0.043, respectively). Mean endophytic diameter and endophytic percentage were found to be statistically significant predictors of PSM in only univariate analysis (
p
= 0.005 and
p
= 0.01, respectively). The value of 0.5 was determined as the cut-off value for the KESKIN ratio. Values higher than 0.5 indicate an increase in PSM.
Conclusions
The KESKIN ratio is a novel, easily measurable, and calculable image-based parameter that can be used to predict PSM after laparascopic PN. If externally validated in a larger patient population, the KESKIN ratio may be used in future versions of the current nephrometry scoring systems for predicting the PSM. |
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ISSN: | 1068-9265 1534-4681 |
DOI: | 10.1245/s10434-024-14970-1 |