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. 2023 Mar 6:10:1118971.
doi: 10.3389/fsurg.2023.1118971. eCollection 2023.

Search for the optimized and key nephrometry elements combination in retroperitoneal laparoscopic partial nephrectomy: A retrospective study

Affiliations

Search for the optimized and key nephrometry elements combination in retroperitoneal laparoscopic partial nephrectomy: A retrospective study

Yanyang Jin et al. Front Surg. .

Abstract

Background: The nephrometry scoring system plays a key role in the preoperative evaluation of partial nephrectomy, and scoring systems based on anatomical characteristics have high similarity in scoring elements. Currently, there is little research on scoring systems related to retroperitoneal laparoscopic partial nephrectomy, and there is a lack of research on the combination of scoring elements, which requires further investigation.

Methods: We retrospectively analyzed the clinical records of 107 patients who underwent retroperitoneal laparoscopic partial nephrectomy conducted by a single operator at a single center. The score and scoring elements were generated based on imaging. The scoring elements of each scoring system and all combinations of two to five elements were extracted. The predictive ability of different score combinations was evaluated by AUC value, and the key parameters of the score were found by taking the intersection. A nomogram was constructed and evaluated.

Results: We observed that with an increase in scoring elements, the strongest combination of elements did not significantly increase the predictive ability of warm ischemia time (P>0.05), postoperative complications (P>0.05), and trifecta achievement (P>0.05). The combination of the maximum tumor diameter and the distance between tumor and collecting system or renal sinus had a good comprehensive predictive ability, and there is no significant difference with the traditional score (P>0.05). The nomogram generated according to this combination has an excellent prediction ability for predicting whether obtain trifecta of partial nephrectomy.

Conclusions: Within the range of two to five elements, the critical degree of elements is more important than the number of elements. The maximum tumor diameter and the distance between the tumor and the collecting system or renal sinus was the key element of the prediction ability.

Keywords: exhaustive method; nephrometry; nomogram; partial nephrectomy; scoring element.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The analysis flowchart.
Figure 2
Figure 2
Comparison between the predictive ability of the number of different elements. WIT2 represents two elements combination to predict warm ischemia time of more than 20 min, WIT3 represents three elements combination to predict, and so on. clavien2 represents two elements combination to predict whether the postoperative complications were greater than or equal to CD II, clavien3 represents three elements combination to predict, and so on. *** Represents P < 0.001.
Figure 3
Figure 3
Identifying combinations of key elements by Venn diagram.
Figure 4
Figure 4
Comparison of AUC value between RN combination and traditional scores. (A–C), RNP score (D–F) and strongest element combination (G–I) in predicting warm ischemia time (wit >20 min, first column), postoperative complications (CD grade ≥II, the second column), and whether to achieve trifecta (Third column).
Figure 5
Figure 5
Tumor maximal diameter (NePhRO.R) and the distance between the tumor and the collecting system or renal sinus (RENAL.N) two elements are used to establish a nomogram (A), the decision curve is used to evaluate the predicted benefit by comparing the nomogram with the traditional scores (B), and the calibration curve is used to evaluate the deviation between the predicted value and the actual value of nomograms (C).
Figure 6
Figure 6
Screening the key factors of clinical variables and scoring elements in predicting whether to obtain trifecta by using lasso regression (A), univariate (B) and multivariate regression analysis (C). (In Figure 6A, 1–6 are tumorSIZE, RENAL.E, RENAL.N, RENAL.L, MAP.FAT, and MAP.Strand, respectively).

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