3D modeling in adherent perinephric fat prediction in nephron-sparing surgery planning in patients with localized renal neoplasms

Abstract

Study objective:

To develop a 3D-image based morphometry scoring system for Adherent Perinephric Fat (APF) prediction in nephron-sparing surgery in renal neoplasm patients.

Materials and methods:

The retrospective study involved 391 patients who underwent a laparoscopic partial nephrectomy performed by five surgeons from January 2014 till December 2018. The surgery involved the 3D virtual operation planning with «Amira» 3D modeling software. With the multivariate logistic regression models, we developed a scoring system based on 3D-models. We tested the significance and sensitivity of new scoring system in a comparative ROC analysis with Mayo Adhesive Probability Score (MAP).

Results:

We found APF in 111 patients (28.4%). The univariate analysis revealed that significant indicators included mean age 59.88 (55–67) (p < 0.001), male sex (p < 0.001), Body Mass Index (BMI) >30 (21.47–35.08) kg/m² (p < 0.001), arterial hypertension (p < 0.001), coronary heart disease (p = 0.019), diabetes mellitus (p = 0.005), urolithiasis (p = 0.002). The multivariate regression analysis identified three most significant indicators in 3D models evaluation: additional >5 mm shadows in perirenal space OR = 7.3 (3.6–15.3) (p < 0.001), the number of shadows >5 OR = 3.8 (2.1–6.8) (p < 0.001), the wide shadow base at the renal parenchymal level OR = 0.293 (0.146–0.588) (p = 0.001). The scoring of these indicators comprises a new prediction scale (0–5). The ROC analysis revealed AUC 0.816 (95% CI 0.772–0.861) p < 0.001 of the MAP score, and AUC = 0.803 (95% CI 0.758–0.848) p < 0.001 of the scoring system developed in the present study.

Conclusions:

The statistical findings comparison of the scoring system that we developed with those of MAP scale suggests that the scoring system is efficient and applicable.