1

Introduction

Renal cell carcinoma (RCC) accounts for 3% of all solid malignancies and ranks tenth in terms of prevalence, with a higher number of cases in western countries [ , ]. The main objective in the treatment of localized stage I renal masses is to obtain complete cancer control together with a maximal preservation of the surrounding heathy renal parenchyma, avoiding positive surgical margins [ , ]. Three-dimensional 3D reconstruction plays an important role approaching both easy and complex tumour masses that overturn renal anatomy, especially when vascular anatomy is surgically challenging [ , ]. Several scores have been considered to approach robot-assisted partial nephrectomy (RAPN) in order to standardize the characterization of renal tumours and assess the complexity of nephron sparing surgery (NSS), correlating with some perioperative outcomes; pure nephrometry scores (radius [R], exophytic/endophytic [E], nearness to collecting system/sinus [N], anterior/posterior [A], and location relative to polar lines [L] [RENAL], preoperative aspects and dimensions used for an anatomical classification [PADUA], Simplified Padua Renal Nephrectomy [SPARE], C-Index, Diameter-Axial-Polar [DAP], Contact Surface Area [CSA]) [ ] can effectively correlate with intra- and post-operative outcomes. However, these nephrometric scores were formulated during a period when 3D reconstruction had not yet been extensively developed and applied. The concept of CSA was introduced by Leslie et al. in 2014 [ ] and it is a variable and an anatomical measure that aims to quantify the contact area between the renal tumor and the surrounding healthy parenchyma. Specifically, the CSA calculation was based on the assumption that all kidney tumors were similar to spheres. It reflects 2 indicative anatomical factors of surgical complexity: the size of the tumor and its degree of intraparenchymal extension [ , ]. In our study, we reassess the values of CSA in our cohort of patients treated with RAPN, using an innovative calculation system based on 3D DICOM to obtain a more reliable calculation of contact surface between tumor mass and surrounding renal parenchyma, correlating the new contact surfaces area values with perioperative outcomes after RAPN.

2

Materials and methods

2.4

Segmentation

Not all images we used for segmentation were acquired with the protocol described above as some patients underwent CT scan in other centers. However, for all patients we utilized the same phase for segmentation, specifically arterial phase. This phase provided the best visualization of the kidneys and tumors, allowing for accurate recognition. For the reconstruction of the kidney and tumors, we utilized a certified medical software Materialize Mimics inPrint. The absence of holes in the parenchyma involved in the contact surface is a necessary condition for computing CSA, as depicted in Fig. 1 A. We used the reconstructed kidneys and tumors to compute the real value of CSA ( Fig. 1 B).

here are represented the segmentation, the reconstruction of the kidney and the tumor and the output of the algorithm. Fig. 1A. There are no holes or openings at the interface where the kidney and tumor meet. Fig. 1B. 3D reconstruction generated and saved as STL files through Materialize. Fig .1C. 3D graphs of the tumor and kidney, alongside the measurements of RCSA, Total Tumor Area, and Total Tumor Volume. Notably, when focusing on the tumor, the blue segment signifies the contact area with the kidney.

3

Results

Of the 78 patients that underwent RAPN, 19 were female (24%) and 59 males (76%). As shown in the Table 1 the median age was 64 year (56–73 as IQR), BMI was 26 kg/m ² (23.88–29.41 as IQR). All of the aforementioned cases showed negative margins in the histopathological examination. The median WIT was 17 min (11–22 IQR), OT was 180 min (130–210 IQR) and EBL was 100 mL (57.5–287.5 IQR). In addition to the postoperative complications listed in Table 2 , 2 intraoperative complications were observed: 1 case of conversion to radical nephrectomy and 1 major vascular lesion. We obtained the following median and IQR of the surface and tumor volume: RCSA was 14.37 cm ² (7.37–24.15), total area and total volume computed using the algorithm were 30.44 cm ² (16.13–66.23) and 14.24 cm ³ (6.54–45.28) respectively, HCSA measured by more experienced radiologist was 15.02 cm ² (6.17–26.28). PADUA score had a median value of 8,5 (7–10 IQR) while R.E.N.A.L. score 7 (5–8 IQR). Median values of pre-operative parameters were HB 13.85 g/dL (12.5–14.9 IQR), CREA 1.0 mg/dL (0.8–1.1 IQR), eGFR 82 mL/min/1.73m ² (64.25–93.25). The post-operative parameters ( Table 1 ) had the following median values HB was 12.1 g/dL (11–13.6 IQR), CREA was 1.0 mg/dL (0.9–1.2 IQR) and eGFR was 75 mL/min/1.73m ² (57–91 IQR). Instead for the follow-up parameters we obtained HB 14 g/dl (12.63–15 IQR), CREA 1.1 mg/dL (0.9–1.2 IQR) and eGFR 73 mL/min/1.73m ² (61–87.5 IQR). As can be seen in Table 3 the Spearman’s test showed a correlation between RCSA and intra-operative parameters. There was no correlation between RCSA and pre-operative parameters. Instead, there was statistically significant correlation with the post-operative CREA post-operative (ρ = 0.27 & P = 0.01) and an inverse correlation with post-operative eGFR post-operative ( Ρ = -0.32 & P = 0.004). Furthermore, we found a significant correlation with the follow-up parameters CREA ( Ρ = 0.38 & P = 0.004) and eGFR ( Ρ = -0,34 & P = 0.01). The correlation between RCSA and follow-up HB turned out to be not significant ( P > 0.05). Also, HCSA, as shown in previous works, was correlated with intra-operative parameters and follow-up parameters. PADUA score was correlated with intra-operative parameter, post-operative eGFR but had no correlation with follow-up parameters. R.E.N.A.L. score exhibits a strong correlation with WIT ( Ρ = 0.47 & p < 0.001), OT ( Ρ = 0.25 & P = 0.03) and EBL ( Ρ = 0.29 & P = 0.01). R.E.N.A.L. was also correlated with follow up HB (ρ = -0.29 & P = 0.03), but there was no correlation with follow-up eGFR ( P > 0.05). We further examined the correlation between RCSA and HCSA, PADUA score, and R.E.N.A.L. score. The PCE was found to be unrelated to both scores and the 2 CSA.

Table 1

Descriptive characteristics of study population

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