Renal volumetric analysis: a new paradigm in renal mass treatment assessment

Volumetric and functional outcomes at 1-year between percutaneous-ablation and partial-nephrectomy for T1b renal tumors

INTRODUCTION

Indication for percutaneous-ablation (PA) is gradually expanding to renal tumors T1b (4-7cm). Few data exist on the alteration of renal functional volume (RFV) post-PA. Yet, it is a surrogate marker of post partial-nephrectomy (PN) glomerular filtration rate (GFR) impairment. The objective was to compare RFV and GFR at 1-year post-PN or PA, in this T1b population.

METHODS

Patients with unifocal renal tumor≥4cm treated between 2014 and 2019 were included. Tumor, homolateral (RFVh), contralateral RFV, and total volumes were assessed by manual segmentation (3D Slicer) before and at 1 year of treatment, as was GFR. The loss of RFV, contralateral hypertrophy, and preservation of GFR were compared between both groups (PN vs. PA).

RESULTS

144 patients were included (87PN, 57PA). Preoperatively, PA group was older (74 vs. 59 years; P<0.0001), had more impaired GFR (73 vs. 85mL/min; P=0.0026) and smaller tumor volume(31.1 vs. 55.9cm3; P=0.0007) compared to PN group. At 1 year, the PN group had significantly more homolateral RFV loss (-19 vs. -14%; P=0.002), and contralateral compensatory hypertrophy (+4% vs. +1,8%; P=0.02, respectively). Total-RFV loss was similar between both (-21.7 vs. -19cm3; P=0.07). GFR preservation was better in the PN group (95.9 vs. 90.7%; P=0.03). In multivariate analysis, age and tumor size were associated with loss of RFVh.

CONCLUSION

For renal tumors T1b, PN is associated with superior compensatory hypertrophy compared with PA, compensating for the higher RFVh loss, resulting in similar ΔRFV-total between both groups. The superior post-PN GFR preservation suggests that the preserved quantitative RFV factor is insufficient. Therefore, the underlying quality of the parenchyma would play a major role in postoperative GFR.

Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron Sparing Interventions

Background Renal cell carcinoma is the most common kidney cancer and the 13th most common cause of cancer death worldwide. Partial nephrectomy and percutaneous ablation, increasingly utilized to treat small renal masses and preserve renal parenchyma, require precise preoperative imaging interpretation. We sought to develop and evaluate a convolutional neural network (CNN), a type of deep learning artificial intelligence, to act as a surgical planning aid by determining renal tumor and kidney volumes via segmentation on single-phase computed tomography (CT). Materials and Methods After institutional review board approval, the CT images of 319 patients were retrospectively analyzed. Two distinct CNNs were developed for (1) bounding cube localization of the right and left hemi-abdomen and (2) segmentation of the renal parenchyma and tumor within each bounding cube. Training was performed on a randomly selected cohort of 269 patients. CNN performance was evaluated on a separate cohort of 50 patients using Sorensen-Dice coefficients (which measures the spatial overlap between the manually segmented and neural network derived segmentations) and Pearson correlation coefficients. Experiments were run on a GPU-optimized workstation with a single NVIDIA GeForce GTX Titan X (12GB, Maxwell architecture). Results Median Dice coefficients for kidney and tumor segmentation were 0.970 and 0.816, respectively; Pearson correlation coefficients between CNN-generated and human-annotated estimates for kidney and tumor volume were 0.998 and 0.993 (p < 0.001), respectively. End-to-end trained CNNs were able to perform renal parenchyma and tumor segmentation on a new test case in an average of 5.6 seconds. Conclusions Initial experience with automated deep learning artificial intelligence demonstrates that it is capable of rapidly and accurately segmenting kidneys and renal tumors on single-phase contrast-enhanced CT scans and calculating tumor and renal volumes.

CT-based renal volume measurements: correlation with renal function in patients with renal tumours

AIM

To evaluate the correlations between renal cortical volume (RCV), renal parenchymal volume (RPV), and renal function in patients with renal tumours before and after laparoscopic partial nephrectomy (LPN).

MATERIALS AND METHODS

Thirty-five patients with a single unilateral renal tumour who had undergone contrast-enhanced computed tomography (CT) and renal nuclear scintigraphy before and after LPN were retrospectively studied. RCV and RPV were calculated as renal volume, excluding tumours or cysts, using a semi-automatic segmentation program. The correlations between RCV, RPV, and glomerular filtration rate (GFR) were undertaken preoperatively and postoperatively using the Pearson correlation coefficient.

RESULTS

Preoperatively, the correlations between RCV and GFR, and RPV and GFR for the operated kidneys was r=0.502 (p=0.002) and 0.527 (p=0.001), respectively, whereas the correlations for the contralateral side were r=0.384 (p=0.023) and r=0.412 (p=0.014). The mean RCV and RPV of the operated kidneys decreased by 27.4% and 24.8%. The mean split GFR of the operated kidneys decreased by 36.4%. Postoperatively, residual RCV (r=0.619, p<0.001) and RPV (r=0.593, p<0.001) correlated moderately with the GFR of the operated kidneys.

CONCLUSIONS

Renal volume, both RCV and RPV, had a moderate relationship with renal function before and after operation. CT-based renal volume measurements could serve as a simple and effective method for estimation of postoperative renal function.

A simple method to estimate renal volume from computed tomography

INTRODUCTION

Renal parenchymal volume can be used clinically to estimate differential renal function. Unfortunately, conventional methods to determine renal volume from computed tomography (CT) are time-consuming or difficult due to software limitations. We evaluated the accuracy of simple renal measurements to estimate renal volume as compared with estimates made using specialized CT volumetric software.

METHODS

We reviewed 28 patients with contrast-enhanced abdominal CT. Using a standardized technique, one urologist and one urology resident independently measured renal length, lateral diameter and anterior-posterior diameter. Using the ellipsoid method, the products of the linear measurements were compared to 3D volume measurements made by a radiologist using specialized volumetric software.

RESULTS

LINEAR KIDNEY MEASUREMENTS WERE HIGHLY CONSISTENT BETWEEN THE UROLOGIST AND THE UROLOGY RESIDENT (INTRACLASS CORRELATION COEFFICIENTS: 0.97 for length, 0.96 for lateral diameter, and 0.90 for anterior-posterior diameter). Average renal volume was 170 (SD: 36) cm(3) using the ellipsoid method compared with 186 (SD 37) cm(3) using volumetric software, for a mean absolute bias of -15.2 (SD 15.0) cm(3) and a relative volume bias of -8.2% (p < 0.001). Thirty-one of 56 (55.3%) estimated volumes were within 10% of the 3D measured volume and 54 of 56 (96.4%) were within 30%.

CONCLUSION

Renal volume can be easily approximated from contrast-enhanced CT scans using the ellipsoid method. These findings may obviate the need for 3D volumetric software analysis in certain cases. Prospective validation is warranted.