Monitoring tumor growth rate to predict immune checkpoint inhibitors' treatment outcome in advanced NSCLC.

INTRODUCTION: Radiological response assessment to immune checkpoint inhibitor is challenging due to atypical pattern of response and commonly used RECIST 1.1 criteria do not take into account the kinetics of tumor behavior. Our study aimed at evaluating the tumor growth rate (TGR) in addition to RECIST 1.1 criteria to assess the benefit of immune checkpoint inhibitors (ICIs). METHODS: Tumor real volume was calculated with a dedicated computed tomography (CT) software that semi-automatically assess tumor volume. Target lesions were identified according to RECIST 1.1. For each patient, we had 3 measurement of tumor volume. CT-1 was performed 8-12 weeks before ICI start, the CT at baseline for ICI was CT0, while CT + 1 was the first assessment after ICI. We calculated the percentage increase in tumor volume before (TGR1) and after immunotherapy (TGR2). Finally, we compared TGR1 and TGR2. If no progressive disease (PD), the group was disease control (DC). If PD but TGR2 < TGR1, it was called LvPD and if TGR2 ⩾ TGR1, HvPD. RESULTS: A total of 61 patients who received ICIs and 33 treated with chemotherapy (ChT) were included. In ICI group, 18 patients were HvPD, 22 LvPD, 21 DC. Median OS was 4.4 months (95% CI: 2.0-6.8, reference) for HvPD, 7.1 months (95% CI 5.4-8.8) for LvPD, p = 0.018, and 20.9 months (95% CI: 12.5-29.3) for DC, p < 0.001. In ChT group, 7 were categorized as HvPD, 17 as LvPD and 9 as DC. No difference in OS was observed in the ChT group (p = 0.786). CONCLUSION: In the presence of PD, a decrease in TGR may result in a clinical benefit in patients treated with ICI but not with chemotherapy. Monitoring TGR changes after ICIs administration can help physician in deciding to treat beyond PD.

3D renal model for surgical planning of partial nephrectomy: A way to improve surgical outcomes.

Objective: to evaluate the impact of 3D model for a comprehensive assessment of surgical planning and quality of partial nephrectomy (PN). Materials and methods: 195 patients with cT1-T2 renal mass scheduled for PN were enrolled in two groups: Study Group (n= 100), including patients referred to PN with revision of both 2D computed tomography (CT) imaging and 3D model; Control group (n= 95), including patients referred to PN with revision of 2D CT imaging. Overall, 20 individuals were switched to radical nephrectomy (RN). The primary outcome was the impact of 3D models-based surgical planning on Trifecta achievement (defined as the contemporary absence of positive surgical margin, major complications and ≤30% postoperative eGFR reduction). The secondary outcome was the impact of 3D models on surgical planning of PN. Multivariate logistic regressions were used to identify predictors of selective clamping and Trifecta's achievement in patients treated with PN (n=175). Results: Overall, 73 (80.2%) patients in Study group and 53 (63.1%) patients in Control group achieved the Trifecta (p=0.01). The preoperative plan of arterial clamping was recorded as clampless, main artery and selective in 22 (24.2%), 22 (24.2%) and 47 (51.6%) cases in Study group vs. 31 (36.9%), 46 (54.8%) and 7 (8.3%) cases in Control group, respectively (p<0.001). At multivariate logistic regressions, the use of 3D model was found to be independent predictor of both selective or super-selective clamping and Trifecta's achievement. Conclusion: 3D-guided approach to PN increase the adoption of selective clamping and better predict the achievement of Trifecta.

Novel Volumetric and Morphological Parameters Derived from Three-dimensional Virtual Modeling to Improve Comprehension of Tumor's Anatomy in Patients with Renal Cancer.

BACKGROUND: Three-dimensional (3D) models improve the comprehension of renal anatomy. OBJECTIVE: To evaluate the impact of novel 3D-derived parameters, to predict surgical outcomes after robot-assisted partial nephrectomy (RAPN). DESIGN, SETTING, AND PARTICIPANTS: Sixty-nine patients with cT1-T2 renal mass scheduled for RAPN were included. Three-dimensional virtual modeling was achieved from computed tomography. The following volumetric and morphological 3D parameters were calculated: VT (volume of the tumor); VT/VK (ratio between tumor volume and kidney volume); CSA3D (ie, contact surface area); UCS3D (contact to the urinary collecting system); Tumor-Artery3D: tumor's blood supply by tertiary segmental arteries (score = 1), secondary segmental artery (score = 2), or primary segmental/main renal artery (scoren = 3); ST (tumor's sphericity); ConvT (tumor's convexity); and Endophyticity3D (ratio between the CSA3D and the global tumor surface). INTERVENTION: RAPN with a 3D model. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Three-dimensional parameters were compared between patients with and without complications. Univariate logistic regression was used to predict overall complications and type of clamping; linear regression was used to predict operative time, warm ischemia time, and estimated blood loss. RESULTS AND LIMITATIONS: Overall, 11 (15%) individuals experienced overall complications (7.2% had Clavien ≥3 complications). Patients with urinary collecting system (UCS) involvement at 3D model (UCS3D = 2), tumor with blood supply by primary or secondary segmentary arteries (Tumor-Artery3D = 1 and 2), and high Endophyticity3D values had significantly higher rates of overall complications (all p ≤ 0.03). At univariate analysis, UCS3D, Tumor-Artery3D, and Endophyticity3D are significantly associated with overall complications; CSA3D and Endophyticity3D were associated with warm ischemia time; and CSA3D was associated with selective clamping (all p ≤ 0.03). Sample size and the lack of interobserver variability are the main limits. CONCLUSIONS: Three-dimensional modeling provides novel volumetric and morphological parameters to predict surgical outcomes after RAPN. PATIENT SUMMARY: Novel morphological and volumetric parameters can be derived from a three-dimensional model to describe surgical complexity of renal mass and to predict surgical outcomes after robot-assisted partial nephrectomy.

Interpreting nephrometry scores with three-dimensional virtual modelling for better planning of robotic partial nephrectomy and predicting complications.

OBJECTIVE: 3D models are increasingly used as additional preoperative tools for renal surgery. We aim to evaluate the impact of 3D renal models in the assessment of PADUA, RENAL, Contact Surface Area (CSA) and Arterial Based Complexity (ABC) for the prediction of complications after Robot assisted Partial Nephrectomy (RAPN). METHODS AND MATERIALS: Overall, 57 patients with T1 and 1 patient with T2 renal mass referred to RAPN, were prospectively enrolled. 3D virtual modelling was obtained from 2D computed tomography (CT). Two radiologists recorded PADUA2D, RENAL2D, CSA2D and ABC2D by evaluation of 2D images; two bioengineers recorded PADUA3D, RENAL3D, CSA3D and ABC3D by evaluation of the 3D model, using MeshMixer software. To evaluate the concordance between 2D and 3D nephrometry scores, Cohen's j coefficient was calculated. Receiver-operating characteristic (ROC) curves were generated to evaluate the accuracy of 3D and 2D nephrometry scores to predict overall complications. Finally, the impact of 3D model on clamping approach during RAPN was compared to 2D imaging. RESULTS: PADUA3D, RENAL3D, CSA3D and ABC3D scores had a significant different distribution compared to PADUA2D, RENAL2D, CSA2D and ABC2D (all p≤0.03). 2D nephrometry scores may be unchanged, reduced or increased after assessment by 3D models: CSA3D, PADUA3D, RENAL3D and ABC3D were reduced in14%, 26%, 29% and 16% and increased in 16%, 36%, 38% and 29% of cases, respectively. At ROC curve analysis, PADUA3D, RENAL3D and ABC3D showed were significantly better accuracy to predict complications compared to PADUA2D, RENAL2D and ABC2D. PADUA3D (OR: 1.66), RENAL3D (OR: 1.69) and ABC3D (OR: 2.44) revealed a significant correlation with postoperative complications (all P ≤0.03). CONCLUSION: Nephrometry scores calculated via 3D models predict complications after RAPN with higher accuracy than conventional 2D imaging.