Novel Intraoperative Navigation Using Ultra-High-Resolution CT in Robot-Assisted Partial Nephrectomy.

To assess the perioperative and short-term functional outcomes of robot-assisted partial nephrectomy (RAPN) with intraoperative navigation using an ultra-high-resolution computed tomography (UHR-CT) scanner, we retrospectively analyzed 323 patients who underwent RAPN using an UHR-CT or area-detector CT (ADCT). Perioperative outcomes and the postoperative preservation ratio of estimated glomerular filtration rate (eGFR) were compared. After the propensity score matching, we evaluated 99 patients in each group. Although the median warm ischemia time (WIT) was less than 25 min in both groups, it was significantly shorter in the UHR-CT group than in the ADCT group (15 min vs. 17 min, p = 0.032). Moreover, the estimated blood loss (EBL) was significantly lower in the UHR-CT group than in the ADCT group (33 mL vs. 50 mL, p = 0.028). However, there were no significant intergroup differences in the postoperative preservation ratio of eGFR at 3 or 6 months of follow-up (ADCT 91.8% vs. UHR-CT 93.5%, p = 0.195; and ADCT 91.7% vs. UHR-CT 94.0%, p = 0.160, respectively). Although no differences in short-term renal function were observed in intraoperative navigation for RAPN in this propensity score-matched cohort, this study is the first to demonstrate that UHR-CT resulted in a shorter WIT and lower EBL than ADCT.

Automated classification of increased uptake regions in bone single-photon emission computed tomography/computed tomography images using three-dimensional deep convolutional neural network.

OBJECTIVE: This study proposes an automated classification of benign and malignant in highly integrated regions in bone single-photon emission computed tomography/computed tomography (SPECT/CT) using a three-dimensional deep convolutional neural network (3D-DCNN). METHODS: We examined 100 regions of 35 patients with bone SPECT/CT classified as benign and malignant by other examinations and follow-ups. First, SPECT and CT images were extracted at the same coordinates in a cube, with a long side two times the diameter of a high concentration in SPECT images. Next, we inputted the extracted image to DCNN and obtained the probability of benignity and malignancy. Integrating the output from DCNN of each SPECT and CT image provided the overall result. To validate the efficacy of the proposed method, the malignancy of all images was assessed using the leave-one-out cross-validation method; besides, the overall classification accuracy was evaluated. Furthermore, we compared the analysis results of SPECT/CT, SPECT alone, CT alone, and whole-body planar scintigraphy in the highly integrated region of the same site. RESULTS: The extracted volume of interest was 50 benign and malignant regions, respectively. The overall classification accuracy of SPECT alone and CT alone was 73% and 68%, respectively, while that of the whole-body planar analysis at the same site was 74%. When SPECT/CT images were used, the overall classification accuracy was the highest (80%), while the classification accuracy of malignant and benign was 82 and 78%, respectively. CONCLUSIONS: This study suggests that DCNN could be used for the direct classification of benign and malignant regions without extracting the features of SPECT/CT accumulation patterns.

Long-Term Functional and Oncologic Outcomes of Robot-Assisted Partial Nephrectomy for Cystic Renal Tumors: A Single-Center Retrospective Study.

Objectives: To evaluate the outcomes of robot-assisted partial nephrectomy (RAPN) in cystic renal tumors. Materials and Methods: We retrospectively analyzed patients who underwent RAPN for either cystic (n = 46) or solid (n = 271) renal tumors at Fujita Health University between 2010 and 2019. Cystic renal tumors were diagnosed using cross-sectional imaging. Perioperative, oncologic, and functional outcomes were assessed. Results: The median follow-up periods were 38, 41, and 37 months in the total, cystic, and solid groups, respectively. Most patient characteristics were similar among both groups, while the median age of the cystic group was significantly lower than that of the solid group (p = 0.02). Most perioperative variables and complications were comparable between the two groups. There was no significant difference between the groups in perioperative renal function. The estimated glomerular filtration rate preservation rates were 93.1% and 89.2% in the cystic and solid groups, respectively (p = 0.17). The cystic group showed a higher benign histology rate (19.6% vs 7%) and lower Fuhrman grade than the solid group (24.3% vs 15.1% in grade 1, and 73% vs 81.3% in grade 2), although there was no statistically significant difference between the two groups. In the solid group, 10 patients (3.7%) experienced recurrence, and 2 patients (0.7%) died of renal-cell carcinoma, while none of the patients with cystic tumors experienced recurrence. There was no statistically significant difference between the cystic and solid tumors with respect to 5-year recurrence-free survival (p = 0.18), cancer-specific survival (p = 0.55), and overall survival (p = 0.35). Conclusions: RAPN for cystic renal tumors appears to be safe and feasible with perioperative, long-term functional and oncologic outcomes comparable with those in solid tumors. RAPN can be a safe and effective surgical option for cystic renal tumors.

Deep learning-based and hybrid-type iterative reconstructions for CT: comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions.

PURPOSE: To determine the image quality improvement including vascular structures using deep learning reconstruction (DLR) for ultra-high-resolution CT (UHR-CT) and area-detector CT (ADCT) compared to a commercially available hybrid-iterative reconstruction (IR) method. MATERIALS AND METHOD: Thirty-two patients suspected of renal cell carcinoma underwent dynamic contrast-enhanced (CE) CT using UHR-CT or ADCT systems. CT value and contrast-to-noise ratio (CNR) on each CT dataset were assessed with region of interest (ROI) measurements. For qualitative assessment of improvement for vascular structure visualization, each artery was assessed using a 5-point scale. To determine the utility of DLR, CT values and CNRs were compared among all UHR-CT data by means of ANOVA followed by Bonferroni post hoc test, and same values on ADCT data were also compared between hybrid IR and DLR methods by paired t test. RESULTS: For all arteries except the aorta, the CT value and CNR of the DLR method were significantly higher compared to those of the hybrid-type IR method in both CT systems reconstructed as 512 or 1024 matrixes (p < 0.05). CONCLUSION: DLR has a higher potential to improve the image quality resulting in a more accurate evaluation for vascular structures than hybrid IR for both UHR-CT and ADCT.