Sunitinib Versus Sorafenib as Initial Targeted Therapy for mCC-RCC With Favorable/Intermediate Risk: Multicenter Randomized Trial CROSS-J-RCC

Purpose: The present study compared the efficacy of sunitinib and sorafenib as first-line treatment of metastatic clear cell renal cell carcinoma (mCC-RCC) with favorable or intermediate Memorial Sloan Kettering Cancer Center (MSKCC) risk. Patients and methods: Treatment-naive patients with mCC-RCC were randomized to receive open-label sunitinib followed by sorafenib (SU/SO) or sorafenib followed by sunitinib (SO/SU). The primary endpoint was first-line progression-free survival (PFS). The secondary endpoints were total PFS and overall survival (OS). Results: Of the 124 patients enrolled at 39 institutions from February 2010 to July 2012, 120 were evaluated. The median first-line PFS duration was 8.7 and 7.0 months in the SU/SO and SO/SU groups, respectively (hazard ratio [HR], 0.67; 95% confidence interval [CI], 0.42-1.08). The total PFS and OS were not significantly different between the SU/SO and SO/SU groups (27.8 and 22.6 months; HR, 0.73; 95% CI, 0.428-1.246; and 38.4 and 30.9 months; HR, 0.934; 95% CI, 0.588-1.485, respectively). The subgroup analysis revealed that the total PFS with SU/SO was superior to the total PFS with SO/SU in the patients with favorable MSKCC risk and those with Conclusions: No statistically significant differences were found in first-line PFS, total PFS, or OS between the 2 treatment arms (ClinicalTrials.gov identifier, NCT01481870)

Automated Classification of Urinary Cells: Using Convolutional Neural Network Pre-trained on Lung Cells

Urine cytology, which is based on the examination of cellular images obtained from urine, is widely used for the diagnosis of bladder cancer. However, the diagnosis is sometimes difficult in highly heterogeneous carcinomas exhibiting weak cellular atypia. In this study, we propose a new deep learning method that utilizes image information from another organ for the automated classification of urinary cells. We first extracted 3137 images from 291 lung cytology specimens obtained from lung biopsies and trained a classification process for benign and malignant cells using VGG-16, a convolutional neural network (CNN). Subsequently, 1380 images were extracted from 123 urine cytology specimens and used to fine-tune the CNN that was pre-trained with lung cells. To confirm the effectiveness of the proposed method, we introduced three different CNN training methods and compared their classification performances. The evaluation results showed that the classification accuracy of the fine-tuned CNN based on the proposed method was 98.8% regarding sensitivity and 98.2% for specificity of malignant cells, which were higher than those of the CNN trained with only lung cells or only urinary cells. The evaluation results showed that urinary cells could be automatically classified with a high accuracy rate. These results suggest the possibility of building a versatile deep-learning model using cells from different organs

Measurement of effective renal plasma flow using model analysis of dynamic CT in the preoperative evaluation of the renal transplant donors

OBJECTIVES: Renal scintigraphy is widely used to evaluate residual function of a transplanted kidney from the donor. Dynamic computed tomography (CT) imaging can evaluate both kidney morphology and regional renal function. The aim of this study was to develop an imaging protocol and a calculation method using dynamic CT for assessing the effective renal plasma flow (ERPF) by model analysis, and to evaluate the validity of the obtained ERPF values. METHODS: Preoperative dynamic CT examination with a low radiation dose exposure system was performed for 25 renal transplant donors, and ERPF was calculated from the obtained images (CT-ERPF). To calculate CT-ERPF, we set the region of interest (ROI) in the renal cortex using automatic ROI-setting software developed in our laboratory. We compared the processing time with automatic and manual ROI settings. To evaluate the validity of CT-ERPF, we examined the correlation of age with CT-ERPF and compared with reported ERPF values. We also compared the uptake rates of technetium-99m-dimercaptosuccinic acid and CT-ERPF in terms of the right-to-left ratio. RESULTS: There was good agreement of CT-ERPF assessed using automatic and manual ROIs. CT-ERPF was negatively correlated with age and showed values below the reference ERPF range in 21 cases. The right-to-left ratio of CT-ERPF showed a significant correlation with that of technetium-99m-dimercaptosuccinic acid. CONCLUSIONS: Using our method, CT-ERPF was a useful indicator for preoperative evaluation of donor’s renal function