Effect of finerenone on nephrotic syndrome in patients with diabetic kidney disease.

Introduction: Diabetic kidney disease (DKD) is an important complication of diabetes as it results in end-stage renal disease; hence, several drugs have been developed for its treatment. However, even with treatment with renin-angiotensin system inhibitors and sodium-glucose cotransporter-2 inhibitors, the residual risk of DKD remains. While this risk is an issue, the renoprotective effects of finerenone, a novel non-steroidal mineralocorticoid receptor antagonist, are becoming evident. High proteinuria increases the risk of cardiovascular death as well as renal failure. Hence, it is especially important to address cases of urine protein to nephrotic levels in DKD, however, no previous studies have assessed the safety and efficacy of finerenone in patients with DKD and nephrotic syndrome. Therefore, this study aimed to assess whether finerenone has a renoprotective effect in advanced DKD complicated by nephrotic syndrome. Methods: Nine patients with DKD and nephrotic syndrome who received 10-20 mg/day of finerenone were retrospectively analyzed. The average observation period was 9.7 ± 3.4 months. Patients with serum potassium levels greater than 5.0 mEq/L at the start of finelenone were excluded. Changes in urinary protein levels, estimated glomerular filtration rate (eGFR), and serum potassium levels were studied before and after finerenone administration. Results: The mean changes in the urinary protein creatinine ratio (UPCR) at baseline were 6.6 ± 2.0. After finerenone treatment, the mean UPCR decreased to -0.6 ± 3.9; however, this change was not statistically significant.The eGFR decline slope also tended to decrease with finerenone treatment (before vs. after: 3.1 ± 4.9 vs. -1.7 ± 3.2 mL/min/1.73 m2. Furthermore, finerenone did not increase serum potassium levels. Conclusions: Patients treated with finerenone showed decreased UPCR; hence, it is suggested that finerenone may be effective in treating nephrotic syndrome in patients with DKD. These findings may be applicable to real-world clinical settings. Nonetheless, it is important to note that this study was a retrospective analysis of a single-center cohort and had a limited sample size, highlighting the need for additional large-scale investigations.

Efficacy and Safety of Vildagliptin for Type 2 Diabetes in Patients With Diabetic Kidney Disease.

BACKGROUND/AIM: Vildagliptin is one of the dipeptidyl peptidase-4 (DPP-4) inhibitors that have been shown to improve hyperglycemia in clinical trials among patients with type 2 diabetes. However, few studies have examined the efficacy of vildagliptin in patients with diabetic kidney disease (DKD). PATIENTS AND METHODS: Eight patients with DKD received oral vildagliptin 50-100 mg/day. The duration of diabetes was 6.7±5.9 years and observation period was 23.6±9.8 months. Changes in fasting blood glucose, and hemoglobin A1c (HbA1c), estimated glomerular filtration rate (eGFR), and urine protein-to-creatinine ratio (UPCR) were studied before and after the administration of vildagliptin. RESULTS: Vildagliptin treatment significantly decreased fasting blood glucose and HbA1c, compared to baseline (132±56 mg/dl, p=0.036, 6.0±0.3, p=0.041, respectively). UPCR tended to be decreased, albeit without statistical significance. However, eGFR was decreased after the administration of vildagliptin. No significant adverse effects were observed in all patients during the study. CONCLUSION: Although the sample size was limited and the observation period was brief, vildagliptin was found to be an effective and reasonably well-tolerated treatment for patients with DKD.

Effect of Sacubitril/Valsartan on Patients Having Heart Failure With Preserved Left Ventricular Ejection Fraction Undergoing Hemodialysis: A Long-term Observational Study.

BACKGROUND/AIM: Sacubitril/valsartan (SV), a novel pharmacological class of angiotensin receptor neprilysin inhibitors, is effective in treating heart failure (HF) by inhibiting the degradation of natriuretic peptides and the renin-angiotensin-aldosterone system. However, no studies have observed the long-term effects of SV on patients with HF and preserved left ventricular ejection fraction (LVEF) undergoing hemodialysis (HD) over a long period. PATIENTS AND METHODS: This single-center retrospective study of 21 months duration involved consecutive patients with HF and preserved LVEF undergoing HD, who received 50-200 mg/day. All patients were followed up regularly, and clinical, biochemical, and echocardiographic parameters were recorded at baseline and during follow-up. The efficacy and safety of SV were also analyzed. RESULTS: This longitudinal study included nine patients, with a median age of 76 years. The median HD duration was 7 years. At baseline, the mean brain natriuretic peptide (BNP) was 133±73.6 pg/ml and that of LVEF was 66%±9%. After SV therapy, the systolic blood pressure, diastolic blood pressure, and heart rate decreased, albeit without statistical significance. BNP levels, LVEF, left atrial anteroposterior dimension, and left ventricular mass index did not change, compared to baseline values. No adverse effects were observed in any of the patients. CONCLUSION: SV tended to decrease blood pressure and heart rate in patients with HF and preserved LVEF undergoing HD but did not alter cardiac function assessments, such as BNP or echocardiography.

Prognostic value of radiomic features in patients with esophageal cancer treated with chemoradiotherapy.

BACKGROUND: The aim of the present study was to evaluate the prognostic value of radiomic features in patients who underwent chemoradiotherapy for esophageal cancer. METHODS: In this retrospective study, two independent cohorts of esophageal cancer patients treated with chemoradiotherapy were included. Radiomics features of each patient were extracted from pre-treatment computed tomography (CT) images. Radiomic features were selected by employing univariate and multivariate analyses in the test cohort. Selected radiomic features were verified in the validation cohort. The endpoint of the present study was overall survival. RESULTS: A total of 101 esophageal cancer patients were included in our study, with 71 patients in the test cohort and 30 patients in the validation cohort. Univariate analysis identified 158 radiomic features as prognostic factors for overall survival in the test cohort. A multivariate analysis revealed that root mean squared and Low-High-High (LHH) median were prognostic factors for overall survival with a hazard ratio of 2.23 (95% confidence interval [CI]: 1.16-4.70, P = 0.017) and 0.26 (95% CI: 0.13-0.54, P < 0.001), respectively. In the validation cohort, root mean squared high/LHH median low group had the most preferable prognosis with a median overall survival of 73.30 months (95% CI: 32.13-NA), whereas root mean squared low/LHH median low group had the poorest prognosis with a median overall survival of 9.72 months (95% CI: 2.50-NA), with a P value of < 0.001. CONCLUSIONS: We identified two radiomic features that might be independent prognostic factors of overall survival of esophageal cancer patients treated with chemoradiotherapy.

Re-salvage focal low-dose rate brachytherapy for local recurrence of prostate cancer after salvage focal low-dose rate brachytherapy.

Introduction: Salvage brachytherapy represents an effective treatment for local recurrence of prostate cancer after prior external beam radiotherapy. However, the optimal therapeutic strategies for local recurrence after salvage brachytherapy have not yet been determined. Case presentation: We describe the case of a 77-year-old man who underwent re-salvage focal low-dose rate brachytherapy for local recurrence after carbon ion radiotherapy and salvage focal low-dose rate brachytherapy. We performed re-salvage focal low-dose rate brachytherapy for the recurrence with a different type of seed, which resulted in a significant reduction in the prostate-specific antigen level. During the 35-month follow-up after re-salvage focal low-dose rate brachytherapy, no recurrence of prostate cancer and no severe radiation-related toxicities were observed. Conclusion: Our patient was successfully treated with re-salvage focal low-dose rate brachytherapy for local recurrence after salvage focal low-dose rate brachytherapy. This treatment strategy might be effective for such patients and is not associated with sexual dysfunction or severe adverse events.

Cone-beam computed-tomography-based delta-radiomic analysis for investigating prognostic power for esophageal squamous cell cancer patients undergoing concurrent chemoradiotherapy.

PURPOSE: To investigate the prognostic power of cone-beam computed-tomography (CBCT)-based delta-radiomics in esophageal squamous cell cancer (ESCC) patients treated with concurrent chemoradiotherapy (CCRT). METHODS: We collected data from 26 ESCC patients treated with CCRT. CBCT images acquired at five time points (1st-5th week) per patient during CCRT were used in this study. Radiomic features were extracted from the five CBCT images on the gross tumor volumes. Then, 17 delta-radiomic feature sets derived from five types of calculations were obtained for all the cases. Leave-one-out cross-validation was applied to investigate the prognostic power of CBCT-based delta-radiomic features. Feature selection and construction of a prediction model using Coxnet were performed using training samples. Then, the test sample was classified into high or low risk in each cross-validation fold. Survival analysis for the two groups were performed to evaluate the prognostic power of the extracted CBCT-based delta-radiomic features. RESULTS: Four delta-radiomic feature sets indicated significant differences between the high- and low-risk groups (p < 0.05). The highest C-index in the 17 delta-radiomic feature sets was 0.821 (95 % confidence interval, 0.735-0.907). That feature set had p-value of the log-rank test and hazard ratio of 0.003 and 4.940 (95 % confidence interval, 1.391-17.544), respectively. CONCLUSIONS: We investigated the potential of using CBCT-based delta-radiomics for prognosis of ESCC patients treated with CCRT. It was demonstrated that delta-radiomic feature sets based on the absolute value of relative difference obtained from the early to the middle treatment stages have high prognostic power for ESCC.

Development and Validation of a Preoperative Nomogram for Endoscopic Management Decision Making in Upper Urinary Tract Urothelial Carcinoma.

OBJECTIVE: We aimed to develop and validate a preoperative nomogram that predicts low-grade, non-muscle invasive upper urinary tract urothelial carcinoma (LG-NMI UTUC), thereby aiding in the accurate selection of endoscopic management (EM) candidates. METHODS: This was a retrospective study that included 454 patients who underwent radical surgery (Cohort 1 and Cohort 2), and 26 patients who received EM (Cohort 3). Utilizing a multivariate logistic regression model, a nomogram predicting LG-NMI UTUC was developed based on data from Cohort 1. The nomogram's accuracy was compared with conventional European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) models. External validation was performed using Cohort 2 data, and the nomogram's prognostic value was evaluated via disease progression metrics in Cohort 3. RESULTS: In Cohort 1, multivariate analyses highlighted the absence of invasive disease on imaging (odds ratio [OR] 7.04; p = 0.011), absence of hydronephrosis (OR 2.06; p = 0.027), papillary architecture (OR 24.9; p < 0.001), and lack of high-grade urine cytology (OR 0.22; p < 0.001) as independent predictive factors for LG-NMI disease. The nomogram outperformed the two conventional models in predictive accuracy (0.869 vs. 0.759-0.821) and exhibited a higher net benefit in decision curve analysis. The model's clinical efficacy was corroborated in Cohort 2. Moreover, the nomogram stratified disease progression-free survival rates in Cohort 3. CONCLUSION: Our nomogram ( https://kmur.shinyapps.io/UTUC_URS/ ) accurately predicts LG-NMI UTUC, thereby identifying suitable candidates for EM. Additionally, the model serves as a useful tool for prognostic stratification in patients undergoing EM.

Effects of Oral Semaglutide on Renal Function in Diabetic Kidney Disease: A Short-term Clinical Study.

BACKGROUND/AIM: In the SUSTAIN-6 trial, semaglutide reduced the risk of worsening nephropathy in patients with type 2 diabetes. The objective of this retrospective study was to elucidate the effect and safety of oral semaglutide (Rybelsus®) in patients with diabetic kidney disease (DKD). PATIENTS AND METHODS: Six patients with DKD received 3 mg/day semaglutide orally. The observation period was 9.0±5.0 months. Changes in estimated glomerular filtration rate (eGFR), urinary protein, fasting blood glucose, and hemoglobin A1c were studied from 6 months before the administration of oral semaglutide until 6 months after administration. RESULTS: The change in eGFR over the 6 months prior to semaglutide administration was -1.2±1.6 ml/min/1.73 m2, showing a trend for a decrease; although not statistically significant, the change at 6 months after oral semaglutide initiation showed improved eGFR (1-50.7±1.8 ml/min/1.73 m2). Proteinuria was not reduced after treatment with oral semaglutide. No significant adverse effects (including retinopathy) were observed in any patient during the study. CONCLUSION: Despite the small sample size and short observation period, oral semaglutide was found to be a relatively well-tolerated drug for patients with DKD.

Hypoxia-inducible factor 2α protein and mRNA expression correlate with histomorphological features in clear cell renal cell carcinoma.

Hypoxia-inducible factor 2α (HIF2α) has been identified as a potential biomarker and novel target for systemic therapy in clear cell renal cell carcinoma (ccRCC). The present study aims to evaluate the association of HIF2α protein and HIF2A mRNA expression with clinicopathological factors and histomorphological features related to vasculature and inflammation of ccRCC using a localized ccRCC cohort (n = 428) and The Cancer Genome Atlas (TCGA)-KIRC cohort (n = 433). HIF2α protein expression was immunohistochemically assessed using tissue microarrays and HIF2A mRNA expression was assessed using the TCGA RNA-sequencing data. Positive HIF2α protein and high HIF2A mRNA expression were observed in 145 (33.9 %) and 142 (32.8 %) patients, respectively. Positive nuclear HIF2α protein expression was significantly associated with the clear histological phenotype and architectural patterns related to rich vascular networks (p < 0.001), and no tumor-associated immune cells status (p < 0.05) in addition to favorable prognostic factors such as lower TNM stage, lower WHO/ISUP grade, or the absence of necrosis (p < 0.001). The HIF2A mRNA expression profile by the TCGA cohort showed similar trends as the HIF2α protein profile. In addition, positive HIF2α protein and high HIF2A mRNA expression were associated with higher recurrence-free survival and overall survival, respectively (both p < 0.001). In conclusion, we comprehensively demonstrated the association of HIF2α profiles with clinicopathological factors and histomorphological features related to vasculature and inflammation at both protein and mRNA levels. Histomorphological features expressing HIF2α may provide information on HIF2α targeted therapeutic response as well as prognosis in ccRCC patients.

Effect of finerenone on diabetic kidney disease outcomes with estimated glomerular filtration rate below 25 mL/min/1.73 m2.

Background: In the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease trial, finerenone reduced the risk of cardiovascular events in patients with chronic kidney disease (CKD) and type 2 diabetes, while in the Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease trial, it improved renal and cardiovascular outcomes in patients with advanced CKD. However, no previous studies have assessed patients with CKD and type 2 diabetes with an estimated glomerular filtration rate (eGFR) below 25 mL/min/1.73 m2. Methods: Nine patients with CKD and type 2 diabetes who received finerenone 10 mg/day were analyzed retrospectively. Changes in eGFR, urinary protein, and serum potassium levels were studied from 1 year before administration of finerenone until 6 months after administration. Results: The mean baseline eGFR slope was -7.63 ± 9.84 (mL/min/1.73 m2/year). After finerenone treatment, the mean eGFR slope significantly improved -1.44 ± 3.17 (mL/min/1.73 m2/6 months, P=0.038). However, finerenone treatment did not significantly reduce proteinuria. Furthermore, finerenone did not increase serum potassium levels. Conclusions: Patients treated with finerenone showed a significantly slower decline in eGFR. Furthermore, aside from the present study, no reports have indicated the effectiveness of finerenone in patients with advanced CKD with an eGFR below 25 mL/min/1.73 m2. As confirmed in our clinical trials, the finding that finerenone is effective in a wide range of renal functions can be generalized to clinical practice. However, sample size in this study was small. Thus, further large-scale investigations will be needed.

Learning from the past and present to change the future: Endoscopic management of upper urinary tract urothelial carcinoma.

Current guidelines recommend endoscopic management (EM) for patients with low-risk upper urinary tract urothelial carcinoma, as well as those with an imperative indication. However, regardless of the tumor risk, radical nephroureterectomy is still mainly performed worldwide despite the benefits of EM, such as renal function maintenance, no hemodialysis requirement, and treatment cost reduction. This might be explained by the association of EM with a high risk of local recurrence and progression. Furthermore, the need for rigorous patient selection and close surveillance following EM may be relevant. Nevertheless, recent developments in diagnostic modalities, pathological evaluation, surgical devices and techniques, and intracavitary regimens have been reported, which may contribute to improved risk stratification and treatments with superior oncological outcomes. In this review, considering recent advances in endourology and oncology, we propose novel treatment strategies for optimal EM.

Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma.

We have recently shown that histological phenotypes focusing on clear and eosinophilic cytoplasm in clear cell renal cell carcinoma (ccRCC) correlated with prognosis and the response to angiogenesis inhibition and checkpoint blockade. This study aims to objectively show the diagnostic utility of clear or eosinophilic phenotypes of ccRCC by developing an artificial intelligence (AI) model using the TCGA-ccRCC dataset and to demonstrate if the clear or eosinophilic predicted phenotypes correlate with pathological factors and gene signatures associated with angiogenesis and cancer immunity. Before the development of the AI model, histological evaluation using hematoxylin and eosin whole-slide images of the TCGA-ccRCC cohort (n = 435) was performed by a urologic pathologist. The AI model was developed as follows. First, the highest-grade area on each whole slide image was captured for image processing. Second, the selected regions were cropped into tiles. Third, the AI model was trained using transfer learning on a deep convolutional neural network, and clear or eosinophilic predictions were scaled as AI scores. Next, we verified the AI model using a validation cohort (n = 95). Finally, we evaluated the accuracy of the prognostic predictions of the AI model and revealed that the AI model detected clear and eosinophilic phenotypes with high accuracy. The AI model stratified the patients' outcomes, and the predicted eosinophilic phenotypes correlated with adverse clinicopathological characteristics and high immune-related gene signatures. In conclusion, the AI-based histologic subclassification accurately predicted clear or eosinophilic phenotypes of ccRCC, allowing for consistently reproducible stratification for prognostic and therapeutic stratification.

MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study.

Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Datasets from the first 47 cases were used for model creation, and those from the subsequent 10 cases were used for validation. Feature extraction was performed semi-automatically, and 2438 features were obtained per image sequence. Machine learning models with logistic regression and a support vector machine were created. The model with the highest accuracy incorporated seven features extracted from GdT1 in the logistic regression. The average area under the curve was 0.93 ± 0.06, and accuracy was 0.90 (9/10) in the validation dataset. The same validation dataset was assessed by 20 board-certified neurosurgeons. Diagnostic accuracy ranged from 0.50 to 0.80 (median 0.60, 95% confidence interval 0.60 ± 0.06%), which was inferior to that of the machine learning model (p = 0.03), although there are some limitations, such as the risk of overfitting and the lack of an extramural cohort for truly independent final validation. In summary, we created a novel MRI-based machine learning model to differentiate skull base chordoma and chondrosarcoma from multiparametric signatures.

Training of deep cross-modality conversion models with a small data set, and their application in megavoltage CT to kilovoltage CT conversion.

PURPOSE: In recent years, deep learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large data set is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. METHODS: The proposed method is based on cycle-consistency generative adversarial network (CycleGAN) with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several data sets acquired from patients with head and neck cancer. The size of the data sets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. RESULTS: The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring. CONCLUSIONS: We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?"

[Improvement in Image Quality of CBCT during Treatment by Cycle Generative Adversarial Network].

PURPOSE: Volumetric modulated arc therapy (VMAT) can acquire projection images during rotational irradiation, and cone-beam computed tomography (CBCT) images during VMAT delivery can be reconstructed. The poor quality of CBCT images prevents accurate recognition of organ position during the treatment. The purpose of this study was to improve the image quality of CBCT during the treatment by cycle generative adversarial network (CycleGAN). METHOD: Twenty patients with clinically localized prostate cancer were treated with VMAT, and projection images for intra-treatment CBCT (iCBCT) were acquired. Synthesis of PCT (SynPCT) with improved image quality by CycleGAN requires only unpaired and unaligned iCBCT and planning CT (PCT) images for training. We performed visual and quantitative evaluation to compare iCBCT, SynPCT and PCT deformable image registration (DIR) to confirm the clinical usefulness. RESULT: We demonstrated suitable CycleGAN networks and hyperparameters for SynPCT. The image quality of SynPCT improved visually and quantitatively while preserving anatomical structures of the original iCBCT. The undesirable deformation of PCT was reduced when SynPCT was used as its reference instead of iCBCT. CONCLUSION: We have performed image synthesis with preservation of organ position by CycleGAN for iCBCT and confirmed the clinical usefulness.

Fast Statistical Iterative Reconstruction for Mega-voltage Computed Tomography.

Statistical iterative reconstruction is expected to improve the image quality of computed tomography (CT). However, one of the challenges of iterative reconstruction is its large computational cost. The purpose of this review is to summarize a fast iterative reconstruction algorithm by optimizing reconstruction parameters. Megavolt projection data was acquired from a TomoTherapy system and reconstructed using in-house statistical iterative reconstruction algorithm. Total variation was used as the regularization term and the weight of the regularization term was determined by evaluating signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and visual assessment of spatial resolution using Gammex and Cheese phantoms. Gradient decent with an adaptive convergence parameter, ordered subset expectation maximization (OSEM), and CPU/GPU parallelization were applied in order to accelerate the present reconstruction algorithm. The SNR and CNR of the iterative reconstruction were several times better than that of filtered back projection (FBP). The GPU parallelization code combined with the OSEM algorithm reconstructed an image several hundred times faster than a CPU calculation. With 500 iterations, which provided good convergence, our method produced a 512 × 512 pixel image within a few seconds. The image quality of the present algorithm was much better than that of FBP for patient data. J. Med. Invest. 67 : 30-39, February, 2020.