In planta evidence that the HAK transporter OsHAK2 is involved in Na+ transport in rice.

HAK family transporters primarily function as K+ transporters and play major roles in K+ uptake and translocation in plants, whereas several HAK transporters exhibit Na+ transport activity. OsHAK2, a rice HAK transporter, was shown to mediate Na+ transport in Escherichia coli in a previous study. In this study, we investigated whether OsHAK2 is involved in Na+ transport in the rice plant. Overexpression of OsHAK2 increased Na+ translocation from the roots to the shoots of transgenic rice. It also increased both root and whole-plant Na+ content, and enhanced shoot length under low Na+ and K+ conditions. Meanwhile, OsHAK2 overexpression increased salt sensitivity under a long-term salt stress condition, indicating that OsHAK2 is not involved in salt tolerance, unlike in the case of ZmHAK4 in maize. These results suggest that OsHAK2 is permeable to Na+ and contributes to shoot growth in rice plants under low Na+ and K+ conditions.

VI-RADS: Multiinstitutional Multireader Diagnostic Accuracy and Interobserver Agreement Study.

BACKGROUND. The Vesical Imaging Reporting and Data System (VI-RADS), based on multiparametric MRI (mpMRI), was developed to provide accurate information for the diagnosis of muscle-invasive bladder cancer (MIBC). OBJECTIVE. The purpose of our study was to evaluate the interobserver agreement and diagnostic performance of VI-RADS among readers with different levels of experience. METHODS. This retrospective study included 91 consecutive patients who underwent mpMRI before transurethral resection of bladder tumor (TURBT) from July 2010 through August 2018. After attending a training session, seven radiologists (five radiologists experienced in bladder MRI and two inexperienced radiologists) reviewed and scored all MRI examinations according to VI-RADS. The interobserver agreement was assessed by kappa statistics. ROC analysis was used to evaluate the diagnostic performance for MIBC. AUCs were estimated. RESULTS. Among 91 patients (72 men and 19 women; mean age ± SD, 73.2 ± 10.2 years), 48 (52.7%) had MIBC and 43 (47.3%) had non-muscle-invasive bladder cancer. Sixty-eight patients were treated with TURBT, and 23 were treated with radical cystectomy. Interobserver agreement was moderate to substantial (κ = 0.60-0.80) among the experienced readers, substantial (κ = 0.67) between the two inexperienced readers, and moderate to substantial (κ = 0.55-0.75) between the experienced and inexperienced readers. The pooled AUC was 0.88 (range, 0.82-0.91) for experienced readers and 0.84 (range, 0.83-0.85) for inexperienced readers, and 0.87 for all readers. Using a VI-RADS score of 4 or greater as the cutoff value for MIBC, the pooled sensitivity and specificity were 74.1% (range, 66.0-80.9%) and 94.1% (range, 88.6-97.7%) for experienced readers and 63.9% (range, 59.6-68.1%) and 86.4% (range, 84.1-88.6%) for inexperienced readers. Using a VI-RADS score of 3 or greater as the cutoff value, the pooled sensitivity and specificity were 83.4% (range, 80.9-85.1%) and 77.3% (range, 61.4-88.6%) for experienced readers and 82.0% (range, 80.9-83.0%) and 73.9% (range, 72.7-75.0%) for inexperienced readers. CONCLUSION. We observed moderate to substantial interobserver agreement and a pooled AUC of 0.87 among radiologists of different levels of expertise using VI-RADS. CLINICAL IMPACT. VI-RADS could help determine the depth and range of excision in TURBT, decreasing the risk of complications and enhancing the accuracy of pathologic diagnosis.

Bayesian multidimensional nominal response model for observer study of radiologists.

PURPOSE: This study proposes a Bayesian multidimensional nominal response model (MD-NRM) to statistically analyze the nominal response of multiclass classifications. MATERIALS AND METHODS: First, for MD-NRM, we extended the conventional nominal response model to achieve stable convergence of the Bayesian nominal response model and utilized multidimensional ability parameters. We then applied MD-NRM to a 3-class classification problem, where radiologists visually evaluated chest X-ray images and selected their diagnosis from one of the three classes. The classification problem consisted of 150 cases, and each of the six radiologists selected their diagnosis based on a visual evaluation of the images. Consequently, 900 (= 150 × 6) nominal responses were obtained. In MD-NRM, we assumed that the responses were determined by the softmax function, the ability of radiologists, and the difficulty of images. In addition, we assumed that the multidimensional ability of one radiologist were represented by a 3 × 3 matrix. The latent parameters of the MD-NRM (ability parameters of radiologists and difficulty parameters of images) were estimated from the 900 responses. To implement Bayesian MD-NRM and estimate the latent parameters, a probabilistic programming language (Stan, version 2.21.0) was used. RESULTS: For all parameters, the Rhat values were less than 1.10. This indicates that the latent parameters of the MD-NRM converged successfully. CONCLUSION: The results show that it is possible to estimate the latent parameters (ability and difficulty parameters) of the MD-NRM using Stan. Our code for the implementation of the MD-NRM is available as open source.

Do the variations in ROI placement technique have influence for prostate ADC measurements?

Background: Prostate apparent diffusion coefficient (ADC) values calculated from diffusion-weighted imaging have been used for evaluating prostate cancer (PCa) aggressiveness. However, the way of measuring ADC values has varied depending on the study. Purpose: To investigate inter- and intra-reader variability and diagnostic performance of three kinds of shaped 2D regions of interests (ROIs) for tumor ADC measurements in PCa. Material and Methods: Seventy-four patients with PCa undergoing 3-T MRI before surgery were included. Histologic findings from radical prostatectomy specimens were reviewed to define each patient's dominant tumor. Three readers independently measured the tumor ADCs using three different ROI methods: freehand, large-circle, and small-circles ROIs. Readers repeated measurements after 3 weeks. Bland-Altman analysis was performed to evaluate the inter- and intra-reader variability. Receiver Operating Characteristic analysis was used for assessment of tumor aggressiveness for PCa. Results: For intra-reader and inter-reader variability, the mean coefficient of repeatability for freehand ROIs, large-circle ROIs, and small-circles ROIs were as follows: 13.7%, 12.4%, and 11.5%; 9.4%, 9.7%, and 9.5%. For differentiating Gleason score (GS) = 3 + 3 from GS ≥ 3 + 4 tumors, the area under the curves were 0.90 for freehand ROIs, 0.89 for large-circle ROIs, and 0.94 small-circles ROIs (p = 0.31). Conclusion: The variations in ROI method did not have a major influence on intra-reader or inter-reader reproducibility or diagnostic performance for prostate ADC measurements.

Comparison of prostate imaging reporting and data system v2.1 and 2 in transition and peripheral zones: evaluation of interreader agreement and diagnostic performance in detecting clinically significant prostate cancer.

OBJECTIVE: To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v. 2.1, in comparison with v. 2. METHODS: Institutional review board approval was obtained for this retrospective study. 77 consecutive patients who underwent a prostate multiparametric magnetic resonance imaging at 3.0 T before radical prostatectomy were included. Four radiologists (two experienced uroradiologists and two inexperienced radiologists) independently scored eight regions [six peripheral zones (PZ) and two transition zones (TZ)] using v. 2.1 and v. 2. Interreader agreement was assessed using κ statistics. To evaluate diagnostic performance for clinically significant prostate cancer (csPC), area under the curve (AUC) was estimated. RESULTS: 228 regions were pathologically diagnosed as positive for csPC. With a cut-off ≥3, the agreement among all readers was better with v. 2.1 than v. 2 in TZ, PZ, or both zones combined (κ-value: TZ, 0.509 vs 0.414; PZ, 0.686 vs 0.568; both zones combined, 0.644 vs 0.531). With a cut-off ≥4, the agreement among all readers was also better with v. 2.1 than v. 2 in the PZ or both zones combined (κ-value: PZ, 0.761 vs 0.701; both zones combined, 0.756 vs 0.709). For all readers, AUC with v. 2.1 was higher than with v. 2 (TZ, 0.826-0.907 vs 0.788-0.856; PZ, 0.857-0.919 vs 0.853-0.902). CONCLUSION: Our study suggests that the PI-RADS v. 2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v. 2. ADVANCES IN KNOWLEDGE: PI-RADS v. 2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.

Deep learning model for the automatic classification of COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy: a multi-center retrospective study.

This retrospective study aimed to develop and validate a deep learning model for the classification of coronavirus disease-2019 (COVID-19) pneumonia, non-COVID-19 pneumonia, and the healthy using chest X-ray (CXR) images. One private and two public datasets of CXR images were included. The private dataset included CXR from six hospitals. A total of 14,258 and 11,253 CXR images were included in the 2 public datasets and 455 in the private dataset. A deep learning model based on EfficientNet with noisy student was constructed using the three datasets. The test set of 150 CXR images in the private dataset were evaluated by the deep learning model and six radiologists. Three-category classification accuracy and class-wise area under the curve (AUC) for each of the COVID-19 pneumonia, non-COVID-19 pneumonia, and healthy were calculated. Consensus of the six radiologists was used for calculating class-wise AUC. The three-category classification accuracy of our model was 0.8667, and those of the six radiologists ranged from 0.5667 to 0.7733. For our model and the consensus of the six radiologists, the class-wise AUC of the healthy, non-COVID-19 pneumonia, and COVID-19 pneumonia were 0.9912, 0.9492, and 0.9752 and 0.9656, 0.8654, and 0.8740, respectively. Difference of the class-wise AUC between our model and the consensus of the six radiologists was statistically significant for COVID-19 pneumonia (p value = 0.001334). Thus, an accurate model of deep learning for the three-category classification could be constructed; the diagnostic performance of our model was significantly better than that of the consensus interpretation by the six radiologists for COVID-19 pneumonia.

Magnetic resonance imaging findings of pure prostatic ductal adenocarcinomas: a case series.

PURPOSE: This study aimed to investigate the characteristics of magnetic resonance imaging (MRI) findings in pure prostatic ductal adenocarcinoma. METHODS: From January 2009 to February 2020, seven patients who were diagnosed with pure prostatic ductal adenocarcinoma and had a referable preoperative MRI scan were included in the study. We evaluated the following MRI findings for each tumor: size, location, presence of multi-cystic component, and apparent diffusion coefficient (ADC) value. RESULTS: The median maximum diameter of the tumors was 22 mm (range 19-70 mm). Regarding transverse distribution, five tumors were located in the periurethral area and two were located peripherally apart from the urethra. Two of the seven tumors had cystic components. The median ADC value of the tumors was 0.754 × 10-3 mm2/s (range 0.570-0.963 × 10-3 mm2/s). Based on the transverse distribution and components of the tumors on MRI, ductal adenocarcinomas were classified into three types: type I as a non-cystic tumor located peripherally apart from the urethra (29%, two cases); type II as a non-cystic tumor located in the periurethral area (43%, three cases); and type III as a tumor with a multi-cystic component (29%, two cases). CONCLUSION: The non-cystic mass with periurethral distribution (type II) and multi-cystic mass (type III) may be characteristic features that differentiate pure ductal adenocarcinoma from ordinary acinar adenocarcinoma on MRI.

Heparin-induced thrombocytopenia during IgA vasculitis: a case report.

Immunoglobulin A (IgA) vasculitis is characterized by small vessel vasculitis involving immune complexes and IgA deposition. The development of heparin-induced thrombocytopenia (HIT) during IgA vasculitis is extremely rare. An 87-year-old man presented with general fatigue, leg edema, purpura, arthritis and renal disease. He was diagnosed with IgA vasculitis and was admitted to our hospital. Hemodialysis with heparin was initiated thrice a week on post-admission Day 11. On Day 21, during hemodialysis, the pressure in the dialysis circuit increased and the dialysis was interrupted. On Day 24, the platelet count rapidly decreased to 18 000/µl. The patient was diagnosed with HIT after testing positive for HIT antibodies; heparin was discontinued at this time. Precautions must be taken against the onset of HIT when initiating hemodialysis in patients with IgA vasculitis.