Combining Magnetic Resonance Diffusion-Weighted Imaging with Prostate-Specific Antigen to Differentiate Between Malignant and Benign Prostate Lesions.

BACKGROUND We aimed to develop a combined model of quantitative parameters derived from 3 different magnetic resonance imaging (MRI) diffusion models and laboratory data related to prostate-specific antigen (PSA) for differentiating between prostate cancer (PCa) and benign lesions. MATERIAL AND METHODS Eighty-four patients pathologically confirmed as having PCa or benign disease were enrolled. All patients underwent multiparametric MRI before biopsy, added intravoxel incoherent motion (IVIM) imaging, and diffusion kurtosis imaging (DKI). The following data were collected: quantitative parameters of diffusion-weighted imaging (DWI), IVIM, and DKI, preoperative total PSA, free/total PSA ratio, and PSA density (PSAD) values. A combined logistic regression model was established by above MRI quantitative parameters and PSA data to diagnose PCa. The Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) was used to assess the lesions for comparison. RESULTS Thirty-two patients had PCa and 52 patients had benign lesions. In multivariate logistic regression analysis, only apparent diffusion coefficient (ADC) and PSAD were significant variables (P<0.05) and were thus retained in the model. The area under curve value of the combined model (0.911) was higher than that of ADC, PSAD, and PI-RADS v2 (0.887, 0.861, and 0.859, respectively) in univariate analysis, but without any statistically significant differences. The combined model generated greater clinical benefit than the independent application of ADC, PSAD, and PI-RADS v2. CONCLUSIONS ADC and PSAD were the 2 most important metrics for distinguishing PCa from benign lesions. The combined model of ADC and PSAD demonstrated satisfactory discrimination and improved clinical net benefit.

A high-throughput phenotyping assay for precisely determining stalk crushing strength in large-scale sugarcane germplasm.

Sugarcane is a major industrial crop around the world. Lodging due to weak mechanical strength is one of the main problems leading to huge yield losses in sugarcane. However, due to the lack of high efficiency phenotyping methods for stalk mechanical strength characterization, genetic approaches for lodging-resistant improvement are severely restricted. This study attempted to apply near-infrared spectroscopy high-throughput assays for the first time to estimate the crushing strength of sugarcane stalks. A total of 335 sugarcane samples with huge variation in stalk crushing strength were collected for online NIRS modeling. A comprehensive analysis demonstrated that the calibration and validation sets were comparable. By applying a modified partial least squares method, we obtained high-performance equations that had large coefficients of determination (R2 > 0.80) and high ratio performance deviations (RPD > 2.4). Particularly, when the calibration and external validation sets combined for an integrative modeling, we obtained the final equation with a coefficient of determination (R2) and ratio performance deviation (RPD) above 0.9 and 3.0, respectively, demonstrating excellent prediction capacity. Additionally, the obtained model was applied for characterization of stalk crushing strength in large-scale sugarcane germplasm. In a three-year study, the genetic characteristics of stalk crushing strength were found to remain stable, and the optimal sugarcane genotypes were screened out consistently. In conclusion, this study offers a feasible option for a high-throughput analysis of sugarcane mechanical strength, which can be used for the breeding of lodging resistant sugarcane and beyond.

The influence of liver fat deposition on the quantification of the liver-iron fraction using fast-kilovolt-peak switching dual-energy CT imaging and material decomposition technique: an in vitro experimental study.

BACKGROUND: To assess the feasibility of dual-energy spectral computed tomography (DECT) for quantifying the liver iron content (LIC) with material decomposition (MD) technique in vitro. METHODS: Liver-iron mixture samples (model A) and liver-iron-fat mixture samples (model B) were prepare and scanned by a single source DECT using GSI mode with successive tube currents of 200, 320, and 485 mA. A standard algorithm of 1.25 mm was used to reconstruct iron (fat) MD images and iron (water) MD images. The iron concentrations of all samples were measured and analyzed by Spearman's rank correlation and linear regression analysis. RESULTS: Significant positive linear correlations were found between virtual iron content (VIC) and LIC in the absence of fat (model A) and in the presence of fat (model B) in the range of LIC 0 to 25 mg/mL. The lines of best fit to model A had slopes around 1.1 and an intercept around (-1.5) mg/mL for iron (water) MD images, and had slopes around 1.1 and an intercept around (-10) mg/mL for iron (fat) MD images. The lines of best fit to the model B had slopes around 1.5 and an intercept around (-15) mg/mL. At the same value of LIC (LIC >0), the VIC values of model A were always higher than those of model B. At the high value of LIC (12.5 mg/mL), the VIC values of model B were similar, but they differed greatly from those of model A. CONCLUSIONS: The fast-kilovolt-peak switching dual-energy CT imaging and MD techniques allow for quantification of iron content. Fat and the post-reconstruction algorithm of iron (fat) MD images, were confounding factors, and led to the underestimation and overestimation of LIC, respectively.