BackgroundWhether men with a prostate‐specific antigen (PSA) level of 4–10 ng/mL should be recommended for a biopsy is clinically challenging.PurposeTo develop and validate a radiomics model based on multiparametric MRI (mp‐MRI) in patients with PSA levels of 4–10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies.Study TypeRetrospective.SubjectsIn all, 199 patients with PSA levels of 4–10 ng/mL.Field Strength/Sequence3T, T2‐weighted, diffusion‐weighted, and dynamic contrast‐enhanced MRI.AssessmentLesion regions of interest (ROIs) from T2‐weighted, diffusion‐weighted, and dynamic contrast‐enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical‐radiological risk factors.Statistical TestsFor continuous variables, variance equality was assessed by Levene's test and Student's t‐test, and Welch's t‐test was used to assess between‐group differences. For categorical variables, Pearson's chi‐square test, Fisher's exact test, or the approximate chi‐square test was used to assess between‐group differences. P < 0.05 was considered statistically significant.ResultsThe combined model incorporating the multi‐imaging fusion model, age, PSA density (PSAD), and the PI‐RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical‐radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical‐radiological model by 18.4%.Data ConclusionThe combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4–10 ng/mL and might aid in treatment decision‐making and reduce unnecessary biopsies.Level of Evidence: 3Technical Efficacy Stage: 3J. Magn. Reson. Imaging 2020;51:1890–1899.

IADE and ICAS had different risk factor profiles and associated with different imaging phenotypes of cerebral small vessel disease, suggesting different underlying mechanisms.

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High-water-cut crude oil is currently the main form of underground oil in China. The high water content and the distribution of oil and water have an important impact on the rheological property and exploration efficiency. Terahertz (THz) waves are extremely sensitive to hydrogen bonds and intermolecular forces, and consequently, the responses of oil, water and air exhibit obvious differences. In this work, a series of crude oils with high water content were investigated by using THz timedomain spectroscopy (THz-TDS). Linear models were built between amplitudes of THz-TDS and the absorbance and water content from 50.05% to 100.00% with correlation coefficients (R) of 0.99 and >0.93, respectively. In addition, oil and water were determined to be distributed at different positions, based on THz signals in a series of oriented scans. Moreover, the transparent parts for visible light are identified by scanning the local points. The positions with low and high THz intensities refer to water and air regions, respectively. Therefore, THz technology is expected to act as a supplementary method to characterize highwater-cut crude oil, which will promote the efficiency and safe operation of pumping units and oil pipelines.

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