This study was performed to evaluate the findings including the time density curve (TD curve), the relative percentage of enhancement washout (Washr) and the absolute percentage of enhancement washout (Washa) at dynamic contrast-enhanced computed tomography (DCE-CT) in 70 patients with 79 adrenal masses (including 44 adenomas and 35 nonadenomas) confirmed histopathologically and/or clinically. The results demonstrated that the TD curves of adrenal masses were classified into 5 types, and the type distribution of the TD curves was significantly different between adenomas and nonadenomas. Types A and C were characteristic of adenomas, whereas types B, D and E were features of nonadenomas. The sensitivity, specificity and accuracy for the diagnosis of adenoma based on the TD curves were 93%, 80% and 87%, respectively. Furthermore, when myelolipomas were excluded, the specificity and accuracy for adenoma were 90% and 92%, respectively. The Washr and the Washa values for the adenomas were higher than those for the nonadenomas. The diagnostic efficiency for adenoma was highest at 7-min delay time at DCE-CT; Washr was more efficient than Washa. Washr ≥34% and Washa ≥43% were both suggestive of adenomas and, on the contrary, suspicious of nonadenomas. The sensitivity, specificity and accuracy for the diagnosis of adenoma were 84%, 77% and 81%, respectively. When myelolipomas were precluded, the diagnostic specificity and accuracy were 87% and 85%, respectively. Therefore, DCE-CT aids in characterization of adrenal tumors, especially for lipid-poor adenomas which can be correctly categorized on the basis of TD curve combined with the percentage of enhancement washout.

To explore the correlation between the typical findings of dynamic contrast-enhanced computed tomography (DCE-CT) and tumoral angiogenesis (microvessel density [MVD] and vascular endothelial growth factor [VEGF]) in adenomas and nonadenomas such that the enhancement mechanism of DCE-CT in adrenal masses can be explained more precisely. Forty-two patients with 46 adrenal masses confirmed by surgery and pathology were included in the study; these masses included 23 adenomas, 18 nonadenomas, and 5 hyperplastic nodules. The findings of DCE-CT and angiogenesis in adrenal masses were studied. The features of DCE-CT in adenomas and nonadenomas were evaluated to determine whether the characteristics of DCE-CT in adrenal masses were closely correlated with tumoral angiogenesis. Adrenal adenomas were significantly different from nonadenomas in the time density curve and the mean percentage of enhancement washout at the 7-minute delay time in DCE-CT. The mean MVD and VEGF expression exhibited significant differences between the rapid washout group (types A and C) and the slow washout group (types B, D, and E) and between the relative washout (Washr) ≥34% and the absolute washout (Washa) ≥43% on the 7-minute enhanced CT scans (P=0.000). Adenomas were suggested when adrenal masses presented as types A and C, and/or the Washr ≥34%, and/or the Washa ≥43%, and the opposite was suggested for nonadenomas. These results showed a close correlation between the characteristics of DCE-CT and both MVD and VEGF expression in adrenal masses. There was also a significant difference in MVD and VEGF expression between adenomas and nonadenomas. In conclusion, MVD and VEGF expression are two important pathological factors that play important roles in the characterization of DCE-CT in adrenal masses because they cause different time density curve types, the Washr and the Washa for adrenal adenomas and nonadenomas.

Purpose

To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images.

Methods

Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis.

Results

The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010).

Conclusion

A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

Materials with high thermal conductivity (κ) are of technological importance and fundamental interest. We grew cubic boron nitride (cBN) crystals with controlled abundance of boron isotopes and measured κ greater than 1600 watts per meter-kelvin at room temperature in samples with enriched 10B or 11B. In comparison, we found that the isotope enhancement of κ is considerably lower for boron phosphide and boron arsenide as the identical isotopic mass disorder becomes increasingly invisible to phonons. The ultrahigh κ in conjunction with its wide bandgap (6.2 electron volts) makes cBN a promising material for microelectronics thermal management, high-power electronics, and optoelectronics applications.