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Purpose: To automatically assess the aggressiveness of prostate cancer (PCa) lesions using zonal-specific image features extracted from diffusion weighted imaging (DWI) and T2W MRI. Methods: Region of interest was extracted from DWI (peripheral zone) and T2W MRI (transitional zone and anterior fibromuscular stroma) around the center of 112 PCa lesions from 99 patients. Image histogram and texture features, 38 in total, were used together with a k-nearest neighbor classifier to classify lesions into their respective prognostic Grade Group (GG) (proposed by the International Society of Urological Pathology 2014 consensus conference). A semi-exhaustive feature search was performed (1-6 features in each feature set) and validated using threefold stratified cross validation in a one-versus-rest classification setup. Results: Classifying PCa lesions into GGs resulted in AUC of 0.87, 0.88, 0.96, 0.98, and 0.91 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5 for the peripheral zone, respectively. The results for transitional zone and anterior fibromuscular stroma were AUC of 0.85, 0.89, 0.83, 0.94, and 0.86 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5, respectively. Conclusion: This study showed promising results with reasonable AUC values for classification of all GG indicating that zonal-specific imaging features from DWI and T2W MRI can be used to differentiate between PCa lesions of various aggressiveness.

The in vivo electrochemical behavior of titanium nitride (TiN) nerve stimulation electrodes was compared to their in vitro behavior for a period of 90 days. Ten electrodes were implanted in two Göttingen minipigs. Four of these were used for electrical stimulation and electrochemical measurements. Five electrodes were kept in Ringer's solution at 37.5°C, of which four were used for electrical stimulation and electrochemical measurements. The voltage transients measured in vivo were 13 times greater than in vitro at implantation and they continued to increase with time. The electrochemical properties in vivo and the tissue resistance (Rtissue) followed a similar trend with time. There was no consistent significant difference between the electrochemical properties of the in vivo and in vitro electrodes after the implanted period. The differences between the in vivo and in vitro electrodes during the implanted period show that the evaluation of electrochemical performance of implantable stimulation electrodes cannot be substituted with in vitro measurements. After the implanted period, however, the performance of the in vivo and in vitro electrodes in saline was similar. In addition, the changes observed over time during the post-implantation period regarding the electrochemical properties of the in vivo electrodes and Rtissue were similar, which indicates that these changes are due to the foreign body response to implantation.

Polydimethylsiloxane (PDMS) is the most common type of silicone polymer for the fabrication of implantable medical devices. Because of its inherent hydrophobic nature, the PDMS surface does not readily promote cellular adhesion, which leads to diverse clinical issues. Previously, we reported a simple water vapor plasma treatment of PDMS surfaces that resulted in stable long-term wettability and excellent in vitro cell compatibility. In this work, we report investigation of the in vivo local responses to PDMS implants treated by water vapor plasma using a subcutaneous rat model. The local tissue responses were assessed after 2 and 4 weeks of implantation by means of macroscopic and histomorphometric analysis. After 2 weeks of implantation, the plasma-treated implants elicited the formation of fibrous tissue capsules that were significantly thinner, more adherent, and vascularized than the control counterparts. The improved cell adhesion was correlated with an increased amount of cells attached to the implant surface after retrieval. There was no difference in the inflammatory response between untreated and treated samples. This study provides a rational approach to optimize the long-term performance of silicone implants, which is likely to have a significant impact in clinical applications demanding enhanced tissue integration of the implants.

Zonal segmentation of the prostate gland using magnetic resonance imaging (MRI) is clinically important for prostate cancer (PCa) diagnosis and image-guided treatments. A two-dimensional convolutional neural network (CNN) based on the U-net architecture was evaluated for segmentation of the central gland (CG) and peripheral zone (PZ) using a dataset of 40 patients (34 PCa positive and 6 PCa negative) scanned on two different MRI scanners (1.5T GE and 3T Siemens). Images were cropped around the prostate gland to exclude surrounding tissues, resampled to 0.5 × 0.5 × 0.5 mm voxels and z-score normalized before being propagated through the CNN. Performance was evaluated using the Dice similarity coefficient (DSC) and mean absolute distance (MAD) in a fivefold cross-validation setup. Overall performance showed DSC of 0.794 and 0.692, and MADs of 3.349 and 2.993 for CG and PZ, respectively. Dividing the gland into apex, mid, and base showed higher DSC for the midgland compared to apex and base for both CG and PZ. We found no significant difference in DSC between the two scanners. A larger dataset, preferably with multivendor scanners, is necessary for validation of the proposed algorithm; however, our results are promising and have clinical potential.

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