Background: Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer. Methods: This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal–Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm–Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves. Results: We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859–0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI. Conclusion: Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

Nanofibrous dressing materials with an antitumor function can potentially inhibit recurrence of melanoma following the surgical excision of skin tumors. In this study, hydrolyzed polyacrylonitrile (hPAN) nanofibers biofunctionalized with L-carnosine (CAR) and loaded with bio (CAR)-synthesized zinc oxide (ZnO) nanoparticles, ZnO/CAR-hPAN (hereafter called ZCPAN), were employed to develop an antimelanoma wound dressing. Inspired by the formulation of the commercial wound healing Zn-CAR complex, i.e., polaprezinc (PLZ), for the first time, we benefitted from the synergy of zinc and CAR to create an antimelanoma nanofibrous wound dressing. According to scanning electron microscopy (SEM) images, ultrafine ZnO nanoparticles were homogenously distributed throughout the nanofibrous dressing. The ZCPAN nanofiber mat showed a significantly higher toughness (18.7 MJ.m⁻³ vs. 1.4 MJ.m⁻³) and an enhanced elongation at break (stretchability) compared to the neat PAN nanofiber mat (12% vs. 9.5%). Additionally, optical coherence elastography (OCE) measurements indicated that the ZCPAN nanofibrous dressing was as stiff as 50.57 ± 8.17 kPa which is notably larger than that of the PAN nanofibrous dressing, i.e., 24.49 ± 6.83 kPa. The optimum mechanical performance of the ZCPAN nanofibers originates from physicochemical interaction of CAR ligands, hPAN nanofibers, and ZnO nanoparticles through hydrogen bonding, electrostatic bonding, and esterification, as verified using ATR-FTIR. An in vitro cell viability assay using human skin melanoma cells implied that the cells are notably killed in the presence of the ZCPAN nanofibers compared to the PAN nanofibers. Thanks to ROS generating ZnO nanoparticles, this behavior originates from the high reactive oxygen species (ROS)-induced oxidative damage of melanoma cells, as verified through a CellROX assay. In this regard, an apoptotic cell response to the ZCPAN nanofibers was recorded through an apoptosis assay. Taken together, the ZCPAN nanofibers induce an antimelanoma effect through oxidative stress and thus are a high potential wound dressing material to suppress melanoma regrowth after surgical excision of skin tumors.

Increased stiffness of tissues has been recognised as a diagnostic feature of pathologies. Tissue stiffness characterisation usually involves the detection of tissue response from mechanical stimulation. Air-puff optical coherence elastography (OCE) can generate impulse surface acoustic waves (SAWs) on tissue surface without contact and evaluate the mechanical properties of tissue. This study endeavours to explore the optimal excitation angle for air-puff OCE, a parameter that lacks standardisation at present, by investigating the relationship between the frequency bandwidth and peak-to-peak signal-to-noise ratio (SNR) of SAWs for different excitation angles (relative to the normal surface) of air-puff on the sample, from 5° to 85°, with an interval of 5° applied on the phantom. Due to the unevenness of human hands, 20°, 45° and 70° angles were employed for human skin (10 healthy adults). The results show that a smaller excitation angle could produce higher wave frequency bandwidth; a 5° angle generated an SAW with 1747 Hz frequency bandwidth, while an 85° angle produced an SAW with 1205 Hz. Significant differences were not shown in peak-to-peak SNR comparison between 5° and 65° on the phantom, but between 65° and 85° at the excitation position, a reduction of 48.6% was observed. Furthermore, the group velocity of the SAWs was used to evaluate the bulk Young’s modulus of the human tissue. The outcomes could provide essential guidance for air-puff-based elastography studies in clinical applications and future tissue research.

The study was aimed to develop a predictive model to identify patients who may benefit from performing systematic random biopsies (SB) in addition to targeted biopsies (TB) in men suspected of having prostate cancer. A total of 198 patients with positive pre-biopsy MRI findings and who had undergone both TB and SB were prospectively recruited into this study. The primary outcome was detection rates of clinically significant prostate cancer (csPCa) in SB and TB approaches. The secondary outcome was net clinical benefits of SB in addition to TB. A logistic regression model and nomogram construction were used to perform a multivariate analysis. The detection rate of csPCa using SB was 51.0% (101/198) compared to a rate of 56.1% (111/198) for TB, using a patient-based biopsy approach. The detection rate of csPCa was higher using a combined biopsy (64.6%; 128/198) in comparison to TB (56.1%; 111/198) alone. This was statistically significant (p < 0.001). Age, PSA density and PIRADS score significantly predicted the detection of csPCa by SB in addition to TB. A nomogram based on the model showed good discriminative ability (C-index; 78%). The decision analysis curve confirmed a higher net clinical benefit at an acceptable threshold.

Objectives: To investigate the impact of ultrasound shear wave elastography (USWE) and multiparametric magnetic resonance imaging (mpMRI) in predicting a change in biopsy-assigned Gleason Score (GS) after radical surgery for localised prostate cancer (PCa). Method: A total of 212 men opting for laparoscopic radical prostatectomy (LRP) between September 2013 and June 2017 were recruited into this study. All the participants had 12-core transrectal ultrasound (TRUS) biopsies and imaging using USWE and mpMRI before radical surgery. The predictive accuracy for imaging modalities was assessed in relation to upgrading and downgrading of PCa GS between the biopsies and radical prostatectomy using Student’s t-test and multivariable logistic regression analyses. A decision analysis curve was constructed assessing the impact of nomogram on clinical situations using different thresholds of upgrading probabilities. Results: Most GS 6 diseases on biopsies were upgraded on radical surgery (37/42, 88.1%). Major downgrading was seen in GS 8 category of disease (14/35; 37.1%), whereas no alteration was observed in GS 7 on biopsies in most men (55/75; 73.3%). In univariate analysis, higher preoperative prostate-specific antigen (PSA) (p = 0.001), higher prostate-specific antigen density (PSAD) (p = 0.002), stiffer USWE lesions (p = 0.009), and higher prostate imaging–reporting and data system (PIRADS) (p = 0.002) on mpMRI were significant predictors of upgrading. In multivariate logistic regression analyses, only PSA (p = 0.016) and USWE-measured tissue stiffness (p = 0.029) showed statistical significance in predicting upgrading. Conclusions: Measurement of tissue stiffness using USWE in clinically localised PCa can predict upgrading of GS and has the potential to improve patient management options.