Development of a novel classification system for anatomical variants of the puboprostatic ligaments with expert validation

Introduction: We propose a novel classification system with a validation study to help clinicians identify and typify commonly seen variants of the puboprostatic ligaments (PPL). Methods: A preliminary dissection of 6 male cadavers and a prospective dataset of over 300 robotic-assisted laparoscopic radical prostatectomies (RARP) recorded on video were used to identify 4 distinct ligament types. Then the prospectively collected database of surgical videos was used to isolate images of the PPL from RARP. Over 300 surgical videos were reviewed and classified with 1 to 5 pictures saved for reference of the type of PPL. To validate the new classification system, we selected 5 independent, blinded expert robotic surgeons to classify 100 ligaments based on morphology into a 4-type system: parallel, V-shaped, inverted V-shape, and fused. One week later, a subset of 25 photographs was sent to the same experts and classified. Statistical analyses were performed to determine both the intra-rater and inter-rater reliability of the proposed system. Results: Inverted V-shaped ligaments were noted most frequently (29.97%), parallel and V-shaped ligaments were found at 19.19% and 11.11%, respectively and fused ligaments were noted less frequently (6.06%). There was good intra-rater agreement (ê = 0.66) and inter-rater agreement (ê = 0.67) for the classification system. Conclusions: This classification system provided standardized descriptions of ligament variations that could be adopted universally to help clinicians categorize the variants. The system, validated by several blinded expert surgeons, demonstrated that surgeons were able to learn and correctly classify the variants. The system may be useful in helping to predict peri- and postoperative outcomes; however, this will require further study

Predicting Gleason Score of Prostate Cancer Patients Using Radiomic Analysis

<p>Purpose: Use of quantitative imaging features and encoding the intra-tumoral heterogeneity from multi-parametric magnetic resonance imaging (mpMRI) for the prediction of Gleason score is gaining attention as a non-invasive biomarker for prostate cancer (PCa). This study tested the hypothesis that radiomic features, extracted from mpMRI, could predict the Gleason score pattern of patients with PCa.</p><p>Methods: This analysis included T2-weighted (T2-WI) and apparent diffusion coefficient (ADC, computed from diffusion-weighted imaging) scans of 99 PCa patients from The Cancer Imaging Archive (TCIA). A total of 41 radiomic features were calculated from a local tumor sub-volume (i.e., regions of interest) that is determined by a centroid coordinate of PCa volume, grouped based on their Gleason score patterns. Kruskal-Wallis and Spearman's rank correlation tests were used to identify features related to Gleason score groups. Random forest (RF) classifier model was used to predict Gleason score groups and identify the most important signature among the 41 radiomic features.</p><p>Results: Gleason score groups could be discriminated based on zone size percentage, large zone size emphasis and zone size non-uniformity values (p < 0.05). These features also showed a significant correlation between radiomic features and Gleason score groups with a correlation value of −0.35, 0.32, 0.42 for the large zone size emphasis, zone size non-uniformity and zone size percentage, respectively (corrected p < 0.05). RF classifier model achieved an average of the area under the curves of the receiver operating characteristic (ROC) of 83.40, 72.71, and 77.35% to predict Gleason score groups (G1) = 6; 6 < (G2) < (3 + 4) and (G3) ≥ 4 + 3, respectively.</p><p>Conclusion: Our results suggest that the radiomic features can be used as a non-invasive biomarker to predict the Gleason score of the PCa patients.</p