External validation of genomic classifier based risk-stratification tool to identify candidates for adjuvant radiation therapy in patients with prostate cancer

Introduction & Objectives: A genomic classifier-based risk stratification nomogram to identify candidates for adjuvant radiation therapy (aRT) after radical prostatectomy (RP) has been proposed (ref: Dalela et al. JCO). Validation study for this model is still lacking. The aim of our study was to externally validate the aforementioned nomogram using a contemporary cohort of men treated with robot-assisted RP. Materials & Methods: A total of 350 patients who underwent RARP, (2013-2018), had adverse pathology features (positive margin,and/or pT3a/b). Genomic profile data was available for all these men. The decision and the timing to administer aRT, and androgen deprivation therapy was based on patient life expectancy, treatment expectations, and PSA kinetics. The metastasis-free survival (MFS) was estimated using the Kaplan-Meier method. The external validity of the nomogram was tested using the concordance index, calibration plot, and decision curve analysis. Results: Median (IQR) follow-up was 26.5 (17.48-36.44) Months. 19.6% had Gleason score ≥8. Non-confined disease (pT3a/b) was noted in 67.61% of the cohort. Overall,14% (49/350) of the patients received aRT. 3.4% of the patients (12/350) developed metastasis. Overall 3-year MFS was 0.95% (95%CI: 0.92 – 0.98). The c-index of the nomogram was 0.837, with favorable calibration characteristics. DCA showed a positive net benefit for probabilities range between a 0.01 and 0.09, with the highest difference at threshold probability around 0.05. At that threshold, the net benefit is 0.06 for the model, and 0 for treating all the patients. Conclusions: Our findings corroborate the validity of this genomic based risk-stratification tool using a contemporary cohort in identifying men who might benefit from aRT after RP. As such, it can be a useful instrument to be incorporated in shared decision making on whether or not administer aRT is worthwhile

Efficacy of single-source rapid kV-switching dual-energy CT for characterization of non-uric acid renal stones: a prospective ex vivo study using anthropomorphic phantom

Purpose To investigate the accuracy of rapid kV-switching single-source dual-energy computed tomography (rsDECT) for prediction of classes of non-uric-acid stones. Materials and methods Non-uric-acid renal stones retrieved via percutaneous nephrolithotomy were prospectively collected between January 2017 and February 2018 in a single institution. Only stones >= 5 mm and with pure composition (i.e., >= 80% composed of one component) were included. Stone composition was determined using Fourier Transform Infrared Spectroscopy. The stones were scanned in 32-cm-wide anthropomorphic whole-body phantom using rsDECT. The effective atomic number (Zeff), the attenuation at 40 keV (HU40), 70 keV (HU70), and 140 keV (HU140) virtual monochromatic sets of images as well as the ratios between the attenuations were calculated. Values of stone classes were compared using ANOVA and Mann-Whitney U test. Receiver operating curves and area under curve (AUC) were calculated. A p value < 0.05 was considered statistically significant. Results The final study sample included 31 stones from 31 patients consisting of 25 (81%) calcium-based, 4 (13%) cystine, and 2 (6%) struvite pure stones. The mean size of the stones was 9.9 +/- 2.4 mm. The mean Zeff of the stones was 12.01 +/- 0.54 for calcium-based, 11.10 +/- 0.68 for struvite, and 10.23 +/- 0.75 for cystine stones (p < 0.001). Zeff had the best efficacy to separate different classes of stones. The calculated AUC was 0.947 for Zeff; 0.833 for HU40; 0.880 for HU70; and 0.893 for HU140. Conclusion Zeff derived from rsDECT has superior performance to HU and attenuation ratios for separation of different classes of non-uric-acid stones

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests