Prostatic arterial embolization versus holmium laser enucleation of the prostate: 1-year outcomes.

PURPOSE: Holmium Laser Enucleation of the Prostate (HoLEP) and Prostatic Artery Embolization (PAE) are novel techniques for the treatment of benign prostatic hyperplasia lower urinary tract symptoms (BPH-LUTS). The objective of this study was to describe and compare the functional results and complications of these two techniques at one year follow-up. MATERIALS AND METHODS: We performed a retrospective, monocentric study of all patients consecutively treated in our center with HoLEP or PAE for symptomatic or complicated BPH between January 2016 and December 2019. Data regarding patient and perioperative characteristics, follow-up biological results, functional questionnaires and uroflowmetry were collected from medical records. RESULTS: A total of 490 and 57 patients were treated with HoLEP and PAE, respectively. The demographic and clinical characteristics of the two groups were similar. The operative time was significantly higher for PAE (p < 0.001) and hospitalization time longer after HoLEP (p = 0.0006). The urinary catheterization time was longer after PAE (p < 0.001). The prostatic volume treated was higher with HoLEP than with PAE (56% versus 26%, p < 0.001). The mean difference in IPSS from baseline to 12 months was significantly higher after HoLEP than after PAE: - 17.58 versus - 8 (p < 0.001). The mean difference in QoL-IPSS from baseline to 12 months was significantly higher after HoLEP: - 4.09 versus - 2.27 (p < 0.001). The rate of postoperative adverse events in the first three months was similar between the two groups:35% after HoLEP and 33% after PAE (p = 0.88). CONCLUSIONS: HoLEP and PAE both significantly improved BPH-LUTS, with HoLEP having an advantage over PAE.

Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation.

OBJECTIVE: To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation. METHODS: The algorithm, combining model-based and deep learning-based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm's mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression. RESULTS: Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm's median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists' delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation. CONCLUSIONS: The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging. KEY POINTS: • Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma). • The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks. • The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists.

The current role of prostate multiparametric magnetic resonance imaging.

Prostate multi-parametric magnetic resonance imaging (mpMRI) has shown excellent sensitivity for Gleason ≥7 cancers, especially when their volume is ≥0.5 mL. As a result, performing an mpMRI before prostate biopsy could improve the detection of clinically significant prostate cancer (csPCa) by adding targeted biopsies to systematic biopsies. Currently, there is a consensus that targeted biopsies improve the detection of csPCa in the repeat biopsy setting and at confirmatory biopsy in patients considering active surveillance. Several prospective multicentric controlled trials recently showed that targeted biopsy also improved csPCa detection in biopsy-naïve patients. The role of mpMRI and targeted biopsy during the follow-up of active surveillance remains unclear. Whether systematic biopsy could be omitted in case of negative mpMRI is also a matter of controversy. mpMRI did show excellent negative predictive values (NPV) in the literature, however, since NPV depends on the prevalence of the disease, negative mpMRI findings should be interpreted in the light of a priori risk for csPCa of the patient. Nomograms combining mpMRI findings and classical risk predictors (age, prostate-specific antigen density, digital rectal examination, etc.) will probably be developed in the future to decide whether a prostate biopsy should be obtained. mpMRI has a good specificity for detecting T3 stage cancers, but its sensitivity is low. It should therefore not be used routinely for staging purposes in low-risk patients. Nomograms combining mpMRI findings and other clinical and biochemical data will also probably be used in the future to better assess the risk of T3 stage disease.