Change of Ultrasound Estimated Bladder Weight and Bladder Wall Thickness After Treatment of Bladder Outlet Obstruction With Dutasteride

The cutoff value of transitional zone index predicting the efficacy of dutasteride on subjective symptoms in patients with benign prostate hyperplasia

OBJECTIVES

We investigated the efficacy of dutasteride add-on therapy to α-1 adrenoceptor antagonists in patients with benign prostate hyperplasia (BPH) in relation to the transitional zone index (TZI) and evaluated the cutoff value of TZI that predicted improvements of subjective symptoms at 6 months.

METHODS

Male BPH patients with prostate volume (PV) ≥ 30 mL receiving dutasteride 0.5 mg/d for 6 months as add-on therapy along with α-1 adrenoceptor antagonists were enrolled. PV, transitional zone volume (TZV), TZI, International Prostate Symptom Score (IPSS), and uroflowmetry parameters before and at 6 months with dutasteride add-on treatment were evaluated.

RESULTS

Eighty-three patients were included. The changes of total IPSS, IPSS voiding subscore, IPSS quality of life score, and voided volume were significantly correlated with TZI. Among baseline parameters, TZV and TZI were significantly associated with the changes of total IPSS in univariate analysis, and only TZI remained as an independent predictive factor for improving total IPSS in multivariate analysis (odds ratio -8.3, P = .048). The cutoff point of TZI for predicting an improvement of the total IPSS by 6 points or more was 0.67 (area under the curve 0.71, sensitivity 0.62, specificity 0.79).

CONCLUSIONS

A higher TZI was significantly associated with improvement of subjective symptoms but not uroflowmetric findings for BPH patients with 6 months of dutasteride add-on therapy along with α-1 adrenoceptor antagonists, and the predictive value of TZI for effective dutasteride add-on therapy was higher than 0.67. BPH patients using α-1 adrenoceptor antagonists with a TZI higher than 0.67 can be good candidates for add-on dutasteride therapy.

Feasibility of a deep learning-based diagnostic platform to evaluate lower urinary tract disorders in men using simple uroflowmetry

Purpose

To diagnose lower urinary tract symptoms (LUTS) in a noninvasive manner, we created a prediction model for bladder outlet obstruction (BOO) and detrusor underactivity (DUA) using simple uroflowmetry. In this study, we used deep learning to analyze simple uroflowmetry.

Materials and Methods

We performed a retrospective review of 4,835 male patients aged ≥40 years who underwent a urodynamic study at a single center. We excluded patients with a disease or a history of surgery that could affect LUTS. A total of 1,792 patients were included in the study. We extracted a simple uroflowmetry graph automatically using the ABBYY Flexicapture^® image capture program (ABBYY, Moscow, Russia). We applied a convolutional neural network (CNN), a deep learning method to predict DUA and BOO. A 5-fold cross-validation average value of the area under the receiver operating characteristic (AUROC) curve was chosen as an evaluation metric. When it comes to binary classification, this metric provides a richer measure of classification performance. Additionally, we provided the corresponding average precision-recall (PR) curves.

Results

Among the 1,792 patients, 482 (26.90%) had BOO, and 893 (49.83%) had DUA. The average AUROC scores of DUA and BOO, which were measured using 5-fold cross-validation, were 73.30% (mean average precision [mAP]=0.70) and 72.23% (mAP=0.45), respectively.

Conclusions

Our study suggests that it is possible to differentiate DUA from non-DUA and BOO from non-BOO using a simple uroflowmetry graph with a fine-tuned VGG16, which is a well-known CNN model.