PURPOSE: We developed an artificial neural network and nomogram using readily available clinical features to model the chance of identifying sperm with microdissection testicular sperm extraction by readily available preoperative clinical parameters for men with nonobstructive azoospermia. MATERIALS AND METHODS: We reviewed the records of 1,026 men who underwent microdissection testicular sperm extraction. Patient age, follicle-stimulating hormone level, testicular volume, history of cryptorchidism, Klinefelter syndrome and presence of varicocele were included in the models. For the artificial neural network the data set was divided randomly into a training set (75%) and a test set (25%) with n1/n2 cross validation used to evaluate model accuracy, and then modeled with a neural computational system. In addition, a nomogram with calibration plots was developed to predict sperm retrieval with microdissection testicular sperm extraction. We compared these models to logistic regression. RESULTS: The ROC area for the neural computational system in the test set was 0.641. The neural network correctly predicted the outcome in 152 of the 256 test set patients (59.4%). The nomogram AUC was 0.59 and adequately calibrated. Multivariable logistic regression demonstrated patient age, history of Klinefelter syndrome and cryptorchidism to be significant predictors of sperm retrieval (p <0.05). However, follicle-stimulating hormone and testicular volume were not significant by internal validation. CONCLUSIONS: We modeled a combination of well described preoperative clinical parameters to predict sperm retrieval using a neural computational system and nomogram with acceptable predictive values. The generalizability of these findings requires external validation.
PURPOSE: We developed an artificial neural network and nomogram using readily available clinical features to model the chance of identifying sperm with microdissection testicular sperm extraction by readily available preoperative clinical parameters for men with nonobstructive azoospermia. MATERIALS AND METHODS: We reviewed the records of 1,026 men who underwent microdissection testicular sperm extraction. Patient age, follicle-stimulating hormone level, testicular volume, history of cryptorchidism, Klinefelter syndrome and presence of varicocele were included in the models. For the artificial neural network the data set was divided randomly into a training set (75%) and a test set (25%) with n1/n2 cross validation used to evaluate model accuracy, and then modeled with a neural computational system. In addition, a nomogram with calibration plots was developed to predict sperm retrieval with microdissection testicular sperm extraction. We compared these models to logistic regression. RESULTS: The ROC area for the neural computational system in the test set was 0.641. The neural network correctly predicted the outcome in 152 of the 256 test set patients (59.4%). The nomogram AUC was 0.59 and adequately calibrated. Multivariable logistic regression demonstrated patient age, history of Klinefelter syndrome and cryptorchidism to be significant predictors of sperm retrieval (p <0.05). However, follicle-stimulating hormone and testicular volume were not significant by internal validation. CONCLUSIONS: We modeled a combination of well described preoperative clinical parameters to predict sperm retrieval using a neural computational system and nomogram with acceptable predictive values. The generalizability of these findings requires external validation.
Authors: Adam O Kadlec; Samuel Ohlander; James Hotaling; Jessica Hannick; Craig Niederberger; Thomas M Turk Journal: Urolithiasis Date: 2014-04-02 Impact factor: 3.436