Open AccessPrediction of prostate cancer by deep learning with multilayer artificial neural network
Author(s) -
Takumi Takeuchi,
Mami Hattori-Kato,
Yukihiro Okuno,
Satoshi Iwai,
Koji Mikami
Publication year - 2018
Publication title -
canadian urological association journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.477
H-Index - 38
eISSN - 1920-1214
pISSN - 1911-6470
DOI - 10.5489/cuaj.5526
Subject(s) - prostate cancer , receiver operating characteristic , logistic regression , artificial neural network , stepwise regression , prostate , medicine , prostate biopsy , artificial intelligence , regression analysis , cancer , statistics , computer science , machine learning , mathematics
To predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning using a multilayer artificial neural network was investigated.
Methods: A total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables, as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis, were input into the constructed multilayer artificial neural network (ANN) programs; 232 patients were used as training cases of ANN programs and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model.
Results: With any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and nine explanatory variables, respectively, from 22. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5–10% higher compared to that with logistic regression analysis (LR). The area under the curves (AUC) with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise LR compared with the AUC with LR. The ANN had a higher net benefit than LR between prostate cancer probability cutoff values of 0.38 and 0.6.
Conclusions: ANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.