Artificial neural network analysis (ANNA) of prostatic transrectal ultrasound

Loch, Tillmann; Leuschner, Ivo; Genberg, Carl; Weichert-Jacobsen, K.; Küppers, Frank; Yfantis, Evangelos A.; Evans, Marilyn; Tsarev, Valeri; Stöckle, Michael

doi:10.1002/(sici)1097-0045(19990515)39:3<198::aid-pros8>3.0.co;2-x

Cited by 88 publications

(36 citation statements)

References 22 publications

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“…ANNs are computer programs that simulate some of the higher-level functions of the architecture of the human brain (11). In ANNs simple processing units (nodes) which simulate neurons are linked via weighted interconnections.…”

Section: Discussionmentioning

confidence: 99%

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Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

et al. 2011

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ObjectiveThe purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models.Materials and MethodsFive hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n = 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05).ResultsThe AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer.ConclusionThe performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

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Section: Discussionmentioning

confidence: 99%

“…There have been several studies to improve the prostate cancer detection rate with the use of prediction models based on logistic regression analysis and an artificial neural network (ANN) (2-11). The use of a support vector machine (SVM) approach has been introduced as a potential alternative to conventional ANNs (2, 12).…”

Section: Introductionmentioning

confidence: 99%

Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

et al. 2011

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“…As in the human brain, they are composed of neurons and synapses with various synaptic connection strengths (referred to as "weights") for each connected pair of neurons. 12 Artificial neural networks consist of 3 types of neurons; input, output, and hidden neurons. The input and output neurons correspond to the inputs to and outputs from a traditional computer program.…”

Section: Discussionmentioning

confidence: 99%

“…Although Loch et al 12 investigated the diagnostic potential of the computerized interpretation of transrectal ultrasonography (TRUS) by ANN analysis, few attempts have been made to introduce TRUS data as an input component in ANNs used for the diagnosis of prostate cancer. Thus, the purpose of this study was to evaluate the diagnostic performance of an ANN model with and without TRUS data.…”

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confidence: 99%

Role of Transrectal Ultrasonography in the Prediction of Prostate Cancer

Lee

Kim

Lee

et al. 2006

Journal of Ultrasound in Medicine

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“…In place of conventional multivariate statistical methods, a new statistical approach, artificial neural networks (ANNs), has recently been introduced to medical analysis 1–3 . Artificial neural networks are algorithms that can be trained to recognize complex patterns in data sets.…”

Section: Introductionmentioning

confidence: 99%

Predicting disease outcome of non‐invasive transitional cell carcinoma of the urinary bladder using an artificial neural network model: Results of patient follow‐up for 15 years or longer

et al. 2003

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Background : Patients with non-invasive (Ta/T1) transitional cell carcinoma (TCC) of the urinary bladder are often observed without progression in the long-term follow-up period, although many of them experience recurrence of disease. It is difficult to accurately predict the disease outcome of each patient with Ta/T1 TCC using conventional prognostic criteria. In this study, we examined the usefulness of artificial neural networks (ANNs) to predict the long-term disease outcome of patients with TCC of the urinary bladder. Methods : A retrospective, prognostic study of 90 patients with Ta/T1 TCC of the urinary bladder, diagnosed by transurethral resection of the bladder tumor between April 1981 and March 1985, and then followed up for 15 years or longer, was carried out. Data were analyzed using the Bayesian network tool of SPSS Neural Connection 2.1. The input neural data consisted of tumor stage, grade, tumor number, age, gender, tumor architecture and estimates of mean nuclear volume. The data set was randomly divided into 68 training and 22 testing examples for the prediction of disease progression and tumor recurrence within 15 years. Results : During 15 years follow-up, tumor recurrence was noted in 42/90 (47%) Ta/T1 tumors. The ANN model could not predict tumor recurrence. Conversely, disease progression was noted in 17/90 (19%) Ta/T1 tumors, and, in the test set, 4/22 (18%) Ta/T1 tumors underwent disease progression. The sensitivity of the ANN model to predict progression was 100% (specificity 67%; positive predictive value 40%; negative predictive value 100%). Patients who were judged to have a favorable prognosis using ANN analysis did not progress within the 15-year follow-up period. Conclusion : The results of the ANN study indicate that long-term progression-free survival of patients with non-invasive TCC of the urinary bladder can be precisely predicted. A favorable prognosis using ANNs would be one of the exclusion criteria for immediate or future total cystectomy.

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Artificial neural network analysis (ANNA) of prostatic transrectal ultrasound

Cited by 88 publications

References 22 publications

Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

Role of Transrectal Ultrasonography in the Prediction of Prostate Cancer

Predicting disease outcome of non‐invasive transitional cell carcinoma of the urinary bladder using an artificial neural network model: Results of patient follow‐up for 15 years or longer

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