Artificial Neural Network Model To Predict Biochemical Failure After Radical Prostatectomy

Porter, Christopher R.; O’Donnell, Colin; Crawford, E. David; Gamito, Eduard J.; Errejon, Abelardo; Genega, Elizabeth M.; Sotelo, Tiffany; Tewari, Ashutosh

doi:10.1089/10915360152745830

Cited by 23 publications

(7 citation statements)

References 10 publications

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“…Logistic regression analysis maximized performance in the training sets only to be outperformed by the ANNs in testing sets. In other articles [18][19][20] the ability of ANNs to assess the preoperative risk of progression was similarly shown.…”

Section: Anns In Predicting Biochemical Failurementioning

confidence: 87%

Artificial Neural Networks for Decision-Making in Urologic Oncology

et al. 2003

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Section: Anns In Predicting Biochemical Failurementioning

confidence: 87%

Artificial Neural Networks for Decision-Making in Urologic Oncology

et al. 2003

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“…Our results agree with those reported in other urological reports supporting the satisfactory diagnostic and prognostic performance of ANNs. Most trials have been focused on prostate cancer, but occasionally ANNs were applied to diagnostic and prognostic problems of kidney, testicle or bladder tumours [9,11–33]. In these studies ANNs were an optimal analytical statistical method to analyse complex problems, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…acute myocardial infarction and pulmonary embolism [7] . In oncological urology ANNs have been investigated specifically to resolve the diagnostic, staging and prognostic problems of prostate cancer, with very promising results [11–24]. Conversely, studies exploring the use of ANNs for kidney, testicle and bladder cancers are uncommon [25–33] (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis

et al. 2007

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predict the same outcome. The LR analysis, based on statistically significant predictors, and the ANN model were the compared for their accuracy in predicting survival. RESULTSThe median age of the patients was 63 years, and overall 201 of them died. The tumour stage and nodal involvement (both P < 0.001) were the only statistically independent predictors of overall 5-year survival on LR analysis. Based on these variables, LR had a sensitivity and specificity for predicting survival of 68.4% and 82.8%, respectively; corresponding values for the ANN were 62.7% and 86.1%. For LR and ANN, the positive predictive values were 78.6% and 76.2%, and the negative predictive values were 73.9% and 76.5%, respectively. The index of diagnostic accuracy was 75.9% for LR and 76.4% for ANN. CONCLUSIONSThe ANN accurately predicted the survival of patients undergoing radical cystectomy for bladder cancer and had a prognostic performance comparable with that of LR. As ANNs are based on easy-to-use software that can identify nonlinear interactions between variables, they might become the preferred tool for predicting outcome.

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“… 25 Some studies have verified that ANN models have the ability to improve prognosis prediction for PCa patients after prostatectomy. 7 10 26 27 28 29 These data have been summarized in Table 5 . One of the first studies in 1994 demonstrated 87% accuracy.…”

Section: Discussionmentioning

confidence: 99%

Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score

Cammann

Meyer

et al. 2014

Asian J Androl

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Many computer models for predicting the risk of prostate cancer have been developed including for prediction of biochemical recurrence (BCR). However, models for individual BCR free probability at individual time-points after a BCR free period are rare. Follow-up data from 1656 patients who underwent laparoscopic radical prostatectomy (LRP) were used to develop an artificial neural network (ANN) to predict BCR and to compare it with a logistic regression (LR) model using clinical and pathologic parameters, prostate-specific antigen (PSA), margin status (R0/1), pathological stage (pT), and Gleason Score (GS). For individual BCR prediction at any given time after operation, additional ANN, and LR models were calculated every 6 months for up to 7.5 years of follow-up. The areas under the receiver operating characteristic (ROC) curve (AUC) for the ANN (0.754) and LR models (0.755) calculated immediately following LRP, were larger than that for GS (AUC: 0.715; P = 0.0015 and 0.001), pT or PSA (AUC: 0.619; P always <0.0001) alone. The GS predicted the BCR better than PSA (P = 0.0001), but there was no difference between the ANN and LR models (P = 0.39). Our ANN and LR models predicted individual BCR risk from radical prostatectomy for up to 10 years postoperative. ANN and LR models equally and significantly improved the prediction of BCR compared with PSA and GS alone. When the GS and ANN output values are combined, a more accurate BCR prediction is possible, especially in high-risk patients with GS ≥7.

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Artificial Neural Network Model To Predict Biochemical Failure After Radical Prostatectomy

Abstract: These results suggest that ANN models can predict PSA failure using readily available preoperative variables. Such predictive models may offer assistance to patients and physicians deciding on definitive therapy for CaP.

Cited by 23 publications

References 10 publications

Artificial Neural Networks for Decision-Making in Urologic Oncology

Artificial Neural Networks for Decision-Making in Urologic Oncology

Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis

Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score

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