Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score
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 underwen...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2014-12-01
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| Series: | Asian Journal of Andrology |
| Subjects: | |
| Online Access: | http://www.ajandrology.com/article.asp?issn=1008-682X;year=2014;volume=16;issue=6;spage=897;epage=901;aulast=Hu |
| _version_ | 1818457398427254784 |
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| author | Xin-Hai Hu Henning Cammann Hellmuth-A Meyer Klaus Jung Hong-Biao Lu Natalia Leva Ahmed Magheli Carsten Stephan Jonas Busch |
| author_facet | Xin-Hai Hu Henning Cammann Hellmuth-A Meyer Klaus Jung Hong-Biao Lu Natalia Leva Ahmed Magheli Carsten Stephan Jonas Busch |
| author_sort | Xin-Hai Hu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T22:41:56Z |
| format | Article |
| id | doaj.art-5b5c2620a8fd438388ef7d2a5db63982 |
| institution | Directory Open Access Journal |
| issn | 1008-682X 1745-7262 |
| language | English |
| last_indexed | 2024-12-14T22:41:56Z |
| publishDate | 2014-12-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Asian Journal of Andrology |
| spelling | doaj.art-5b5c2620a8fd438388ef7d2a5db639822022-12-21T22:44:58ZengWolters Kluwer Medknow PublicationsAsian Journal of Andrology1008-682X1745-72622014-12-0116689790110.4103/1008-682X.129940Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason scoreXin-Hai HuHenning CammannHellmuth-A MeyerKlaus JungHong-Biao Lu Natalia LevaAhmed MagheliCarsten StephanJonas BuschMany 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.http://www.ajandrology.com/article.asp?issn=1008-682X;year=2014;volume=16;issue=6;spage=897;epage=901;aulast=Huartificial neural network; prostate cancer; recurrence |
| spellingShingle | Xin-Hai Hu Henning Cammann Hellmuth-A Meyer Klaus Jung Hong-Biao Lu Natalia Leva Ahmed Magheli Carsten Stephan Jonas Busch Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score Asian Journal of Andrology artificial neural network; prostate cancer; recurrence |
| title | Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score |
| title_full | Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score |
| title_fullStr | Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score |
| title_full_unstemmed | Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score |
| title_short | Risk prediction models for biochemical recurrence after radical prostatectomy using prostate-specific antigen and Gleason score |
| title_sort | risk prediction models for biochemical recurrence after radical prostatectomy using prostate specific antigen and gleason score |
| topic | artificial neural network; prostate cancer; recurrence |
| url | http://www.ajandrology.com/article.asp?issn=1008-682X;year=2014;volume=16;issue=6;spage=897;epage=901;aulast=Hu |
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