Cargando…

A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer

Metastatic castration resistant prostate cancer (mCRPC) is one of the most common cancers with a poor prognosis. To improve prognostic models of mCRPC, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Consortium organized a crowdsourced competition known as the Prostate Cancer DR...

Descripción completa

Detalles Bibliográficos
Autores principales: Mahmoudian, Mehrad, Seyednasrollah, Fatemeh, Koivu, Liisa, Hirvonen, Outi, Jyrkkiö, Sirkku, Elo, Laura L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556990/
https://www.ncbi.nlm.nih.gov/pubmed/31231503
http://dx.doi.org/10.12688/f1000research.8192.2
_version_ 1783425401893158912
author Mahmoudian, Mehrad
Seyednasrollah, Fatemeh
Koivu, Liisa
Hirvonen, Outi
Jyrkkiö, Sirkku
Elo, Laura L.
author_facet Mahmoudian, Mehrad
Seyednasrollah, Fatemeh
Koivu, Liisa
Hirvonen, Outi
Jyrkkiö, Sirkku
Elo, Laura L.
author_sort Mahmoudian, Mehrad
collection PubMed
description Metastatic castration resistant prostate cancer (mCRPC) is one of the most common cancers with a poor prognosis. To improve prognostic models of mCRPC, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Consortium organized a crowdsourced competition known as the Prostate Cancer DREAM Challenge. In the competition, data from four phase III clinical trials were utilized. A total of 1600 patients’ clinical information across three of the trials was used to generate prognostic models, whereas one of the datasets (313 patients) was held out for blinded validation. The previously introduced prognostic model of overall survival of chemotherapy-naive mCRPC patients treated with docetaxel or prednisone (so called Halabi model) was used as a performance baseline. This paper presents the model developed by the team TYTDreamChallenge and its improved version to predict the prognosis of mCRPC patients within the first 30 months after starting the treatment based on available clinical features of each patient. In particular, by replacing our original larger set of eleven features with a smaller more carefully selected set of only five features the prediction performance on the independent validation cohort increased up to 5.4 percent. While the original TYTDreamChallenge model (iAUC=0.748) performed similarly as the performance-baseline model developed by Halabi et al. (iAUC=0.743), the improved post-challenge model (iAUC=0.779) showed markedly improved performance by using only PSA, ALP, AST, HB, and LESIONS as features. This highlights the importance of the selection of the clinical features when developing the predictive models.
format Online
Article
Text
id pubmed-6556990
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher F1000 Research Limited
record_format MEDLINE/PubMed
spelling pubmed-65569902019-06-20 A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer Mahmoudian, Mehrad Seyednasrollah, Fatemeh Koivu, Liisa Hirvonen, Outi Jyrkkiö, Sirkku Elo, Laura L. F1000Res Method Article Metastatic castration resistant prostate cancer (mCRPC) is one of the most common cancers with a poor prognosis. To improve prognostic models of mCRPC, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Consortium organized a crowdsourced competition known as the Prostate Cancer DREAM Challenge. In the competition, data from four phase III clinical trials were utilized. A total of 1600 patients’ clinical information across three of the trials was used to generate prognostic models, whereas one of the datasets (313 patients) was held out for blinded validation. The previously introduced prognostic model of overall survival of chemotherapy-naive mCRPC patients treated with docetaxel or prednisone (so called Halabi model) was used as a performance baseline. This paper presents the model developed by the team TYTDreamChallenge and its improved version to predict the prognosis of mCRPC patients within the first 30 months after starting the treatment based on available clinical features of each patient. In particular, by replacing our original larger set of eleven features with a smaller more carefully selected set of only five features the prediction performance on the independent validation cohort increased up to 5.4 percent. While the original TYTDreamChallenge model (iAUC=0.748) performed similarly as the performance-baseline model developed by Halabi et al. (iAUC=0.743), the improved post-challenge model (iAUC=0.779) showed markedly improved performance by using only PSA, ALP, AST, HB, and LESIONS as features. This highlights the importance of the selection of the clinical features when developing the predictive models. F1000 Research Limited 2019-05-17 /pmc/articles/PMC6556990/ /pubmed/31231503 http://dx.doi.org/10.12688/f1000research.8192.2 Text en Copyright: © 2019 Mahmoudian M et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method Article
Mahmoudian, Mehrad
Seyednasrollah, Fatemeh
Koivu, Liisa
Hirvonen, Outi
Jyrkkiö, Sirkku
Elo, Laura L.
A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer
title A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer
title_full A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer
title_fullStr A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer
title_full_unstemmed A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer
title_short A predictive model of overall survival in patients with metastatic castration-resistant prostate cancer
title_sort predictive model of overall survival in patients with metastatic castration-resistant prostate cancer
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556990/
https://www.ncbi.nlm.nih.gov/pubmed/31231503
http://dx.doi.org/10.12688/f1000research.8192.2
work_keys_str_mv AT mahmoudianmehrad apredictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT seyednasrollahfatemeh apredictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT koivuliisa apredictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT hirvonenouti apredictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT jyrkkiosirkku apredictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT elolaural apredictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT mahmoudianmehrad predictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT seyednasrollahfatemeh predictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT koivuliisa predictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT hirvonenouti predictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT jyrkkiosirkku predictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer
AT elolaural predictivemodelofoverallsurvivalinpatientswithmetastaticcastrationresistantprostatecancer