A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma

Abstract After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. Model training was perf...

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Autores principales:	Kalle E. Mattila, Teemu D. Laajala, Sara V. Tornberg, Tuomas P. Kilpeläinen, Paula Vainio, Otto Ettala, Peter J. Boström, Harry Nisen, Laura L. Elo, Panu M. Jaakkola
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f097036ba65447a59e243f07da006031
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spelling	oai:doaj.org-article:f097036ba65447a59e243f07da0060312021-12-02T17:32:56ZA three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma10.1038/s41598-021-88177-92045-2322https://doaj.org/article/f097036ba65447a59e243f07da0060312021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88177-9https://doaj.org/toc/2045-2322Abstract After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. Model training was performed on 196 patients. Right-censored metastasis-free survival was analysed using LASSO-regularized Cox regression, which identified three key prediction features. The model was validated in an external cohort of 714 patients. 55 (28%) and 134 (19%) patients developed distant metastases during the median postoperative follow-up of 6.3 years (interquartile range 3.4–8.6) and 5.4 years (4.0–7.6) in the training and validation cohort, respectively. Patients were stratified into clinically meaningful risk categories using only three features: tumor size, tumor grade and microvascular invasion, and a representative nomogram and a visual prediction surface were constructed using these features in Cox proportional hazards model. Concordance indices in the training and validation cohorts were 0.755 ± 0.029 and 0.836 ± 0.015 for our novel model, which were comparable to the C-indices of the original Leibovich prediction model (0.734 ± 0.035 and 0.848 ± 0.017, respectively). Thus, the presented model retains high accuracy while requiring only three features that are routinely collected and widely available.Kalle E. MattilaTeemu D. LaajalaSara V. TornbergTuomas P. KilpeläinenPaula VainioOtto EttalaPeter J. BoströmHarry NisenLaura L. EloPanu M. JaakkolaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kalle E. Mattila Teemu D. Laajala Sara V. Tornberg Tuomas P. Kilpeläinen Paula Vainio Otto Ettala Peter J. Boström Harry Nisen Laura L. Elo Panu M. Jaakkola A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
description	Abstract After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. Model training was performed on 196 patients. Right-censored metastasis-free survival was analysed using LASSO-regularized Cox regression, which identified three key prediction features. The model was validated in an external cohort of 714 patients. 55 (28%) and 134 (19%) patients developed distant metastases during the median postoperative follow-up of 6.3 years (interquartile range 3.4–8.6) and 5.4 years (4.0–7.6) in the training and validation cohort, respectively. Patients were stratified into clinically meaningful risk categories using only three features: tumor size, tumor grade and microvascular invasion, and a representative nomogram and a visual prediction surface were constructed using these features in Cox proportional hazards model. Concordance indices in the training and validation cohorts were 0.755 ± 0.029 and 0.836 ± 0.015 for our novel model, which were comparable to the C-indices of the original Leibovich prediction model (0.734 ± 0.035 and 0.848 ± 0.017, respectively). Thus, the presented model retains high accuracy while requiring only three features that are routinely collected and widely available.
format	article
author	Kalle E. Mattila Teemu D. Laajala Sara V. Tornberg Tuomas P. Kilpeläinen Paula Vainio Otto Ettala Peter J. Boström Harry Nisen Laura L. Elo Panu M. Jaakkola
author_facet	Kalle E. Mattila Teemu D. Laajala Sara V. Tornberg Tuomas P. Kilpeläinen Paula Vainio Otto Ettala Peter J. Boström Harry Nisen Laura L. Elo Panu M. Jaakkola
author_sort	Kalle E. Mattila
title	A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
title_short	A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
title_full	A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
title_fullStr	A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
title_full_unstemmed	A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
title_sort	three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f097036ba65447a59e243f07da006031
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A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma

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