Mattila, Kalle E.;
Laajala, Teemu D.;
Tornberg, Sara V.;
Kilpeläinen, Tuomas P.;
Vainio, Paula;
Ettala, Otto;
Boström, Peter J.;
Nisen, Harry;
Elo, Laura L.;
Jaakkola, Panu M.
A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
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Medientyp:
E-Artikel
Titel:
A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma
Beteiligte:
Mattila, Kalle E.;
Laajala, Teemu D.;
Tornberg, Sara V.;
Kilpeläinen, Tuomas P.;
Vainio, Paula;
Ettala, Otto;
Boström, Peter J.;
Nisen, Harry;
Elo, Laura L.;
Jaakkola, Panu M.
Erschienen:
Springer Science and Business Media LLC, 2021
Erschienen in:Scientific Reports
Sprache:
Englisch
DOI:
10.1038/s41598-021-88177-9
ISSN:
2045-2322
Entstehung:
Anmerkungen:
Beschreibung:
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>