Your browser doesn't support javascript.
loading
A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma.
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.
Afiliação
  • Mattila KE; Department of Oncology and Radiotherapy, Fican West Cancer Centre, University of Turku and Turku University Hospital, Hämeentie 11, Post Box 52, 20521, Turku, Finland. kalle.mattila@tyks.fi.
  • Laajala TD; Biomathematics Research Group, Fican West Cancer Centre, University of Turku, Turku, Finland.
  • Tornberg SV; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
  • Kilpeläinen TP; Department of Urology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Vainio P; Department of Urology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Ettala O; Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland.
  • Boström PJ; Department of Urology, University of Turku and Turku University Hospital, Turku, Finland.
  • Nisen H; Department of Urology, University of Turku and Turku University Hospital, Turku, Finland.
  • Elo LL; Department of Urology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
  • Jaakkola PM; Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
Sci Rep ; 11(1): 8650, 2021 04 21.
Article em En | MEDLINE | ID: mdl-33883645
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carcinoma de Células Renais / Neoplasias Renais Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Finlândia

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Carcinoma de Células Renais / Neoplasias Renais Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: Sci Rep Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Finlândia