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Peeken, J.C.* ; Goldberg, T.* ; Knie, C.* ; Komboz, B.* ; Bernhofer, M.* ; Pasa, F.* ; Kessel, K.A. ; Tafti, P.D.* ; Rost, B.* ; Nüsslin, F.* ; Braun, A.E.* ; Combs, S.E.

Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients.

Strahlenther. Onkol. 194, 824-834 (2018)
DOI Verlagsversion bestellen
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these models.A single-center retrospective cohort of 136 STS patients treated with radiotherapy (RT) was analyzed for patients' characteristics, staging information, and treatment-related data. Therapeutic imaging studies and pathology reports of neoadjuvantly treated patients were analyzed for signs of response. Random forest machine learning-based models were used to predict patients' death and disease progression at 2 years. Pre-treatment and treatment models were compared.The prognostic models achieved high performances. Using treatment features improved the overall performance for all three classification types: prediction of death, and of local and systemic progression (area under the receiver operatoring characteristic curve (AUC) of 0.87, 0.88, and 0.84, respectively). Overall, RT-related features, such as the planning target volume and total dose, had preeminent importance for prognostic performance. Therapy response features were selected for prediction of disease progression.A machine learning-based prognostic model combining known prognostic factors with treatment- and response-related information showed high accuracy for individualized risk assessment. This model could be used for adjustments of follow-up procedures.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Biomarker ; Precision Medicine ; Prognostic Model ; Random Forest ; Decision Support Systems; Random Forests; Postoperative Nomogram; Cancer; Classification; Extremities
ISSN (print) / ISBN 0179-7158
e-ISSN 1439-099X
Quellenangaben Band: 194, Heft: 9, Seiten: 824-834 Artikelnummer: , Supplement: ,
Verlag Urban & Vogel
Verlagsort Tiergartenstrasse 17, D-69121 Heidelberg, Germany
Begutachtungsstatus Peer reviewed