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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)

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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.

Abstract
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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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