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

Therapieinformationen verbessern auf maschinellem Lernen basierende prognostische Einschätzungen für Patienten mit Weichteilsarkomen

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Abstract

Background and purpose

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.

Materials and methods

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.

Results

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.

Conclusions

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.

Zusammenfassung

Hintergrund und Zielsetzung

Aktuelle prognostische Modelle für Patienten mit Weichteilsarkomen basieren primär auf Staginginformationen. Therapieinformationen werden dabei nicht berücksichtigt. Die Berücksichtigung solcher Daten könnte die Vorhersage verbessern.

Material und Methoden

Für eine retrospektive, monozentrische, strahlentherapeutisch behandelte Kohorte mit 136 Weichteilsarkompatienten wurden Patientencharakteristika, Staging und therapieassoziierte Daten erhoben. Potenzielle mit dem Therapieansprechen assoziierte Informationen von neoadjuvant behandelten Patienten wurden aus therapeutischen Magnetresonanztomographie(MRT)-Datensätzen und pathologischen Befunden erhoben. Auf Basis dieser Informationen wurden Random-Forest-Modelle für die Vorhersage des 2‑Jahres-Überlebens bzw. des Progresses generiert. Prätherapie- und Therapiemodelle wurden verglichen.

Ergebnisse

Die prognostischen Modelle zeigten insgesamt eine gute Vorhersagekraft. Die Hinzunahme von Therapieinformationen konnte die Vorhersageeffizienz der 3 Klassifikationen verbessern: Vorhersage des Versterbens sowie des lokalen und systemischen Progresses („area under the receiver operating or characteristic curve“ [AUC] je 0,87, 0,88 und 0,84). Strahlentherapieassoziierte Informationen wie das Planungszielvolumen und die Gesamtdosis hatten einen großen Einfluss auf die Vorhersagekraft. Mit dem Therapieansprechen assoziierte Informationen wurden für die Vorhersage des Progresses selektiert und zeigten so eine mögliche prognostische Bedeutung.

Schlussfolgerung

Auf maschinellem Lernen basierende prognostische Modelle zeigten eine hohe Genauigkeit für die Vorhersage des Überlebens und Krankheitsprogresses durch Einschluss von Informationen zur Therapie und zum Therapieansprechen. Diese Modelle könnten für die individuelle Risikoabschätzung in der Nachsorge verwendet werden.

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Funding

Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich (to JP, KK, SC), Alexander von Humboldt Foundation through German Federal Ministry for Education and Research, and the Bavarian Competence Network for Technical and Scientific High Performance Computing (to MB and BR), Allianz (to TG, BK, PT and AB).

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Authors and Affiliations

  1. Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany

    Jan C. Peeken, Christoph Knie, Kerstin A. Kessel, Fridtjof Nüsslin & Stephanie E. Combs

  2. Allianz SE, Königinstraße 28, 80802, Munich, Germany

    Tatyana Goldberg, Basil Komboz, Pouya D. Tafti & Andreas E. Braun

  3. Department for Bioinformatics and Computational Biology, Informatik 12, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany

    Michael Bernhofer & Burkhard Rost

  4. Department of Computer Science, Informatik 9, Technical University of Munich (TUM), Boltzmannstraße 3, 85748, Garching, Germany

    Francesco Pasa

  5. Chair of Biomedical Physics, Department of Physics, Technical University of Munich (TUM), James-Franck-Straße 1, 85748, Garching, Germany

    Francesco Pasa

  6. Institute of Innovative Radiotherapy (iRT), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Ingolstaedter Landstraße 1, 85764, Neuherberg, Germany

    Kerstin A. Kessel & Stephanie E. Combs

  7. Partner Site Munich, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Munich, Germany

    Jan C. Peeken, Kerstin A. Kessel & Stephanie E. Combs

Authors
  1. Jan C. Peeken
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  2. Tatyana Goldberg
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  3. Christoph Knie
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  4. Basil Komboz
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  10. Fridtjof Nüsslin
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  12. Stephanie E. Combs
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Corresponding author

Correspondence to Jan C. Peeken.

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Conflict of interest

J.C. Peeken, T. Goldberg, C. Knie, B. Komboz, M. Bernhofer, F. Pasa, K.A. Kessel, P.D. Tafti, B. Rost, F. Nüsslin, A.E. Braun, and S.E. Combs declare that they have no competing interests.

Ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Additional information

Both authors contributed equally: Jan C. Peeken, Tatyana Goldberg.

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66_2018_1294_MOESM1_ESM.docx

Supplemental Tables and Supplemental Figure. Table S1: Performance assessment of six random forest-based prediction models. Table S2: Comparison of AUC values on training and test sets of six random forest-based prediction models. Figure S1: Precision/Recall curves for the spectrum of reliability scores for the pre-treatment models.

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Peeken, J.C., Goldberg, T., Knie, C. et al. Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients. Strahlenther Onkol 194, 824–834 (2018). https://doi.org/10.1007/s00066-018-1294-2

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