Therapieinformationen verbessern auf maschinellem Lernen basierende prognostische Einschätzungen für Patienten mit Weichteilsarkomen.
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 retr...
| Publicado en: | Strahlentherapie und Onkologie Vol. 194; no. 9; pp. 824 - 835 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131372860&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131372860 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01797158 NZE jtl: Strahlentherapie und Onkologie issn: 01797158 maglogo: N pubinfo: dt: Sep2018 vid: 194 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131372860 131372860 NLM29557486 131372860 10.1007/s00066-018-1294-2 NLM29557486 131372860 ppf: 824 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Therapieinformationen verbessern auf maschinellem Lernen basierende prognostische Einschätzungen für Patienten mit Weichteilsarkomen. aug: au: Peeken, Jan C. Goldberg, Tatyana Knie, Christoph Komboz, Basil Bernhofer, Michael Pasa, Francesco Kessel, Kerstin A. Tafti, Pouya D. Rost, Burkhard Nüsslin, Fridtjof Braun, Andreas E. Combs, Stephanie E. affil: Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Straße 22, 81675, Munich, Germany sug: subj: Sarcoma Radiotherapy Cox Proportional Hazards Model Sarcoma Pathology Adult Male Survival Sarcoma Mortality Female Neoadjuvant Therapy Aged, 80 and Over Risk Assessment Retrospective Design Prognosis Middle Age Aged Disease Progression Neoplasm Staging Human Prospective Studies Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Adult: 19-44 years Aged, 80 & over Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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