Frequently Asked Questions
For a treatment plan with a defined dose prescription to one or more target volumes, Oncospace Predictive Planning predicts achievable dose volume histograms (DVH) for organs at risk (OAR). The machine learning (ML) model used for dose predictions takes as inputs the prescription and a range of features describing the spatial relationship between the target(s) and each OAR. For each DVH volume, a dose range is predicted representing the level of OAR dose sparing that should be achievable.
Oncospace Predictive Planning uses a regression ensemble ML model, including feature extraction and 5-fold cross-validation for hyperparameter optimization.
- Wu, B., Ricchetti, F., Sanguineti, G., Kazhdan, M., Simari, P., Jacques, R., Taylor, R., McNutt, T. Data-driven approach to generating achievable dose-volume histogram objectives in intensity modulated radiotherapy planning. International Journal of Radiation Oncology, Biology, Physics 2011 Mar 15;79(4):1241-7. Epub 2010 Aug
Yibing Wang, Andras Zolnay, Luca Incrocci, Hans Joosten, Todd McNutt, Ben Heijmen, Steven Petit. A quality control model that uses PTV-rectal distances to predict the lowest achievable rectum dose, improves IMRT planning for patients with prostate cancer. Radiotherapy and Oncology Volume 107, Issue 3, June 2013, Pages 352-357
- American Association of Medical Physicists (AAPM) Task Group (TG)-263
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