Frequently Asked Questions
Predictive Planning supports prostate and head neck radiotherapy.
In Oncospace, Predictive Planning makes use of sophisticated machine learning models built to support a wide range of flexible protocols.
For a given anatomical treatment site like prostate, these models support protocols for target volumes that may include the prostate bed, or lymph nodes, or not, and delivery options like SIB, hypofractionation, and SBRT.
For H&N planning these models support protocols with target volumes in various anatomies, in unilateral or bilateral configurations, and deliveries with multiple dose levels.
What might be separate models with alternate knowledge-based approaches, and the curating, training, and validating they entail, is covered out of the box cloud with Predictive Planning.
Philips Pinnacle™ 9.8, or later
Oncospace is an International Standards Organization (ISO) 27001 Information Security Management Systems (ISMS) certified company (link) that encompasses all our products, services, and business practices.
To highlight some specific areas of interest,
Patient information: Prior to being uploaded to Oncospace patient data is anonymized and double encrypted via customer and Oncospace key (RSA-2048 implemented in HSM).
- Data in transit: Patient data is protected via browser supported SSH/SSL (TLS 1.2).
- Data at rest: Once in your organization’s dedicated subscription, patient data protected via Microsoft Azure at-rest encryption for file and database storage.
- User Authentication: For a more secure deployment, Oncospace leverages the active directory (AD; Azure or Microsoft) policies and requirements of your organization.
We look forward to reviewing our implementation in the context of your information security requirements
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
Look here for more information on the rich history of Oncospace related publications.
- American Association of Medical Physicists (AAPM) Task Group (TG)-263
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Access Oncospace from almost anywhere with these supported web browsers:
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