Oncospace Presents at Big Data Workshop 2023
Oncospace congratulates the team of Julie Shade Ph.D., Pranav Lakshminarayanan, Michael Bowers, David Murray Ph.D., Peter Hoban Ph.D., Todd McNutt Ph.D. on having our latest accomplishments selected for presentation at this year’s Practical Big Data Workshop in Ann Arbor:
“The Practical Big Data Workshop series brings together global innovators in radiation therapy and diagnostic imaging who are leading in development and use of big data and artificial intelligence to improve care for cancer patients.”
The presentations focused on describing Oncospace accomplishments in delivering on two key aspects our vision to enable radiation oncology clinicians to optimize outcomes for the patient and the practice: data curation and Predictive Planning*.
In the first abstract, Oncospace demonstrated success with two curation steps essential for radiation therapy planning data use in retrospective analytical studies and to develop machine learning models that predict achievable dose for a patient: 1) standardizing organ-at-risk (OAR) nomenclature across training and validation datasets and 2) ensuring that target volume(s) and associated intended doses are accurately identified (intended target/dose or ITD).
https://oncospace.com/shade-pbdw-2023-abstract-data-curation/
In the second abstract, Oncospace demonstrated success creating a comprehensive, clinical-site-independent dose-volume-histogram (DVH) prediction platform for photon beam intensity-modulated radiotherapy. In contrast to prior work, a single model per OAR to predict DVHs for plans with any protocol, number of targets, and target geometry (ex. prostate-only vs. prostate-plus-nodes, nasopharynx vs. neck) is used. The predicted DVHs can be used for plan evaluation or to set initial optimization objectives for treatment planning.
https://oncospace.com/shade-pbdw-2023-abstract-dose-prediction/
* Predictive Planning for prostate and head & neck radiotherapy is FDA 510(k) cleared and for sale in the United States only.
