Mr. Jorge Galvan Profile

Jorge Galvan

at Univ of Southern California

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Publications (1)

Proceedings Article | 2 April 2024 Presentation + Paper

Data-mining and machine learning for knowledge-based treatment planning support in radiation therapy of head and neck cancer using anatomical structure and tumor position

Trent Benedick, Jorge Galvan, Wejdan Ali Alshehri, Ryan Fue, John Asbach, Anh Le, Brent Liu

Proceedings Volume 12931, 1293107 (2024) https://doi.org/10.1117/12.3006269

KEYWORDS: Radiotherapy, Tumors, Anatomy, Neck, Head, Cancer, Oncology, Databases, Information science, Biomedical applications

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Radiation Therapy seeks to treat cancers through the dosage of destructive radiation to target volumes. The treatment plans, detailing the application of radiation dosage, are currently created to adhere to formal guidelines and target dose levels based on physician experience and trial-and-error rather than standard quantitative methods. We propose a web-based informatics application to introduce data driven methods and uniformity into radiation therapy treatment plan creation. We use a quantitative comparison of tumor position and structural anatomy between retrospective cases and a current case undergoing treatment planning to identify useful and relevant retrospective treatment plans for use as templates and reference during current treatment plan creation. The system is based on a database of 403 retrospective DICOM RT objects from University of California Los Angeles and State University of New York Buffalo; Roswell Park as well as the quantitative features we extract from each case. The quantitative identifiers we develop and use in our feature extraction process are the Overlap Value Histogram (OVH) and the Spatial Target Similarity (STS) calculated between the tumor volume and each Organ At Risk (OAR) of irradiation. The similarity between each retrospective case and the current case is the gower’s distance sum of all the earth mover’s distance values calculated between the OVHs and STSs for each OAR in common between the two cases. By calculating the similarity between the current case and each retrospective case we construct a similarity index from which clinicians can select cases they deem useful in their current treatment planning process. Case outcomes will be stored in our database allowing the discovery of correlations between the structural anatomy, tumor position, treatment plans, and outcome, enabling treatment plan benchmarking. These methods allow our informatics system to increase usage of data driven methodologies and standardized practices in radiation therapy treatment planning.

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