Mr. Trent Benedick Profile

Trent Benedick

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Publications (4)

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.

Proceedings Article | 10 April 2023 Presentation + Paper

Utilizing data-mining and deep learning methods on retrospective head and neck radiation therapy cases for decision support of individualized treatment planning

Trent Benedick, Mengxuan Li, Emilio Wang, Sachi Pawooskar-Almeida, Anh Le, Brent Liu

Proceedings Volume 12469, 1246906 (2023) https://doi.org/10.1117/12.2656180

KEYWORDS: Radiotherapy, Feature extraction, Cancer, Neck, Head, Databases, Tumors, Decision support systems, Pathology, Standards development

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Proceedings Article | 4 April 2022 Presentation

Head and neck cancer radiation therapy treatment planning decision support and benchmarking

Trent Benedick, Xinming Li, Lisa Meng, Eric Manning, Ahn Le, Brent Liu

Proceedings Volume PC12037, PC120370C (2022) https://doi.org/10.1117/12.2613538

KEYWORDS: Radiotherapy, Tumors, Databases, Feature extraction, Detection and tracking algorithms, Information science

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Proceedings Article | 15 March 2019 Presentation + Paper

Head and neck cancer radiation therapy decision support

Trent Benedick, Veda Murthy, Rhea Koparde, Maying Shi, Sabrina Lieu, Siliang Zhang, Benjamin Yao, Anh Le, Brent Liu

Proceedings Volume 10954, 109540Y (2019) https://doi.org/10.1117/12.2513075

KEYWORDS: Cancer, Radiotherapy, Decision support systems, Head, Neck, Feature extraction, Tumors, Databases, Oncology, Information science

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Currently the methods used to develop radiation therapy treatment plans for head and neck cancers rely on clinician experience and a small set of universal guidelines which result in inconsistent and variable methods. Data driven support can provide assistance to clinicians by reducing inconsistency associated with treatment planning and provide empirical estimates to minimize the radiation to healthy organs near the tumor. We created a database of DICOM RT objects which stores historical cases and when a new DICOM object is uploaded it will return a set of similar treatment plans to assist the clinician in creating the treatment plan for the current patient. The database works first by extracting features from DICOM RT object to quantitatively compare and evaluate the similarity of cases enabling the system to mine for cases with defined similarity. The feature extraction methods are based on the spatial relationships between the tumors and organs at risk which allows the generation the overlap volume histogram and spatial target similarity which demonstrate the volumetric and locational similarity between the organ at risk and the tumor. It is useful to find cases with similar tumor anatomy because this similarity translates to similarity in radiation dosage. The developed system was applied to three different RT sites, University of California Los Angeles, Technical University at Munich and State University of New York Buffalo; Roswell Park, with a total of 247 cases to evaluate the system for both inter- and intra- institutional best practices and results. Future roadmap will be discussed for correlating outcomes results to the decision support system which will enhance the overall performance and utilization of the decision support system in the RT workflow. In the future, because this database returns similar historical cases to a current one this could be a worthwhile decision support tool for clinicians as they create new radiation therapy treatment plans.

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