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Dose Prediction and Customized Optimization Settings by Learning From Previous Cases: Application to SBRT Treatment Planning

E Schreibmann*, Department of Radiation Oncology and Winship Cancer Institute of Emory University


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: To report on the dose-prediction approach that uses a search engine to find cases of similar anatomy in a database of peer-reviewed cases. We show that contrary to the traditional knowledge-based algorithms, the presented approach retains all information within the original plans and can be used in clinical practice to automate the planning process.

Methods: This work uses a recent mathematical formalism developed for shape analysis to quantify segmentations. To construct the search data, all cases were aligned to a reference dataset. Variance for each PTV point was quantified to obtain a shape’s eigenvectors describing its discrepancies from the reference dataset. When planning a new case, the procedure is repeated to align the new case to the reference dataset. Corresponding eigenvalues are compared to each database case using a Mahalanobis distance quantifying their similarities. The search procedure outputs the list of cases in the database ordered by anatomical similarity to a new patient’s volume. The previously attained dose is used as a prediction. Additionally, the strategy to create the dose fall-off with ring structures is replicated from the database match to the new patient CT dataset, with an ESAPI script that automatically creates a treatment plan based on settings reproduced from the previous treatment.

Results: The approach was tested on lung cases treated with SBRT, where a script using common arcs and optimization options did produce sub-optimal results when compared to the clinical plans. However, when using settings learned from the database, plans of comparable quality were obtained across a wide variety of tumor locations and sizes.

Conclusion: From a practical point of view, the system delivered feedback on beam/arc directions that worked in the past for tumors of similar shapes, guiding plan parameters selection that improved the accuracy of automated planning methods.


Image Analysis, Shape Analysis, Shape Deformation


TH- Dataset Analysis/Biomathematics: Machine learning techniques

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