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A rapid learning approach for the knowledge modeling of radiation therapy plan

Publication ,  Conference
Yuan, L; Ge, Y; Yin, F; Wu, QJ
Published in: IFMBE Proceedings
January 1, 2015

The purpose of this study is to implement a rapid learning method to train the knowledge models to predict the organ-at-risk (OAR) dose sparing in radiation therapy (RT) based on an array of patient anatomical features. We also aim to establish the evaluation criteria and validation method to ensure an accurate and efficient learning process. A rapid learning approach is utilized to train the knowledge models in this study. 100 clinical cancer cases in the pelvic region were retrospectively analyzed. Among them, 40 cases are low-to-intermediate risk prostate cases (Type I), 20 are high-risk prostate cases with lymph node irradiation (Type II), 40 are anorectal cancer cases (Type III). Starting from a base model for type I cases, increasing number of cases with more complex planning-target-volume (PTV)-OAR anatomies (type II and type III) were continuously added into the training case pool. The studentized residual and the leverage values are calculated as evaluation criteria at each step. The efficiency and accuracy of the learning method was quantified by the learning curve. The gEUD in the bladder and rectum are compared between the model predictions and actual values for the validation cases. The Median of the Absolute value of their Differences (MAD) are calculated for the validation cases. The MAD of the predicted OAR gEUD in all three types of cases gradually decreases when increasing number of training cases are added in training. The knowledge models learned by this method reach comparable level of prediction accuracies as in batch training mode even less training cases. The rapid learning approach is able to learn knowledge models for multiple cancer types in the pelvic region with comparable accuracy to the batch training method and with improved efficiency. This approach will facilitate the implementation of the knowledge based radiation therapy planning in clinics.

Duke Scholars

Published In

IFMBE Proceedings

DOI

ISSN

1680-0737

ISBN

9783319193878

Publication Date

January 1, 2015

Volume

51

Start / End Page

1492 / 1494
 

Citation

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Yuan, L., Ge, Y., Yin, F., & Wu, Q. J. (2015). A rapid learning approach for the knowledge modeling of radiation therapy plan. In IFMBE Proceedings (Vol. 51, pp. 1492–1494). https://doi.org/10.1007/978-3-319-19387-8_362
Yuan, L., Y. Ge, F. Yin, and Q. J. Wu. “A rapid learning approach for the knowledge modeling of radiation therapy plan.” In IFMBE Proceedings, 51:1492–94, 2015. https://doi.org/10.1007/978-3-319-19387-8_362.
Yuan L, Ge Y, Yin F, Wu QJ. A rapid learning approach for the knowledge modeling of radiation therapy plan. In: IFMBE Proceedings. 2015. p. 1492–4.
Yuan, L., et al. “A rapid learning approach for the knowledge modeling of radiation therapy plan.” IFMBE Proceedings, vol. 51, 2015, pp. 1492–94. Scopus, doi:10.1007/978-3-319-19387-8_362.
Yuan L, Ge Y, Yin F, Wu QJ. A rapid learning approach for the knowledge modeling of radiation therapy plan. IFMBE Proceedings. 2015. p. 1492–1494.
Journal cover image

Published In

IFMBE Proceedings

DOI

ISSN

1680-0737

ISBN

9783319193878

Publication Date

January 1, 2015

Volume

51

Start / End Page

1492 / 1494