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Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.

Publication ,  Journal Article
Sheng, Y; Zhang, J; Wang, C; Yin, F-F; Wu, QJ; Ge, Y
Published in: Technol Cancer Res Treat
January 1, 2019

Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively (P = .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction (P = .1972). By retaining novel cases, under the Ample scenario, significant statistical improvement was observed over the Scarce scenario (P = .0398) for the bladder model. We expect that the incorporation of case-based reasoning that judiciously applies appropriate predictive models could improve overall prediction accuracy and robustness in clinical practice.

Duke Scholars

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Published In

Technol Cancer Res Treat

DOI

EISSN

1533-0338

Publication Date

January 1, 2019

Volume

18

Start / End Page

1533033819874788

Location

United States

Related Subject Headings

  • Reproducibility of Results
  • Radiotherapy, Intensity-Modulated
  • Radiotherapy, Image-Guided
  • Radiotherapy Planning, Computer-Assisted
  • Radiometry
  • Pelvis
  • Organs at Risk
  • Oncology & Carcinogenesis
  • Models, Theoretical
  • Humans
 

Citation

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Sheng, Y., Zhang, J., Wang, C., Yin, F.-F., Wu, Q. J., & Ge, Y. (2019). Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study. Technol Cancer Res Treat, 18, 1533033819874788. https://doi.org/10.1177/1533033819874788
Sheng, Yang, Jiahan Zhang, Chunhao Wang, Fang-Fang Yin, Q Jackie Wu, and Yaorong Ge. “Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.Technol Cancer Res Treat 18 (January 1, 2019): 1533033819874788. https://doi.org/10.1177/1533033819874788.
Sheng Y, Zhang J, Wang C, Yin F-F, Wu QJ, Ge Y. Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study. Technol Cancer Res Treat. 2019 Jan 1;18:1533033819874788.
Sheng, Yang, et al. “Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.Technol Cancer Res Treat, vol. 18, Jan. 2019, p. 1533033819874788. Pubmed, doi:10.1177/1533033819874788.
Sheng Y, Zhang J, Wang C, Yin F-F, Wu QJ, Ge Y. Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study. Technol Cancer Res Treat. 2019 Jan 1;18:1533033819874788.
Journal cover image

Published In

Technol Cancer Res Treat

DOI

EISSN

1533-0338

Publication Date

January 1, 2019

Volume

18

Start / End Page

1533033819874788

Location

United States

Related Subject Headings

  • Reproducibility of Results
  • Radiotherapy, Intensity-Modulated
  • Radiotherapy, Image-Guided
  • Radiotherapy Planning, Computer-Assisted
  • Radiometry
  • Pelvis
  • Organs at Risk
  • Oncology & Carcinogenesis
  • Models, Theoretical
  • Humans