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Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases.

Publication ,  Journal Article
Sheng, Y; Ge, Y; Yuan, L; Li, T; Yin, F-F; Wu, QJ
Published in: Med Phys
November 2017

PURPOSE: The purpose of this study was to apply statistical metrics to identify outliers and to investigate the impact of outliers on knowledge-based planning in radiation therapy of pelvic cases. We also aimed to develop a systematic workflow for identifying and analyzing geometric and dosimetric outliers. METHODS: Four groups (G1-G4) of pelvic plans were sampled in this study. These include the following three groups of clinical IMRT cases: G1 (37 prostate cases), G2 (37 prostate plus lymph node cases) and G3 (37 prostate bed cases). Cases in G4 were planned in accordance with dynamic-arc radiation therapy procedure and include 10 prostate cases in addition to those from G1. The workflow was separated into two parts: 1. identifying geometric outliers, assessing outlier impact, and outlier cleaning; 2. identifying dosimetric outliers, assessing outlier impact, and outlier cleaning. G2 and G3 were used to analyze the effects of geometric outliers (first experiment outlined below) while G1 and G4 were used to analyze the effects of dosimetric outliers (second experiment outlined below). A baseline model was trained by regarding all G2 cases as inliers. G3 cases were then individually added to the baseline model as geometric outliers. The impact on the model was assessed by comparing leverages of inliers (G2) and outliers (G3). A receiver-operating-characteristic (ROC) analysis was performed to determine the optimal threshold. The experiment was repeated by training the baseline model with all G3 cases as inliers and perturbing the model with G2 cases as outliers. A separate baseline model was trained with 32 G1 cases. Each G4 case (dosimetric outlier) was subsequently added to perturb the model. Predictions of dose-volume histograms (DVHs) were made using these perturbed models for the remaining 5 G1 cases. A Weighted Sum of Absolute Residuals (WSAR) was used to evaluate the impact of the dosimetric outliers. RESULTS: The leverage of inliers and outliers was significantly different. The Area-Under-Curve (AUC) for differentiating G2 (outliers) from G3 (inliers) was 0.98 (threshold: 0.27) for the bladder and 0.81 (threshold: 0.11) for the rectum. For differentiating G3 (outlier) from G2 (inlier), the AUC (threshold) was 0.86 (0.11) for the bladder and 0.71 (0.11) for the rectum. Significant increase in WSAR was observed in the model with 3 dosimetric outliers for the bladder (P < 0.005 with Bonferroni correction), and in the model with only 1 dosimetric outlier for the rectum (P < 0.005). CONCLUSIONS: We established a systematic workflow for identifying and analyzing geometric and dosimetric outliers, and investigated statistical metrics for outlier detection. Results validated the necessity for outlier detection and clean-up to enhance model quality in clinical practice.

Duke Scholars

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

November 2017

Volume

44

Issue

11

Start / End Page

5617 / 5626

Location

United States

Related Subject Headings

  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Radiometry
  • Prostatic Neoplasms
  • Pelvis
  • Organs at Risk
  • Nuclear Medicine & Medical Imaging
  • Male
  • Humans
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Sheng, Y., Ge, Y., Yuan, L., Li, T., Yin, F.-F., & Wu, Q. J. (2017). Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases. Med Phys, 44(11), 5617–5626. https://doi.org/10.1002/mp.12556
Sheng, Yang, Yaorong Ge, Lulin Yuan, Taoran Li, Fang-Fang Yin, and Qingrong Jackie Wu. “Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases.Med Phys 44, no. 11 (November 2017): 5617–26. https://doi.org/10.1002/mp.12556.
Sheng Y, Ge Y, Yuan L, Li T, Yin F-F, Wu QJ. Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases. Med Phys. 2017 Nov;44(11):5617–26.
Sheng, Yang, et al. “Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases.Med Phys, vol. 44, no. 11, Nov. 2017, pp. 5617–26. Pubmed, doi:10.1002/mp.12556.
Sheng Y, Ge Y, Yuan L, Li T, Yin F-F, Wu QJ. Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases. Med Phys. 2017 Nov;44(11):5617–5626.

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

November 2017

Volume

44

Issue

11

Start / End Page

5617 / 5626

Location

United States

Related Subject Headings

  • Radiotherapy Planning, Computer-Assisted
  • Radiotherapy Dosage
  • Radiometry
  • Prostatic Neoplasms
  • Pelvis
  • Organs at Risk
  • Nuclear Medicine & Medical Imaging
  • Male
  • Humans
  • Algorithms