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

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Abstract

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).

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.

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