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Technical note: Determining the applicability of a clinical knowledge-based learning model via prospective outlier
Jiahan Zhang1, Yang Sheng2, Jonathan Wolf1
1Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA.
Medical Physics
|February 14, 2022
Summary
This study introduces a data-driven method using local outlier factor (LOF) to identify patients with high uncertainties in knowledge-based planning (KBP), improving treatment plan quality.
Area of Science:
- Medical Physics
- Radiation Oncology
- Machine Learning in Healthcare
Background:
- Knowledge-based planning (KBP) enhances treatment planning efficiency and consistency.
- High uncertainties in dose-volume histogram (DVH) predictions can necessitate manual adjustments for optimal plan quality.
Purpose of the Study:
- To develop a data-driven approach for detecting patients with significant KBP prediction uncertainties.
- To enable targeted manual intervention for patients requiring extra attention.
Main Methods:
- Applied local outlier factor (LOF) anomaly detection to identify outlier cases.
- Utilized anatomical features and DVH principal component scores for LOF analysis.
- Validated the model on 142 prostate cancer patients (100 training, 42 testing).
Main Results:
- The LOF method successfully distinguished between inlier and outlier cases.
- Outlier cases exhibited significantly higher root-mean-squared errors (RMSEs) for bladder and rectum DVH predictions (e.g., 13.0 vs. 6.7 for rectum).
Conclusions:
- A novel method is proposed to identify prospective patients with outlier status in KBP.
- Integration into automated workflows can mitigate risks of suboptimal plans and alert planners proactively.
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