Technical note: Determining the applicability of a clinical knowledge‐based learning model via prospective outlier detection

Abstract

Background: Knowledge-based planning (KBP) is increasingly implemented clinically because of its demonstrated ability to improve treatment planning efficiency and reduce plan quality variations. However, cases with large dosevolume histogram (DVH) prediction uncertainties may still need manual adjustments by the planner to achieve high plan quality. Purpose: The purpose of this study is to develop a data-driven method to detect patients with high prediction uncertainties so that intentional effort is directed to these patients. Methods: We apply an anomaly detection method known as the local outlier factor (LOF) to a dataset consisting of the training set and each of the prospective patients considered, to evaluate their likelihood of being an anomaly when compared with the training cases. Features used in the LOF analysis include anatomical features and the model-generated DVH principal component scores. To test the efficacy of the proposed model, 142 prostate patients were retrieved from the clinical database and split into a training dataset of 100 patients and a test dataset of 42 patients. The outlier identification performance was quantified by the difference between the DVH prediction root-mean-squared errors (RMSE) of the identified outlier cases and that of the remaining inlier cases. Results: With a predefined LOF threshold of 1.4, the inlier cases achieved average RMSEs of 5.0 and 6.7 for bladder and rectum, while the outlier cases have substantially higher RMSEs of 6.7 and 13.0 in comparison. Conclusions:We propose a method that can determine the prospective patient's outlier status. This method can be integrated into existing automated treatment planning workflows to reduce the risk of generating suboptimal treatment plans while providing an upfront alert to the treatment planner.

Deep learning–based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers

“…Geometric models that correlate patient geometry with achievable dose‐volume histograms (DVHs) 4–8 are advantageous for patient‐specific treatment plan quality assurance 9–11 . With the advent of deep learning, more advanced models that enable the prediction of entire 3D dose distributions have been developed in recent years 12–33 .…”