An anomaly detector as a clinical decision support system for parotid gland delineations

Abstract

Purpose. Auto-contouring (AC) is rapidly becoming standard practice for OAR contouring. However, in clinical practice, clinicians still need to manually check and correct contours. Anomaly detection systems (ADS) can aid the clinical decision process by suggesting which structures require corrections or not, greatly enhancing the value of AC. The purpose of this work is to develop and evaluate a decision support system for detecting anomalies in the case of parotid gland delineations. Methods. Head and neck parotid gland delineations (1037 right, 1038 left), were retrieved from the Netherlands Cancer Institute (NKI) database. Morphological and image-based features were extracted from each patient’s CT and structure set. An isolation forest model was initially trained on 70% of the data, of which 10% had synthetically generated anomalies and validated on the remaining 30% of clinical data. The ADS was tested on an independent set of 250 patients (Normal: 174, Anomalies: 76) and on a clinical autocontouring software. Results. Applied to the validation set, the ADS system resulted in area under the curve (AUC) values of 0.93 and 0.94 for the parotid left and right respectively. Image features appeared more important than morphological, but using all features resulted marginally in the best model. Applied to the test set the ADS system reached an accuracy level of 0.83 and 0.81 for the parotid left and right respectively. The ADS was particularly sensitive to uniform expansions/contractions, misplacements, extra/missing slices and anisotropic over-contouring. Conclusion. Anomaly detection can serve as a powerful contour quality assurance tool, especially for cases of organ misplacement and over-contouring.