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Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach
Author(s) -
Hui Cheukkai B.,
Nourzadeh Hamidreza,
Watkins William T.,
Trifiletti Daniel M.,
Alonso Clayton E.,
Dutta Sunil W.,
Siebers Jeffrey V.
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12835
Subject(s) - quality assurance , outlier , computer science , parametric statistics , anomaly detection , artificial intelligence , medical physics , data mining , statistics , medicine , mathematics , external quality assessment , pathology
Purpose To develop a quality assurance ( QA ) tool that identifies inaccurate organ at risk ( OAR ) delineations. Methods The QA tool computed volumetric features from prior OAR delineation data from 73 thoracic patients to construct a reference database. All volumetric features of the OAR delineation are computed in three‐dimensional space. Volumetric features of a new OAR are compared with respect to those in the reference database to discern delineation outliers. A multicriteria outlier detection system warns users of specific delineation outliers based on combinations of deviant features. Fifteen independent experimental sets including automatic, propagated, and clinically approved manual delineation sets were used for verification. The verification OAR s included manipulations to mimic common errors. Three experts reviewed the experimental sets to identify and classify errors, first without; and then 1 week after with the QA tool. Results In the cohort of manual delineations with manual manipulations, the QA tool detected 94% of the mimicked errors. Overall, it detected 37% of the minor and 85% of the major errors. The QA tool improved reviewer error detection sensitivity from 61% to 68% for minor errors ( P = 0.17), and from 78% to 87% for major errors ( P = 0.02). Conclusions The QA tool assists users to detect potential delineation errors. QA tool integration into clinical procedures may reduce the frequency of inaccurate OAR delineation, and potentially improve safety and quality of radiation treatment planning.