Clinically-oriented Contour Evaluation using Dosimetric Indices Generated from Automated Knowledge-based Planning.

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Clinically-oriented Contour Evaluation using Dosimetric Indices Generated from Automated Knowledge-based Planning.

Int J Radiat Oncol Biol Phys. 2018 Nov 30;:

Authors: Lim TY, Gillespie E, Murphy J, Moore KL

Abstract
PURPOSE: Geometric indicators of contouring accuracy suffer from lack of clinical context in radiotherapy. To provide clinical relevance, treatment plans should be generated from the candidate contours, but manual planning could introduce confounding variations. Therefore, our objectives in this study were to: (i) determine the feasibility of using automated knowledge-based planning (KBP) as an objective tool to generate dosimetric parameters for contour evaluation, (ii) evaluate the correlation between geometric indices and dosimetric endpoints, and (iii) report the dosimetric impact of multiple observations of resident-contoured head-and-neck target and organ-at-risk (OAR) volumes.
METHODS/MATERIALS: 22 residents contoured the clinical target volumes, parotids and cochleae for a nasopharyngeal cancer case, and expert-generated contours were defined as gold-standard for this study. A validated KBP routine generated 67 treatment plans with various resident/gold-standard and target/OAR combinations. Dosimetric indices (PTV:D98%; OAR:Dmean) were calculated on gold-standard contours. Commonly-used geometric indices (Dice coefficients, Hausdorff maximum/mean/median distances, volume differences, and centroid distances) were also calculated. R2 quantified correlation between geometric and dosimetric indices.
RESULTS: The correlation between geometric and dosimetric indices were weak (R2<0.2 for 61% of the correlations studied - 77 out of 126) and inconsistent (no single geometric index consistently exhibited superior/inferior correlation with dosimetric endpoints). The lack of consistent correlations between geometric and dosimetric indices resulted in the inability to define any geometric index thresholds for clinical acceptability. Geometric indices also exhibited a high propensity for false-positives and false-negatives as a classifier of dosimetric impact. Lastly, we found substantial inter-resident contour variation, whether quantified using geometric or dosimetric indices, with significant negative dosimetric impact were these contours used for treatment planning.
CONCLUSION: Contour variation among residents significantly affected dosimetric endpoints, highlighting the importance of resident education in head-and-neck anatomy delineation. Whenever available, dosimetric indices generated from automated planning should be used alongside geometric indices in radiotherapy contouring studies.

PMID: 30508619 [PubMed - as supplied by publisher]

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