A dose based approach for evaluation of inter-observer variations in target delineation

Abstract

a b s t r a c tBackground and purpose: Substantial inter-observer variations in target delineation have been presented previously. Target delineation for paediatric cases is difficult due to the small number of children, the variation in paediatric targets, the number of study protocols, and the individual patient's specific needs and demands. Uncertainties in target delineation might lead to under-dosage or over-dosage. The aim of this work is to apply the concept of a consensus volume and good quality treatment plans to visualise and quantify inter-observer target delineation variations in dosimetric terms in addition to conventional geometrically based volume concordance indices. Material and methods: Two paediatric cases were used to demonstrate the potential of adding dose metrics when evaluating target delineation diversity; Hodgkin's disease (case 1) and rhabdomyosarcoma of the parotid gland (case 2). The variability in target delineation (PTV delineations) between six centres was quantified using the generalised conformity index, CIgen, generated for volume overlap. The STAPLE algorithm, as implemented in CERR, was used for both cases to derive a consensus volumes. STAPLE is a probabilistic estimate of the true volume generated from all observers. Dose distributions created by each centre for the original target volumes were then applied to this consensus volume. Results: A considerable variation in target segmentation was seen in both cases. For case 1 the variation was 374-960 cm 3 (average 669 cm 3 ) and for case 2; 65-126 cm 3 (average 109 cm 3 ). CIgen were 0.53 and 0.70, respectively. The DVHs in absolute volume displayed for the delineated target volume as well as for the consensus volume adds information on both ''compliant" target volumes as well as outliers which are hidden with just the use of concordance indices. Conclusions: The DVHs in absolute volume add valuable and easily understood information to various indices for evaluating uniformity in target delineation.

Combining inter-observer variability, range and setup uncertainty in a variance-based sensitivity analysis for proton therapy

“…The relative contribution to the overall uncertainty is broken down to first order indices S setup , S range and S IOV , higher order indices with involvement of IOV (SI IOV ) and higher order indices without involvement of IOV (SI other ). work, the consensus target volume created with the STAPLE algorithm was used to define a "ground truth" target volume, as has been done previously [14]. Since this algorithm provides a maximum likelyhood estimate for the actual CTV based on the observer CTVs themselves, this approach is well suited to capture the variability within a group of observers.…”

Organ at risk delineation for radiation therapy clinical trials: Global Harmonization Group consensus guidelines

“…This request was sel-dom accompanied by contouring guidance. Review of clinical and dosimetric evaluation studies demonstrates variation in practice [24][25][26][27][28][29][30][31][32]. Recommended skin thickness for contouring from clinical trial documentation ranged from 3 to 6 mm; anatomically the thickness of the skin is dependent on the location, ranging from 1.5 to 5 mm [33].…”

Resection cavity auto‐contouring for patients with pediatric medulloblastoma using only CT information

“…Relying on CT information alone can make it difficult to delineate the boundaries of the postoperative volume 18,19 . Contouring pediatric post‐operative GTV volumes introduces an added challenge of relying on limited and more heterogenous datasets as pediatric cancers are less common than adult cancers and pediatric patient anatomy changes with age 20,21 …”

“…18,19 Contouring pediatric post-operative GTV volumes introduces an added challenge of relying on limited and more heterogenous datasets as pediatric cancers are less common than adult cancers and pediatric patient anatomy changes with age. 20,21 There is extensive literature demonstrating varying success in automatically delineating solid GTV volumes using MRI information or multi-modality imaging information. 16,17,[22][23][24] There is limited literature on automatically delineating post-operative GTV volumes.…”