Target volume delineation variation in radiotherapy for early stage rectal cancer in the Netherlands

Nijkamp, Jasper; Haas-Kock, Danielle F. M. de; Beukema, Jannet C.; Neelis, Karen J.; Woutersen, D.; Ceha, Heleen M.; Rozema, Tom; Slot, Annerie; Vos-Westerman, Hanneke; Intven, Martijn; Spruit, Patty H.; Linden, Yvette M. van der; Geijsen, Debby; Verschueren, Kristin; Herk, Marcel van; Marijnen, Corrie A.M.

doi:10.1016/j.radonc.2011.08.011

Cited by 65 publications

(37 citation statements)

References 22 publications

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“…Already auto-segmentation or semi-automated segmentation assessment solutions are likely to become a part of radiotherapy clinical trial efforts sooner rather than later [21]. A flexible, robust software solution, capable of both manual and auto-segmentation assessment might also have applicability for both “fixed-location” [22] and “remote” [11,12] web-based training solutions that are likely to become increasingly important as the availability of new technologies increases. Having a no-cost open-source solution, as presented herein, also opens the possibility of end-users adding desired metrics [9,17,18] on a clinical trial or training needs-based situation.…”

Section: Resultsmentioning

confidence: 99%

Auto-segmentation of the brachial plexus assessed with TaCTICS – A software platform for rapid multiple-metric quantitative evaluation of contours

et al. 2015

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Section: Resultsmentioning

confidence: 99%

Auto-segmentation of the brachial plexus assessed with TaCTICS – A software platform for rapid multiple-metric quantitative evaluation of contours

et al. 2015

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“…SPARC incorporates a comprehensive radiotherapy quality assurance programme to ensure consistency in target definition and radiotherapy delivery. This includes a radiotherapy manual with atlas, and pre-trial contouring and planning test-cases followed by a workshop, both of which have been shown to reduce variation in target volume definition [13], [14], [15]. The radiotherapy manual specifies that the target structure for the margin-directed boost should be defined following discussion with the radiologist and/or Hepato-Pancreato-Biliary (HPB) surgeon to identify the vascular structures that are responsible for the tumour being classified as borderline resectable according to the NCCN criteria.…”

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confidence: 99%

Conformity analysis to demonstrate reproducibility of target volumes for Margin-Intense Stereotactic Radiotherapy for borderline-resectable pancreatic cancer

Holyoake

Robinson

Grose

et al. 2016

Radiotherapy and Oncology

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Background and purposeMargin-directed neoadjuvant radiotherapy for borderline-resectable pancreatic cancer (BRPC) aims to facilitate clear surgical margins. A systematic method was developed for definition of a boost target volume prior to a formal phase-I study.Material and methodsReference structures were defined by two oncologists and one radiologist, target structures were submitted by eight oncologist investigators and compared using conformity indices. Resultant risk of duodenal bleed (NTCP) was modelled.ResultsFor GTV, reference volume was 2.1 cm3 and investigator mean was 6.03 cm3 (95% CI 3.92–8.13 cm3), for boost volume 1.1 cm3 and 1.25 cm3 (1.02–1.48 cm3). Mean Dice conformity coefficient for GTV was 0.47 (0.38–0.56), and for boost volume was significantly higher at 0.61 (0.52–0.70, p = 0.01). Discordance index (DI) for GTV was 0.65 (0.56–0.75) and for boost volume was significantly lower at 0.39 (0.28–0.49, p = 0.001). NTCP using reference contours was 2.95%, with mean for investigator contour plans 3.93% (3.63–4.22%). Correlations were seen between NTCP and GTV volume (p = 0.02) and NTCP and DI (correlation coefficient 0.83 (0.29–0.97), p = 0.01).ConclusionsBetter conformity with reference was shown for boost volume compared with GTV. Investigator GTV volumes were larger than reference, had higher DI scores and modelled toxicity risk. A consistent method of target structure definition for margin-directed pancreatic radiotherapy is demonstrated.

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“…International or institutional guidelines, contouring atlases, case libraries and numerous recommendations have thus recently been developed [2][3][4][5][6][7][8][9][10][11][12]. Atlases and guidelines are widely recognized and contribute to reducing inter-observer variability, but they are static documents that also lack interactivity.…”

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Recommendations on how to establish evidence from auto-segmentation software in radiotherapy

Valentini

Boldrini

Damiani

et al. 2014

Radiotherapy and Oncology

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Along with the improved treatment conformity achieved with the recently implemented radiotherapy (RT) planning and delivery approaches [1], there is growing awareness of the uncertainties connected to the definition and delineation of the RT targets as well as the organs at risk (ORs). To assure accurate reproducibility of the planned treatment in order to avoid 'geographical miss' of the target, it is mandatory to correctly identify the volumes to be irradiated and to appropriately manage these uncertainties in the definition of the gross target volume (GTV) as well as of standardized, disease-specific, nodal clinical target volumes (CTVs).Accurate segmentation of primarily the GTVs and CTVs, as well as the ORs, therefore represents the foundation for successful RT. International or institutional guidelines, contouring atlases, case libraries and numerous recommendations have thus recently been developed [2][3][4][5][6][7][8][9][10][11][12]. Atlases and guidelines are widely recognized and contribute to reducing inter-observer variability, but they are static documents that also lack interactivity.To address this challenge, several commercial auto-contouring software solutions have recently been released, representing an opportunity for individualizing the existing atlases, automatically propagating clinically reliable contours to patients' specific anatomies [13]. They also have potential for lowering the segmentation time, increasing the adherence to existing guidelines and, not the least, to reduce the inter-observer variability that still may be a major source of uncertainty in RT. The current issue presents several studies related to the use of such software in RT [14][15][16].A basic requirement of the application of auto-segmentation software is the existence of a widely recognized and reliable definition of the volumes, according to the anatomical region, the histology as well as the stage of the disease. This can be described as software ontology [17], covering clinical, anatomical, pathological and imaging information. This ontology should be linked to benchmark performance values obtained through comparisons with the agreement between multiple observers/operators, or between manual delineations and auto-contouring. There are several metrics for the agreement between the contours, and establishing ranges indicating clinically acceptable agreement on the scales of these metrics plays an essential role in translating the obtained observations to everyday practice.In this paper we would like to share comments and criticisms on how evidence can be derived from the use of auto-segmentation software, addressing the key aspects that should be considered in future studies in this field: ontology definition, benchmark evaluation methods and performance evaluation tools. We also discuss the potential benefits that can be achieved with these tools. Ontology definitionThe term ontology refers to a form of dictionary where information is specified and organized in a well-defined semantic data collection model; a se...

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Target volume delineation variation in radiotherapy for early stage rectal cancer in the Netherlands

Cited by 65 publications

References 22 publications

Auto-segmentation of the brachial plexus assessed with TaCTICS – A software platform for rapid multiple-metric quantitative evaluation of contours

Auto-segmentation of the brachial plexus assessed with TaCTICS – A software platform for rapid multiple-metric quantitative evaluation of contours

Conformity analysis to demonstrate reproducibility of target volumes for Margin-Intense Stereotactic Radiotherapy for borderline-resectable pancreatic cancer

Recommendations on how to establish evidence from auto-segmentation software in radiotherapy

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