Four years with FALCON – An ESTRO educational project: Achievements and perspectives

Eriksen, Jesper Grau; Salembier, Carl; Rivera, Sofía; Bari, Berardino De; Berger, Daniel; Mantello, G.; Müller, Arndt-Christian; Martin, A. Navarro; Pasini, D.; Tanderup, Kari; Palmu, M.; Verfaillie, Christine; Pötter, Richard; Valentini, Vincenzo

doi:10.1016/j.radonc.2014.06.017

Cited by 47 publications

(31 citation statements)

References 23 publications

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“…This study has some limitations that have to be acknowledged. Following the courses only 20% of the participants submitted the delineations, similar to other reported training studies [17]. We are aware that this may have biased our results.…”

Section: Discussionsupporting

confidence: 77%

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The impact of training and professional collaboration on the interobserver variation of lung cancer delineations: a multi-institutional study

et al. 2018

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Background: To assess the impact of training and interprofessional collaboration on the interobserver variation in the delineation of the lung gross tumor volume (GTVp) and lymph node (GTVln). Material and methods: Eight target volume delineations courses were organized between 2008 and 2013. Specialists and trainees in radiation oncology were asked to delineate the GTVp and GTVln on four representative CT images of a patient diagnosed with lung cancer individually prior each course (baseline), together as group (interprofessional collaboration) and post-training. The mean delineated volume and local standard deviation (local SD) between the contours for each course group were calculated and compared with the expert delineations. Results: A total 410 delineations were evaluated. The average local SD was lowest for the interprofessional collaboration (GTVp ¼ 0.194 cm, GTVln ¼ 0.371 cm) followed by the post-training (GTVp ¼ 0.244 cm, GTVln ¼ 0.607 cm) and baseline delineations (GTVp ¼ 0.274 cm, GTVln: 0.718 cm). The mean delineated volume was smallest for the interprofessional (GTVp ¼ 4.93 cm 3 , GTVln ¼ 4.34 cm 3) followed by the post-training (GTVp ¼ 5.68 cm 3 , GTVln ¼ 5.47 cm 3) and baseline delineations (GTVp ¼ 6.65 cm 3 , GTVln ¼ 6.93 cm 3). All delineations were larger than the expert for both GTVp and GTVln (p < .001). Conclusion: Our findings indicate that image interpretational differences can lead to large interobserver variation particularly when delineating the GTVln. Interprofessional collaboration was found to have the greatest impact on reducing interobserver variation in the delineation of the GTVln. This highlights the need to develop a clinical workflow so as to ensure that difficult cases are reviewed routinely by a second radiation oncologist or radiologist so as to minimize the risk of geographical tumor miss and unnecessary irradiation to normal tissue.

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Section: Discussionsupporting

confidence: 77%

“…To tackle this issue, in 1999, the European society for therapeutic radiology and oncology (ESTRO) started courses on target volume delineations [16,17]. Here we report on 410 delineations for a single lung cancer case that was consistently used within one software system for courses delivered between 2008 and 2013.…”

Section: Introductionmentioning

confidence: 99%

The impact of training and professional collaboration on the interobserver variation of lung cancer delineations: a multi-institutional study

et al. 2018

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“…These types of interventions are resource intensive and may not be readily available. However, there are a number of major conferences which include small group contouring workshops on their agenda as well as specific stand‐alone volume delineation workshops addressing this . Participation in contouring workshops is a recognised Continuing Professional Development activity of RANZCR.…”

Section: Discussionmentioning

confidence: 99%

A review of interventions to reduce inter‐observer variability in volume delineation in radiation oncology

et al. 2016

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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.…”

mentioning

confidence: 99%

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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Four years with FALCON – An ESTRO educational project: Achievements and perspectives

Cited by 47 publications

References 23 publications

The impact of training and professional collaboration on the interobserver variation of lung cancer delineations: a multi-institutional study

The impact of training and professional collaboration on the interobserver variation of lung cancer delineations: a multi-institutional study

A review of interventions to reduce inter‐observer variability in volume delineation in radiation oncology

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

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