Delineation of small mobile tumours with FDG-PET/CT in comparison to pathology in breast cancer patients

Hapdey, Sébastien; Edet-Sanson, Agathe; Gouel, Pierrick; Martin, Benoı̂t; Modzelewski, Romain; Baron, Marc; Berghian, Anca; Forestier-Lebreton, F.; Georgescu, D.; Picquenot, Jean‐Michel; Gardin, I.; Dubray, Bernard; Véra, Pierre

doi:10.1016/j.radonc.2014.08.005

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…However, building up clinical evidence is time consuming (not the least within a prospective setting), and there are considerable future challenges in constructing reliable predictive models [39][40][41]. Quantifying the potential clinical benefits of auto-contouring in a retrospective way could be a first step in this direction.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…The study was declared to the ClinicalTrials.gov Protocol Registration System (PRS) (VoSeTep study, N°RCB: 2009-A00602-55). Patient characteristics with respect to tumour localization and TNM classification were described in [39]. All patients underwent procubitus ungated PET-CT acquisition, centred on the breast region, immobilized with a device fixing the mammalian gland to avoid tumour movement.…”

Section: Iv) Breast Tumours (Centre-4)mentioning

confidence: 99%

“…The specimen was sectioned with a macrotome (EH-170T, Sofraca, France) into 5 µm thick slices at 2 mm intervals. Digitized slides of each slice were recorded and evaluated as described in [39] to estimate the pathological volume that was used as the gold standard volume of the lesion.…”

Section: Iv) Breast Tumours (Centre-4)mentioning

confidence: 99%

Impact of consensus contours from multiple PET segmentation methods on the accuracy of functional volume delineation

Schaefer

Vermandel

Baillet

et al. 2015

Eur J Nucl Med Mol Imaging

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This study showed that combining different PET segmentation methods by the use of a consensus algorithm offers robustness against the variable performance of individual segmentation methods and this approach would therefore be useful in radiation oncology. It might also be relevant for other scenarios such as the merging of expert recommendations in clinical routine and trials or the multiobserver generation of contours for standardization of automatic contouring.

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Place de l’imagerie fonctionnelle dans la définition des volumes cible en cancérologie pulmonaire

Thureau

Hapdey

Véra

2016

Cancer/Radiothérapie

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Delineation of small mobile tumours with FDG-PET/CT in comparison to pathology in breast cancer patients

Cited by 6 publications

References 27 publications

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

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

Impact of consensus contours from multiple PET segmentation methods on the accuracy of functional volume delineation

Place de l’imagerie fonctionnelle dans la définition des volumes cible en cancérologie pulmonaire

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