Target volume delineation in external beam partial breast irradiation: Less inter-observer variation with preoperative- compared to postoperative delineation

Leij, Femke van der; Elkhuizen, Paula H.M.; Janssen, Tomas; Poortmans, P.; Sangen, M.J.C. van der; Scholten, Astrid N.; Vliet-Vroegindeweij, Corine van; Boersma, Liesbeth

doi:10.1016/j.radonc.2013.10.033

Cited by 45 publications

(39 citation statements)

References 18 publications

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“…Also in this issue, Conson et al reported on the dose/volume effects of the use of auto-segmentation for normal tissues in the brain [15]. It is therefore necessary to develop this field further to fully describe the existing correlation between the considered similarity indices and dose/volume effects [43]. This must still be considered along with what we refer to as the ''benchmark trap'' [17]: are we confident that the daily ''human'' inter-observer variability could show better performances, in terms of dose and volumes, when considering a comparison with the same manual benchmark?…”

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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“…There were 31 studies which evaluated the effect of imaging on IOV, but they varied in methodology (Table 3). Some studies compared volume delineation on Volume delineation in radiation oncology two separate imaging modalities including two different CT scans, 41,42 CT vs MRI [43][44][45][46][47][48][49] , CT vs cone-beam CT [50][51][52] and MRI scans using different sequences. 53,54 Other studies compared standard imaging with the use of additional registered data sets such as specialised CT 55 , MRI [56][57][58] and PET.…”

Section: The Effect Of Additional Imaging Interventions On Iov In Volmentioning

confidence: 99%

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

et al. 2016

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“…[21] Furthermore, compared to postoperative EB-PBI, preoperative target volume delineation leads to considerably less interobserver variation. [22,23] Hence, in order to improve the balance between local control and cosmesis outcome, it is necessary to ascertain the extent of the tumor as accurately as possible by preoperative imaging.…”

Section: Discussionmentioning

confidence: 99%

A comparison study between gross tumor volumes defined by preoperative magnetic resonance imaging, postoperative specimens, and tumor bed for radiotherapy after breast-conserving surgery

Zhang

Li²,

Wang³

et al. 2017

Medicine

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Background:The identification and contouring of target volume is important for breast-conserving therapy. The aim of the study was to compare preoperative magnetic resonance imaging (MRI), postoperative pathology, excised specimens’ (ES) size, and tumor bed (TB) delineation as methods for determining the gross tumor volume (GTV) for radiotherapy after breast-conserving surgery (BCS).Methods:Thirty-three patients with breast cancer who underwent preoperative MRI and radiotherapy after BCS were enrolled. The GTVs determined by MRI, pathology, and the ES were defined as GTVMRI, GTVPAT, and GTVES, respectively. GTVMRI+1 was defined as a 1.0-cm margin around the GTVMRI. The radiation oncologist delineated GTV of the TB (GTVTB) using planning computed tomography according to ≥5 surgical clips placed in the lumpectomy cavity (LC).Results:The median GTVMRI, GTVMRI+1, GTVPAT, GTVES, and GTVTB were 0.97 cm3 (range, 0.01–6.88), 12.58 cm3 (range, 3.90–34.13), 0.97 cm3 (range, 0.01–6.36), 15.46 cm3 (range, 1.15–70.69), and 19.24 cm3 (range, 4.72–54.33), respectively. There were no significant differences between GTVMRI and GTVPAT, GTVMRI+1 and GTVES, GTVES and GTVTB (P = 0.188, 0.070, and 0.264, respectively). GTVMRI is positively related with GTVPAT. However, neither GTVES nor GTVTB correlated with GTVMRI (P = 0.071 and 0.378, respectively). Furthermore, neither GTVES nor GTVTB correlated with GTVMRI+1 (P = 0.068 and 0.375, respectively).Conclusion:When ≥5 surgical clips were placed in the LC for BCS, the volume of TB was consistent with the volume of ES. Neither the volume of TB nor the volume of ES correlated significantly with the volume of tumor defined by preoperative MRI.

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Target volume delineation in external beam partial breast irradiation: Less inter-observer variation with preoperative- compared to postoperative delineation

Cited by 45 publications

References 18 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

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

A comparison study between gross tumor volumes defined by preoperative magnetic resonance imaging, postoperative specimens, and tumor bed for radiotherapy after breast-conserving surgery

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