Automated delineation of radiotherapy volumes: are we going in the right direction?

Whitfield, Gillian A; Price, Patricia M; Price, Gareth J; Moore, Christopher

doi:10.1259/bjr.20110718

Cited by 41 publications

(41 citation statements)

References 22 publications

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“…By invading the surrounding structures, such as blood vessels, chest and mediastinal walls, the NSCLCs may occlude the existent boundaries of the lungs, which increases the difficulty of accurately segmenting the lung thoracic areas on the chest CT images and hinders the subsequent detection and segmentation of lung cancers. The existing algorithms for delineating lung nodules are insufficiently efficient to segment the relatively larger NSCLC masses . To rectify this problem, we proposed an algorithm that acknowledges the radio‐morphological condition of a NSCLC.…”

Section: Discussionmentioning

confidence: 94%

Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Saad

Lee

Choi

2019

Int J Imaging Syst Tech

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Quantitative reasoning in medical decision science relies on the delineation of pathological objects. For example, evidence‐based clinical decisions regarding lung diseases require the segmentation of nodules, tumors, or cancers. Non‐small cell lung cancer (NSCLC) tends to be large sized, irregularly shaped, and grows against surrounding structures imposing challenges in the segmentation, even for expert clinicians. An automated delineation tool based on spatial analysis was developed and studied on 25 sets of computed tomography scans of NSCLC. Manual and automated delineations were compared, and the proposed method exhibited robustness in terms of the tumor size (5.32–18.24 mm), shape (spherical or irregular), contouring (lobulated, spiculated, or cavitated), localization (solitary, pleural, mediastinal, endobronchial, or tagging), and laterality (left or right lobe) with accuracy between 80% and 99%. Small discrepancies observed between the manual and automated delineations may arise from the variability in the practitioners' definitions of region of interest or imaging artifacts that reduced the tissue resolution.

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Section: Discussionmentioning

confidence: 94%

Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Saad

Lee

Choi

2019

Int J Imaging Syst Tech

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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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“…This is because delineation of OAR remains the ‘gold standard’ in terms of accuracy (21) . Agreement of autocontours with manually delineated contours was assessed using positional contour comparison metrics calculated by ImSimQA v3.1.5 (OSL, Shrewsbury, UK): mean distance to conformity (MDC); DICE index; sensitivity index (Se Idx); and inclusion index (Incl Idx).…”

Section: Methodsmentioning

confidence: 99%

The feasibility of atlas‐based automatic segmentation of MRI for H&N radiotherapy planning

Wardman

Prestwich

Gooding

et al. 2016

J Applied Clin Med Phys

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Atlas‐based autosegmentation is an established tool for segmenting structures for CT‐planned head and neck radiotherapy. MRI is being increasingly integrated into the planning process. The aim of this study is to assess the feasibility of MRI‐based, atlas‐based autosegmentation for organs at risk (OAR) and lymph node levels, and to compare the segmentation accuracy with CT‐based autosegmentation. Fourteen patients with locally advanced head and neck cancer in a prospective imaging study underwent a T1‐weighted MRI and a PET‐CT (with dedicated contrast‐enhanced CT) in an immobilization mask. Organs at risk (orbits, parotids, brainstem, and spinal cord) and the left level II lymph node region were manually delineated on the CT and MRI separately. A ‘leave one out’ approach was used to automatically segment structures onto the remaining images separately for CT and MRI. Contour comparison was performed using multiple positional metrics: Dice index, mean distance to conformity (MDC), sensitivity index (Se Idx), and inclusion index (Incl Idx). Automatic segmentation using MRI of orbits, parotids, brainstem, and lymph node level was acceptable with a DICE coefficient of 0.73−0.91, MDC 2.0−5.1 mm, Se Idx 0.64−0.93, Incl Idx 0.76−0.93. Segmentation of the spinal cord was poor (Dice coefficient 0.37). The process of automatic segmentation was significantly better on MRI compared to CT for orbits, parotid glands, brainstem, and left lymph node level II by multiple positional metrics; spinal cord segmentation based on MRI was inferior compared with CT. Accurate atlas‐based automatic segmentation of OAR and lymph node levels is feasible using T1‐MRI; segmentation of the spinal cord was found to be poor. Comparison with CT‐based automatic segmentation suggests that the process is equally as, or more accurate, using MRI. These results support further translation of MRI‐based segmentation methodology into clinical practice.PACS number(s): 87.55.de

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Automated delineation of radiotherapy volumes: are we going in the right direction?

Cited by 41 publications

References 22 publications

Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

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

The feasibility of atlas‐based automatic segmentation of MRI for H&N radiotherapy planning

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