Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies

Vinod, Shalini; Jameson, Michael; Min, Myo; Holloway, Lois

doi:10.1016/j.radonc.2016.09.009

Cited by 249 publications

(231 citation statements)

References 158 publications

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“…The DICE Similarity Coefficient (DSC), Centre of Mass shift (COMshift) and Jaccard Conformity Index (CI) for each observer (observers 1–4) volume were compared to the patient's STAPLE volume in MATLAB for each dataset (CT alone, CT/PET, CT/PET/T2W and CT/PET/T2W/DWI) and for each patient volume that is, the GTV and CTV. The CI, DSC, COMshift and Kappa statistic are all metrics commonly used to establish IOV and have been explained and reviewed in the literature …”

Section: Methodsmentioning

confidence: 99%

Evaluating diffusion‐weighted magnetic resonance imaging for target volume delineation in head and neck radiotherapy

et al. 2019

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Introduction Inter‐observer variability (IOV) in target volume delineation is a source of error in head and neck radiotherapy. Diffusion‐weighted imaging (DWI) has been shown to be useful in detecting recurrent head and neck cancer. This study aims to determine whether DWI improves target volume delineation and IOV. Methods Four radiation oncologists delineated the gross tumour volume (GTV) for ten head and neck cancer patients. Delineation was performed on CT alone as well as fused image sets which incorporated fluorodeoxyglucose (FDG)‐positron emission tomography (PET) and magnetic resonance imaging (MRI) in the form of CT/PET, CT/PET/T2W and CT/PET/T2W/DWI image sets. Analysis of the variability of contour volumes was completed by comparison to the simultaneous truth and performance level estimation (STAPLE) volumes. The DICE Similarity Coefficient (DSC) and other IOV metrics for each observer's contour were compared to the STAPLE for each patient and image dataset. A DWI usability scoresheet for delineation was completed. Results The CT/PET/T2W/DWI mean GTV volume of 13.37 (10.35–16.39)cm3 was shown to be different to the mean GTV of 10.92 (8.32–13.51)cm3 when using CT alone (P < 0.001). The GTV DSC amongst observers for CT alone was 0.72 (0.65–0.79), CT/PET was 0.73 (0.67–0.80), CT/PET/T2W was 0.71 (0.64–0.77) and CT/PET/T2W/DWI was 0.69 (0.61–0.75). Conclusion Mean GTVs with the addition of DWI had slightly larger volumes compared to standard CT and CT/PET volumes. DWI may add supplemental visual information for GTV delineation while having a small impact on IOV, therefore potentially improving target volume delineation.

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Section: Methodsmentioning

confidence: 99%

Evaluating diffusion‐weighted magnetic resonance imaging for target volume delineation in head and neck radiotherapy

et al. 2019

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“…CT images provide accurate geometry information and electron density for inhomogeneity correction but are of low soft tissue contrast. This makes the manual delineation of soft tissues, such as the esophagus, particularly difficult and prone to errors arising from inter‐ and intraobserver variability . For the last few decades, researchers and clinicians have spent enormous effort to develop automatic contouring methods to provide accurate and consistent organ delineation.…”

Section: Introductionmentioning

confidence: 99%

Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

et al. 2019

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Purpose Accurate and timely organs‐at‐risk (OARs) segmentation is key to efficient and high‐quality radiation therapy planning. The purpose of this work is to develop a deep learning‐based method to automatically segment multiple thoracic OARs on chest computed tomography (CT) for radiotherapy treatment planning. Methods We propose an adversarial training strategy to train deep neural networks for the segmentation of multiple organs on thoracic CT images. The proposed design of adversarial networks, called U‐Net‐generative adversarial network (U‐Net‐GAN), jointly trains a set of U‐Nets as generators and fully convolutional networks (FCNs) as discriminators. Specifically, the generator, composed of U‐Net, produces an image segmentation map of multiple organs by an end‐to‐end mapping learned from CT image to multiorgan‐segmented OARs. The discriminator, structured as an FCN, discriminates between the ground truth and segmented OARs produced by the generator. The generator and discriminator compete against each other in an adversarial learning process to produce the optimal segmentation map of multiple organs. Our segmentation results were compared with manually segmented OARs (ground truth) for quantitative evaluations in geometric difference, as well as dosimetric performance by investigating the dose‐volume histogram in 20 stereotactic body radiation therapy (SBRT) lung plans. Results This segmentation technique was applied to delineate the left and right lungs, spinal cord, esophagus, and heart using 35 patients’ chest CTs. The averaged dice similarity coefficient for the above five OARs are 0.97, 0.97, 0.90, 0.75, and 0.87, respectively. The mean surface distance of the five OARs obtained with proposed method ranges between 0.4 and 1.5 mm on average among all 35 patients. The mean dose differences on the 20 SBRT lung plans are −0.001 to 0.155 Gy for the five OARs. Conclusion We have investigated a novel deep learning‐based approach with a GAN strategy to segment multiple OARs in the thorax using chest CT images and demonstrated its feasibility and reliability. This is a potentially valuable method for improving the efficiency of chest radiotherapy treatment planning.

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“…However, lack of sufficient contrast resolution and functional information can lead to inadequate information on the tumor spatial extents. This limitation leads to large variability in defining the extent of disease, regardless of the specific disease site . To address those limitations, other imaging exams are frequently incorporated to complement CT images.…”

Section: Introductionmentioning

confidence: 99%

Task Group 174 Report: Utilization of [¹⁸F]Fluorodeoxyglucose Positron Emission Tomography ([¹⁸F]FDG‐PET) in Radiation Therapy

et al. 2019

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The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is 18F‐fluorodeoxyglucose ([18F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [18F]FDG‐PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [18F]FDG‐PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [18F]FDG‐PET for RT.

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Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies

Abstract: There is a lack of consistency in conducting and reporting analyses from IOV studies. We suggest a framework to use for future studies evaluating IOV.

Cited by 249 publications

References 158 publications

Evaluating diffusion‐weighted magnetic resonance imaging for target volume delineation in head and neck radiotherapy

Evaluating diffusion‐weighted magnetic resonance imaging for target volume delineation in head and neck radiotherapy

Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

Task Group 174 Report: Utilization of [¹⁸F]Fluorodeoxyglucose Positron Emission Tomography ([¹⁸F]FDG‐PET) in Radiation Therapy

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Uncertainties in volume delineation in radiation oncology: A systematic review and recommendations for future studies

Abstract: There is a lack of consistency in conducting and reporting analyses from IOV studies. We suggest a framework to use for future studies evaluating IOV.

Cited by 249 publications

References 158 publications

Evaluating diffusion‐weighted magnetic resonance imaging for target volume delineation in head and neck radiotherapy

Evaluating diffusion‐weighted magnetic resonance imaging for target volume delineation in head and neck radiotherapy

Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN

Task Group 174 Report: Utilization of [18F]Fluorodeoxyglucose Positron Emission Tomography ([18F]FDG‐PET) in Radiation Therapy

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Product

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Task Group 174 Report: Utilization of [¹⁸F]Fluorodeoxyglucose Positron Emission Tomography ([¹⁸F]FDG‐PET) in Radiation Therapy