Validated guidelines for tumor delineation on magnetic resonance imaging for laryngeal and hypopharyngeal cancer

Jager, E.A.; Ligtenberg, H.; Caldas-Magalhaes, J.; Schäkel, T.; Philippens, M.E.P.; Pameijer, Frank A.; Kasperts, N.; Willems, Stefan M.; Terhaard, C.; Raaijmakers, Cornelis P.J.

doi:10.1080/0284186x.2016.1219048

Cited by 34 publications

(34 citation statements)

References 21 publications

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“…The degree of distortion in DWI images was difficult to determine and could have had a small impact on the results obtained when assessing volumes against CT. Literature suggests that when contouring with MRI, guidelines should be carefully followed . There are currently no DWI contouring guidelines available.…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

“…Literature suggests that when contouring with MRI, guidelines should be carefully followed. 25 There are currently no DWI contouring guidelines available. The spatial resolution of both PET and DWI is another significant limitation.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2019

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“…The use of magnetic resonance imaging (MRI) in radiotherapy is growing rapidly, due to its superior soft tissue contrast as compared to computed tomography (CT) . In radiation treatment of head and neck cancers, MRI is often used in addition to CT to reduce interobserver variability of target delineation . The excellent soft‐tissue contrast of anatomical MRI makes this modality ideally suited for staging and follow‐up in the head and neck region .…”

Section: Introductionmentioning

confidence: 99%

“…1 In radiation treatment of head and neck cancers, MRI is often used in addition to CT to reduce interobserver variability of target delineation. 2,3 The excellent soft-tissue contrast of anatomical MRI makes this modality ideally suited for staging and follow-up in the head and neck region. 4 In addition, reduced dental artifacts compared to CT may improve delineation of target and organs at risk (OARs) for treatment planning.…”

Section: Introductionmentioning

confidence: 99%

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

et al. 2019

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Purpose To develop and evaluate a patch‐based convolutional neural network (CNN) to generate synthetic computed tomography (sCT) images for magnetic resonance (MR)‐only workflow for radiotherapy of head and neck tumors. A patch‐based deep learning method was chosen to improve robustness to abnormal anatomies caused by large tumors, surgical excisions, or dental artifacts. In this study, we evaluate whether the generated sCT images enable accurate MR‐based dose calculations in the head and neck region. Methods We conducted a retrospective study on 34 patients with head and neck cancer who underwent both CT and MR imaging for radiotherapy treatment planning. To generate the sCTs, a large field‐of‐view T2‐weighted Turbo Spin Echo MR sequence was used from the clinical protocol for multiple types of head and neck tumors. To align images as well as possible on a voxel‐wise level, CT scans were nonrigidly registered to the MR (CTreg). The CNN was based on a U‐net architecture and consisted of 14 layers with 3 × 3 × 3 filters. Patches of 48 × 48 × 48 were randomly extracted and fed into the training. sCTs were created for all patients using threefold cross validation. For each patient, the clinical CT‐based treatment plan was recalculated on sCT using Monaco TPS (Elekta). We evaluated mean absolute error (MAE) and mean error (ME) within the body contours and dice scores in air and bone mask. Also, dose differences and gamma pass rates between CT‐ and sCT‐based plans inside the body contours were calculated. Results sCT generation took 4 min per patient. The MAE over the patient population of the sCT within the intersection of body contours was 75 ± 9 Hounsfield Units (HU) (±1 SD), and the ME was 9 ± 11 HU. Dice scores of the air and bone masks (CTreg vs sCT) were 0.79 ± 0.08 and 0.70 ± 0.07, respectively. Dosimetric analysis showed mean deviations of −0.03% ± 0.05% for dose within the body contours and −0.07% ± 0.22% inside the >90% dose volume. Dental artifacts obscuring the CT could be circumvented in the sCT by the CNN‐based approach in combination with Turbo Spin Echo (TSE) magnetic resonance imaging (MRI) sequence that typically is less prone to susceptibility artifacts. Conclusions The presented CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate, and can therefore be used for MR‐only radiotherapy treatment planning of the head and neck.

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“…As indicated above, implementation of MRI in radiotherapy is expected to facilitate tumor delineation [21][22][23]. In the current paper by Damkjaer et al [24], multiparametric MRI was used to define MRI-positive parts of the seminal vesicles in prostate cancer patients treated with external beam radiotherapy.…”

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confidence: 99%

Bridging imaging and therapy: the role of medical physics in development of precision cancer care

et al. 2017

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Validated guidelines for tumor delineation on magnetic resonance imaging for laryngeal and hypopharyngeal cancer

Cited by 34 publications

References 21 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

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

Bridging imaging and therapy: the role of medical physics in development of precision cancer care

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