International guideline for the delineation of the clinical target volumes (CTV) for nasopharyngeal carcinoma

Lee, Anne W; Ng, Wai Tong; Pan, Jianji; Poh, Sharon Shuxian; Ahn, Yong Chan; AlHussain, Hussain; Corry, June; Grau, Cai; Grégoire, Vincent; Harrington, Kevin; Hu, Chao; Kwong, Dora Lai-Wan; Langendijk, Johannes A.; Le, Quynh Thu; Lee, Nancy Y.; Jia, Lin; Lu, Tai Xiang; Mendenhall, William M.; O’Sullivan, Brian; Özyar, Enis; Peters, Lester J.; Rosenthal, David I.; Soong, Yoke Lim; Tao, Yungan; Yom, Sue S.; Wee, Ji‐Hyang

doi:10.1016/j.radonc.2017.10.032

Cited by 253 publications

(246 citation statements)

References 63 publications

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“…4,5 Contrast-enhanced (CE) computed tomography (CT) images acquired with iodinated contrast materials play a key role in treatment planning, allowing the accurate delineation of structures. 6 However, the CE and noncontrast CT scans are acquired sequentially in clinical practice, requiring subsequent image registration. Consequently, there are potential concerns about uncertainties related to image registration and potential geometric misalignment between acquisitions.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Koike¹,

Ohira²,

Akino

et al. 2019

Medical Physics

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Purpose The aim of this study was to develop a deep learning (DL) method for generating virtual noncontrast (VNC) computed tomography (CT) images from contrast‐enhanced (CE) CT images (VNCDL) and to evaluate its performance in dose calculations for head and neck radiotherapy in comparison with VNC images derived from a dual‐energy CT (DECT) scanner (VNCDECT). Methods This retrospective study included data for 61 patients who underwent head and neck radiotherapy. All planning CT images were obtained with a single‐source DECT scanner (80 and 140 kVp) with rapid kVp switching. The DL‐based method used a pair of virtual monochromatic images (VMIs) at 70 keV with and without contrast materials. VMIs without contrast materials were used as reference true noncontrast (TNC) images. Deformable image registration was used between the TNC and CE images. We used the data of 45 patients, chosen randomly, for training (7922 paired images), and data from the other 16 patients as test data. We generated the VNCDL images with a densely connected convolutional network. As the VNCDECT images, we used VMIs with the iodine signal suppressed, reconstructed from the CE images of the 16 test patients. The CT numbers of the tumor, common carotid artery, internal jugular vein, muscle, fat, bone marrow, cortical bone, and mandible of each VNC image were compared with those of the TNC image. The dose of the reference TNC plan was recalculated using the CE, VNCDL, and VNCDECT images. Difference maps of the dose distributions and dose–volume histograms were evaluated. Results The mean prediction time for the VNCDL images was 3.4 s per patient, and the mean number of slices was 204. The absolute differences in CT numbers of the VNCDL images were significantly smaller than those of the VNCDECT images for the bone marrow (8.0 ± 6.5 vs 175.1 ± 40.9 HU; P < 0.001) and mandible (20.3 ± 19.3 vs 106.2 ± 80.5 HU; P = 0.002). The DL‐based model provided the dose distribution most similar to that of the TNC plan. With the VNCDECT plans, dose errors >1.0% were observed in bone regions. The dose–volume histogram analysis showed that the VNCDL plans yielded the smallest errors for the primary target, although dose differences were <1.0% for all the approaches. For the maximum dose to the mandible, the mean ± SD errors for the CE, VNCDL, and VNCDECT plans were –0.13% ± 0.23% (range: −0.46% to 0.31%; P = 0.037), –0.01% ± 0.22% (range: −0.40% to 0.36%; P = 1.0), and 0.53% ± 0.47% (range: −0.21% to 1.41%; P < 0.001), respectively. Conclusions In this study, we developed a method based on DL that can rapidly generate VNC images from CE images without a DECT scanner. Compared with the DECT approach, the DL‐based method improved the prediction accuracy of CT numbers in bone regions. Consequently, there was greater agreement between the VNCDL and TNC plan dose distributions than with the CE and VNCDECT plans, achieved by suppressing the contrast material signals while retaining the CT numbers of bone structures.

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Section: Introductionmentioning

confidence: 99%

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Koike¹,

Ohira²,

Akino

et al. 2019

Medical Physics

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show abstract

“…Su et al 36 extended the GTV to the CTV with a 1-cm margin for pathology-confirmed lateralized NPC and did not contain the entire nasopharyngeal mucosa of the contralateral nasopharynx. 37 Our preliminary study based on MRI and biopsy data is supporting adopting the guideline, specifically, we also did not emphasize covering of the whole nasopharynx in high-risk CTV. Similarly, Lin et al carried out a retrospective analysis of the reduced-volume IMRT for NPC and suggested that the high-dose treatment volume may only require a 5-mm margin expansion.…”

Section: Discussionmentioning

confidence: 82%

“…The guideline recommended covering only the GTV with 5 mm expansion as high-risk CTV for choice in clinical practice to decrease toxicities. 37 Our preliminary study based on MRI and biopsy data is supporting adopting the guideline, specifically, we also did not emphasize covering of the whole nasopharynx in high-risk CTV. However, as the revision of the international guideline is based on the case that MRI is widely used in the detection of NPC but lacking the pathological data and clinical verification, and the delineation protocol is generalized without individualization, prospective studies are important to validate it to avoid sacrificing tumour control unnecessarily.…”

Section: Discussionmentioning

confidence: 82%

Pathologic study of tumour extension for clinically localized unilateral nasopharyngeal carcinoma: Should the contralateral side be included in the clinical target volume?

Zhang

et al. 2018

J Med Imag Rad Onc

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“…Third, it is notable that ENPG in this study entailed the removal of the entire NP mucosa. This extent of resection contradicts the principle of target contouring in head and neck cancer, which ascribes to the 5+5 mm rule for the definition of high‐risk and low‐risk subclinical disease [12]. Fourth and most importantly, the authors had not actually outlined a detailed protocol in the instance when adjuvant/salvage treatment is required for margin‐positive disease or local/nodal recurrence.…”

mentioning

confidence: 99%

Surgery as an alternative to radiotherapy in early‐stage nasopharyngeal carcinoma: innovation at the expense of uncertainty

Chua

2020

Cancer Communications

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International guideline for the delineation of the clinical target volumes (CTV) for nasopharyngeal carcinoma

Cited by 253 publications

References 63 publications

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Pathologic study of tumour extension for clinically localized unilateral nasopharyngeal carcinoma: Should the contralateral side be included in the clinical target volume?

Surgery as an alternative to radiotherapy in early‐stage nasopharyngeal carcinoma: innovation at the expense of uncertainty

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