The impact of margin reduction on outcome and toxicity in head and neck cancer patients treated with image-guided volumetric modulated arc therapy (VMAT)

Navran, Arash; Heemsbergen, Wilma D.; Janssen, Tomas; Hamming‐Vrieze, Olga; Jonker, M.; Zuur, Charlotte L.; Verheij, Marcel; Remeijer, P.; Sonke, Jan‐Jakob; Brekel, Michiel W. M. van den

doi:10.1016/j.radonc.2018.06.032

Cited by 67 publications

(61 citation statements)

References 26 publications

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“…Because the prescribed dose was not very different, the significant variance of LRC in the low‐dose region could be associated to the use of thinner CTV to PTV margin of 3 mm used in target delineation in group B compared to the 5 mm of the group A. On the contrary, a recently published article confirmed an equivalent outcome between patients treated with 3 versus 5 mm of margins . The analysis included 414 patients, the 2‐year LRC control rates of patients treated in 3 mm and 5 mm groups were 79.9% and 79.2% ( P = 1.0), whereas DFSs were 71.5% and 72.7 ( P = 0.6) and OSs were 75.2% and 75.1% ( P = 0.9).…”

Section: Discussionmentioning

confidence: 96%

“…On the contrary, a recently published article confirmed an equivalent outcome between patients treated with 3 versus 5 mm of margins. 20 The analysis included 414 patients, the 2-year LRC control rates of patients treated in 3 mm and 5 mm groups were 79.9% and 79.2% (P = 1.0), whereas DFSs were 71.5% and 72.7 (P = 0.6) and OSs were 75.2% and 75.1% (P = 0.9). Our analysis showed OS rates higher compared to the reported literature, with 95.9% AE 1.8% and 91.6% AE 2.7% at 1 and 2 years.…”

Section: Discussionmentioning

confidence: 97%

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Impact of hypofractionated schemes in radiotherapy for locally advanced head and neck cancer patients

et al. 2019

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Objective To analyze two cohorts of locally advanced head and neck cancer (LAHNC) patients treated with two schedules of hypofractionated radiotherapy and 5 versus 3 mm clinical target volume–planning target volume (CTV‐PTV) margins. Methods 145 patients were included. The patient cohort was divided into two groups. Group A included 97 patients treated with 69.96/54.45 grays (Gy) to the high‐risk and low‐risk volumes, respectively, in 33 fractions, with 5 mm margins. Group B included 48 patients treated with 66/54 Gy in 30 fractions using 3 mm margins. Toxicity was recorded according to the Common Toxicity Criteria version 3. Patient outcomes for locoregional control (LRC), disease‐free survival (DFS), and overall survival (OS) were determined. Results Median follow‐up was 26 months. In terms of toxicity, acute dysphagia worsened significantly (P = 0.003) in group B, whereas acute salivary toxicity tended to significance (P = 0.071). No significant late toxicity differences were recorded. Rates of LRC at 1 and 2 years were 90.1% ± 2.6% and 84.2% ± 3.4%, respectively, with superiority for group A (P = 0.045). The statistical significance remained for the low‐dose level (P = 0.007) but not for high dose (P = 0.110). Rates of DFS at 1 and 2 years were 84.9% ± 3.1% and 76.5% ± 3.9%, respectively, whereas rates of OS were 95.9% ± 1.8% and 91.6% ± 2.7%, respectively. No differences were observed between the two groups in terms of DFS (P = 0.270) and OS (P = 0.152). Conclusion The use of reduced CTV to PTV margins and dose reduction with hypofractionation schedule can improve the pattern of toxicity in LAHNC patients. This approach resulted in an increased rate of locoregional progression, particularly in low‐risk regions. Level of Evidence 4 Laryngoscope, 130:E163–E170, 2020

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 97%

Impact of hypofractionated schemes in radiotherapy for locally advanced head and neck cancer patients

et al. 2019

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“…Patients were positioned and fixated using a five point thermoplastic mask and a personal best fitting headrest [11] and knee support. The clinical target volume (CTV) involving the primary tumour, pathological lymph nodes and elective lymph node regions were delineated on the pCT, expanded with a uniform PTV margin of 3 mm [12]. The gross tumour volume (GTV) of the primary tumour and the involved node(s) were delineated.…”

Section: Radiation Treatmentmentioning

confidence: 99%

Protocolised way to cope with anatomical changes in head & neck cancer during the course of radiotherapy

Beek¹,

Jonker²,

Hamming‐Vrieze

et al. 2019

Technical Innovations & Patient Support in Radiation Oncolo

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Introduction: During a course of radiotherapy for head-and-neck-cancer (HNC), non-rigid anatomical changes can be observed on daily Cone Beam CT (CBCT). To objectify responses to these changes, we use a decision support system (traffic light protocol). Action levels orange and red may lead to replanning. The purpose of this study was to evaluate how often re-planning was done for non-rigid anatomical changes, which anatomical changes led to re-planning and in which subgroups of patients treatment adaptation was deemed necessary. Materials and methods: A consecutive series of 388 HNC patients were retrospectively selected using the digital log of CBCT scans. The logs were analyzed for the number of new plans on an original planning CT scan (O-pCT) or a new pCT scan (N-pCT). Reasons for re-planning were categorized into: target volume increase/decrease, body contour decrease/increase and local shift of target volume. Subgroup analysis was performed to investigate relative differences of re-planning between treatment modalities. Results: For 33 patients the treatment plan was adapted due to anatomical changes, resulting in 37 new plans in total. Re-planning on a N-pCT with complete re-delineation was done 22 times. In fifteen cases a new plan was created after adjustment of contours on the O-pCT. Main reasons for re-planning were target volume increase, body contour decrease and local shifts of target volume. Most re-planning (23%) was seen in patients treated with chemoradiotherapy. Conclusion: Visual detection of anatomical changes on CBCT during treatment of HNC, results in replanning in 1 out of 10 patients.

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“…In these complex regions, a smaller margin size is desirable to minimize radiation-related toxicities. 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.…”

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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The impact of margin reduction on outcome and toxicity in head and neck cancer patients treated with image-guided volumetric modulated arc therapy (VMAT)

Cited by 67 publications

References 26 publications

Impact of hypofractionated schemes in radiotherapy for locally advanced head and neck cancer patients

Impact of hypofractionated schemes in radiotherapy for locally advanced head and neck cancer patients

Protocolised way to cope with anatomical changes in head & neck cancer during the course of radiotherapy

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

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