Recommendations for MRI-based contouring of gross tumor volume and organs at risk for radiation therapy of pancreatic cancer

Heerkens, H.D.; Hall, William A.; Li, X. A.; Knechtges, Paul; Dalah, Entesar Zawam; Paulson, E.S.; Berg, Cornelis A.T. van den; Meijer, G.J.; Koay, Eugene J.; Crane, Christopher H.; Aitken, K.; Vulpen, Marco van; Erickson, Beth

doi:10.1016/j.prro.2016.10.006

Cited by 50 publications

(41 citation statements)

References 34 publications

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“…Both 1.5-Tesla and 3-Tesla MRI showed advantages and disadvantages, as reported in several studies. In fact, in 3-Tesla MRI motion artifacts resulted more pronounced whereas, in 1.5-Tesla MRI, scan time will be prolonged in order to reach similar contrast to noise as obtained at 3-Tesla MRI [21]. Probably, a wider availability of 3-Tesla MRI system, with optimized protocols for abdominal malignancies, could lead to an improvement of volume delineation in pancreatic cancer, as already demonstrated in others sites [38].…”

Section: Discussionmentioning

confidence: 88%

“…All members were asked to contour the GTV and the duodenum in the corresponding slides where GTV had been delineated. Contouring guidelines with reference to GTV and OARs delineation on diagnostic MRI in pancreatic cancer were provided to all the participants [21].…”

Section: Contouring Sectionmentioning

confidence: 99%

“…Recently, recommendations for Gross Tumor Volume (GTV) and OARs contouring of pancreatic cancer using MRI have been provided [21].…”

Section: Introductionmentioning

confidence: 99%

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Magnetic resonance imaging (MRI) compared with computed tomography (CT) for interobserver agreement of gross tumor volume delineation in pancreatic cancer: a multi-institutional contouring study on behalf of the AIRO group for gastrointestinal cancers

et al. 2019

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Genovesi (2019) Magnetic resonance imaging (MRI) compared with computed tomography (CT) for interobserver agreement of gross tumor volume delineation in pancreatic cancer: a multiinstitutional contouring study on behalf of the AIRO group for gastrointestinal cancers, Acta Oncologica, 58:4, 439-447, ABSTRACT Background: Due to the high soft tissue resolution, magnetic resonance imaging (MRI) could improve the accuracy of pancreatic tumor delineation in radiation treatment planning. A multi-institutional study was proposed to evaluate the impact of MRI on inter-observer agreement in gross tumor volume (GTV) and duodenum delineation for pancreatic cancer compared with computer tomography (CT). Material and Methods: Two clinical cases of borderline resectable (Case 1) and unresectable (Case 2) pancreatic cancer were selected. In two sequential steps, diagnostic contrast-enhanced CT scan and MRI sequences were sent to the participating centers. CT-GTVs were contoured while blinded to MRI data sets. DICE index was used to evaluate the spatial overlap accuracy. Results: Thirty-one radiation oncologists from different Institutions submitted the delineated volumes. CT-and MRI-GTV mean volumes were 21.6 ± 9.0 cm 3 and 17.2 ± 6.0 cm 3 , respectively for Case 1, and 31.3 ± 15.6 cm 3 and 33.2 ± 20.2 cm 3 , respectively for Case 2. Resulting MRI-GTV mean volume was significantly smaller than CT-GTV in the borderline resectable case (p < .05). A substantial agreement was shown by the median DICE index for CT-and MRI-GTV resulting as 0.74 (IQR: 0.67-0.75) and 0.61 (IQR: 0.57-0.67) for Case 1; a moderate agreement was instead reported for Case 2: 0.59 (IQR:0.52-0.66) and 0.53 (IQR:0.42-0.62) for CT-and MRI-GTV, respectively. Conclusion: Diagnostic MRI resulted in smaller GTV in borderline resectable case with a substantial agreement between observers, and was comparable to CT scan in interobserver variability, in both cases. The greater variability in the unresectable case underlines the critical issues related to the outlining when vascular structures are more involved. The integration of MRI with contrast-enhancement CT, thanks to its high definition of tumor relationship with neighboring vessels, could offer a greater accuracy of target delineation.

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Section: Discussionmentioning

confidence: 88%

Section: Contouring Sectionmentioning

confidence: 99%

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Magnetic resonance imaging (MRI) compared with computed tomography (CT) for interobserver agreement of gross tumor volume delineation in pancreatic cancer: a multi-institutional contouring study on behalf of the AIRO group for gastrointestinal cancers

et al. 2019

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“…More abdominal patients will be enrolled in the future to further evaluate two models. Keywords: Generative adversarial network, Synthetic CT, MR-guided radiotherapy planning workflows for pelvic or abdominal cancer radiotherapy (Villeirs et al 2005, Lim et al 2011, Heerkens et al 2017, Mittauer et al 2018. Since there is no direct relationship between MR intensity values and electron densities, the standard MR-guided radiotherapy workflow still requires the acquisition of a CT image for dose calculation.…”

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confidence: 99%

Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy

Fu¹,

Singhrao²,

Cao³

et al. 2020

Biomed. Phys. Eng. Express

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Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy.A retrospective study was performed using CT images and 0.35T MR images of 12 patients with liver (n=8) and non-liver abdominal (n=4) cancer. CT images were deformably registered to the corresponding MR images to generate deformed CT (dCT) images for treatment planning. Both cGAN and cycleGAN were trained using MR and dCT transverse slices. Four-fold cross-validation testing was conducted to generate sCT images for all patients. The HU prediction accuracy was evaluated by voxel-wise similarity metric between each dCT and sCT image for all 12 patients. dCT-based and sCT-based dose distributions were compared using gamma and dose-volume histogram (DVH) metric analysis for 8 liver patients. sCTcycleGAN achieved the average mean absolute error (MAE) of 94.1 HU, while sCTcGAN achieved 89.8 HU. In both models, the average gamma passing rates within all volumes of interest were higher than 95% using a 2%, 2 mm criterion, and 99% using a 3%, 3 mm criterion. The average differences in the mean dose and DVH metrics were within ±0.6% for the planning target volume and within ±0.15% for evaluated organs in both models.Results demonstrated that abdominal sCT images generated by both cGAN and cycleGAN achieved accurate dose calculation for 8 liver radiotherapy plans. sCTcGAN images had smaller average MAE and achieved better dose calculation accuracy than sCTcyleGAN images. More abdominal patients will be enrolled in the future to further evaluate two models. Keywords: Generative adversarial network, Synthetic CT, MR-guided radiotherapy planning workflows for pelvic or abdominal cancer radiotherapy (Villeirs et al 2005, Lim et al 2011, Heerkens et al 2017, Mittauer et al 2018. Since there is no direct relationship between MR intensity values and electron densities, the standard MR-guided radiotherapy workflow still requires the acquisition of a CT image for dose calculation. However, registration between CT and MR images for transferring target delineations introduces systematic uncertainties that propagate throughout the treatment (Edmund and Nyholm 2017). Acquiring an additional CT image also increases unwanted radiation exposure, clinical workload, and financial cost (Karlsson et al 2009). MR-only radiotherapy can avoid these downsides.A few methods have been proposed to generate synthetic CT (sCT) images from MR images. These methods include atlas-based methods, voxel-based methods, and hybrid methods (Edmund and Nyholm 2017). In atlas-based methods (Sjölund et al 2015, Dowling et al 2015, the target MR image was first deformably registered to atlas-MR images to acquire deformation vector fields. The acquired vector fi...

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“…MRI is characterized by its superior soft-tissue contrast which is important for the identification of lesions e.g. within the prostate, the liver or the pancreas [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Non-rigid image registration of 4D-MRI data for improved delineation of moving tumors

et al. 2020

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Background: To increase the image quality of end-expiratory and end-inspiratory phases of retrospective respiratory self-gated 4D MRI data sets using non-rigid image registration for improved target delineation of moving tumors. Methods: End-expiratory and end-inspiratory phases of volunteer and patient 4D MRI data sets are used as targets for non-rigid image registration of all other phases using two different registration schemes: In the first, all phases are registered directly (dir-Reg) while next neighbors are successively registered until the target is reached in the second (nn-Reg). Resulting data sets are quantitatively compared using diaphragm and tumor sharpness and the coefficient of variation of regions of interest in the lung, liver, and heart. Qualitative assessment of the patient data regarding noise level, tumor delineation, and overall image quality was performed by blinded reading based on a 4 point Likert scale. Results: The median coefficient of variation was lower for both registration schemes compared to the target. Median dir-Reg coefficient of variation of all ROIs was 5.6% lower for expiration and 7.0% lower for inspiration compared with nn-Reg. Statistical significant differences between the two schemes were found in all comparisons. Median sharpness in inspiration is lower compared to expiration sharpness in all cases. Registered data sets were rated better compared to the targets in all categories. Over all categories, mean expiration scores were 2.92 ± 0.18 for the target, 3.19 ± 0.22 for nn-Reg and 3.56 ± 0.14 for dir-Reg and mean inspiration scores 2.25 ± 0.12 for the target, 2.72 ± 215 0.04 for nn-Reg and 3.78 ± 0.04 for dir-Reg. Conclusions: In this work, end-expiratory and inspiratory phases of a 4D MRI data sets are used as targets for nonrigid image registration of all other phases. It is qualitatively and quantitatively shown that image quality of the targets can be significantly enhanced leading to improved target delineation of moving tumors.

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Recommendations for MRI-based contouring of gross tumor volume and organs at risk for radiation therapy of pancreatic cancer

Cited by 50 publications

References 34 publications

Magnetic resonance imaging (MRI) compared with computed tomography (CT) for interobserver agreement of gross tumor volume delineation in pancreatic cancer: a multi-institutional contouring study on behalf of the AIRO group for gastrointestinal cancers

Magnetic resonance imaging (MRI) compared with computed tomography (CT) for interobserver agreement of gross tumor volume delineation in pancreatic cancer: a multi-institutional contouring study on behalf of the AIRO group for gastrointestinal cancers

Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy

Non-rigid image registration of 4D-MRI data for improved delineation of moving tumors

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