Abstract：
OBJECTIVE: To investigate the potential of virtual contrast-enhanced MRI (VCE-MRI) for gross-tumor-volume (GTV) delineation of nasopharyngeal carcinoma (NPC) using multi-institutional data.
METHODS: This study retrospectively retrieved T1-weighted (T1w), T2-weighted (T2w) MRI, gadolinium-based contrast-enhanced MRI (CE-MRI) and planning CT of 348 biopsy-proven NPC patients from three oncology centers. A multimodality-guided synergistic neural network (MMgSN-Net) was trained using 288 patients to leverage complementary features in T1w and T2w MRI for VCE-MRI synthesis, which was independently evaluated using 60 patients. Three board-certified radiation oncologists and two medical physicists participated in clinical evaluations in three aspects: image quality assessment of the synthetic VCE-MRI, VCE-MRI in assisting target volume delineation, and effectiveness of VCE-MRI-based contours in treatment planning. The image quality assessment includes distinguishability between VCE-MRI and CE-MRI, clarity of tumor-to-normal tissue interface and veracity of contrast enhancement in tumor invasion risk areas. Primary tumor delineation and treatment planning were manually performed by radiation oncologists and medical physicists, respectively.
RESULTS: The mean accuracy to distinguish VCE-MRI from CE-MRI was 31.67%; no significant difference was observed in the clarity of tumor-to-normal tissue interface between VCE-MRI and CE-MRI; for the veracity of contrast enhancement in tumor invasion risk areas, an accuracy of 85.8% was obtained. The image quality assessment results suggest that the image quality of VCE-MRI is highly similar to real CE-MRI. The mean dosimetric difference of planning target volumes were less than 1Gy.
CONCLUSIONS: The VCE-MRI is highly promising to replace the use of gadolinium-based CE-MRI in tumor delineation of NPC patients.
METHODS: This study retrospectively retrieved T1-weighted (T1w), T2-weighted (T2w) MRI, gadolinium-based contrast-enhanced MRI (CE-MRI) and planning CT of 348 biopsy-proven NPC patients from three oncology centers. A multimodality-guided synergistic neural network (MMgSN-Net) was trained using 288 patients to leverage complementary features in T1w and T2w MRI for VCE-MRI synthesis, which was independently evaluated using 60 patients. Three board-certified radiation oncologists and two medical physicists participated in clinical evaluations in three aspects: image quality assessment of the synthetic VCE-MRI, VCE-MRI in assisting target volume delineation, and effectiveness of VCE-MRI-based contours in treatment planning. The image quality assessment includes distinguishability between VCE-MRI and CE-MRI, clarity of tumor-to-normal tissue interface and veracity of contrast enhancement in tumor invasion risk areas. Primary tumor delineation and treatment planning were manually performed by radiation oncologists and medical physicists, respectively.
RESULTS: The mean accuracy to distinguish VCE-MRI from CE-MRI was 31.67%; no significant difference was observed in the clarity of tumor-to-normal tissue interface between VCE-MRI and CE-MRI; for the veracity of contrast enhancement in tumor invasion risk areas, an accuracy of 85.8% was obtained. The image quality assessment results suggest that the image quality of VCE-MRI is highly similar to real CE-MRI. The mean dosimetric difference of planning target volumes were less than 1Gy.
CONCLUSIONS: The VCE-MRI is highly promising to replace the use of gadolinium-based CE-MRI in tumor delineation of NPC patients.
摘要：
目的：使用多机构数据，探讨虚拟对比增强MRI（VCE-MRI）在鼻咽癌（NPC）大体肿瘤体积（GTV）勾画中的潜力。
方法：本研究回顾性检索T1加权（T1w），T2加权(T2w)MRI,来自三个肿瘤中心的348例经活检证实的NPC患者的钆对比增强MRI（CE-MRI）和计划CT。使用288名患者训练了多模态引导协同神经网络（MmgSN-Net），以利用T1w和T2wMRI中的互补特征进行VCE-MRI合成，对60例患者进行了独立评估。三名获得委员会认证的放射肿瘤学家和两名医学物理学家参与了三个方面的临床评估：合成VCE-MRI的图像质量评估，VCE-MRI辅助靶区勾画,以及基于VCE-MRI的轮廓在治疗计划中的有效性。图像质量评估包括VCE-MRI和CE-MRI的可区分性。肿瘤与正常组织界面的清晰度和肿瘤侵袭风险区域对比增强的准确性。原发性肿瘤的描绘和治疗计划由放射肿瘤学家和医学物理学家手动进行，分别。
结果：区分VCE-MRI和CE-MRI的平均准确率为31.67％；VCE-MRI和CE-MRI在肿瘤与正常组织界面的清晰度方面没有观察到显着差异；对于肿瘤侵袭风险区域的对比增强的准确性，准确率为85.8%。图像质量评估结果表明，VCE-MRI的图像质量与真实的CE-MRI高度相似。计划目标体积的平均剂量学差异小于1Gy。
结论：VCE-MRI在NPC患者的肿瘤勾画中非常有希望取代基于钆的CE-MRI。
方法：本研究回顾性检索T1加权（T1w），T2加权(T2w)MRI,来自三个肿瘤中心的348例经活检证实的NPC患者的钆对比增强MRI（CE-MRI）和计划CT。使用288名患者训练了多模态引导协同神经网络（MmgSN-Net），以利用T1w和T2wMRI中的互补特征进行VCE-MRI合成，对60例患者进行了独立评估。三名获得委员会认证的放射肿瘤学家和两名医学物理学家参与了三个方面的临床评估：合成VCE-MRI的图像质量评估，VCE-MRI辅助靶区勾画,以及基于VCE-MRI的轮廓在治疗计划中的有效性。图像质量评估包括VCE-MRI和CE-MRI的可区分性。肿瘤与正常组织界面的清晰度和肿瘤侵袭风险区域对比增强的准确性。原发性肿瘤的描绘和治疗计划由放射肿瘤学家和医学物理学家手动进行，分别。
结果：区分VCE-MRI和CE-MRI的平均准确率为31.67％；VCE-MRI和CE-MRI在肿瘤与正常组织界面的清晰度方面没有观察到显着差异；对于肿瘤侵袭风险区域的对比增强的准确性，准确率为85.8%。图像质量评估结果表明，VCE-MRI的图像质量与真实的CE-MRI高度相似。计划目标体积的平均剂量学差异小于1Gy。
结论：VCE-MRI在NPC患者的肿瘤勾画中非常有希望取代基于钆的CE-MRI。
