Sahoo Prativa, Gupta Rakesh K, Gupta Pradeep K, Awasthi Ashish, Pandey Chandra M, Gupta Mudit, Patir Rana, Vaishya Sandeep, Ahlawat Sunita, Saha Indrajit
Division of Mathematical oncology, City of Hope National Medical Center, CA, USA.
Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, India.
Magn Reson Imaging. 2017 Dec;44:32-37. doi: 10.1016/j.mri.2017.08.003. Epub 2017 Aug 4.
Aim of this retrospective study was to compare diagnostic accuracy of proposed automatic normalization method to quantify the relative cerebral blood volume (rCBV) with existing contra-lateral region of interest (ROI) based CBV normalization method for glioma grading using T1-weighted dynamic contrast enhanced MRI (DCE-MRI).
Sixty patients with histologically confirmed gliomas were included in this study retrospectively. CBV maps were generated using T1-weighted DCE-MRI and are normalized by contralateral ROI based method (rCBV_contra), unaffected white matter (rCBV_WM) and unaffected gray matter (rCBV_GM), the latter two of these were generated automatically. An expert radiologist with >10years of experience in DCE-MRI and a non-expert with one year experience were used independently to measure rCBVs. Cutoff values for glioma grading were decided from ROC analysis. Agreement of histology with rCBV_WM, rCBV_GM and rCBV_contra respectively was studied using Kappa statistics and intra-class correlation coefficient (ICC).
The diagnostic accuracy of glioma grading using the measured rCBV_contra by expert radiologist was found to be high (sensitivity=1.00, specificity=0.96, p<0.001) compared to the non-expert user (sensitivity=0.65, specificity=0.78, p<0.001). On the other hand, both the expert and non-expert user showed similar diagnostic accuracy for automatic rCBV_WM (sensitivity=0.89, specificity=0.87, p=0.001) and rCBV_GM (sensitivity=0.81, specificity=0.78, p=0.001) measures. Further, it was also observed that, contralateral based method by expert user showed highest agreement with histological grading of tumor (kappa=0.96, agreement 98.33%, p<0.001), however; automatic normalization method showed same percentage of agreement for both expert and non-expert user. rCBV_WM showed an agreement of 88.33% (kappa=0.76,p<0.001) with histopathological grading.
It was inferred from this study that, in the absence of expert user, automated normalization of CBV using the proposed method could provide better diagnostic accuracy compared to the manual contralateral based approach.
本回顾性研究的目的是比较所提出的自动归一化方法与现有的基于对侧感兴趣区域（ROI）的脑血容量（CBV）归一化方法在使用T1加权动态对比增强磁共振成像（DCE-MRI）进行胶质瘤分级时量化相对脑血容量（rCBV）的诊断准确性。
本研究回顾性纳入了60例经组织学确诊的胶质瘤患者。使用T1加权DCE-MRI生成CBV图，并通过基于对侧ROI的方法（rCBV_contra）、未受影响的白质（rCBV_WM）和未受影响的灰质（rCBV_GM）进行归一化，后两者是自动生成的。分别由一位在DCE-MRI方面有超过10年经验的专家放射科医生和一位有一年经验的非专家独立测量rCBV。通过ROC分析确定胶质瘤分级的临界值。使用Kappa统计量和组内相关系数（ICC）分别研究组织学与rCBV_WM、rCBV_GM和rCBV_contra的一致性。
与非专家用户相比（敏感性=0.65，特异性=0.78，p<0.001），专家放射科医生使用测量的rCBV_contra进行胶质瘤分级的诊断准确性较高（敏感性=1.00，特异性=0.96，p<0.001）。另一方面，专家和非专家用户在自动rCBV_WM（敏感性=0.89，特异性=0.87，p=0.001）和rCBV_GM（敏感性=0.81，特异性=0.78，p=0.001）测量方面显示出相似的诊断准确性。此外，还观察到，专家用户基于对侧的方法与肿瘤组织学分级的一致性最高（kappa=0.96，一致性98.33%，p<0.001），然而；自动归一化方法对专家和非专家用户显示出相同的一致性百分比。rCBV_WM与组织病理学分级的一致性为88.33%（kappa=0.76，p<0.001）。
从本研究推断，在没有专家用户的情况下，与基于手动对侧的方法相比，使用所提出的方法对CBV进行自动归一化可以提供更好的诊断准确性。
