Diagnostic accuracy of automatic normalization of CBV in glioma grading using T1- weighted DCE-MRI |
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| Institution: | 1. Division of Mathematical oncology, City of Hope National Medical Center, CA, USA;2. Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, India;3. Department of Biostatistics and Health Informatics, SGPGIMS, Lucknow, India;4. Department of Neurosurgery, Fortis Memorial Research Institute, Gurgaon, India;5. SRL Diagnostics, Fortis Memorial Research Institute, Gurgaon, India;6. Philips Health System, Philips India Limited, Gurgaon, India;1. Changchun Institute of Applied Chemistry Chinese Academy of Sciences, No. 5625, Renmin Street, Changchun 130022, China;2. University of Chinese Academy of Sciences, No. 19, Yuquan Road 19, Beijing 100049, China;1. Institute of Clinical Physiology, National Council of Research, Pisa, Italy;2. Fondazione G. Monasterio CNR - Regione Toscana, Pisa, Italy;3. Laboratory of Medical Physics and Biotechnologies for Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy;4. Imago7 Foundation, Pisa, Italy;1. Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA;2. Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huai-hai Road, Xuzhou, Jiangsu 221002, PR China;3. Division of Nephrology and Hypertension, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA;1. Division of Radiology, Department of Radiology and Medical Informatics, Geneva University Hospital and Faculty of Medicine, University of Geneva, 4 rue Gabrielle-Perret-Gentil, 1205 Geneva, Switzerland;2. First Clinic of Internal Medicine, Department of Internal Medicine, University of Genoa, 6 viale Benedetto XV, 16132 Genoa, Italy;3. IRCCS AOU San Martino - IST, Genova, 10 Largo Rosanna Benzi, 16132 Genoa, Italy;4. Division of Pathology, Department of Genetics and Laboratory Medicine, Geneva University Hospitals, 4 rue Gabrielle-Perret-Gentil, 1205 Geneva, Switzerland;5. Division of Cardiology, Foundation for Medical Researches, Faculty of Medicine, Department of Internal Medicine, University of Geneva, 64 avenue de la Roseraie, 1211 Geneva, Switzerland;1. Department of Reparative Materials, Institute for Frontier Medical Sciences, Kyoto University, Japan;2. Department of Neurosurgery, Jikei University School of Medicine, Japan;3. Department of Biomedical Sciences, College of Life and Health Sciences, Chubu University, Japan;4. Hiratsuka Technical Center, Tanaka Kikinzoku Kogyo K.K., Japan;5. Isehara Technical Center, Tanaka Kikinzoku Kogyo K.K., Japan |
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| Abstract: | PurposeAim 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).Material and methodsSixty 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 > 10 years 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).ResultThe 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.ConclusionIt 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. |
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