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Differentiation of Recurrent Glioblastoma from Delayed Radiation Necrosis by Using Voxel-based Multiparametric
Ra Gyoung Yoon1, Ho Sung Kim1, Myeong Ju Koh1
1From the Department of Radiology, Catholic Kwandong University College of Medicine, Catholic Kwandong University International St. Mary's Hospital, Incheon, Korea (R.G.Y.); Department of Radiology, Jeju National University Hospital, Jeju, Korea (M.J.G.); Department of Radiology and Research Institute of Radiology (H.S.K., W.H.S., S.C.J., S.J.K.) and Department of Neurosurgery (J.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-736, Korea.
Abstract:
Purpose To assess a volume-weighted voxel-based multiparametric (MP) clustering method as an imaging biomarker to differentiate recurrent glioblastoma from delayed radiation necrosis. Materials and Methods The institutional review board approved this retrospective study and waived the informed consent requirement. Seventy-five patients with pathologic analysis-confirmed recurrent glioblastoma (n = 42) or radiation necrosis (n = 33) who presented with enlarged contrast material-enhanced lesions at magnetic resonance (MR) imaging after they completed concurrent chemotherapy and radiation therapy were enrolled. The diagnostic performance of the total MP cluster score was determined by using the area under the receiver operating characteristic curve (AUC) with cross-validation and compared with those of single parameter measurements (10% histogram cutoffs of apparent diffusion coefficient [ADC10] or 90% histogram cutoffs of normalized cerebral blood volume and initial time-signal intensity AUC). Results Receiver operating characteristic curve analysis showed that an AUC for differentiating recurrent glioblastoma from delayed radiation necrosis was highest in the total MP cluster score and lowest for ADC10 for both readers. The total MP cluster score had significantly better diagnostic accuracy than any single parameter (corrected P = .001-.039 for reader 1; corrected P = .005-.041 for reader 2). The total MP cluster score was the best predictor of recurrent glioblastoma (cross-validated AUCs, 0.942-0.946 for both readers), with a sensitivity of 95.2% for reader 1 and 97.6% for reader 2. Conclusion Quantitative analysis with volume-weighted voxel-based MP clustering appears to be superior to the use of single imaging parameters to differentiate recurrent glioblastoma from delayed radiation necrosis. © RSNA, 2017 Online supplemental material is available for this article.
Insights
Multiparametric (MP) clustering accurately distinguishes recurrent glioblastoma from radiation necrosis. This advanced imaging biomarker shows superior performance compared to single imaging parameters for improved patient diagnosis.
Area of Science:
- Radiology
- Oncology
- Medical Imaging
Background:
- Differentiating recurrent glioblastoma from delayed radiation necrosis is critical for treatment planning.
- Conventional magnetic resonance (MR) imaging parameters can be ambiguous in these cases.
- Novel imaging biomarkers are needed for accurate diagnosis.
Purpose of the Study:
- To evaluate a volume-weighted voxel-based multiparametric (MP) clustering method as an imaging biomarker.
- To assess its efficacy in differentiating recurrent glioblastoma from delayed radiation necrosis.
- To compare its diagnostic performance against single imaging parameters.
Main Methods:
- Retrospective analysis of 75 patients with confirmed recurrent glioblastoma or radiation necrosis.
- Utilized multiparametric (MP) MR imaging data including apparent diffusion coefficient (ADC) and normalized cerebral blood volume (nCBV).
- Calculated total MP cluster score and compared its diagnostic performance (AUC) with single parameters using receiver operating characteristic analysis.
Main Results:
- The total MP cluster score demonstrated the highest area under the receiver operating characteristic curve (AUC) for differentiating the two conditions.
- MP clustering significantly outperformed single parameters (ADC10, nCBV, time-signal intensity AUC) in diagnostic accuracy.
- The MP cluster score was the best predictor of recurrent glioblastoma with high sensitivity (95.2%-97.6%).
Conclusions:
- Quantitative volume-weighted voxel-based MP clustering is a superior imaging biomarker.
- It offers improved diagnostic accuracy over single imaging parameters for distinguishing recurrent glioblastoma from radiation necrosis.
- This method holds promise for more precise patient management in neuro-oncology.
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