Combined Diagnostic Accuracy of Diffusion and Perfusion MR Imaging to Differentiate Radiation-Induced Necrosis from Recurrence in Glioblastoma

• Published in: Diagnostics (Basel) Vol. 12; no. 3; p. 718
• Main Authors: Jajodia, Ankush, Goel, Varun, Goyal, Jitin, Patnaik, Nivedita, Khoda, Jeevitesh, Pasricha, Sunil, Gairola, Munish
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.03.2022; MDPI
• Subjects: Accuracy; Blood; Chemotherapy; clinical decision-making; diffusion-weighted imaging; glioblastoma multiforme; Histopathology; perfusion weighted imaging; Radiation therapy; radiation-induced necrosis; Statistical analysis; Tumors; Work stations; radiation-induced necrosis; glioblastoma multiforme; clinical decision-making; diffusion-weighted imaging; perfusion weighted imaging
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics12030718

We aimed to use quantitative values derived from perfusion and diffusion-weighted MR imaging (PWI and DWI) to differentiate radiation-induced necrosis (RIN) from tumor recurrence in Glioblastoma (GBM) and investigate the best parameters for improved diagnostic accuracy and clinical decision-making. Methods: A retrospective analysis of follow-up MRI with new enhancing observations was performed in histopathologically confirmed subjects of post-treated GBM, who underwent re-surgical exploration. Quantitative estimation of rCBV (relative cerebral blood volume) from PWI and three methods of apparent diffusion coefficient (ADC) estimation were performed, namely ADC R1 (whole cross-sectional area of tumor), ADC R2 (only solid enhancing lesion), and ADC R3 (central necrosis). ROC curve and logistic regression analysis was completed. A confusion matrix table created using Excel provided the best combination parameters to ameliorate false-positive and false-negative results. Results: Forty-four subjects with a mean age of 46 years (range, 19–70 years) underwent re-surgical exploration with RIN in 28 (67%) and recurrent tumor in 16 (33%) on histopathology. rCBV threshold of >3.4 had the best diagnostic accuracy (AUC = 0.93, 81% sensitivity and 89% specificity). A multiple logistic regression model showed significant contributions from rCBV (p < 0.001) and ADC R3 (p = 0.001). After analysis of confusion matrix ADC R3 > 2032 × 10−6 mm2 achieved 100% specificity with gain in sensitivity (94% vs. 56%). Conclusions: A combination of parameters had better diagnostic performance, and a stepwise combination of rCBV and ADC R3 obviated unnecessary biopsies in 10% (3/28), leading to improved clinical decision-making.