PURPOSE: To determine the accuracy of relative cerebral blood volume (rCBV) fraction for distinguishing high-grade recurrent neoplasm from treatment-related necrosis (TRN) in enhancing masses identified on surveillance magnetic resonance (MR) images following treatment for primary or secondary brain neoplasm. MATERIALS AND METHODS: This institutional review board approved and HIPAA-compliant retrospective study included 30 patients undergoing resection of recurrent enhancing mass appearing after treatment with surgery and radiation, with or without chemotherapy. The enhancing mass volume was manually segmented on three-dimensional T1-weighted images. The rCBV maps were created by using T2-weighted dynamic susceptibility contrast perfusion MR imaging and registered to T1-weighted images, and the fraction of enhancing mass with rCBV above a range of thresholds was calculated. A receiver operating characteristic (ROC) curve was created by calculating sensitivity-specificity pairs at each threshold for rCBV fraction (< or = 20% or > 20%) by using percentage of malignant features at histologic evaluation as the reference criterion. Relationships between rCBV and probability of recurrence were estimated by using logistic regression analysis. RESULTS: ROC analysis showed excellent discriminating accuracy of rCBV fraction (area under the ROC curve, 0.97 +/- 0.03 [standard error]) and high efficiency (93%) with an rCBV threshold of 1.8 times that of normal-appearing white matter. Logistic regression analysis showed that a unit increase of rCBV is associated with a 254-fold increase (95% confidence interval: 43, 1504, P < .001) of the odds that enhanced tissue is recurrence, adjusting for age, treatment, volume of enhancing tissue, and time to suspected recurrence. CONCLUSION: The fraction of malignant histologic features in enhancing masses recurring after treatment for brain neoplasms can be predicted by using the rCBV fraction, with improved differentiation between recurrent neoplasm and TRN. RSNA, 2009
PURPOSE: To determine the accuracy of relative cerebral blood volume (rCBV) fraction for distinguishing high-grade recurrent neoplasm from treatment-related necrosis (TRN) in enhancing masses identified on surveillance magnetic resonance (MR) images following treatment for primary or secondary brain neoplasm. MATERIALS AND METHODS: This institutional review board approved and HIPAA-compliant retrospective study included 30 patients undergoing resection of recurrent enhancing mass appearing after treatment with surgery and radiation, with or without chemotherapy. The enhancing mass volume was manually segmented on three-dimensional T1-weighted images. The rCBV maps were created by using T2-weighted dynamic susceptibility contrast perfusion MR imaging and registered to T1-weighted images, and the fraction of enhancing mass with rCBV above a range of thresholds was calculated. A receiver operating characteristic (ROC) curve was created by calculating sensitivity-specificity pairs at each threshold for rCBV fraction (< or = 20% or > 20%) by using percentage of malignant features at histologic evaluation as the reference criterion. Relationships between rCBV and probability of recurrence were estimated by using logistic regression analysis. RESULTS: ROC analysis showed excellent discriminating accuracy of rCBV fraction (area under the ROC curve, 0.97 +/- 0.03 [standard error]) and high efficiency (93%) with an rCBV threshold of 1.8 times that of normal-appearing white matter. Logistic regression analysis showed that a unit increase of rCBV is associated with a 254-fold increase (95% confidence interval: 43, 1504, P < .001) of the odds that enhanced tissue is recurrence, adjusting for age, treatment, volume of enhancing tissue, and time to suspected recurrence. CONCLUSION: The fraction of malignant histologic features in enhancing masses recurring after treatment for brain neoplasms can be predicted by using the rCBV fraction, with improved differentiation between recurrent neoplasm and TRN. RSNA, 2009
Authors: Asim K Bag; Phillip C Cezayirli; Jake J Davenport; Santhosh Gaddikeri; Hassan M Fathallah-Shaykh; Alan Cantor; Xiaosi S Han; Louis B Nabors Journal: J Neurooncol Date: 2014-08-07 Impact factor: 4.130
Authors: J I Traylor; D C A Bastos; D Fuentes; M Muir; R Patel; V A Kumar; R J Stafford; G Rao; S S Prabhu Journal: AJNR Am J Neuroradiol Date: 2019-08-01 Impact factor: 3.825
Authors: Keith R Thulborn; Aiming Lu; Ian C Atkinson; Mohan Pauliah; Kathryn Beal; Timothy A Chan; Antonio Omuro; Josh Yamada; Michelle S Bradbury Journal: Clin Cancer Res Date: 2018-11-28 Impact factor: 12.531
Authors: Zachary S Kelm; Panagiotis D Korfiatis; Ravi K Lingineni; John R Daniels; Jan C Buckner; Daniel H Lachance; Ian F Parney; Rickey E Carter; Bradley J Erickson Journal: J Med Imaging (Bellingham) Date: 2015-05-26