BACKGROUND: A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. PURPOSE: To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. MATERIAL AND METHODS: T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. RESULTS: Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). CONCLUSION: Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients.
BACKGROUND: A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. PURPOSE: To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in gliomapatients. MATERIAL AND METHODS: T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 gliomapatients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. RESULTS: Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant (P < 0.0001). CONCLUSION: Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in gliomapatients.
Authors: Michael R Folkert; Jeremy Setton; Aditya P Apte; Milan Grkovski; Robert J Young; Heiko Schöder; Wade L Thorstad; Nancy Y Lee; Joseph O Deasy; Jung Hun Oh Journal: Phys Med Biol Date: 2017-06-12 Impact factor: 3.609
Authors: David A Gutman; Lee A D Cooper; Scott N Hwang; Chad A Holder; Jingjing Gao; Tarun D Aurora; William D Dunn; Lisa Scarpace; Tom Mikkelsen; Rajan Jain; Max Wintermark; Manal Jilwan; Prashant Raghavan; Erich Huang; Robert J Clifford; Pattanasak Mongkolwat; Vladimir Kleper; John Freymann; Justin Kirby; Pascal O Zinn; Carlos S Moreno; Carl Jaffe; Rivka Colen; Daniel L Rubin; Joel Saltz; Adam Flanders; Daniel J Brat Journal: Radiology Date: 2013-02-07 Impact factor: 11.105