PURPOSE: Assessment of radiologic response (RR) for brain tumors utilizes the Macdonald criteria 8 to 10 weeks from the start of treatment. Diffusion magnetic resonance imaging (MRI) using a functional diffusion map (fDM) may provide an earlier measure to predict patient survival. PATIENTS AND METHODS: Sixty patients with high-grade glioma were enrolled onto a study of intratreatment MRI at 1, 3, and 10 weeks. Receiver operating characteristic curve analysis was used to evaluate imaging parameters as a function of patient survival at 1 year. Both log-rank and Cox proportional hazards models were utilized to assess overall survival. RESULTS: Greater increases in diffusion in response to therapy over time were observed in those patients alive at 1 year compared with those who died as a result of disease. The volume of tumor with increased diffusion by fDM at 3 weeks was the strongest predictor of patient survival at 1 year, with larger fDM predicting longer median survival (52.6 v 10.9 months; log-rank, P < .003; hazard ratio [HR] = 2.7; 95% CI, 1.5 to 5.9). Radiologic response at 10 weeks had similar prognostic value (median survival, 31.6 v 10.9 months; log-rank P < .0007; HR = 2.9; 95% CI, 1.7 to 7.2). Radiologic response and fDM differed in 25% of cases. A composite index of response including fDM and RR provided a robust predictor of patient survival and may identify patients in whom RR does not correlate with clinical outcome. CONCLUSION: Compared with conventional neuroimaging, fDM provided an earlier assessment of equal predictive value, and the combination of fDM and RR provided a more accurate prediction of patient survival than either metric alone.
PURPOSE: Assessment of radiologic response (RR) for brain tumors utilizes the Macdonald criteria 8 to 10 weeks from the start of treatment. Diffusion magnetic resonance imaging (MRI) using a functional diffusion map (fDM) may provide an earlier measure to predict patient survival. PATIENTS AND METHODS: Sixty patients with high-grade glioma were enrolled onto a study of intratreatment MRI at 1, 3, and 10 weeks. Receiver operating characteristic curve analysis was used to evaluate imaging parameters as a function of patient survival at 1 year. Both log-rank and Cox proportional hazards models were utilized to assess overall survival. RESULTS: Greater increases in diffusion in response to therapy over time were observed in those patients alive at 1 year compared with those who died as a result of disease. The volume of tumor with increased diffusion by fDM at 3 weeks was the strongest predictor of patient survival at 1 year, with larger fDM predicting longer median survival (52.6 v 10.9 months; log-rank, P < .003; hazard ratio [HR] = 2.7; 95% CI, 1.5 to 5.9). Radiologic response at 10 weeks had similar prognostic value (median survival, 31.6 v 10.9 months; log-rank P < .0007; HR = 2.9; 95% CI, 1.7 to 7.2). Radiologic response and fDM differed in 25% of cases. A composite index of response including fDM and RR provided a robust predictor of patient survival and may identify patients in whom RR does not correlate with clinical outcome. CONCLUSION: Compared with conventional neuroimaging, fDM provided an earlier assessment of equal predictive value, and the combination of fDM and RR provided a more accurate prediction of patient survival than either metric alone.
Authors: Kuei C Lee; Bradford A Moffat; Anne F Schott; Rachel Layman; Steven Ellingworth; Rebecca Juliar; Amjad P Khan; Mark Helvie; Charles R Meyer; Thomas L Chenevert; Alnawaz Rehemtulla; Brian D Ross Journal: Clin Cancer Res Date: 2007-01-15 Impact factor: 12.531
Authors: C R Meyer; J L Boes; B Kim; P H Bland; K R Zasadny; P V Kison; K Koral; K A Frey; R L Wahl Journal: Med Image Anal Date: 1997-04 Impact factor: 8.545
Authors: Victor D Schepkin; Kuei C Lee; Kyle Kuszpit; Mukilan Muthuswami; Timothy D Johnson; Thomas L Chenevert; Alnawaz Rehemtulla; Brian D Ross Journal: NMR Biomed Date: 2006-12 Impact factor: 4.044
Authors: L D Stegman; A Rehemtulla; D A Hamstra; D J Rice; S J Jonas; K L Stout; T L Chenevert; B D Ross Journal: Gene Ther Date: 2000-06 Impact factor: 5.250
Authors: Daniel A Hamstra; Thomas L Chenevert; Bradford A Moffat; Timothy D Johnson; Charles R Meyer; Suresh K Mukherji; Douglas J Quint; Stephen S Gebarski; Xiaoying Fan; Christina I Tsien; Theodore S Lawrence; Larry Junck; Alnawaz Rehemtulla; Brian D Ross Journal: Proc Natl Acad Sci U S A Date: 2005-11-02 Impact factor: 11.205
Authors: June L Chan; Susan W Lee; Benedick A Fraass; Daniel P Normolle; Harry S Greenberg; Larry R Junck; Stephen S Gebarski; Howard M Sandler Journal: J Clin Oncol Date: 2002-03-15 Impact factor: 44.544
Authors: W J Curran; C B Scott; J Horton; J S Nelson; A S Weinstein; A J Fischbach; C H Chang; M Rotman; S O Asbell; R E Krisch Journal: J Natl Cancer Inst Date: 1993-05-05 Impact factor: 13.506
Authors: Benjamin M Ellingson; Timothy F Cloughesy; Taryar Zaw; Albert Lai; Phioanh L Nghiemphu; Robert Harris; Shadi Lalezari; Naveed Wagle; Kourosh M Naeini; Jose Carrillo; Linda M Liau; Whitney B Pope Journal: Neuro Oncol Date: 2012-01-22 Impact factor: 12.300
Authors: Craig J Galbán; Stefanie Galbán; Marcian E Van Dort; Gary D Luker; Mahaveer S Bhojani; Alnawaz Rehemtulla; Brian D Ross Journal: Prog Mol Biol Transl Sci Date: 2010 Impact factor: 3.622
Authors: Reza Farjam; Christina I Tsien; Felix Y Feng; Diana Gomez-Hassan; James A Hayman; Theodore S Lawrence; Yue Cao Journal: Neuro Oncol Date: 2013-12-09 Impact factor: 12.300
Authors: Alexander D Cohen; Peter S LaViolette; Melissa Prah; Jennifer Connelly; Mark G Malkin; Scott D Rand; Wade M Mueller; Kathleen M Schmainda Journal: J Magn Reson Imaging Date: 2013-02-06 Impact factor: 4.813
Authors: Jennifer L Boes; Benjamin A Hoff; Nola Hylton; Martin D Pickles; Lindsay W Turnbull; Anne F Schott; Alnawaz Rehemtulla; Ryan Chamberlain; Benjamin Lemasson; Thomas L Chenevert; Craig J Galbán; Charles R Meyer; Brian D Ross Journal: Transl Oncol Date: 2014-02-01 Impact factor: 4.243
Authors: John H Rossmeisl; Paulo A Garcia; Gregory B Daniel; John Daniel Bourland; Waldemar Debinski; Nikolaos Dervisis; Shawna Klahn Journal: Vet Radiol Ultrasound Date: 2013-11-13 Impact factor: 1.363