Reevaluating the imaging definition of tumor progression: Perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival

Leland S Hu, Jennifer M. Eschbacher, Joseph E. Heiserman, Amylou Dueck, William R. Shapiro, Seban Liu, John P. Karis, Kris A. Smith, Stephen W. Coons, Peter Nakaji, Robert F. Spetzler, Burt G. Feuerstein, Josef Debbins, Leslie C. Baxter

Research output: Contribution to journalArticle

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Abstract

Introduction: Contrast-enhanced MRI (CE-MRI) represents the current mainstay for monitoring treatment response in glioblastoma multiforme (GBM), based on the premise that enlarging lesions reflect increasing tumor burden, treatment failure, and poor prognosis. Unfortunately, irradiating such tumors can induce changes in CE-MRI that mimic tumor recurrence, so called post treatment radiation effect (PTRE), and in fact, both PTRE and tumor re-growth can occur together. Because PTRE represents treatment success, the relative histologic fraction of tumor growth versus PTRE affects survival. Studies suggest that Perfusion MRI (pMRI)based measures of relative cerebral blood volume (rCBV) can noninvasively estimate histologic tumor fraction to predict clinical outcome. There are several proposed pMRI-based analytic methods, although none have been correlated with overall survival (OS). This study compares how well histologic tumor fraction and OS correlate with several pMRI-based metrics. Methods: We recruited previously treated patients with GBM undergoing surgical re-resection for suspected tumor recurrence and calculated preoperative pMRI-based metrics within CE-MRI enhancing lesions: rCBV mean, mode, maximum, width, and a new thresholding metric called pMRIfractional tumor burden (pMRI-FTB). We correlated all pMRI-based metrics with histologic tumor fraction and OS. Results: Among 25 recurrent patients with GBM, histologic tumor fraction correlated most strongly with pMRI-FTB (r 0.82; P <. 0001), which was the only imaging metric that correlated with OS (P<.02). Conclusion: The pMRI-FTB metric reliably estimates histologic tumor fraction (i.e., tumor burden) and correlates with OS in the context of recurrent GBM. This technique may offer a promising biomarker of tumor progression and clinical outcome for future clinical trials.

Original languageEnglish (US)
Pages (from-to)919-930
Number of pages12
JournalNeuro-Oncology
Volume14
Issue number7
DOIs
StatePublished - Jul 2012

Fingerprint

Glioblastoma
Necrosis
Perfusion
Radiation
Survival
Radiation Effects
Neoplasms
Tumor Burden
Therapeutics
Recurrence
Tumor Biomarkers
Growth
Treatment Failure
Clinical Trials

Keywords

  • glioblastoma
  • histologic tumor fraction
  • perfusion MRI
  • pseudoprogression
  • radiation necrosis
  • recurrent
  • relative cerebral blood volume
  • survival

ASJC Scopus subject areas

  • Cancer Research
  • Oncology
  • Clinical Neurology

Cite this

Reevaluating the imaging definition of tumor progression : Perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. / Hu, Leland S; Eschbacher, Jennifer M.; Heiserman, Joseph E.; Dueck, Amylou; Shapiro, William R.; Liu, Seban; Karis, John P.; Smith, Kris A.; Coons, Stephen W.; Nakaji, Peter; Spetzler, Robert F.; Feuerstein, Burt G.; Debbins, Josef; Baxter, Leslie C.

In: Neuro-Oncology, Vol. 14, No. 7, 07.2012, p. 919-930.

Research output: Contribution to journalArticle

Hu, LS, Eschbacher, JM, Heiserman, JE, Dueck, A, Shapiro, WR, Liu, S, Karis, JP, Smith, KA, Coons, SW, Nakaji, P, Spetzler, RF, Feuerstein, BG, Debbins, J & Baxter, LC 2012, 'Reevaluating the imaging definition of tumor progression: Perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival', Neuro-Oncology, vol. 14, no. 7, pp. 919-930. https://doi.org/10.1093/neuonc/nos112
Hu, Leland S ; Eschbacher, Jennifer M. ; Heiserman, Joseph E. ; Dueck, Amylou ; Shapiro, William R. ; Liu, Seban ; Karis, John P. ; Smith, Kris A. ; Coons, Stephen W. ; Nakaji, Peter ; Spetzler, Robert F. ; Feuerstein, Burt G. ; Debbins, Josef ; Baxter, Leslie C. / Reevaluating the imaging definition of tumor progression : Perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. In: Neuro-Oncology. 2012 ; Vol. 14, No. 7. pp. 919-930.
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abstract = "Introduction: Contrast-enhanced MRI (CE-MRI) represents the current mainstay for monitoring treatment response in glioblastoma multiforme (GBM), based on the premise that enlarging lesions reflect increasing tumor burden, treatment failure, and poor prognosis. Unfortunately, irradiating such tumors can induce changes in CE-MRI that mimic tumor recurrence, so called post treatment radiation effect (PTRE), and in fact, both PTRE and tumor re-growth can occur together. Because PTRE represents treatment success, the relative histologic fraction of tumor growth versus PTRE affects survival. Studies suggest that Perfusion MRI (pMRI)based measures of relative cerebral blood volume (rCBV) can noninvasively estimate histologic tumor fraction to predict clinical outcome. There are several proposed pMRI-based analytic methods, although none have been correlated with overall survival (OS). This study compares how well histologic tumor fraction and OS correlate with several pMRI-based metrics. Methods: We recruited previously treated patients with GBM undergoing surgical re-resection for suspected tumor recurrence and calculated preoperative pMRI-based metrics within CE-MRI enhancing lesions: rCBV mean, mode, maximum, width, and a new thresholding metric called pMRIfractional tumor burden (pMRI-FTB). We correlated all pMRI-based metrics with histologic tumor fraction and OS. Results: Among 25 recurrent patients with GBM, histologic tumor fraction correlated most strongly with pMRI-FTB (r 0.82; P <. 0001), which was the only imaging metric that correlated with OS (P<.02). Conclusion: The pMRI-FTB metric reliably estimates histologic tumor fraction (i.e., tumor burden) and correlates with OS in the context of recurrent GBM. This technique may offer a promising biomarker of tumor progression and clinical outcome for future clinical trials.",
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T1 - Reevaluating the imaging definition of tumor progression

T2 - Perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival

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AU - Eschbacher, Jennifer M.

AU - Heiserman, Joseph E.

AU - Dueck, Amylou

AU - Shapiro, William R.

AU - Liu, Seban

AU - Karis, John P.

AU - Smith, Kris A.

AU - Coons, Stephen W.

AU - Nakaji, Peter

AU - Spetzler, Robert F.

AU - Feuerstein, Burt G.

AU - Debbins, Josef

AU - Baxter, Leslie C.

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N2 - Introduction: Contrast-enhanced MRI (CE-MRI) represents the current mainstay for monitoring treatment response in glioblastoma multiforme (GBM), based on the premise that enlarging lesions reflect increasing tumor burden, treatment failure, and poor prognosis. Unfortunately, irradiating such tumors can induce changes in CE-MRI that mimic tumor recurrence, so called post treatment radiation effect (PTRE), and in fact, both PTRE and tumor re-growth can occur together. Because PTRE represents treatment success, the relative histologic fraction of tumor growth versus PTRE affects survival. Studies suggest that Perfusion MRI (pMRI)based measures of relative cerebral blood volume (rCBV) can noninvasively estimate histologic tumor fraction to predict clinical outcome. There are several proposed pMRI-based analytic methods, although none have been correlated with overall survival (OS). This study compares how well histologic tumor fraction and OS correlate with several pMRI-based metrics. Methods: We recruited previously treated patients with GBM undergoing surgical re-resection for suspected tumor recurrence and calculated preoperative pMRI-based metrics within CE-MRI enhancing lesions: rCBV mean, mode, maximum, width, and a new thresholding metric called pMRIfractional tumor burden (pMRI-FTB). We correlated all pMRI-based metrics with histologic tumor fraction and OS. Results: Among 25 recurrent patients with GBM, histologic tumor fraction correlated most strongly with pMRI-FTB (r 0.82; P <. 0001), which was the only imaging metric that correlated with OS (P<.02). Conclusion: The pMRI-FTB metric reliably estimates histologic tumor fraction (i.e., tumor burden) and correlates with OS in the context of recurrent GBM. This technique may offer a promising biomarker of tumor progression and clinical outcome for future clinical trials.

AB - Introduction: Contrast-enhanced MRI (CE-MRI) represents the current mainstay for monitoring treatment response in glioblastoma multiforme (GBM), based on the premise that enlarging lesions reflect increasing tumor burden, treatment failure, and poor prognosis. Unfortunately, irradiating such tumors can induce changes in CE-MRI that mimic tumor recurrence, so called post treatment radiation effect (PTRE), and in fact, both PTRE and tumor re-growth can occur together. Because PTRE represents treatment success, the relative histologic fraction of tumor growth versus PTRE affects survival. Studies suggest that Perfusion MRI (pMRI)based measures of relative cerebral blood volume (rCBV) can noninvasively estimate histologic tumor fraction to predict clinical outcome. There are several proposed pMRI-based analytic methods, although none have been correlated with overall survival (OS). This study compares how well histologic tumor fraction and OS correlate with several pMRI-based metrics. Methods: We recruited previously treated patients with GBM undergoing surgical re-resection for suspected tumor recurrence and calculated preoperative pMRI-based metrics within CE-MRI enhancing lesions: rCBV mean, mode, maximum, width, and a new thresholding metric called pMRIfractional tumor burden (pMRI-FTB). We correlated all pMRI-based metrics with histologic tumor fraction and OS. Results: Among 25 recurrent patients with GBM, histologic tumor fraction correlated most strongly with pMRI-FTB (r 0.82; P <. 0001), which was the only imaging metric that correlated with OS (P<.02). Conclusion: The pMRI-FTB metric reliably estimates histologic tumor fraction (i.e., tumor burden) and correlates with OS in the context of recurrent GBM. This technique may offer a promising biomarker of tumor progression and clinical outcome for future clinical trials.

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KW - histologic tumor fraction

KW - perfusion MRI

KW - pseudoprogression

KW - radiation necrosis

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KW - relative cerebral blood volume

KW - survival

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