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A Simple Automated Method for Detecting Recurrence in High-Grade Glioma.

T K Yanagihara1, J Grinband2, J Rowley3

  • 1From the Departments of Radiation Oncology (T.K.Y., J.R., A.L., M.G., M.A., A.C., T.J.C.W.) tky2102@columbia.edu.

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|July 16, 2016
PubMed
Summary
This summary is machine-generated.

An automated multiparametric MRI analysis effectively identifies recurrent high-grade glioma. This method uses subtraction maps from routine imaging to predict tumor progression, aiding treatment planning for patients with high-grade glioma.

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Area of Science:

  • Neuroimaging
  • Oncology
  • Radiology

Background:

  • High-grade gliomas often recur, necessitating accurate identification of recurrent disease for effective management.
  • Current methods for detecting recurrence can be subjective and time-consuming.
  • Focal re-irradiation requires precise localization of recurrent tumor areas.

Purpose of the Study:

  • To develop and validate an automated multiparametric MRI analysis for identifying focally recurrent high-grade glioma.
  • To assess the utility of this automated method in guiding treatment decisions for recurrent gliomas.
  • To establish a tool for improved treatment planning and post-treatment surveillance.

Main Methods:

  • Retrospective review of MRI data (T1WI, FLAIR, DWI) from 141 patients with high-grade glioma treated with radiation therapy.
  • Development of automated multiparametric subtraction maps to identify patterns of change indicative of recurrence.
  • Validation of the method in a cohort of 12 patients with nodular recurrence and assessment in a separate cohort of 4 patients treated with radiosurgery.

Main Results:

  • Automated subtraction maps accurately predicted radiologist-identified recurrence in cohort 1 (median AUC = 0.91).
  • The model correctly identified recurrent lesions in cohort 2, guiding treatment decisions.
  • In cohort 2, treated lesions showed control, while untreated progressing lesions aligned with model predictions.

Conclusions:

  • Automated multiparametric MRI subtraction maps can reliably predict nodular progression in previously treated high-grade gliomas.
  • This automated approach, utilizing routine imaging sequences, offers a valuable tool for clinical decision-making.
  • Prospective validation is recommended for treatment planning and surveillance of high-grade glioma recurrence.