Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: Conventional versus advanced imaging

Evan D.H. Gates, Jonathan S. Lin, Jeffrey S. Weinberg, Jackson Hamilton, Sujit S. Prabhu, John D. Hazle, Gregory N. Fuller, Veera Baladandayuthapani, David Fuentes, Dawid Schellingerhout

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Background. Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard. Methods. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a prospective clinical trial. Stereotactic biopsies were harvested from each patient immediately prior to surgical resection. For each biopsy, an imaging description (23 parameters) was developed, and the Ki-67 index was recorded. Machine learning models were built to estimate Ki-67 from imaging inputs, and cross validation was undertaken to determine the error in estimates. The best model was used to generate graphical maps of Ki-67 estimates across the whole brain. Results. Fifty-two image-guided biopsies were collected from 23 evaluable patients. The random forest algorithm best modeled Ki-67 with 4 imaging inputs (T2-weighted, fractional anisotropy, cerebral blood flow, Ktrans). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R2 = 0.75). A less accurate predictive result (RMS error 5.4%, R2 = 0.50) was found using conventional imaging only. Conclusion. Ki-67 can be predicted to clinically useful accuracies using clinical imaging data. Advanced imaging (diffusion, perfusion, and permeability) improves predictive accuracy over conventional imaging alone. Ki-67 predictions, displayed as graphical maps, could be used to guide biopsy, resection, and/or radiation in the care of glioma patients.

Original languageEnglish (US)
Pages (from-to)527-536
Number of pages10
JournalNeuro-oncology
Volume21
Issue number4
DOIs
StatePublished - Mar 18 2019

Keywords

  • glioma
  • machine learning
  • magnetic resonance imaging

ASJC Scopus subject areas

  • Oncology
  • Clinical Neurology
  • Cancer Research

MD Anderson CCSG core facilities

  • Biostatistics Resource Group
  • Clinical Trials Office

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