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

• Published in: Neuro-oncology (Charlottesville, Va.) Vol. 21; no. 4; pp. 527 - 536
• Main Authors: Gates, Evan D H, Lin, Jonathan S, Weinberg, Jeffrey S, Hamilton, Jackson, Prabhu, Sujit S, Hazle, John D, Fuller, Gregory N, Baladandayuthapani, Veera, Fuentes, David, Schellingerhout, Dawid
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 18-03-2019
• Subjects: Adult; Aged; Aged, 80 and over; Biomarkers, Tumor - analysis; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; Female; Glioma - diagnostic imaging; Glioma - pathology; Humans; Image Interpretation, Computer-Assisted - methods; Image-Guided Biopsy - methods; Ki-67 Antigen - analysis; Machine Learning; Magnetic Resonance Imaging - methods; Male; Middle Aged; Neuroimaging; Neuroimaging - methods; Young Adult; machine learning; glioma; magnetic resonance imaging
• ISSN: 1522-8517; 1523-5866
• DOI: 10.1093/neuonc/noz004

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.