期刊
NEURO-ONCOLOGY
卷 21, 期 4, 页码 527-536出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/neuonc/noz004
关键词
glioma; machine learning; magnetic resonance imaging
资金
- National Institutes of Health [R01-CA194391, R01-CA160736]
- National Cancer Institute [R01-CA194391, R01-CA160736]
- Cancer Center Support grant [P30 CA016672]
- Baylor College of Medicine Medical Scientist Training Program
- Cullen Trust for Higher Education Physician/Scientist Fellowship Programs
- John S. Dunn Sr. Distinguished Chair in Diagnostic Imaging
- M D Anderson Cancer Center Internal Research Grant
- Clinical Research Support mechanisms
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, K-trans). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R-2 = 0.75). A less accurate predictive result (RMS error 5.4%, R-2 = 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.
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