Predicting Glioblastoma Cellular Motility from In Vivo MRI with a Radiomics Based Regression Model

Kellen Mulford, Mariah Mcmahon, Andrew M. Gardeck, Matthew Hunt, Clark C. Chen, David J. Odde, Christopher Wilke

Research output: Contribution to journalArticlepeer-review

Abstract

Characterizing the motile properties of glioblastoma tumor cells could provide a useful way to predict the spread of tumors and to tailor the therapeutic approach. Radiomics has emerged as a diagnostic tool in the classification of tumor grade, stage, and prognosis. The purpose of this work is to examine the potential of radiomics to predict the motility of glioblastoma cells. Tissue specimens were obtained from 31 patients undergoing surgical resection of glioblastoma. Mean tumor cell motility was calculated from time-lapse videos of specimen cells. Manual segmentation was used to define the border of the enhancing tumor T1-weighted MR images, and 107 radiomics features were extracted from the normalized image volumes. Model parameter coefficients were estimated using the adaptive lasso technique validated with leave-one-out cross validation (LOOCV) and permutation tests. The R-squared value for the predictive model was 0.60 with p-values for each individual parameter estimate less than 0.0001. Permutation test models trained with scrambled motility failed to produce a model that out-performed the model trained on the true data. The results of this work suggest that it is possible for a quantitative MRI feature-based regression model to non-invasively predict the cellular motility of glioblastomas.

Original languageEnglish (US)
Article number578
JournalCancers
Volume14
Issue number3
DOIs
StatePublished - Feb 1 2022

Bibliographical note

Funding Information:
Funding: This research was supported by the National Institutes of Health’s National Center for Advancing Translational Sciences, grants TL1R002493 and UL1TR002494 to K.M. and National Cancer Institute U54 CA210190 to D.J.O. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health’s National Center for Advancing Translational Sciences or National Cancer Institute.

Funding Information:
This research was supported by the National Institutes of Health?s National Center for Advancing Translational Sciences, grants TL1R002493 and UL1TR002494 to K.M. and National Cancer Institute U54 CA210190 to D.J.O. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health?s National Center for Advancing Translational Sciences or National Cancer Institute.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Cellular motility
  • Glioblastoma
  • MRI
  • Radiomics

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'Predicting Glioblastoma Cellular Motility from In Vivo MRI with a Radiomics Based Regression Model'. Together they form a unique fingerprint.

Cite this