Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI

Nathan Gaw, Andrea Hawkins-Daarud, Leland S. Hu, Hyunsoo Yoon, Lujia Wang, Yanzhe Xu, Pamela R. Jackson, Kyle W. Singleton, Leslie C. Baxter, Jennifer Eschbacher, Ashlyn Gonzales, Ashley Nespodzany, Kris Smith, Peter Nakaji, J. Ross Mitchell, Teresa Wu, Kristin R. Swanson, Jing Li

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.

Original languageEnglish
Article number10063
JournalScientific reports
Volume9
Issue number1
DOIs
Publication statusPublished - 2019 Dec 1

Bibliographical note

Funding Information:
We would like to acknowledge the funding received for this work from the National Institutes of Health (NS082609) (L.S.H.) (R01CA16437, R01NS060752, U54CA210180, U54CA143970, U54193489, U01CA220378) (K.R.S), the James S. McDonnell Foundation (K.R.S.) the Ben & Catherine Ivy Foundation (K.R.S.) and the Mayo Clinic Foundation (L.S.H.).

Publisher Copyright:
© 2019, The Author(s).

All Science Journal Classification (ASJC) codes

  • General

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