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Machine Learning Prediction of Collagen Fiber Orientation and Proteoglycan Content From Multiparametric Quantitative
Seyed Amir Mirmojarabian1, Abdul Wahed Kajabi2, Juuso H J Ketola1
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
Background:
Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage.
Purpose:
To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content.
Study Type:
Retrospective, animal model.
Animal Model:
An open-source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points, including 10% for test and 90% for train validation.
Field Strength/Sequence:
A 9.4 T MRI scanner/qMRI sequences: T1 , T2 , adiabatic T1ρ and T2ρ , continuous-wave T1ρ and relaxation along a fictitious field (TRAFF ) maps.
Assessment:
Five machine learning regression models were developed: random forest (RF), support vector regression (SVR), gradient boosting (GB), multilayer perceptron (MLP), and Gaussian process regression (GPR). A nested cross-validation was used for performance evaluation. For reference, proteoglycan content and collagen fiber orientation were determined by quantitative histology from digital densitometry (DD) and polarized light microscopy (PLM), respectively.
Statistical Tests:
Normality was tested using Shapiro-Wilk test, and association between predicted and measured values was evaluated using Spearman's Rho test. A P-value of 0.05 was considered as the limit of statistical significance.
Results:
Four out of the five models (RF, GB, MLP, and GPR) yielded high accuracy (R2 = 0.68-0.75 for PLM and 0.62-0.66 for DD), and strong significant correlations between the reference measurements and predicted cartilage matrix properties (Spearman's Rho = 0.72-0.88 for PLM and 0.61-0.83 for DD). GPR algorithm had the highest accuracy (R2 = 0.75 and 0.66) and lowest prediction-error (root mean squared [RMSE] = 1.34 and 2.55) for PLM and DD, respectively.
Data Conclusion:
Multiparametric qMRIs in combination with regression models can determine cartilage compositional and structural features, with higher accuracy for collagen fiber orientation than proteoglycan content.
Evidence Level:
2 TECHNICAL EFFICACY: Stage 2.
