Non-Gaussian Diffusion Imaging with a Fractional Order Calculus Model to Predict Response of Gastrointestinal Stromal Tumor to Second-line Sunitinib Therapy

Abstract

Purpose

To demonstrate the clinical value of a non-Gaussian diffusion model using fractional order calculus (FROC) for early prediction of the response of gastrointestinal stromal tumor (GIST) to second-line sunitinib targeted therapy.

Materials and Methods

The institutional review board (IRB) approved this prospective study and written informed consents were obtained from all participating patients. Fifteen patients underwent sunitinib treatment after imatinib resistance. Diffusion-weighted imaging (DWI) with multiple b-values was performed prior to treatment (baseline) and two weeks (for early prediction of response) after initiating sunitinib treatment. Conventional MRI images at twelve weeks were used to determine the good and poor responders according to the modified Choi criteria for MRI. Diffusion coefficient D, fractional order parameter β (which correlates with intravoxel tissue heterogeneity), and a microstructural quantity μ were calculated using the FROC model. The FROC parameters and the longest diameter of the lesion, as well as their changes after two weeks of treatment, were compared between the good and poor responders. Additionally, the pre-treatment FROC parameters were individually combined with the change in D (ΔD) using a logistic regression model to evaluate response to sunitinib treatment with a receiver operating characteristic (ROC) analysis.

Results

Forty-two good responding and thirty-two poor responding lesions were identified. Significant differences were detected in pre-treatment β (0.67 vs. 0.74, p=0.011) and ΔD (45.7% vs. 12.4%, p=0.001) between the two groups. The ROC analysis showed that ΔD had a significantly higher predictive power than the tumor size change (AUC: 0.725 vs. 0.580; 0.95 confidence interval). When ΔD was combined with pre-treatment β, the AUC improved to 0.843 with a predictive accuracy of 75.7% (56/74).

Conclusion

The non-Gaussian FROC diffusion model showed clinical value in early prediction of GIST response to second-line sunitinib targeted therapy. The pre-treatment FROC parameter β can increase the predictive accuracy when combined with the change in diffusion coefficient during treatment.