TU-F-CAMPUS-J-02: Evaluation of Textural Feature Extraction for Radiotherapy Response Assessment of Early Stage Breast Cancer Patients Using Diffusion Weighted MRI and Dynamic Contrast Enhanced MRI

Authors


Abstract

Purpose:

To investigate the feasibility of using classic textural feature extraction in radiotherapy response assessment, we studied a unique cohort of early stage breast cancer patients with paired pre - and post-radiation Diffusion Weighted MRI (DWI-MRI) and Dynamic Contrast Enhanced MRI (DCE-MRI).

Methods:

15 female patients from our prospective phase I trial evaluating preoperative radiotherapy were included in this retrospective study. Each patient received a single-fraction radiation treatment, and DWI and DCE scans were conducted before and after the radiotherapy. DWI scans were acquired using a spin-echo EPI sequence with diffusion weighting factors of b = 0 and b = 500 mm2 /s, and the apparent diffusion coefficient (ADC) maps were calculated. DCE-MRI scans were acquired using a T1-weighted 3D SPGR sequence with a temporal resolution of about 1 minute. The contrast agent (CA) was intravenously injected with a 0.1 mmol/kg bodyweight dose at 2 ml/s. Two parameters, volume transfer constant (Ktrans ) and kep were analyzed using the two-compartment Tofts kinetic model. For DCE parametric maps and ADC maps, 33 textural features were generated from the clinical target volume (CTV) in a 3D fashion using the classic gray level co-occurrence matrix (GLCOM) and gray level run length matrix (GLRLM). Wilcoxon signed-rank test was used to determine the significance of each texture feature's change after the radiotherapy. The significance was set to 0.05 with Bonferroni correction.

Results:

For ADC maps calculated from DWI-MRI, 24 out of 33 CTV features changed significantly after the radiotherapy. For DCE-MRI pharmacokinetic parameters, all 33 CTV features of Ktrans and 33 features of kep changed significantly.

Conclusion:

Initial results indicate that those significantly changed classic texture features are sensitive to radiation-induced changes and can be used for assessment of radiotherapy response in breast cancer.

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