Diffusion MRI with Semi-Automated Segmentation Can Serve as a Restricted Predictive Biomarker of the Therapeutic Response of Liver Metastasis

Abstract

Purpose

To assess the value of semi-automated segmentation applied to diffusion MRI for predicting the therapeutic response of liver metastasis.

Methods

Conventional diffusion weighted magnetic resonance imaging (MRI) was performed using b-values of 0, 150, 300 and 450 s/mm² at baseline and days 4, 11 and 39 following initiation of a new chemotherapy regimen in a pilot study with 18 women with 37 liver metastases from primary breast cancer. A semi-automated segmentation approach was used to identify liver metastases. Linear regression analysis was used to assess the relationship between baseline values of the apparent diffusion coefficient (ADC) and change in tumor size by day 39.

Results

A semi-automated segmentation scheme was critical for obtaining the most reliable ADC measurements. A statistically significant relationship between baseline ADC values and change in tumor size at day 39 was observed for minimally treated patients with metastatic liver lesions measuring 2–5 cm in size (p = 0.002), but not for heavily treated patients with the same tumor size range (p = 0.29), or for tumors of smaller or larger sizes. ROC analysis identified a baseline threshold ADC value of 1.33 μm²/ms as 75% sensitive and 83% specific for identifying non-responding metastases in minimally treated patients with 2–5 cm liver lesions.

Conclusion

Quantitative imaging can substantially benefit from a semi-automated segmentation scheme. Quantitative diffusion MRI results can be predictive of therapeutic outcome in selected patients with liver metastases, but not for all liver metastases, and therefore should be considered to be a restricted biomarker.

Introduction

Diffusion-weighted magnetic resonance imaging (DW-MRI) is sensitive to the microscopic random motion of water molecules, which can be quantified via the apparent diffusion coefficient (ADC) [1]. The ADC provides quantitative information regarding the cellularity and cell membrane integrity of biologic tissue. The initial average ADC value has been shown to predict tumor response to therapy [2], [3]. For example, DW-MRI has been used to measure therapy response during pre-clinical [4], [5], [6], [7], [8], [9], [10] and clinical studies of primary brain cancer [11], [12], [13], colorectal cancer [14], [15], [16], [17], osteosarcomas [18], [19] and breast cancer [20], [21], [22]. Moreover, the use of non-ionizing radiation as well as the short time and ease of acquisition permit longitudinal information to be safely collected, which are attractive features for clinical assessments of cancer.

ADC values measured prior to initiating treatment have been shown to potentially predict treatment response [23] and disease survival [24] in colorectal cancer patients with liver metastases. However, measuring the ADC value of a liver lesion is technically challenging, because diffusional motion in a liver lesion is confounded by non-rigid body visceral motion due to respiration, cardiac motion, blood flow and motility [25], [26]. These confounding factors may be eliminated or mitigated by using advanced post-processing methods that improve the identification of lesion borders and the quantitative estimation and evaluation of ADC values. Furthermore, the manual evaluation of large volumes of DW-MR images can be a daunting challenge in a radiology clinic. Therefore, a primary aim of this study was to develop a robust post-processing protocol for obtaining reliable results from DW-MRI of liver metastases, by evaluating the merits of automatic and semi-automatic lesion segmentation relative to manual segmentation.

Previous DW-MRI studies of liver metastases have evaluated patients with specific conditions [23], [24]. Patients enrolled in these studies were undergoing their first chemotherapy treatment regimen, and had a minimum number of liver lesions. The DW-MRI analyses were restricted to lesions in specific liver lobes and morphologic locations, and to lesions that were greater than 1 or 1.6 cm in diameter. These criteria for patient enrollment and for selecting liver lesions for analyses suggest that DW-MRI may only apply as a “restricted” predictive biomarker, known as a biomarker for predicting treatment response under restricted conditions. Therefore, another primary aim of our study was to evaluate whether DW-MRI can serve as a robust biomarker for all patients with all sizes of liver metastases in all liver locations, or whether DW-MRI should be used as a restricted biomarker for assessing liver metastases. To investigate this aim, we conducted a pilot clinical trial in breast cancer patients with known liver metastases.