Renu M. Stephen

Promise and Progress for Functional and Molecular Imaging of Response to Targeted Therapies

Biomarkers to predict or monitor therapy response are becoming essential components of drug developer’s armamentaria. Molecular and functional imaging has particular promise as a biomarker for anticancer therapies because it is non-invasive, can be used longitudinally and provides information on the whole patient or tumor. Despite this promise, molecular or functional imaging endpoints are not routinely incorporated into clinical trial design. As the costs of clinical trials and drug development become prohibitively more expensive, the need for improved biomarkers has become imperative and thus, the relatively high cost of imaging is justified. Imaging endpoints, such as Diffusion-Weighted MRI, DCE-MRI and FDG-PET have the potential to make drug development more efficient at all phases, from discovery screening with in vivo pharmacodynamics in animal models through the phase III enrichment of the patient population for potential responders. This review focuses on the progress of imaging responses to new classes of anti-cancer therapies targeted against PI3 kinase/AKT, HIF-1α and VEGF. The ultimate promise of molecular and functional imaging is to theragnostically predict response prior to commencement of targeted therapy.

A clustering algorithm for liver lesion segmentation of diffusion-weighted MR images

In diffusion-weighted magnetic resonance imaging, accurate segmentation of liver lesions in the diffusion-weighted images is required for computation of the apparent diffusion coefficient (ADC) of the lesion, the parameter that serves as an indicator of lesion response to therapy. However, the segmentation problem is challenging due to low SNR, fuzzy boundaries and speckle and motion artifacts. We propose a clustering algorithm that incorporates spatial information and a geometric constraint to solve this issue. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms.

Task-based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard

In many studies, the estimation of the apparent diffusion coefficient (ADC) of lesions in visceral organs in diffusion-weighted (DW) magnetic resonance images requires an accurate lesion-segmentation algorithm. To evaluate these lesion-segmentation algorithms, region-overlap measures are used currently. However, the end task from the DW images is accurate ADC estimation, and the region-overlap measures do not evaluate the segmentation algorithms on this task. Moreover, these measures rely on the existence of gold-standard segmentation of the lesion, which is typically unavailable. In this paper, we study the problem of task-based evaluation of segmentation algorithms in DW imaging in the absence of a gold standard. We first show that using manual segmentations instead of gold-standard segmentations for this task-based evaluation is unreliable. We then propose a method to compare the segmentation algorithms that does not require gold-standard or manual segmentation results. The no-gold-standard method estimates the bias and the variance of the error between the true ADC values and the ADC values estimated using the automated segmentation algorithm. The method can be used to rank the segmentation algorithms on the basis of both the ensemble mean square error and precision. We also propose consistency checks for this evaluation technique.

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

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 450s/mm(2) 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 2-5cm 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 1.33μm(2)/ms as 75% sensitive and 83% specific for identifying non-responding metastases in minimally treated patients with 2-5cm 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.

Monitoring the development of xenograft triple-negative breast cancer models using diffusion-weighted magnetic resonance imaging

Evaluations of tumor growth rates and molecular biomarkers are traditionally used to assess new mouse models of human breast cancers. This study investigated the utility of diffusion weighted (DW)-magnetic resonance imaging (MRI) for evaluating cellular proliferation of new tumor models of triple-negative breast cancer, which may augment traditional analysis methods. Eleven human breast cancer cell lines were used to develop xenograft tumors in severe combined immunodeficient mice, with two of these cell lines exhibiting sufficient growth to be serially passaged. DW-MRI was performed to measure the distributions of the apparent diffusion coefficient (ADC) in these two tumor xenograft models, which showed a correlation with tumor growth rates and doubling times during each passage. The distributions of the ADC values were also correlated with expression of Ki67, a biomarker of cell proliferation, and hypoxia inducible factor (HIF)-1α and vascular endothelial growth factor receptor-2 (VEGFR2), which are essential proteins involved in regulating aerobic glycolysis and angiogenesis that support tumor cell proliferation. Although phosphatase and tensin homolog (PTEN) levels were different between the two xenograft models, AKT levels did not differ nor did they correlate with tumor growth. This last result demonstrates the complexity of signaling protein pathways and the difficulty in interpreting the effects of protein expression on tumor cell proliferation. In contrast, DW-MRI may be a more direct assessment of tumor growth and cancer cell proliferation.

ADC estimation of lesions in diffusion-weighted MR images: A maximum-likelihood approach

In recent years, the apparent diffusion coefficient (ADC) of lesions obtained using diffusion-weighted magnetic resonance imaging (DWMRI) has emerged as a potentially novel non-invasive imaging bio-marker for prediction and monitoring of anti-cancer therapy response. However, the motion in visceral organs and different variances in DWMRI measurements at different magnetic diffusion gradient values can make ADC estimation a challenging task. We propose a maximum-likelihood method for ADC estimation of lesions in DWMRI. We show through simulations that our method outperforms the standard linear-least-squares and diffusion-map methods.