Predicting and monitoring cancer treatment response with diffusion-weighted MRI

Abstract

An imaging biomarker that would provide for an early quantitative metric of clinical treatment response in cancer patients would provide for a paradigm shift in cancer care. Currently, nonimage based clinical outcome metrics include morphology, clinical, and laboratory parameters, however, these are obtained relatively late following treatment. Diffusion-weighted MRI (DW-MRI) holds promise for use as a cancer treatment response biomarker as it is sensitive to macromolecular and microstructural changes which can occur at the cellular level earlier than anatomical changes during therapy. Studies have shown that successful treatment of many tumor types can be detected using DW-MRI as an early increase in the apparent diffusion coefficient (ADC) values. Additionally, low pretreatment ADC values of various tumors are often predictive of better outcome. These capabilities, once validated, could provide for an important opportunity to individualize therapy thereby minimizing unnecessary systemic toxicity associated with ineffective therapies with the additional advantage of improving overall patient health care and associated costs. In this report, we provide a brief technical overview of DW-MRI acquisition protocols, quantitative image analysis approaches and review studies which have implemented DW-MRI for the purpose of early prediction of cancer treatment response.

Figure 1

Schematic representation of the relationship between change in cellular density following an effective therapy and the corresponding distribution of water diffusion values within the tumor. Note that the mean diffusion value of a tumor increases early following the loss of cellular density.

Figure 2

Measured signal decay in kidney cortex of as a function of b-value. Perfusion effect is dominant in the low b-value range (0–100s/mm²) and would inflate ADC if included in slope calculations. By extinguishing most of the vascular signal at b values greater than 100s/mm², a true estimate of ADC is derived. Using b-values 100 to 750s/mm², the estimated kidney ADC = 2.03×10⁻³mm²/s. The dashed line represents the noise floor. DWI measurements where signal falls within the noise floor lead to erroneous ADC estimates and a false multi-exponential appearance to the log(signal) versus b-value curve. Tissues do exhibit true multi-exponential diffusion decays over a wide b-value range (b=500 to > 3000s/mm²), although very high SNR is required to document these features.

Figure 3

Effects of b-values on blood flow contribution to images and ADC maps. DW-MRI of the liver with b-values of 0, 100 and 600 s/mm² (center column) revealing suppression of vascular signal with increasing b-value. ADC maps generated using b-value images of 0 and 600 s/mm² as well as 100 and 600 s/mm². ADC maps generated using b-values of 100 and 600 s/mm² have attenuated contribution/contamination from perfusion effects (right ADC map) compared with the ADC map generated using b-values of 0 and 600 s/mm².

Figure 4

A patient with a hepatocellular carcinoma in the liver undergoing treatment with transcatheter arterial chemoembolization (TACE) (top row) pre and (bottom row) post-therapy. Each set of images consists of the DW-MRI (left images) along with their corresponding color ADC overlay maps (right column).

Figure 5

(Left panel) Diagrammatic representation of two possible pathways of cell death associated with therapy. Induction of apoptosis which would lead to an increase in tumor ADC values due to cell shrinkage and loss of cellular density. Processes involved with the induction of necrosis which might exhibit a transient initial drop in ADC values due to cell swelling following by an increase in ADC values during cell lysis during the later stages of this death pathway. (Right panel) The voxel-by-voxel analytical approach (functional diffusion map (fDM) or parametric response map (PRM_ADC)) used to quantify diffusion changes following therapy within a tumor using ADC maps pre- and post-treatment initiation. (Used with permission. Moffat BA, Chenevert TL, Lawrence TS, et al. Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci U S A 2005;102:5524–5529. Copyright (2005) National Academy of Sciences, U.S.A.).

Figure 6

Patient with a soft tissue sarcoma. (Left) Pre-treatment high b-value image shows revealing large dense (fibrosis) region on the lower left along with a high cellular region. ADC color overlay maps of the sarcoma (Middle) before and (Right) 3 weeks following chemotherapy. Note that the tumor mass (arrow) exhibited a large increase in tumor ADC values following treatment indicating positive response. (Image kindly provided by T.L. Chenevert, University of Michigan).

Figure 7

Representative slices of PRM_ADC for patients whose conditions were diagnosed as (A) complete response (CR) and (B) partial response (PR); color-coded VOIs are shown as overlays on contrast-enhanced T1-weighted MR images before therapy and corresponding scatter plots for quantification and distribution of ADC before and 3 weeks after treatment initiation for the entire tumor volume. Unity and threshold designating significant change in ADC within the scatter plot are presented by red and black lines, respectively. Voxels with significant increased, decreased, or unchanged ADC values were assigned as red, blue, and green, respectively. Used with permission from Neoplasia Press. Galbán CJ, Mukherji SK, Chenevert TL, et al. Parametric Response Map Analysis of DW-MRI Scans of Head and Neck Cancer Patients Provides for Early Detection of Therapeutic Efficacy. Translational Oncology 2009;2:184–190.).