Physical Address
304 North Cardinal St.
Dorchester Center, MA 02124
Disease and Treatment Monitoring
Preoperative, or neoadjuvant chemotherapy (NAC), in which systemic therapy is administered before surgery, is used to downstage primary breast cancers while reducing the risk of recurrence. NAC often results in complete eradication of tumor at the time of surgery (pathological complete response [pCR]), and it is well established that pCR confers excellent survival outcomes. The US Food and Drug Administration (FDA) now accepts pCR as an endpoint in clinical trials to support accelerated drug approval for high-risk early-stage breast cancer.
Among breast imaging methods, dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is particularly effective for visualizing the effects of neoadjuvant treatment on breast tumors. DCE MRI has been found to be more effective than clinical examination and other routine imaging modalities (mammography and ultrasound) for detecting residual disease and defining its extent. In addition to its high sensitivity, DCE MRI noninvasively provides information about breast tumor biology that can be used to predict response.
Although DCE is the standard MRI method for evaluating breast cancer, diffusion-weighted imaging (DWI) has been shown to provide additional and complementary information about tissue cellularity and microstructure of the tumor and surrounding tissue environment, which can also be used to characterize breast tumors and to monitor their response to treatment. In fact, several studies have suggested that changes in quantitative diffusion measurements from DWI can be detectable earlier in the course of NAC than changes in tumor size or vascularity measured by DCE MRI. This is because effective drugs induce apoptosis and/or necrosis in the tumor, which alters the cell density and reduces barriers to water diffusion. Before the cell death associated with NAC, cell swelling or damage to membrane integrity can occur, which may also affect the water diffusion in the tumor. For this reason, many studies have evaluated DWI for prediction of breast cancer treatment response.
Diffusion-Weighted Magnetic Resonance Imaging Approaches for Treatment Monitoring
Three primary requirements common to almost all studies investigating quantitative DWI for treatment outcome prediction are DWI acquisition; image analysis, in particular the selection of regions of interest (ROIs) for quantitation; and statistical analysis for prediction of a clinically useful patient outcome. In the setting of neoadjuvant treatment of invasive breast cancer, the outcome of interest is generally the pathological response at surgery or disease-free survival at 3 to 5 years following treatment—both short-term endpoints commonly used as surrogates of overall survival. Fig. 5.1 illustrates a typical multiregimen NAC treatment timeline, showing sequential MRI examinations at baseline (pretreatment), early in the first drug regimen, between the first and second regimens, and posttreatment before surgery, similar to that used in the I-SPY 2 TRIAL. Timing of the early treatment MRI varies among published studies and is generally between 1 and 6 weeks of treatment. Fig. 5.2 illustrates serial DWI acquisitions for a patient undergoing NAC. In this example, multiparametric MRI examinations including DCE MRI and DWI acquisitions were conducted at fixed time points before, during (early and midtreatment), and after the full course of NAC treatment (see Fig. 5.1 ). The patient showed a positive but incomplete response to treatment with both MRI modalities. DCE MRI showed a large reduction in tumor volume but residual enhancing tumor following treatment. On DWI, tumor apparent diffusion coefficient (ADC) increased steadily with treatment but remained lower than that of normal fibroglandular tissue.
Dwi Acquisition and ADC Mapping
For DWI acquisition, the most common sequence is a 2D, fat-suppressed single-shot echo-planar imaging (SS-EPI) using a single low b value (generally 0) and a single high b value applied in three gradient directions (isotropic acquisition). This sequence has the advantages of simplicity and speed, with reasonably good signal-to-noise ratio (SNR). There is currently no consensus on the optimum high b value for evaluating therapeutic effects, with a range of at least 600 to 1500 s/mm 2 reported in the majority of treatment response studies. The primary disadvantages of the two b value SS-EPI DWI approach are the limitation to monoexponential modeling (yielding a single ADC metric) and image quality issues common to EPI-based acquisitions including distortions, ghosting, and incomplete fat suppression. These issues are of particular concern in treatment monitoring as they can be inconsistent over the course of multiple longitudinal examinations, contributing errors to the measurement of changes in diffusion parameters.
Image Analysis
For image analysis, a particular challenge of DWI treatment response studies is appropriate definition of ROIs. Tumor localization and delineation on breast DWI can be difficult or even impossible in this setting. As opposed to DCE MRI, which is generally obtained with relatively high spatial resolution and high SNR, DWI scans are typically of lower resolution and poorer SNR. Furthermore, spatial resolution differences and geometrical distortions that are common in DWI also make it difficult to transfer tumor ROIs directly from DCE MRI, where they can be more accurately defined.
Currently, most breast DWI studies employ manually drawn ROIs done either on a picture archiving and communication system (PACS) workstation (where ROI geometries may be limited) or on a dedicated research workstation. It is a common practice to avoid necrotic and cystic areas and clip artifacts so that only viable tissues are included. Fig. 5.3 illustrates some challenges of ROI definition by contrasting a single mass lesion with a diffusely enhancing tumor being evaluated for ADC before treatment. The guideline applied in these examples was to identify the lesion on a DCE MRI image and then manually delineate the ROI at the corresponding locations on the diffusion scan, referencing the high b value DWI (e.g., b = 800 s/mm 2 ), the ADC map, or a combination thereof, selecting regions hyperintense on DWI and hypointense on ADC. The challenge of getting a true whole-tumor segmentation in diffuse or multifocal disease is clearly illustrated by the number of individual contours seen in the second example in Fig. 5.3 . In a consensus publication by Padhani and colleagues, the recommendation was made that ROIs be drawn to completely delineate lesions on images that have the highest contrast between lesion and normal tissue and to avoid defining smaller ROIs within lesions, which is considered more subjective and not recommended for assessing treatment response.
It is common to define “pseudo-3D” regions by selecting tumor on multiple planes to reduce sampling errors inherent with single-plane definitions. However, this can greatly increase the skilled-operator time required for analysis, and there are some indications there may be no benefit over single-slice ROIs. Studies with multiple readers, unless explicitly investigating interreader reproducibility, generally use consensus ROIs from multiple radiologists or trained researchers. For longitudinal trials, most commonly a single operator or consensus team would evaluate all DWI studies for an individual subject to minimize errors from interoperator variability. We also note that in longitudinal studies, the challenge of defining reproducible tumor ROIs tends to increase in difficulty over the course of NAC, as the contrast between tumor and normal fibroglandular tissue decreases, particularly in good responders. This may be less of a concern in studies investigating the early-treatment prediction of patient outcome; however, even as early as 3 weeks into NAC, there are examples of excellent responders where the tumor is no longer discernable on MRI ( Fig. 5.4 ).
Statistical Analysis for Response Prediction
Selection of outcome variable(s) and methods for evaluation of predictive capability of the predictor variable(s) can vary between treatment response trials. As mentioned earlier, the most common outcome variable across studies is pCR, defined as the absence of residual invasive cancer in the breast and all sampled axillary lymph nodes. The FDA also allows a stricter guideline requiring absence of both residual invasive cancer and in situ cancer, and all sampled axillary lymph nodes. For binary endpoints like pCR predictive ability is generally evaluated using receiver operating characteristic (ROC) analysis with area under the receiver-operator curve (AUC) as the primary metric. In some studies, response is defined by clinical or imaging measurements by applying the response evaluation criteria in solid tumor (RECIST) definition of less than 30% reduction to identify nonresponders.
Tumor Mean ADC for Predicting Response
The most common diffusion measurement for NAC monitoring and outcome prediction is the mean ADC value within the tumor. ADC is typically calculated pixel-wise by fitting a monoexponential function to all b values acquired in the DWI sequence (or directly if there are only two b values) and then taking the mean over the region representing some or all of the cancerous lesion. Alternatively, mean ADC can be calculated using the average DWI signal intensities (e.g., b = 0, 800 s/mm 2 ) over the region. Iima and colleagues demonstrated that the second approach is less affected by noise than the first approach and thus is more accurate. As mentioned earlier, the region is generally delineated by user-defined ROIs. Numerous studies have investigated the value of tumor mean ADC measures before, during and after NAC, as well as ADC changes from pretreatment values to later time points during NAC, for the prediction of response to treatment.
You're Reading a Preview
Become a Clinical Tree membership for Full access and enjoy Unlimited articles
If you are a member. Log in here
