Diffusion-Weighted MRI as a Biomarker of Tumor Radiation Treatment Response Heterogeneity: A Comparative Study of Whole-Volume Histogram Analysis versus Voxel-Based Functional Diffusion Map Analysis

Spatiotemporal Heterogeneity in Multiparametric Physiologic MRI Is Associated with Patient Outcomes in IDH-Wildtype Glioblastoma

PURPOSE

Heterogeneity in glioblastomas is associated with poorer outcomes, and physiologic heterogeneity can be quantified with noninvasive imaging. We developed spatial habitats based on multiparametric physiologic MRI and evaluated associations between temporal changes in these habitats and progression-free survival (PFS) after concurrent chemoradiotherapy (CCRT) in patients with glioblastoma.

EXPERIMENTAL DESIGN

Ninety-seven patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma were enrolled and two serial MRI examinations after CCRT were analyzed. Cerebral blood volumes and apparent diffusion coefficients were grouped using k-means clustering into three spatial habitats. Associations between temporal changes in spatial habitats and PFS were investigated using Cox proportional hazard modeling. The performance of significant predictors for PFS and overall survival (OS) was measured using a discrete increase of habitat (habitat risk score) in a temporal validation set from a prospective registry (n = 53, ClinicalTrials.gov NCT02619890). The site of progression was matched with the spatiotemporal habitats.

RESULTS

Three spatial habitats of hypervascular cellular, hypovascular cellular, and nonviable tissue were identified. A short-term increase in the hypervascular cellular habitat (HR, 40.0; P = 0.001) and hypovascular cellular habitat was significantly associated with shorter PFS (HR, 3.78; P < 0.001) after CCRT. Combined with clinical predictors, the habitat risk score showed a C-index of 0.79 for PFS and 0.74 for OS and stratified patients with short, intermediate, and long PFS (P = 0.016). An increase in the hypovascular cellular habitat predicted tumor progression sites.

CONCLUSIONS

Hypovascular cellular habitats derived from multiparametric physiologic MRIs may be useful predictors of clinical outcomes in patients with posttreatment glioblastoma.

ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging

Medical imaging is critical for assessing the response of patients to new cancer therapies. Quantitative lesion assessment on images is time-consuming, and adopting new promising quantitative imaging biomarkers of response in clinical trials is challenging. The electronic Physician Annotation Device (ePAD) is a freely available web-based zero-footprint software application for viewing, annotation, and quantitative analysis of radiology images designed to meet the challenges of quantitative evaluation of cancer lesions. For imaging researchers, ePAD calculates a variety of quantitative imaging biomarkers that they can analyze and compare in ePAD to identify potential candidates as surrogate endpoints in clinical trials. For clinicians, ePAD provides clinical decision support tools for evaluating cancer response through reports summarizing changes in tumor burden based on different imaging biomarkers. As a workflow management and study oversight tool, ePAD lets clinical trial project administrators create worklists for users and oversee the progress of annotations created by research groups. To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases. We present an overview of ePAD as a platform for medical image annotation and quantitative analysis. We also discuss use cases and collaborations with different groups in the Quantitative Imaging Network and future directions.

Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps

Purpose

The purpose of this study was to investigate whether a voxel-wise analysis of apparent diffusion coefficient (ADC) values may differentiate between progressive disease (PD) and pseudoprogression (PsP) in patients with high-grade glioma using the parametric response map, a newly introduced postprocessing tool.

Methods

Twenty-eight patients with proven PD and seven patients with PsP were identified in this retrospective feasibility study. For all patients ADC baseline and follow-up maps on four subsequent MRIs were available. ADC maps were coregistered on contrast enhanced T1-weighted follow-up images. Subsequently, enhancement in the follow-up contrast enhanced T1-weighted image was manually delineated and a reference region of interest (ROI) was drawn in the contralateral white matter. Both ROIs were transferred to the ADC images. Relative ADC (rADC) (baseline)/reference ROI values and rADC (follow up)/reference ROI values were calculated for each voxel within the ROI. The corresponding voxels of rADC (follow up) and rADC (baseline) were subtracted and the percentage of all voxels within the ROI that exceeded the threshold of 0.25 was quantified.

Results

rADC voxels showed a decrease of 59.2% (1^st quartile (Q1) 36.7; 3^rd quartile (Q3) 78.6) above 0.25 in patients with PD and 18.6% (Q1 3.04; Q3 26.5) in patients with PsP (p = 0.005). Receiver operating characteristic curve analysis showed the optimal decreasing rADC cut-off value for identifying PD of > 27.05% (area under the curve 0.844±0.065, sensitivity 0.86, specificity 0.86, p = 0.014).

Conclusion

This feasibility study shows that the assessment of rADC using parametric response maps might be a promising approach to contribute to the differentiation between PD and PsP. Further research in larger patient cohorts is necessary to finally determine its clinical utility.

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors

Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.

A Pilot Study of Quantitative MRI Parametric Response Mapping of Bone Marrow Fat for Treatment Assessment in Myelofibrosis

Myelofibrosis (MF) is a hematologic neoplasm arising as a primary disease or secondary to other myeloproliferative neoplasms (MPNs). Both primary and secondary MF are uniquely associated with progressive bone marrow fibrosis, displacing normal hematopoietic cells from the marrow space and disrupting normal production of mature blood cells. Activation of the JAK2 signaling pathway in hematopoietic stem cells commonly causes MF, and ruxolitinib, a drug targeting this pathway, is the treatment of choice for many patients. However, current measures of disease status in MF do not necessarily predict response to treatment with ruxolitinib or other drugs in MF. Bone marrow biopsies are invasive and prone to sampling error, while measurements of spleen volume only indirectly reflect bone marrow status. Toward the goal of developing an imaging biomarker for treatment response in MF, we present preliminary results from a prospective clinical study evaluating parametric response mapping (PRM) of quantitative Dixon MRI bone marrow fat fraction maps in four MF patients treated with ruxolitinib. PRM allows for the voxel-wise identification of significant change in quantitative imaging readouts over time, in this case the bone marrow fat content. We identified heterogeneous response patterns of bone marrow fat among patients and within different bone marrow sites in the same patient. We also observed discordance between changes in bone marrow fat fraction and reductions in spleen volume, the standard imaging metric for treatment efficacy. This study provides initial support for PRM analysis of quantitative MRI of bone marrow fat to monitor response to therapy in MF, setting the stage for larger studies to further develop and validate this method as a complementary imaging biomarker for this disease.

Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma

Aside from bidimensional measurements from conventional contrast-enhanced MRI, there are no validated or FDA-qualified imaging biomarkers for high-grade gliomas. However, advanced functional MRI techniques, including perfusion- and diffusion-weighted MRI, have demonstrated much potential for determining prognosis, predicting therapeutic response, and assessing early treatment response. They may also prove useful for differentiating pseudoprogression from true progression after temozolomide chemoradiation and pseudoresponse from true response after anti-angiogenic therapy. This review will highlight recent developments using these techniques and emphasize the need for technical standardization and validation in prospective studies in order for these methods to become incorporated into standard-of-care imaging for brain tumor patients.

Development of a Multiparametric Voxel-Based Magnetic Resonance Imaging Biomarker for Early Cancer Therapeutic Response Assessment

Quantitative magnetic resonance imaging (MRI)-based biomarkers, which capture physiological and functional tumor processes, were evaluated as imaging surrogates of early tumor response following chemoradiotherapy in glioma patients. A multiparametric extension of a voxel-based analysis, referred as the parametric response map (PRM), was applied to quantitative MRI maps to test the predictive potential of this metric for detecting response. Fifty-six subjects with newly diagnosed high-grade gliomas treated with radiation and concurrent temozolomide were enrolled in a single-site prospective institutional review board-approved MRI study. Apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps were acquired before therapy and 3 weeks after therapy was initiated. Multiparametric PRM (mPRM) was applied to both physiological MRI maps and evaluated as an imaging biomarker of patient survival. For comparison, single-biomarker PRMs were also evaluated in this study. The simultaneous analysis of ADC and rCBV by the mPRM approach was found to improve the predictive potential for patient survival over single PRM measures. With an array of quantitative imaging parameters being evaluated as biomarkers of therapeutic response, mPRM shows promise as a new methodology for consolidating physiologically distinct imaging parameters into a single interpretable and quantitative metric.

Functional diffusion map: A biomarker of brain metastases response to treatment based on magnetic resonance image analysis

Validated biomarkers for treatment response in patients suffering from brain metastases are needed in daily clinical practice as they may improve survival by providing reliable prognostic information and allowing alternative therapies. This work presents a new analysis tool for an early and non-invasive evaluation of treatment response in patients with brain metastases. A set of twenty-five metastases from sixteen patients were examined by T1-weighted and diffusion magnetic resonance imaging before starting radiotherapy and at least once after treatment. Diffusion MRI can show a correlation between water diffusion variation within metastasis area and its clinical evolution. Images were co-registered to pretreatment scans. Diffusion changes, resulting in spatially varying changes in apparent diffusion coefficient values of metastatic lesions, were quantified and presented as a functional diffusion map (fDM). These functional maps were compared to two traditional criteria for assessing oncological response. Of the twenty-five metastases analyzed, seven were classified as partial response (PR), eight as stable disease (SD) and nine as progressive disease (PD). Normalized volume values of the metastases for each response group were obtained, disclosing that apparent diffusion coefficient increase was a good predictor of response. Sensitivity was 88%, specificity 100%, positive predictive value 100% and negative predictive value was 94%. Outcome reveals that the implemented tool, based on functional diffusion mapping as evolution biomarker, provides a reliable prediction of metastases response to treatment.

Integrated multimodal imaging of dynamic bone-tumor alterations associated with metastatic prostate cancer

Bone metastasis occurs for men with advanced prostate cancer which promotes osseous growth and destruction driven by alterations in osteoblast and osteoclast homeostasis. Patients can experience pain, spontaneous fractures and morbidity eroding overall quality of life. The complex and dynamic cellular interactions within the bone microenvironment limit current treatment options thus prostate to bone metastases remains incurable. This study uses voxel-based analysis of diffusion-weighted MRI and CT scans to simultaneously evaluate temporal changes in normal bone homeostasis along with prostate bone metatastsis to deliver an improved understanding of the spatiotemporal local microenvironment. Dynamic tumor-stromal interactions were assessed during treatment in mouse models along with a pilot prospective clinical trial with metastatic hormone sensitive and castration resistant prostate cancer patients with bone metastases. Longitudinal changes in tumor and bone imaging metrics during delivery of therapy were quantified. Studies revealed that voxel-based parametric response maps (PRM) of DW-MRI and CT scans could be used to quantify and spatially visualize dynamic changes during prostate tumor growth and in response to treatment thereby distinguishing patients with stable disease from those with progressive disease (p<0.05). These studies suggest that PRM imaging biomarkers are useful for detection of the impact of prostate tumor-stromal responses to therapies thus demonstrating the potential of multi-modal PRM image-based biomarkers as a novel means for assessing dynamic alterations associated with metastatic prostate cancer. These results establish an integrated and clinically translatable approach which can be readily implemented for improving the clinical management of patients with metastatic bone disease.

Pseudoprogression in Patients with Glioblastoma: Assessment by Using Volume-weighted Voxel-based Multiparametric Clustering of MR Imaging Data in an Independent Test Set

PURPOSE

To validate a volume-weighted voxel-based multiparametric clustering (VVMC) method for magnetic resonance imaging data that is designed to differentiate between pseudoprogression and early tumor progression (ETP) in patients with glioblastoma in an independent test set.

MATERIALS AND METHODS

This retrospective study was approved by the local institutional review board, with waiver of the need to obtain informed consent. The study patients were grouped chronologically into a training set (108 patients) and a test set (54 patients). The reference standard was pathologic findings or subsequent clinical-radiologic study results. By using the optimal cutoff determined in the training set, the diagnostic performance of VVMC was subsequently tested in the test set and was compared with that of single-parameter measurements (apparent diffusion coefficient [ADC], normalized cerebral blood volume [nCBV], and initial area under the time-signal intensity curve).

RESULTS

Interreader agreement was highest for VVMC (intraclass correlation coefficient, 0.87-0.89). Receiver operating characteristic curve analysis revealed that VVMC performed the best as a classifier, although statistical significance was not demonstrated with respect to the nCBV in the training set. In the test set, the diagnostic accuracy of VVMC was higher than that of any single-parameter measurements, but this trend reached significance only for the ADC. When the entire population was considered, VVMC had significantly better diagnostic accuracy than did any single parameter (P = .003-.046 for reader 1; P = .002-.016 for reader 2). Results of fivefold cross validation confirmed the trends in both the training set and the test set.

CONCLUSION

VVMC is a superior and more reproducible imaging biomarker than single-parameter measurements for differentiating between pseudoprogression and ETP in patients with glioblastoma. Online supplemental material is available for this article.

Image registration for quantitative parametric response mapping of cancer treatment response

Imaging biomarkers capable of early quantification of tumor response to therapy would provide an opportunity to individualize patient care. Image registration of longitudinal scans provides a method of detecting treatment associated changes within heterogeneous tumors by monitoring alterations in the quantitative value of individual voxels over time, which is unattainable by traditional volumetric-based histogram methods. The concepts involved in the use of image registration for tracking and quantifying breast cancer treatment response using parametric response mapping (PRM), a voxel-based analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) scans, are presented. Application of PRM to breast tumor response detection is described, wherein robust registration solutions for tracking small changes in water diffusivity in breast tumors during therapy are required. Methodologies that employ simulations are presented for measuring expected statistical accuracy of PRM for response assessment. Test-retest clinical scans are used to yield estimates of system noise to indicate significant changes in voxel-based changes in water diffusivity. Overall, registration-based PRM image analysis provides significant opportunities for voxel-based image analysis to provide the required accuracy for early assessment of response to treatment in breast cancer patients receiving neoadjuvant chemotherapy.