Shutter-Speed Model DCE-MRI for Assessment of Response to Cancer Therapy U01 CA154602; Wei Huang, PhD, Christopher Ryan, MD; Oregon Health & Science University, Portland, Oregon

Specific Aims•Specific Aim 1: Compare Shutter-Speed model (SSM) DCE-MRI with standard model (SM) DCE-MRI, diffusion-weighted (DW) MRI, and tumor size for early prediction of therapy response and accurate assessment of post-therapy residual cancer.• Breast cancer neoadjuvant chemotherapy• Soft tissue sarcoma phase I/II trial with Sorafenib• Correlation with pathology endpoints

•Specific Aim 2: Investigate the effects of data acquisition and processing schemes on DCE-MRI biomarkers for the purpose of assessing therapy response.• Temporal resolution• Acquisition time• AIF variation

•Specific Aim 3: Develop software tools that can provide clinicians with a single and/or a set of biomarker values to aid clinical decision-making.

Study Schema

Progress ReportSpecific Aim 1

Early Prediction of Soft-Tissue Sarcoma Response to Antiangiogenic Therapy

SM, SSM Ktrans and ΔKtrans maps of two sarcomas at time point zero – baseline (TP0) and TP1 (after two weeks of antiangiogenic therapy only). Pathology reviews of surgical specimens at TP2 revealed that one tumor had optimal response to treatment (>95% necrosis) (top panels), while the other had sub-optimal response (50% necrosis) (bottom panels).

Scatter plots of % changes at TP1 (relative to TP0) in RECIST, ROI ADC, ROI and histogram median ΔKtrans values vs. % necrosis at surgery for 9 sarcoma patients. There were significant linear correlations between % necrosis and % changes in ROI ΔKtrans (R = -0.93, P = 0.0003; Spearman’s correlation), and histogram median ΔKtrans (R = -0.71, P = 0.03).

Evidence for Caution in DCE-MRI Assessment of Response to Antiangiogenic Treatment Using Reference Tissue Method

Left: SM and SSM Ktrans maps of normal appearing muscle ROIs adjacent to the sarcoma of one patient at TP0 and TP1. Right: the decreases of SM and SSM muscle Ktrans in eight sarcoma patients from TP0 to TP1 were statistically significant: paired t test, P < 0.05.

Early Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy

Ktrans (SSM), Δktrans, and τi maps of two breast tumors before neoadjuvant chemotherapy (NACT) (V1, or TP0) and after one NACT cycle (V2, or TP1). One is a pathologic complete responder (pCR, bottom two rows) and the other is a pathologic partial responder (pPR, top two rows) – non-pCR. Response status was determined by surgical pathology after 6 NACT cycles.

Column graph of mean % changes of MRI metrics after one NACT cycle for the pCR (n = 4) and the non-pCR (n = 11) groups. The error bar represents the SD value. Except for RECIST, the other DCE-MRI metrics are excellent early predictors of response.

Evaluation of Residual Cancer Burden (RCB) after Neoadjuvant Chemotherapy

Significant correlations between residual cancer burden (RCB) and MRI metrics measured after NACT completion (V4, or TP3, before surgery).

Specific Aim 2Effects of Acquisition Time on DCE-MRI Assessment of Breast Cancer Response to Neoadjuvant Chemotherapy

Column graphs of (a) TP1 tumor mean Ktrans (SM and SSM) and kep (SM and SSM) values and (b) their corresponding % changes (TP1 relative to TP0) at simulated, varying DCE-MRI acquisition time (Tacq). The blue column represents the mean pCR value, while the red column represents the mean non-pCR value. The results suggest that for typical responder and non-responder breast tumor Ktrans or kep values and their changes after one chemotherapy cycle, a DCE-MRI Tacq of 5-6 min is sufficient to achieve the goal of early prediction of therapeutic response when either parameter is used as the discriminatory biomarker.

Effects of Model/Algorithm on DCE-MRI Assessment of Breast Cancer Response to Neoadjuvant Chemotherapy•See DCE-/DW-MRI WG poster

Specific Aim 3Image Analysis Pipeline and Workflow. The OHSU informatics group is working on several tasks pertaining to Specific Aim 3:•Modeling existing imaging workflow at OHSU Advanced Imaging Research Center (AIRC) and draft design of new, integrated, workflow (Image Analysis Pipeline).•Beginning procurement and setup up computing environment for Image Analysis Pipeline.•Beginning process to install and test open source software needed for Image Analysis Pipeline.•Establish a project management framework (project plan/tasks, communication plan, deliverables and timeline).•Submitting images to the central TCIA repository.•Optimizing a pipeline for image registration across time (longitudinal) and image type (T1-weighted DCE-MRI and parametric maps overlaid on anatomical images).•Optimizing a pipeline for motion correction using open-source algorithms (FLIRT, FNIRT, ANTS, BrainsFit are being evaluated currently).•Modifying the user-interface for XNAT to incorporate a clinician centric view.•Begin development of a DCE-MRI data analysis module in 3D Slicer.•Begin a clinical needs assessment with clinicians.