Data Analysis

• MRI: Three PK models, ADC maps

• FDG-PET: SUL maps

• Semi-quantitative and quantitative analyses at the ROI and voxel levels

Development and Application of MRI & PET Methods for Predicting Therapeutic Response of Breast Cancers During Neoadjuvant Chemotherapy

Xia Li, Lori R. Arlinghaus, Richard G. Abramson, A. Bapsi Chakravarthy, Vandana G. Abramson, Jaime Farley, Hakmook Kang, Jason Williams, Melinda Sanders, Thomas E. Yankeelov

Vanderbilt University

Overall goal

• Provide the breast cancer community with acquisition & analysis tools for:

integration of PET and MRI data

early predictive indices of NAC

Data Acquisition

• Woman undergoing NAC are scanned with

DCE-MRI, DW-MRI, FDG-PET/CT:

1) pre-NAC

2) after one cycle of NAC

3) after all NAC Deliverables

• Data acquisition Methods for practical quantitative DCE-MRI and DW-MRI of the breast Methods for integrating MRI-PET data

• Data Analysis Quantitative DCE-MRI analysis Integrating quantitative DCE-MRI with ADC Importance of spatial heterogeneity

• Bottom line: Using combination of quantitative DCE- and DW-MRI, and an analysis of intra-tumoral spatial heterogeneity, we have a method that achieves an AUC = 0.91 after one cycle of NAC

Results – qMRI

• Quantitative (n = 37)AUCkep = 0.77AUCADC = 0.81

• Quantitative at ROI levelkep/ADC at t2 discriminated

pCR patients w/AUC of 0.86

• Quantitative at voxel levelkep/ADC at t2 on highly

perfused voxels achieved a sensitivity, specificity, accuracy, precision, and AUC of 0.91, 0.84, 0.87, 0.79, and 0.91

Results – PET

• Prone vs supine (n = 34)

Prone and supine yield consistent anatomical disease categorization

Prone scanning visualizes a higher number of lymph node mets

No significant differences in SUV

• Serial alignment