Functional 4-D clustering for characterizing intratumor heterogeneity in dynamic imaging: evaluation in FDG PET as a prognostic biomarker for breast cancer

European Journal of Nuclear Medicine - 2021
Rhea Chitalia1, Varsha Viswanath1, Austin R. Pantel2, Lanell M. Peterson3, Aimilia Gastounioti2, Éric A. Cohen2, Mark Muzi3, Joel S. Karp2, David A. Mankoff2, Despina Kontos2
1Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
2Department of Radiology, University of Pennsylvania, Philadelphia, USA
3Department of Radiology, University of Washington, Seattle, USA

Tóm tắt

Abstract Purpose Probe-based dynamic (4-D) imaging modalities capture breast intratumor heterogeneity both spatially and kinetically. Characterizing heterogeneity through tumor sub-populations with distinct functional behavior may elucidate tumor biology to improve targeted therapy specificity and enable precision clinical decision making. Methods We propose an unsupervised clustering algorithm for 4-D imaging that integrates Markov-Random Field (MRF) image segmentation with time-series analysis to characterize kinetic intratumor heterogeneity. We applied this to dynamic FDG PET scans by identifying distinct time-activity curve (TAC) profiles with spatial proximity constraints. We first evaluated algorithm performance using simulated dynamic data. We then applied our algorithm to a dataset of 50 women with locally advanced breast cancer imaged by dynamic FDG PET prior to treatment and followed to monitor for disease recurrence. A functional tumor heterogeneity (FTH) signature was then extracted from functionally distinct sub-regions within each tumor. Cross-validated time-to-event analysis was performed to assess the prognostic value of FTH signatures compared to established histopathological and kinetic prognostic markers. Results Adding FTH signatures to a baseline model of known predictors of disease recurrence and established FDG PET uptake and kinetic markers improved the concordance statistic (C-statistic) from 0.59 to 0.74 (p = 0.005). Unsupervised hierarchical clustering of the FTH signatures identified two significant (p < 0.001) phenotypes of tumor heterogeneity corresponding to high and low FTH. Distributions of FDG flux, or Ki, were significantly different (p = 0.04) across the two phenotypes. Conclusions Our findings suggest that imaging markers of FTH add independent value beyond standard PET imaging metrics in predicting recurrence-free survival in breast cancer and thus merit further study.

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European Journal of Nuclear Medicine

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