Functional 4-D clustering for characterizing intratumor heterogeneity in dynamic imaging: evaluation in FDG PET as a prognostic biomarker for breast cancer
| Autor: | David A. Mankoff, Eric A. Cohen, Austin R. Pantel, Aimilia Gastounioti, Mark Muzi, Varsha Viswanath, Rhea Chitalia, Joel S. Karp, Despina Kontos, Lanell M. Peterson |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Oncology
medicine.medical_specialty Dynamic imaging Concordance medicine.medical_treatment Breast Neoplasms Dynamic PET 030218 nuclear medicine & medical imaging Targeted therapy 03 medical and health sciences 0302 clinical medicine Breast cancer Text mining Fluorodeoxyglucose F18 Internal medicine Cluster Analysis Humans Medicine Radiology Nuclear Medicine and imaging Cluster analysis business.industry Correction General Medicine Image segmentation Prognosis medicine.disease Hierarchical clustering Positron-Emission Tomography 030220 oncology & carcinogenesis Imaging markers Intratumor heterogeneity Female Original Article Neoplasm Recurrence Local business Biomarkers |
| Zdroj: | European Journal of Nuclear Medicine and Molecular Imaging |
| ISSN: | 1619-7089 1619-7070 |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
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