TU-CD-BRB-08: Radiomic Analysis of FDG-PET Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated with SBRT

Journal Article · Mon Jun 15 04:00:00 EDT 2015 · Medical Physics

DOI:https://doi.org/10.1118/1.4925593· OSTI ID:22563058

Cui, Y; Shirato, H ^[1]; Song, J; Pollom, E; Chang, D; Koong, A ^[2]; Li, R ^[1]

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1. Hokkaido University, Global Institute for Collaborative Research and Educat, Sapporo, Hokkaido (Japan)
2. Stanford University, Palo Alto, CA (United States)

Purpose: This study aims to identify novel prognostic imaging biomarkers in locally advanced pancreatic cancer (LAPC) using quantitative, high-throughput image analysis. Methods: 86 patients with LAPC receiving chemotherapy followed by SBRT were retrospectively studied. All patients had a baseline FDG-PET scan prior to SBRT. For each patient, we extracted 435 PET imaging features of five types: statistical, morphological, textural, histogram, and wavelet. These features went through redundancy checks, robustness analysis, as well as a prescreening process based on their concordance indices with respect to the relevant outcomes. We then performed principle component analysis on the remaining features (number ranged from 10 to 16), and fitted a Cox proportional hazard regression model using the first 3 principle components. Kaplan-Meier analysis was used to assess the ability to distinguish high versus low-risk patients separated by median predicted survival. To avoid overfitting, all evaluations were based on leave-one-out cross validation (LOOCV), in which each holdout patient was assigned to a risk group according to the model obtained from a separate training set. Results: For predicting overall survival (OS), the most dominant imaging features were wavelet coefficients. There was a statistically significant difference in OS between patients with predicted high and low-risk based on LOOCV (hazard ratio: 2.26, p<0.001). Similar imaging features were also strongly associated with local progression-free survival (LPFS) (hazard ratio: 1.53, p=0.026) on LOOCV. In comparison, neither SUVmax nor TLG was associated with LPFS (p=0.103, p=0.433) (Table 1). Results for progression-free survival and distant progression-free survival showed similar trends. Conclusion: Radiomic analysis identified novel imaging features that showed improved prognostic value over conventional methods. These features characterize the degree of intra-tumor heterogeneity reflected on FDG-PET images, and their biological underpinnings warrant further investigation. If validated in large, prospective cohorts, this method could be used to stratify patients based on individualized risk.

• OSTI ID: 22563058
• Journal Information: Medical Physics, Journal Name: Medical Physics Journal Issue: 6 Vol. 42; ISSN 0094-2405; ISSN MPHYA6
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
61 RADIATION PROTECTION AND DOSIMETRY
BIOLOGICAL MARKERS
BIOMEDICAL RADIOGRAPHY
CHEMOTHERAPY
HAZARDS
IMAGE PROCESSING
IMAGES
NEOPLASMS
PANCREAS
PATIENTS
POSITRON COMPUTED TOMOGRAPHY