(Tuesday, 7/31/2018) 4:30 PM - 6:00 PM
Room: Davidson Ballroom B
Purpose: Radiomics provides quantitative tissue heterogeneity profiling and is an exciting approach to developing imaging biomarkers in the context of precision medicine. Normal-appearing parenchymal tissues surrounding primary tumors can harbor microscopic disease that leads to increased risk of distant metastasis (DM). This study assesses whether computed-tomography (CT) imaging features of such peritumoral tissues can predict DM in locally advanced non-small cell lung cancer (NSCLC).
Methods: 200 NSCLC patients of histological adenocarcinoma were included in this study. The investigated lung tissues were tumor rim, defined to be 3mm of tumor and parenchymal tissue on either side of the tumor border and the exterior region extended from 3 to 9mm outside of the tumor. Fifteen stable radiomic features were extracted and evaluated from each of these regions on pre-treatment CT images. For comparison, features from expert-delineated tumor contours were similarly prepared. The patient cohort was separated into training and validation datasets for prognostic power evaluation. Both univariate and multivariate analyses were performed for each region using concordance index (CI).
Results: Eight out of fifteen tumor rim features were associated with DM (CI > 0.6, p-value < 0.05), as were five features from the visible tumor, while none of the exterior features was. Multivariately, a rim radiomic signature achieved the highest prognostic performance in the independent validation sub-cohort (CI = 0.64, p-value = 8.1×10-7). A combined clinical and rim radiomic model (CI = 0.65) found significantly better prediction than either a multivariate clinical model (CI = 0.57, p-value = 0.02) or a combined clinical and visible tumor radiomics model (CI = 0.59, p-value = 0.02).
Conclusion: We identified peritumoral rim radiomic features significantly associated with DM. This study demonstrated that peritumoral imaging characteristics may provide additional valuable information over the visible tumor features for patient stratification due to cancer metastasis.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the Kaye Scholar Award, and the BWH/DFCI Department of Radiation Oncology Clinical-Translational Award.
Keywords
Lung, CT, Quantitative Imaging
Taxonomy
TH- response assessment : CT imaging-based
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