Program Information
A PET-CT Radiomics Comparison to Predict Distant Metastasis in Lung Adenocarcinoma
T Coroller1*, S Yip1 , J Kim2 , S Lee1 , R Mak1 , H Aerts1 , (1) Dana Farber Cancer Institute, Brigham and Women's Hospital, Havard Medical School, Boston, MA, (2) Brigham and Women's Hospital, Children's hospital, Harvard Medical School, Boston, MA
Presentations
SU-D-207B-3 (Sunday, July 31, 2016) 2:05 PM - 3:00 PM Room: 207B
Purpose: Early prediction of distant metastasis may provide crucial information for adaptive therapy, subsequently improving patient survival. Radiomic features that extracted from PET and CT images have been used for assessing tumor phenotype and predicting clinical outcomes. This study investigates the values of radiomic features in predicting distant metastasis (DM) in non-small cell lung cancer (NSCLC).
Methods: A total of 108 patients with stage II-III lung adenocarcinoma were included in this retrospective study. Twenty radiomic features were selected (10 from CT and 10 from PET). Conventional features (metabolic tumor volume, SUV, volume and diameter) were included for comparison. Concordance index (CI) was used to evaluate features prognostic value. Noether test was used to compute p-value to consider CI significance from random (CI = 0.5) and were adjusted for multiple testing using false rate discovery (FDR).
Results: A total of 70 patients had DM (64.8%) with a median time to event of 8.8 months. The median delivered dose was 60 Gy (range 33-68 Gy). None of the conventional features from PET (CI ranged from 0.51 to 0.56) or CT (CI ranged from 0.57 to 0.58) were significant from random. Five radiomics features were significantly prognostic from random for DM (p-values < 0.05). Four were extracted from CT (CI = 0.61 to 0.63, p-value <0.01) and one from PET which was also the most prognostic (CI = 0.64, p-value <0.001).
Conclusion: This study demonstrated significant association between radiomic features and DM for patients with locally advanced lung adenocarcinoma. Moreover, conventional (clinically utilized) metrics were not significantly associated with DM. Radiomics can potentially help classify patients at higher risk of DM, allowing clinicians to individualize treatment, such as intensification of chemotherapy) to reduce the risk of DM and improve survival.
Funding Support, Disclosures, and Conflict of Interest: R.M. has consulting interests with Amgen.
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