Room: Karl Dean Ballroom C
Purpose: Recent studies show 18-F FDG PET for triple-negative breast cancer (TNBC) patients has significant tumor uptake compared to other breast cancers. This study uses a deep learning approach to identify the prognostic value of the combination of image and clinicopathological data for improving clinical outcomes of TNBC patients treated with radiotherapy.
Methods: With IRB approval, 43 consecutive TNBC breast cancer patients treated with radiotherapy were identified. Tissue samples were examined by immunohistochemistry, including P53, Ki-67, CK5/6 and EGFR, and clinical variables were collected, including age, tumor size, grade, pathologic tumor status. All patients had pre-treatment PET/CT scans, and FDG uptake was measured by standard uptake value (SUV). Deep learning convolutional neural networks (DL-CNNs) were used to extract imaging features with 2 convolution layers, and then were connected with a separate regression model to include clinicopathological information. The final layer was constructed to account for image-clinical features for prediction of survival risk of patients. Further, the prognostic values of individual variables were examined by disease-free survival (DFS), and the results of neural networks were compared with multivariate Cox analysis.
Results: DFS was significantly associated with tumor size(p=0.03), lymph node status(p=0.04), T stage(p=0.02), EGFR(p=0.002), and mean uptake(SUVmean, p=0.016). The survival regression was validated using 5-fold cross-validation. The proposed imaging-clinical learning approach achieved better performance for prediction of DFS(C-Index=0.745) than multivariate Cox analysis(C-index=0.692). As a result, patients were stratified into two groups based on the risk score with log-rank p=0.016: a low risk group(n=24) with a longer mean DFS(38.1 months), and a high risk group(n=19) with a shorter mean DFS(22.2 months).
Conclusion: The integration of learning-based deep features and clinicopathological information provides a feasible approach for predicting clinical outcomes for TNBC patients. The proposed method can potentially be used to stratify patient risk and select appropriate clinical strategies before treatment.