Abstract P1-02-16: Prediction of breast ductal carcinoma in situ recurrence using histomics analysis of stromal collagen from second-harmonic generation and hematoxylin and eosin stain-based images

Abstract OBJECTIVE. A clinical challenge of ductal carcinoma in situ (DCIS) is to accurately predict individual risk for recurrence. Prior studies have highlighted the importance of stromal collagen surrounding breast ducts in breast cancer aggression. We studied the collagen structure surrounding DCIS in a matched case-control cohort of women with DCIS using second-harmonic generation (SHG) and hematoxylin and eosin (H&E) images to determine whether stromal collagen could provide additional risk information. METHODS. A cohort of 122 patients with DCIS (61 cases with recurrence matched with 61 controls) were retrospectively analyzed. Whole-side SHG and H&E images were obtained and regions-of-interest (ROI) of stromal collagen adjacent to DCIS was automatically segmented using an in-house trained deep learning algorithm. A quantitative histology image analysis pipeline (termed “histomics”) was exploited to extract a total of 123 histomics features (intensity, statistical and textural) from the segmented stromal region from both SHG and H&E ROI images. Clinical features previously shown to associate with DCIS recurrence including tumor size, margins and treatment were also collected. Conditional logistic regression model with lasso penalty was used to assess the performance of these features to stratify patients with and without recurrence. RESULTS. We found the combination of SHG, H&E and clinical features reached the highest concordance of 0.93 and the combination of only SHG and H&E features reached a concordance of 0.91. In contrast, clinical features alone attained a concordance index of 0.86. Model comparison by the Akaike information criterion (AIC​) shows that the higher concordance of the combination of SHG, H&E and clinical features may indicate better discrimination, whereas the higher concordance of combination of only SHG and H&E features was merely the result of including the larger number of features. Various textural features were found to be statistically significant (p<0.05) in both the SHG and H&E images, while the intensity or statistical features did not reach statistical significance. CONCLUSION. Textural stromal features extracted from SHG and H&E image-based histomics indicate a significant association of some features with DCIS recurrence. Combination of these histomics features with conventional clinical features are a promising quantitative tool to predict recurrence in patients with DCIS. Citation Format: Taman Upadhaya, Mary-Kate Hayward, Mi-Ok Kim, Ronald Balassanian, Valerie Weaver, Olivier Morin, Catherine Park. Prediction of breast ductal carcinoma in situ recurrence using histomics analysis of stromal collagen from second-harmonic generation and hematoxylin and eosin stain-based images [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-02-16.