Abstract PO3-09-01: Integrating pathomic and radiomic images to classify risk of subsequent events among women with DCIS
Authors: Huang Shih-Ting , Bennett Debbie , West Robert , Colditz Graham , Jiang Shu 
Published: May 2024

Background Multiple sources of ~omic data can be generated from women at different stages of developing breast cancer, the leading cancer diagnosed in women worldwide. Traditionally interrogation of risk factors to study associations and develop prediction models for future breast events has been limited to one or few risk factors, or summary scores of clinical and tumor characteristics. Methods to bring mammography images and breast biopsies of precancer lesions together to summarize risk of cancer developing in the breast are urgently needed. Integration of these two sources has not been performed to date, but has potential to increase accuracy of risk prediction. Approach The Repository of Archival Human Breast Tissue (RAHBT) was established in 2007 at Washington University School of Medicine (WUSM) and maintains biospecimens and medical record data of women treated with breast-conserving surgery (BCS) or mastectomy for breast cancer at WUSM and six other St Louis metropolitan hospitals between 1981-2016. Prospective follow-up of cases is achieved through health records and Siteman tumor registry. Among 1831 patients with pathologically confirmed DCIS who had no prior cancer, 174 developed breast events at least six months after initial DCIS diagnosis. For each case diagnosed between January 1998 and March 2016 with subsequent breast events, two DCIS controls were matched on race, year of diagnosis (±5 years), age (±5 years), and type of surgery. Tissue micro arrays (TMAs) are constructed after breast pathology review and processed for H&E and imaged. Breast cancer risk factor data are uniformly retrieved form the medical records at time of processing cases of DCIS. Mammogram images are retrieved for all women. Full-field digital mammograms (FFDM) are all using the same technology (Hologic). Risk factors and outcome variables: ipsilateral breast events (IBE) was defined as the development of invasive cancer or DCIS in the treated breast at least 6 months post-diagnosis. Follow-up time was calculated from the date of DCIS diagnosis to the date of first IBE, death, or last follow-up, whichever occurred first. The predictors included commonly used clinicopathological factors: age, tumor grade (high vs. low/intermediate), comedonecrosis, surgical margins (close (≤2mm) vs. negative ( &gt;2mm)), local treatment (BCS only and mastectomy vs. BCS+radiation), and endocrine therapy. BMI, menopausal status, and ER were also available for evaluation as predictors. We limited this analysis to women with digital mammograms immediately prior to diagnosis and include 128 cases and controls. Validation cohort As the RAHBT cohort continues to be followed additional second breast events have been documented after the cut off for events identified through March 2016, and the identical procedures used to review/confirm DCIS and subsequent breast events, construct TMAs, process H&E and images, and assemble risk factor data, and the associated digital mammograms. We have 13 additional ipsilateral breast events with complete data and their controls for independently testing the model performance. Statistical methods Propose novel supervised learning approach to integrate image data from mammograms and TMA slides that accommodates agreement between multi-omics. Results 128 (cases and controls, 21.9% ipsilateral subsequent events) identified for development and internal cross validation. Median age at diagnosis was 52 and median time to subsequent breast event was 63 mo. Controlling for the clinical/pathologic risk factors (age, BMI, BI-RADs density, treatment, tumor grade, parous, menopausal status, ER status, and race) and integrating the whole mammogram and TMA pathology images, we observe a 5-yr ipsilateral AUC = 0.74; 10-yr AUC 0.75. The independent validation is ongoing. Conclusions Integrating heterogeneous multiomic data sources can generate significant improvement in long term risk prediction after initial DCIS diagnosis.

Citation Format: Shih-Ting Huang, Debbie Bennett, Robert West, Graham Colditz, Shu Jiang. Integrating pathomic and radiomic images to classify risk of subsequent events among women with DCIS [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-09-01.