Abstract PD11-03: Deep learning applied on resection specimen tissue slides of ‘pure’ ductal carcinoma in situ predicts ipsilateral invasive breast cancer recurrence
Authors: Doyle Shannon , Canton Francesco Dal , Koostra Timo , van Seijen Maartje , Groen Emilie , Gavves Efstratios , Horlings Hugo , Lips Esther , Wesseling Jelle , Teuwen Jonas ,   Grand Challenge PRECISION Consortium
Published: March 2022

Abstract
Clinical problem. Ductal Carcinoma in Situ (DCIS) is a potential precursor for invasive breast cancer (IBC). Therefore, DCIS is currently treated with surgical excision, often supplemented with radiotherapy to prevent progression to ipsilateral IBC (iIBC). However, many DCIS lesions will never do so. Estimating the risk of progression is a grand challenge, as neither the histopathological grade of the DCIS lesion nor other biological markers are conclusively associated with the disease outcome. Aim. We aimed to develop a deep-learning based pipeline for estimating the risk of iIBC recurrence following DCIS using a dataset of 235 H&E-stained whole-slide images (WSIs) of the primary DCIS lesions with corresponding 10-year follow up metadata of DCIS recurrence collected at the Netherlands Cancer Institute. The patients included in our dataset did not receive radiotherapy and experienced recurrence in 167 of the cases. Results. We developed a two-step pipeline that is able to find predictive features for 10-year iIBC prediction. First, tissue regions on WSIs were divided into equally sized tiles. Mammary ducts were detected in the tiles using a RetinaNet with ResNet101 backbone that was implemented in Detectron2 and pre-trained on ImageNet. Selecting only tiles containing ducts served to reduce the input dimensionality of typically giga-pixel WSIs for the second step. Here, DCIS recurrence was predicted by a weakly-supervised multi-instance learning (MIL) classification model where the label of the WSI was determined by average weighting of duct labels. The performance of this model was enhanced by pre-training it with SimCLR, a self-supervised learning method, on image data from the histopathology domain. Our proposed model achieved an AUC of .93 ± .005, with a sensitivity of .83 ± .27 and a specificity of .85± .09. These results show that the model was able to correctly distinguish patients with low subsequent IBC-risk from those with a substantially higher risk. An active research pursuit of our group is now to develop a model which is able to predict iIBC progression for patients treated with radiotherapy. This poses additional challenges to the model, as the effect of radiotherapy, as well as disease outcome must be predicted. Conclusion and impact. Our method opens up an avenue for identifying biologically relevant features for estimating DCIS progression risk into invasive breast cancer. This knowledge may be used for appropriately choosing a personalized DCIS management option for patients - which may be active surveillance rather than surgical removal of the lesion. JW and JT were equal senior co-authors on this project. This work was supported by Cancer Research UK and by KWF Dutch Cancer Society (ref.C38317/A24043)
Citation Format: Shannon Doyle, Francesco Dal Canton, Timo Koostra, Maartje van Seijen, Emilie Groen, Efstratios Gavves, Hugo Horlings, Esther Lips, Jelle Wesseling, Jonas Teuwen, Grand Challenge PRECISION Consortium. Deep learning applied on resection specimen tissue slides of ‘pure’ ductal carcinoma in situ predicts ipsilateral invasive breast cancer recurrence [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 PD11-03.