Abstract PO5-07-05: Deep learning can diagnose axillary lymph node metastases on optical virtual histologic images in breast cancer patients during surgery

Abstract

Abstract Background: Reliable identification of axillary lymph node (ALN) involvement in patients with breast cancer allows for definitive axillary dissection at the time of the initial surgery, thus avoiding the need for a separate axillary surgery. However, conventional intraoperative ALN diagnostic methods are time-consuming and labor-intensive and can result in tissue destruction. Dynamic full field optical coherence tomography, also called dynamic cell imaging (DCI), has been developed and validated to offer rapid and label-free histologic approximations of metastatic and non-metastatic ALNs. In this study, we aim to optimize the diagnostic pipeline with an automated approach and present the results of using a deep learning (DL) algorithm with DCI to predict ALN status intraoperatively in patients with breast cancer. Methods: Breast cancer patients who required ALN staging were enrolled prospectively in this study. DCI was applied to bisected fresh lymph nodes in a non-destructive manner, and the specimens were subsequently sent for histopathological examination, regarded as the gold standard for comparison. A DL model was trained and fine-tuned on over 80,000 DCI images, and the results were mapped to slide level to predict ALN diagnosis. Results: Total 607 DCI slides of ALNs with 112,852 cropped patches were included in the study. The DL model was trained and validated on a dataset containing 481 slides and tested on an independent testing dataset with 126 slides. In the test set, the DL algorithm yielded accuracy for prediction of ALN status, with sensitivity and specificity of 91.9% and 95.5% and an area under the receiver operating characteristic curve (AUC) of 0.937 (95% confidence interval [CI]: 0.912-0.957) at slide level. Conclusion: These results demonstrate that the integration of DCI with DL is rapid, reduces labor requirements and minimizes tissue destruction. Meanwhile, this algorithm had high classification accuracy to predict the metastatic burden of ALNs for patients with breast cancer. Citation Format: Shuwei Zhang, Houpu Yang, Jin Zhao, Shu Wang. Deep learning can diagnose axillary lymph node metastases on optical virtual histologic images in breast cancer patients during surgery [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 PO5-07-05.