Potential of an AI-based digital biomarker to predict neoadjuvant chemotherapy response from preoperative biopsies of Luminal-B breast cancer

W. Aswolinskiy, H. Horlings, L. Mulder, J. van der Laak, J. Wesseling, E. Lips and F. Ciompi

European Congress of Pathology 2019.

Background & objectives: Invasive breast cancer (IBC) is increasingly treated with neoadjuvant chemotherapy. Yet, only 15-20% of Luminal-B patients achieve pathological complete response (pCR). We developed an AI-based biomarker to predict pCR of Luminal-B IBC from preoperative biopsies stained with H&E.

Methods: First, we trained a deep learning model on a multi-centric dataset of n=277 manually annotated breast cancer H&E-stained histopathology images to segment tumour, lymphocytes and other tissue. Second, we applied the segmentation model to an independent set of n=297 Luminal-B pre-treatment biopsies. For each case, we computed our biomarker: the proportion of tumour within 80mm distance from lymphocyte regions.

Results: From the Luminal-B cohort, 32/297 cases (11%) were labelled as "pCR" when no remaining cancer cells were reported for the post-operative surgical resection. The biomarker showed significant (p<<0.01) correlation with pCR with a point biserial correlation coefficient of 0.27. Setting a cut-off value based on the optimal operating point of the ROC curve (AUC=0.69), we reached a sensitivity of 0.53 and a specificity of 0.74.

Conclusion: The developed deep-learning based biomarker quantifies the proportion of inflammatory tumour regions. It shows promising results for predicting pCR for Luminal-B breast cancer from pre-treatment biopsies stained with H&E.