  Computational pathology methods have the potential to improve access to
precision medicine, as well as the reproducibility and accuracy of pathological
diagnoses. Particularly the analysis of whole-slide-images (WSIs) of
immunohistochemically (IHC) stained tissue sections could benefit from
computational pathology methods. However, scoring biomarkers such as KI67 in
IHC WSIs often necessitates the detection of areas of invasive cancer. Training
cancer detection models often requires annotations, which is time-consuming and
therefore costly. Currently, cancer regions are typically annotated in WSIs of
haematoxylin and eosin (H&E) stained tissue sections. In this study, we
investigate the possibility to register annotations that were made in H&E WSIs
to their IHC counterparts. Two pathologists annotated regions of invasive
cancer in WSIs of 272 breast cancer cases. For each case, a matched H&E and
KI67 WSI are available, resulting in 544 WSIs with invasive cancer annotations.
We find that cancer detection CNNs that were trained with annotations
registered from the H&E to the KI67 WSIs only differ slightly in calibration
but not in performance compared to cancer detection models trained on
annotations made directly in the KI67 WSIs in a test set consisting of 54
cases. The mean slide-level AUROC is 0.974 [0.964, 0.982] for models trained
with the KI67 annotations and 0.974 [0.965, 0.982] for models trained using
registered annotations. This indicates that WSI registration has the potential
to reduce the need for IHC-specific annotations. This could significantly
increase the usefulness of already existing annotations.
