  Cribriform growth patterns in prostate carcinoma are associated with poor
prognosis. We aimed to introduce a deep learning method to detect such patterns
automatically. To do so, convolutional neural network was trained to detect
cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning
taking into account other tumor growth patterns during training was used to
cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC
analyses were applied to assess network performance regarding detection of
biopsies harboring cribriform growth pattern. The ROC analysis yielded a mean
area under the curve up to 0.81. FROC analysis demonstrated a sensitivity of
0.9 for regions larger than 0.0150 mm2 with on average 7.5 false positives. To
benchmark method performance for intra-observer annotation variability, false
positive and negative detections were re-evaluated by the pathologists.
Pathologists considered 9% of the false positive regions as cribriform, and 11%
as possibly cribriform; 44% of the false negative regions were not annotated as
cribriform. As a final experiment, the network was also applied on a dataset of
60 biopsy regions annotated by 23 pathologists. With the cut-off reaching
highest sensitivity, all images annotated as cribriform by at least 7/23 of the
pathologists, were all detected as cribriform by the network and 9/60 of the
images were detected as cribriform whereas no pathologist labelled them as
such. In conclusion, the proposed deep learning method has high sensitivity for
detecting cribriform growth patterns at the expense of a limited number of
false positives. It can detect cribriform regions that are labelled as such by
at least a minority of pathologists. Therefore, it could assist clinical
decision making by suggesting suspicious regions.
