Automated Detection of Cribriform Growth Patterns in Prostate Histology Images

• URL: http://arxiv.org/abs/2003.10543v2
• Date: Fri, 11 Sep 2020 13:15:52 GMT
• Title: Automated Detection of Cribriform Growth Patterns in Prostate Histology Images
• Authors: Pierre Ambrosini, Eva Hollemans, Charlotte F. Kweldam, Geert J. L. H. van Leenders, Sjoerd Stallinga, Frans Vos
• Abstract summary: Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies.
• Score: 0.13048920509133805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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