Automated annotation of virtual dual stains to generate convolutional neural network for detecting cancer metastases in H&E-stained lymph nodes.

CONTEXT: Staging cancer patients is crucial and requires analyzing all removed lymph nodes microscopically for metastasis. For this pivotal task, convolutional neural networks (CNN) can reduce workload and improve diagnostic accuracy.

OBJECTIVE

This study aimed to develop a CNN for detecting lymph node metastases (LNM) in colorectal (CRC) and head and neck cancer patients (HNC) while also demonstrating how routine pathology departments can build tailored AI models without the need for large datasets or extensive manual annotations.

DESIGN

From 40 CRC and 40 HCN patients with LNM, we scanned 40 hematoxylin and eosin-stained (H&E) slides with and 40 slides without LNM. The same slides were re-stained with immunohistochemistry for pan-cytokeratin and re-scanned. The two corresponding whole slide images were aligned digitally before having the metastatic areas annotated by a color threshold-based algorithm on the immunohistochemistry slide. These annotations were digitally transferred onto H&E whole slide images, which served as the CNN training cohort. The two developed CNNs were tested on 388 lymph nodes from 20 CRC and 138 lymph nodes from 20 HNC patients.

RESULTS

The areas under the ROC curve were 0.9968 [95 %CI, 0.9925-0.9996] for CRC and 0.9485 [95 %CI, 0.8938-0.9888] for HNC patients representing a high sensitivity and specificity for both CNNs. The results are comparable to studies based on huge data sets or exhaustively manually annotated whole slide images.

CONCLUSIONS

Our study showed that it is possible to develop a high-performing CNN with no requirements for huge datasets or time-consuming manual annotations.