TY - JOUR
T1 - Automated annotation of virtual dual stains to generate convolutional neural network for detecting cancer metastases in H&E-stained lymph nodes
AU - Højlund, Sebastian Andsager
AU - Mandrup, Johan Bjorholm
AU - Nielsen, Patricia Switten
AU - Georgsen, Jeanette Bæhr
AU - Steiniche, Torben
N1 - .
PY - 2025/6/1
Y1 - 2025/6/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Automated annotation
KW - CNN
KW - Lymph node metastasis
KW - Virtual double stain
UR - http://www.scopus.com/inward/record.url?scp=105005879444&partnerID=8YFLogxK
U2 - 10.1016/j.prp.2025.155977
DO - 10.1016/j.prp.2025.155977
M3 - Journal article
C2 - 40300522
SN - 0344-0338
VL - 270
JO - Pathology, Research and Practice
JF - Pathology, Research and Practice
M1 - 155977
ER -