Identification lymph node metastasis in esophageal squamous cell carcinoma using whole slide images and a hybrid network of multiple instance and transfer learning.

Difficulties associated with identifying lymph nodes metastasis in esophageal squamous cell carcinoma (ESCC LNM) can make it challenging to determine the clinical stage and devize precise treatment strategies for patients with esophageal cancer (EC). The lack of a large public dataset and expensive expert annotation are the factors responsible for the slow development of clinical computer-aided diagnosis for ESCC LNM. In this study, we collected 863 whole slide images from 198 patients at two hospitals, and developed a weakly supervised workflow based on a hybrid network of multiple instance and transfer learning (MITL) for automating identi-fication of ESCC LNM. The results showed that MITL achieved a significant performance advantage over its competitors. The accuracy (ACC), F1-score, Matthews correlation coefficient, and areas under the curve were 0.976, 0.944, 0.929, and 0.991 for the internal testing data, and 0.969, 0.925, 0.905, and 0.988 for the external testing data, respectively. Compared to pre-train feature extractor, the improvement in ACC of pre-training aggregation network on the internal testing data was approximately twice. Furthermore, ACCs were deter-mined using MITL from micrometastasis and macrometastasis (0.824 and 0.95, respectively). Visualization showed that the key features of ESCC LNM can be extracted by MITL for accurate detection and classification. In summary, our findings illustrated that MITL can achieve the high-efficiency identification and prediction of ESCC LNM with less investment of resources, and provide a new research strategy for the diagnosis of it.