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INTRODUCTION: Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent. METHODS: Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers. RESULTS: It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925. CONCLUSION: Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.
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Esophageal squamous cell carcinoma (ESCC) is more prevalent than esophageal adenocarcinoma in Asia, especially in China, where more than half of ESCC cases occur worldwide. Many studies have reported that the automatic detection of lymph node metastasis using semantic segmentation shows good performance in breast cancer and other adenocarcinomas. However, the detection of squamous cell carcinoma metastasis in hematoxylin-eosin (H&E)-stained slides has never been reported. We collected a training set of 110 esophageal lymph node slides with metastasis and 132 lymph node slides without metastasis. An iPad-based annotation system was used to draw the contours of the cancer metastasis region. A DeepLab v3 model was trained to achieve the best fit with the training data. The learned model could estimate the probability of metastasis. To evaluate the effectiveness of the detection model of learned metastasis, we used another large cohort of clinical H&E-stained esophageal lymph node slides containing 795 esophageal lymph nodes from 154 esophageal cancer patients. The basic authenticity label for each slide was confirmed by experienced pathologists. After filtering isolated noise in the prediction, we obtained an accuracy of 94%. Furthermore, we applied the learned model to throat and lung lymph node squamous cell carcinoma metastases and achieved the following promising results: an accuracy of 96.7% in throat cancer and an accuracy of 90% in lung cancer. In this work, we organized an annotated dataset of H&E-stained esophageal lymph node and trained a deep neural network to detect lymph node metastasis in H&E-stained slides of squamous cell carcinoma automatically. Moreover, it is possible to use this model to detect lymph nodes metastasis in squamous cell carcinoma from other organs. This study directly demonstrates the potential for determining the localization of squamous cell carcinoma metastases in lymph node and assisting in pathological diagnosis.
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MET overactivation is one of the crucial reasons for tyrosine kinase inhibitor (TKI) resistance, but the mechanisms are not wholly clear. Here, COX2, TOPK, and MET expression were examined in EGFR-activating mutated NSCLC by immunohistochemical (IHC) analysis. The relationship between COX2, TOPK, and MET was explored in vitro and ex vivo. In addition, the inhibition of HCC827GR cell growth by combining COX2 inhibitor (celecoxib), TOPK inhibitor (pantoprazole), and gefitinib was verified ex vivo and in vivo. We found that COX2 and TOPK were highly expressed in EGFR-activating mutated NSCLC and the progression-free survival (PFS) of triple-positive (COX2, MET, and TOPK) patients was shorter than that of triple-negative patients. Then, we observed that the COX2-TXA2 signaling pathway modulated MET through AP-1, resulting in an inhibition of apoptosis in gefitinib-resistant cells. Moreover, we demonstrated that MET could phosphorylate TOPK at Tyr74 and then prevent apoptosis in gefitinib-resistant cells. In line with these findings, the combination of celecoxib, pantoprazole, and gefitinib could induce apoptosis in gefitinib-resistant cells and inhibit tumor growth ex vivo and in vivo. Our work reveals a novel COX2/MET/TOPK signaling axis that can prevent apoptosis in gefitinib-resistant cells and suggests that a triple combination of FDA-approved drugs would provide a low-cost and practical strategy to overcome gefitinib resistance.
