BackgroundLymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to disease recurrence and mortality, thereby impacting therapeutic schedule choices for patients. However, accurately predicting LNM prior to treatment remains challenging. Consequently, this study seeks to utilize digital pathological features extracted from histopathological slides of primary cervical cancer patients to preoperatively predict the presence of LNM.MethodsA deep learning (DL) model was trained using the Vision transformer (ViT) and recurrent neural network (RNN) frameworks to predict LNM. This prediction was based on the analysis of 554 histopathological whole‐slide images (WSIs) obtained from Qilu Hospital of Shandong University. To validate the model's performance, an external test was conducted using 336 WSIs from four other hospitals. Additionally, the efficiency of the DL model was evaluated using 190 cervical biopsies WSIs in a prospective set.ResultsIn the internal test set, our DL model achieved an area under the curve (AUC) of 0.919, with sensitivity and specificity values of 0.923 and 0.905, respectively, and an accuracy (ACC) of 0.909. The performance of the DL model remained strong in the external test set. In the prospective cohort, the AUC was 0.91, and the ACC was 0.895. Additionally, the DL model exhibited higher accuracy compared to imaging examination in the evaluation of LNM. By utilizing the transformer visualization method, we generated a heatmap that illustrates the local pathological features in primary lesions relevant to LNM.ConclusionDL‐based image analysis has demonstrated efficiency in predicting LNM in early operable cervical cancer through the utilization of biopsies WSI. This approach has the potential to enhance therapeutic decision‐making for patients diagnosed with cervical cancer.
Studies have shown that stress such as hypoxia, chemotherapy, radiotherapy can lead to polyploidization of tumor cells, which play an important role in tumor heterogeneity and malignant phenotype. Paclitaxel (PTX) treatment promoted polyploid cancer cells (PCCs) formation, and miR-378d is sharply reduced in PCCs of esophageal squamous cell carcinomas (ESCC) cells, but miR-378d participation PCCs formation and the impact on the biological behavior of ESCC remains unclear. We analyzed the PCCs formation and biological behavior of ESCC cells in vivo and in vitro, and the related proteins regulated by miR-378d. Results showed that miR-378d expression was associated with good prognosis in ESCC patients. miR-378d inhibition promoted PCCs formation, heterogenicity, chemo-resistance, monoclonal formation, EMT, migration, invasion, stemness and metastasis of ESCC cells. miR-378d can target downregulated AKT1, and inactivating the AKT-β-catenin signaling pathway, miR-378d and AKT can also regulated RhoA expression. AKT and RhoA regulated polyploidization and depolyploidization. Therefore, miR-378d expression is a good prognostic factor of ESCC patients and regulates polyploidization and malignant phenotype of tumor cells through AKT and RhoA.
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