Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer

Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach

OBJECTIVES

The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and clinical features for AC patients.

METHODS

Clinical and pathological information from one hundred and ninety-seven patients with cervical AC was collected and analyzed. For each patient, 107 radiomics features were extracted from T2-weighted MRI images. Feature selection was performed using Spearman correlation and random forest (RF) algorithms, and predictive models were built using support vector machine (SVM) technique. Deep learning models were also trained with T2-weighted MRI images and clinicopathological features through Convolutional Neural Network (CNN). Kaplan-Meier curve was analyzed using significant features. In addition, information from another group of 56 AC patients was used for the independent validation.

RESULTS

A total of 107 radiomics features and 6 clinicopathological features (age, FIGO stage, differentiation, invasion depth, lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) were included in the analysis. When predicting the 3-year, 4-year, and 5-year DFS, the model trained solely on radiomics features achieved AUC values of 0.659 (95%CI: 0.620-0.716), 0.791 (95%CI: 0.603-0.922), and 0.853 (95%CI: 0.745-0.912), respectively. However, the combined model, incorporating both radiomics and clinicopathological features, outperformed the radiomics model with AUC values of 0.934 (95%CI: 0.885-0.981), 0.937 (95%CI: 0.867-0.995), and 0.916 (95%CI: 0.857-0.970), respectively. For deep learning models, the MRI-based models achieved an AUC of 0.857, 0.777 and 0.828 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. And the combined deep learning models got a improved performance, the AUCs were 0.903. 0.862 and 0.969. In the independent test set, the combined model achieved an AUC of 0.873, 0.858 and 0.914 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively.

CONCLUSIONS

We demonstrated the prognostic value of integrating MRI-based radiomics and clinicopathological features in cervical adenocarcinoma. Both radiomics and deep learning models showed improved predictive performance when combined with clinical data, emphasizing the importance of a multimodal approach in patient management.

Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023

BACKGROUND

Recently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer.

METHODS

We retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords.

RESULTS

We found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer.

CONCLUSIONS

Overall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.

KLF5 inhibits the migration and invasion in cervical cancer cell lines by regulating SNAI1

BACKGROUND

Epithelial-mesenchymal transition (EMT) is an important biological process by which malignant tumor cells to acquire migration and invasion abilities. This study explored the role of KLF5 in the EMT process of in cervical cancer cell lines.

OBJECTIVE

Krüpple-like factor 5 (KLF5) is a basic transcriptional factor that plays a key role in cell-cycle arrest and inhibition of apoptosis. However, the molecular mechanism by which KLF5 mediates the biological functions of cervical cancer cell lines has not been elucidated. Here, we focus on the potential function of ELF5 in regulating the EMT process in in vitro model of cervical cancer cell lines.

METHOD

Western-blot and real-time quantitative PCR were used to detect the expression of EMT-related genes in HeLa cells. MTT assays, cell scratch and Transwell assays were used to assess HeLa cells proliferation and invasion capability. Using the bioinformatics tool JASPAR, we identified a high-scoring KLF5-like binding sequence in the SNAI1 gene promoter. Luciferase reporter assays was used to detect transcriptional activity for different SNAI1 promoter truncates.

RESULT

After overexpressing the KLF5 gene in HeLa cells, KLF5 not only significantly inhibited the invasion and migration of HeLa cells, but also increased the expression of E-cadherin and decreased the expression of N-cadherin and MMP9. In addition, the mRNA expression of upstream regulators of E-cadherin, such as SNAI1, SLUG, ZEB1/2 and TWIST1 was also decreased. Furthermore, KLF5 inhibiting the expression of the SNAI1 gene via binding its promoter region, and the EMT of Hela cells was promoted after overexpression of the SNAI1 gene.

CONCLUSION

These results indicate that KLF5 can downregulate the EMT process of HeLa cells by decreasing the expression of the SNAI1 gene, thereby inhibiting the migration and invasion of HeLa cervical cancer cells.