Predicting the risk of squamous dysplasia and esophageal squamous cell carcinoma using minimum classification error method

A screened predictive model for esophageal squamous cell carcinoma based on salivary flora data

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor of the digestive system in the esophageal squamous epithelium. Many studies have linked esophageal cancer (EC) to the imbalance of oral microecology. In this work, different machine learning (ML) models including Random Forest (RF), Gaussian mixture model (GMM), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM) and extreme gradient boosting (XGBoost) based on Genetic Algorithm (GA) optimization was developed to predict the relationship between salivary flora and ESCC by combining the relative abundance data of Bacteroides, Firmicutes, Proteobacteria, Fusobacteria and Actinobacteria in the saliva of patients with ESCC and healthy control. The results showed that the XGBoost model without parameter optimization performed best on the entire dataset for ESCC diagnosis by cross-validation (Accuracy = 73.50%). Accuracy and the other evaluation indicators, including Precision, Recall, F1-score and the area under curve (AUC) of the receiver operating characteristic (ROC), revealed XGBoost optimized by the GA (GA-XGBoost) achieved the best outcome on the testing set (Accuracy = 89.88%, Precision = 89.43%, Recall = 90.75%, F1-score = 90.09%, AUC = 0.97). The predictive ability of GA-XGBoost was validated in phylum-level salivary microbiota data from ESCC patients and controls in an external cohort. The results obtained in this validation (Accuracy = 70.60%, Precision = 46.00%, Recall = 90.55%, F1-score = 61.01%) illustrate the reliability of the predictive performance of the model. The feature importance rankings obtained by XGBoost indicate that Bacteroides and Actinobacteria are the two most important factors in predicting ESCC. Based on these results, GA-XGBoost can predict and diagnose ESCC according to the relative abundance of salivary flora, providing an effective tool for the non-invasive prediction of esophageal malignancies.

Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer

Objective

Using visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.

Methods

On April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.

Results

A total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.

Conclusions

An increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.

Survival risk prediction model for ESCC based on relief feature selection and CNN

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive system with poor prognosis and high mortality. It is of great significance to predict the prognosis risk of patients with cancer by using medical pathology information. To take full advantage of the clinic pathological information of ESCC patients and improve the accuracy of postoperative survival risk prediction, this paper proposes an ESCC survival risk prediction model based on Relief feature selection and convolutional neural network (CNN). Firstly, statistical analysis methods and relief feature selection algorithm are used to extract the important risk factors related to the survival risk of patients. Then, One-dimensional convolutional neural network (1D-CNN) is used to establish the survival risk prediction model of patients with esophageal cancer. Finally, the data of patients with esophageal cancer provided by the First Affiliated Hospital of Zhengzhou University is used to assess the performance of the model. The results show that the model proposed in this paper has a high accuracy rate, which can effectively predict the postoperative survival risk of the patient through the clinical phenotypic index of the patient.

Glioblastoma multiforme prognosis: MRI missing modality generation, segmentation and radiogenomic survival prediction

The accurate prognosis of glioblastoma multiforme (GBM) plays an essential role in planning correlated surgeries and treatments. The conventional models of survival prediction rely on radiomic features using magnetic resonance imaging (MRI). In this paper, we propose a radiogenomic overall survival (OS) prediction approach by incorporating gene expression data with radiomic features such as shape, geometry, and clinical information. We exploit TCGA (The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities using a fully convolutional network (FCN) in a conditional generative adversarial network (cGAN). Meanwhile, the same FCN architecture enables the tumor segmentation from the available and the synthesized MRI modalities. The proposed FCN architecture comprises octave convolution (OctConv) and a novel decoder, with skip connections in spatial and channel squeeze & excitation (skip-scSE) block. The OctConv can process low and high-frequency features individually and improve model efficiency by reducing channel-wise redundancy. Skip-scSE applies spatial and channel-wise excitation to signify the essential features and reduces the sparsity in deeper layers learning parameters using skip connections. The proposed approaches are evaluated by comparative experiments with state-of-the-art models in synthesis, segmentation, and overall survival (OS) prediction. We observe that adding missing MRI modality improves the segmentation prediction, and expression levels of gene markers have a high contribution in the GBM prognosis prediction, and fused radiogenomic features boost the OS estimation.

Effect of external application of traditional Chinese medicine combined with chewing gum on gastrointestinal function in patients after surgery for esophageal carcinoma

AIM

To explore the effect of external application of traditional Chinese medicine combined with chewing gum on the recovery of gastrointestinal function in patients after surgery for esophageal carcinoma (EC).

METHODS

One hundred and eight patients who underwent surgical treatment for EC at our hospital from March 2014 to August 2016 were selected and randomly divided into an observation group (abdominal application of traditional Chinese medicine combined with chewing gum) and a control group (routine treatment and nursing care). Clinical efficacy, the recovery of gastrointestinal function and the levels of gastrin and motilin were compared between the two groups.

RESULTS

The effective rate was 94.44% in the observation group and 85.19% in the control group, and there was a significant difference between the two groups (χ² = 6.635, P < 0.05). Times to first defecation, anal exhaust, and recovery of bowel sounds and hospitalization time were significantly shorter in the observation group than in the control group (49.27 h ± 4.82 h vs 65.83 h ± 5.26 h, 31.45 h ± 3.72 h vs 59.26 h ± 4.01 h, 18.43 h ± 2.83 h vs 24.05 h ± 3.26 h, 8.72 h ± 1.26 h vs 11.45 h ± 2.74 h, P < 0.05). Before treatment, there was no significant difference in the levels of gastrin and motilin between the two groups (P > 0.05). After treatment, the levels of gastrin and motilin in the observation group were significantly higher than those in the control group (108.24 ng/L ± 21.36 ng/L vs 91.38 ng/L ± 22.36 ng/L, 612.79 ng/L ± 42.35 ng/L vs 506.23 ng/L ± 51.25 ng/L, P < 0.05).

CONCLUSION

Abdominal application of traditional Chinese medicine combined with chewing gum can significantly improve clinical symptoms and signs and promote the recovery of gastrointestinal function in patients with EC after surgery.

Reducing Cardiovascular and Cancer Risk: How to Address Global Primary Prevention in Clinical Practice

Emerging evidence suggesting the possibility that interventions able to prevent cardiovascular disease (CVD) may also be effective in the prevention of cancer have recently stimulated great interest in the medical community. In particular, data from both experimental and observational studies have demonstrated that aspirin may play a role in preventing different types of cancer. Although the use of aspirin in the secondary prevention of CVD is well established, aspirin in primary prevention is not systematically recommended because the absolute cardiovascular event reduction is similar to the absolute excess in major bleedings. By adding to its cardiovascular prevention benefits, the potential beneficial effect of aspirin in reducing the incidence of mortality and cancer could tip the balance between risks and benefits of aspirin therapy in primary prevention in favor of the latter and broaden the indication for treatment with aspirin in populations at average risk. Prospective and randomized studies are currently investigating the effect of aspirin in prevention of both cancer and CVD; however, clinical efforts at the individual level to promote the use of aspirin in global (or total) primary prevention already could be made on the basis of a balanced evaluation of the benefit/risk ratio.