Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network

Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer

Background and objective

Nasopharyngeal carcinoma (NPC) is a rare disease in most parts of the world, but it is highly prevalent in South China. Epstein-Barr virus (EBV) is one of the major risk factors for NPC. Hence, understanding the factors associated with the reactivation of EBV from the latent stage is crucial for preventing NPC. This study aimed to investigate the risk factors for EBV reactivation associated with NPC in high-prevalence areas in China using a Bayesian network (BN) model combined with structural equation modeling tools.

Methods

The baseline information for this study was derived from NPC screening data from a population-based prospective cohort in Sihui City, Guangdong Province, China. We divided the data into a training dataset and a test dataset. We then constructed an interaction networktionba BN prediction model to explore the risk factors for EBV reactivation, which was compared with a conventional logistic regression model.

Results

A total of 12,579 participants were included in the analyses, with 1596 participant pairs finally included after the use of a nested case-control study. The results of multivariable logistic regression showed that only being older than 60 years (OR = 1.718, 95% CI = 1.273,2.322) and being a current smoker (OR = 1.477, 95% CI = 1.167 - 1.872) were the risk factors for EBV reactivation. The results of the model constructed using BN showed that age and smoking were directly associated with EBV reactivation. In contrast, sex, education level, tea drinking, cooking, and family history of cancer were indirectly associated with EBV reactivation. Further, we predicted the risk of EBV reactivation using Bayesian inference and visualized the BN inference. Model prediction performance was evaluated using the test dataset. The results showed that the BN model slightly outperformed the traditional logistic regression model in all metrics.

Conclusions

BN not only reflects the complex interaction between factors but also visualizes the prediction results. It has a promising application potential in the risk prediction of EBV reactivation associated with NPC.

Artificial intelligence methods available for cancer research

Cancer is a heterogeneous and multifaceted disease with a significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis and selection of effective treatment remains a challenge. With the convenience of large-scale datasets including multiple levels of data, new bioinformatic tools are needed to transform this wealth of information into clinically useful decision-support tools. In this field, artificial intelligence (AI) technologies with their highly diverse applications are rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, and K-nearest neighbors, including neural network models like deep learning, have proven valuable in predictive, prognostic, and diagnostic studies. Researchers have recently employed large language models to tackle new dimensions of problems. However, leveraging the opportunity to utilize AI in clinical settings will require surpassing significant obstacles-a major issue is the lack of use of the available reporting guidelines obstructing the reproducibility of published studies. In this review, we discuss the applications of AI methods and explore their benefits and limitations. We summarize the available guidelines for AI in healthcare and highlight the potential role and impact of AI models on future directions in cancer research.

Added value of DCER-features to clinicopathologic model for predicting metachronous metastases in rectal cancer patients

RATIONALE AND OBJECTIVES

The study aimed to investigate whether dynamic contrast-enhanced MRI parameters and preoperative radiological features (DCER-Features) add value to the clinicopathologic model for predicting metachronous metastases in rectal cancer patients.

MATERIALS AND METHODS

From January 2014 to December 2020, 859 patients in the PACS system were retrospectively screened. Of the initial 722 patients with surgically confirmed rectal cancer and no synchronous metastases, 579 patients were excluded for various reasons such as lack of clinicopathological or radiological information. 143 patients were finally included in this study. And 73 Patients of them developed metachronous metastasis within five years. After stepwise multiple regression analyses, we constructed three distinct models. Model 1 was developed solely based on clinicopathological factors, and model 2 incorporated clinicopathological characteristics along with DCE-MRI parameters. Finally, model 3 was built on all available factors, including clinicopathological characteristics, DCE-MRI parameters, and radiological features based on rectal magnetic resonance imaging. The radiological features assessed in this study encompass tumor imaging staging, location, and circumferential resection margin (CRM) for primary tumors, as well as the number of visible lymph nodes and suspected metastatic lymph nodes. Receiver operating characteristic (ROC) and decision curve analysis (DCA) were conducted to evaluate whether the diagnostic efficiency was improved.

RESULTS

The performance of model 3 (including clinicopathologic characteristics and DCER-Features) was the best (AUC: 0.856, 95% CI 0.778-0.886), whereas it was 0.796 (95% CI 0.720-0.828) for model 2 and 0.709 (95% CI 0.612-0.778) for model 1 (DeLong test: model 1 vs model 2, p = 0.004; model 2 vs model 3, p = 0.037; model 1 vs model 3, p < 0.001). The decision curves indicated that the net benefit of model 3 was higher than the other two models at each referral threshold. The calibration plot of the three models revealed an excellent predictive accuracy.

CONCLUSION

This study suggests that DCER-Features have added value for the clinicopathological model to predict metachronous metastasis in patients with rectal cancers.

Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension

BACKGROUND

Portal hypertension (PHT), primarily induced by cirrhosis, manifests severe symptoms impacting patient survival. Although transjugular intrahepatic portosystemic shunt (TIPS) is a critical intervention for managing PHT, it carries risks like hepatic encephalopathy, thus affecting patient survival prognosis. To our knowledge, existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes. Consequently, the development of an innovative modeling approach is essential to address this limitation.

AIM

To develop and validate a Bayesian network (BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.

METHODS

The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed. Variables were selected using Cox and least absolute shrinkage and selection operator regression methods, and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.

RESULTS

Variable selection revealed the following as key factors impacting survival: age, ascites, hypertension, indications for TIPS, postoperative portal vein pressure (post-PVP), aspartate aminotransferase, alkaline phosphatase, total bilirubin, prealbumin, the Child-Pugh grade, and the model for end-stage liver disease (MELD) score. Based on the above-mentioned variables, a BN-based 2-year survival prognostic prediction model was constructed, which identified the following factors to be directly linked to the survival time: age, ascites, indications for TIPS, concurrent hypertension, post-PVP, the Child-Pugh grade, and the MELD score. The Bayesian information criterion was 3589.04, and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16. The model's accuracy, precision, recall, and F1 score were 0.90, 0.92, 0.97, and 0.95 respectively, with the area under the receiver operating characteristic curve being 0.72.

CONCLUSION

This study successfully developed a BN-based survival prediction model with good predictive capabilities. It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.

Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling

Colorectal cancer is a prevalent malignancy with global significance. This retrospective study aimed to investigate the influence of stage and tumor site on survival outcomes in 284 colorectal cancer patients diagnosed between 2001 and 2017. Patients were categorized into four groups based on tumor site (colon and rectum) and disease stage (early stage and advanced stage). Demographic characteristics, treatment modalities, and survival outcomes were recorded. Bayesian survival modeling was performed using semi-competing risks illness-death models with an accelerated failure time (AFT) approach, utilizing R 4.1 software. Results demonstrated significantly higher time ratios for disease recurrence (TR = 1.712, 95% CI 1.489-2.197), mortality without recurrence (TR = 1.933, 1.480-2.510), and mortality after recurrence (TR = 1.847, 1.147-2.178) in early-stage colon cancer compared to early-stage rectal cancer. Furthermore, patients with advanced-stage rectal cancer exhibited shorter survival times for disease recurrence than patients with early-stage colon cancer. The interaction effect between the disease site and cancer stage was not significant. These findings, derived from the optimal Bayesian log-normal model for terminal and non-terminal events, highlight the importance of early detection and effective management strategies for colon cancer. Early-stage colon cancer demonstrated improved survival rates for disease recurrence, mortality without recurrence, and mortality after recurrence compared to other stages. Early intervention and comprehensive care are crucial to enhance prognosis and minimize adverse events in colon cancer patients.

Risk prediction of second primary malignancies in patients after rectal cancer: analysis based on SEER Program

BACKGROUND

This study will focus on exploring the clinical characteristics of rectal cancer (RC) patients with Second Primary Malignancies (SPMs) and constructing a prognostic nomogram to provide clinical treatment decisions.

METHODS

We determined the association between risk factors and overall survival (OS) while establishing a nomogram to forecast the further OS status of these patients via Cox regression analysis. Finally, we evaluated the performance of the prognostic nomogram to predict further OS status.

RESULTS

Nine parameters were identified to establish the prognostic nomogram in this study, and, the C-index of the training set and validation set was 0.691 (95%CI, 0.662-0.720) and 0.731 (95%CI, 0.676-0.786), respectively. The calibration curve showed a high agreement between the predicted and actual results, and the receiver operating characteristic (ROC) curves verified the superiority of our model for clinical usefulness. In addition, the nomogram classification could more precisely differentiate risk subgroups and improved the discrimination of SPMs' prognosis.

CONCLUSIONS

We systematically explored the clinical characteristics of SPMs after RC and constructed a satisfactory nomogram.

Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis

PURPOSE

At present, there are few effective method to predict metachronous liver metastasis (MLM) from rectal cancer. We aim to investigate the efficacy of radiomics based on multiparametric MRI of first diagnosed rectal cancer in predicting MLM from rectal cancer.

METHODS

From 301 consecutive histopathologically confirmed rectal cancer patients, 130 patients who have no distant metastasis detected at the time of diagnosis were enrolled and divided into MLM group (n = 49) and non-MLM group (n = 81) according to whether liver metastasis be detected later than 6 month after the first diagnosis of rectal cancer within 3 years' follow-up. The 130 patients were divided into a training set (n = 91) and a testing set (n = 39) at a ratio of 7:3 by stratified sampling using SPSS 24.0 software. The DWI model, HD T₂WI model, and DWI + HD T₂WI model were constructed respectively. The best performing model was selected and combined with the screened clinical features (including non-radiomics MRI features) to construct a fusion model. The testing set was used to evaluate the performance of the models, and the area under the curve (AUC) of receiver operating characteristics (ROC) was calculated for both the training set and the testing set.

RESULTS

The AUC of the DWI + HD T₂WI model in the testing set was higher than that of the DWI or the HD T₂ model alone with statistically significance (P < 0.05). The screened clinical features were extramural vascular invasion (EMVI), T and N stages in MRI (_mrT, _mrN), and the distance from the lower edge of the tumor to the anal verge. The AUC of the fusion model in the testing set was 0.911. Decision curves and nomogram also showed that the fusion model had excellent clinical performance.

CONCLUSION

The fusion model of primary rectal cancer MRI based radiomics combing clinical features can effectively predict MLM from rectal cancer, which may assist clinicians in formulating individualized monitoring and treatment plans.

Establishment of a Bayesian network model to predict the survival of malignant peritoneal mesothelioma patients after cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy

OBJECTIVES

To establish a Bayesian network (BN) model to predict the survival of patients with malignant peritoneal mesothelioma (MPM) treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC).

METHODS

The clinicopathological data of 154 MPM patients treated with CRS + HIPEC at our hospital from April 2015 to November 2022 were retrospectively analyzed. They were randomly divided into two groups in a 7:3 ratio. Survival analysis was conducted on the training set and a BN model was established. The accuracy of the model was validated using a confusion matrix of the testing set. The receiver operating characteristic (ROC) curve and area under the curve were used to evaluate the overall performance of the BN model.

RESULTS

Survival analysis of 107 patients (69.5%) in the training set found ten factors affecting patient prognosis: age, Karnofsky performance score, surgical history, ascites volume, peritoneal cancer index, organ resections, red blood cell transfusion, pathological types, lymphatic metastasis, and Ki-67 index (all p < 0.05). The BN model was successfully established after the above factors were included, and the BN model structure was adjusted according to previous research and clinical experience. The results of confusion matrix obtained by internal validation of 47 cases in the testing set showed that the accuracy of BN model was 72.7%, and the area under ROC was 0.74.

CONCLUSIONS

The BN model was established successfully with good overall performance and can be used as a clinical decision reference.