ArticleFrontiers in public health2022
Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Prognostic nomogram for early-onset locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: a SEER analysis.Updates in surgery · 2026Article
- Bayesian networks as prognostic models in oncology: a systematic review and recommendations for clinical practice.BMJ oncology · 2026Article
- Comprehensive review of Bayesian network applications in gastrointestinal cancers.World journal of clinical oncology · 2025Review
- Changes in facial expressions can distinguish Parkinson's disease via Bayesian inference.Frontiers in neurology · 2025Article
- Artificial intelligence methods available for cancer research.Frontiers of medicine · 2024Review
- Added value of DCER-features to clinicopathologic model for predicting metachronous metastases in rectal cancer patients.Abdominal radiology (New York) · 2024Article
- Bayesian network-based survival prediction model for patients having undergone post-transjugular intrahepatic portosystemic shunt for portal hypertension.World journal of gastroenterology · 2024Article
- Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling.Scientific reports · 2024Article
- Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer.Frontiers in oncology · 2024Article
- Risk prediction of second primary malignancies in patients after rectal cancer: analysis based on SEER Program.BMC gastroenterology · 2023Article
- Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis.Abdominal radiology (New York) · 2023Article
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14 authors.
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Abstract
Background: The existing prognostic models of rectal cancer after radical resection ignored the relationships among prognostic factors and their mutual effects on prognosis. Thus, a new modeling method is required to remedy this defect. The present study aimed to construct a new prognostic prediction model based on the Bayesian network (BN), a machine learning tool for data mining, clinical decision-making, and prognostic prediction. Methods: From January 2015 to December 2017, the clinical data of 705 patients with rectal cancer who underwent radical resection were analyzed. The entire cohort was divided into training and testing datasets. A new prognostic prediction model based on BN was constructed and compared with a nomogram. Results: A univariate analysis showed that age, Carcinoembryonic antigen (CEA), Carbohydrate antigen19-9 (CA19-9), Carbohydrate antigen 125 (CA125), preoperative chemotherapy, macropathology type, tumor size, differentiation status, T stage, N stage, vascular invasion, KRAS mutation, and postoperative chemotherapy were associated with overall survival (OS) of the training dataset. Based on the above-mentioned variables, a 3-year OS prognostic prediction BN model of the training dataset was constructed using the Tree Augmented Naïve Bayes method. In addition, age, CEA, CA19-9, CA125, differentiation status, T stage, N stage, KRAS mutation, and postoperative chemotherapy were identified as independent prognostic factors of the training dataset through multivariate Cox regression and were used to construct a nomogram. Then, based on the testing dataset, the two models were evaluated using the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) of ROC of the BN model and nomogram was 80.11 and 74.23%, respectively. Conclusion: The present study established a BN model for prognostic prediction of rectal cancer for the first time, which was demonstrated to be more accurate than a nomogram.
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