Bayesian networks established functional differences between breast cancer subtypes

Predictive model of positive surgical margins after radical prostatectomy based on Bayesian network analysis

Objective

This study aimed to analyze the independent risk factors for marginal positivity after radical prostatectomy and to evaluate the clinical value of the predictive model based on Bayesian network analysis.

Methods

We retrospectively analyzed the clinical data from 238 patients who had undergone radical prostatectomy, between June 2018 and May 2022. The general clinical data, prostate specific antigen (PSA)-derived indicators, puncture factors, and magnetic resonance imaging (MRI) characteristics were included as predictive variables, and univariate and multivariate analyses were conducted. We established a nomogram model based on the independent predictors and adopted BayesiaLab software to generate tree-augmented naive (TAN) and naive Bayesian models based on 15 predictor variables.

Results

Of the 238 patients included in the study, 103 exhibited positive surgical margins. Univariate analysis revealed that PSA density (PSAD) (P = 0.02), Gleason scores for biopsied tissue (P = 0.002) and the ratio of positive biopsy cores (P < 0.001), preoperative T staging (P < 0.001), and location of abnormal signals (P = 0.002) and the side of the abnormal signal (P = 0.009) were all statistically significant. The area under curve (AUC) of the established nomogram model based on independent predictors was 73.80%, the AUC of the naive Bayesian model based on 15 predictors was 82.71%, and the AUC of the TAN Bayesian model was 80.80%.

Conclusion

The predictive model of positive resection margin after radical prostatectomy based on Bayesian network demonstrated high accuracy and usefulness.

Methylation of ESR1 promoter induced by SNAI2-DNMT3B complex promotes epithelial-mesenchymal transition and correlates with poor prognosis in ERα-positive breast cancers

Estrogen receptor α (ERα) serves as an essential therapeutic predictor for breast cancer (BC) patients and is regulated by epigenetic modification. Abnormal methylation of cytosine phosphoric acid guanine islands in the estrogen receptor 1 (ESR1) gene promoter could silence or decrease ERα expression. In ERα-negative BC, we previously found snail family transcriptional repressor 2 (SNAI2), a zinc-finger transcriptional factor, recruited lysine-specific demethylase 1 to the promoter to transcriptionally suppress ERα expression by demethylating histone H3 lysine 4 dimethylation (H3K4me2). However, the role of SNAI2 in ERα-positive BC remains elusive. In this study, we observed a positive correlation between SNAI2 and ESR1 methylation, and SNAI2 promoted ESR1 methylation by recruiting DNA methyltransferase 3 beta (DNMT3B) rather than DNA methyltransferase 1 (DNMT1) in ERα-positive BC cells. Subsequent enrichment analysis illustrated that ESR1 methylation is strongly correlated with cell adhesion and junction. Knocking down DNMT3B could partially reverse SNAI2 overexpression-induced cell proliferation, migration, and invasion. Moreover, high DNMT3B expression predicted poor relapse-free survival and overall survival in ERα-positive BC patients. In conclusion, this study demonstrated the novel mechanisms of the ESR1 methylation mediated with the SNAI2/DNMT3B complex and enhanced awareness of ESR1 methylation's role in promoting epithelial-mesenchymal transition in BC.

Integration of artificial intelligence in lung cancer: Rise of the machine

The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelligence (AI) provides the opportunity to use available data from molecular information to radiomics, in combination with patient and tumor characteristics, to help clinicians provide individualized care. In doing so, AI can help create models to identify cancer early in diagnosis and deliver tailored therapy on the basis of available information, both at the time of diagnosis and in real time as they are undergoing treatment. The purpose of this review is to summarize the current literature in AI specific to lung cancer and how it applies to the multidisciplinary team taking care of these complex patients.

Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review

Background and Objective

Machine learning (ML) models are increasingly being utilized in oncology research for use in the clinic. However, while more complicated models may provide improvements in predictive or prognostic power, a hurdle to their adoption are limits of model interpretability, wherein the inner workings can be perceived as a "black box". Explainable artificial intelligence (XAI) frameworks including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are novel, model-agnostic approaches that aim to provide insight into the inner workings of the "black box" by producing quantitative visualizations of how model predictions are calculated. In doing so, XAI can transform complicated ML models into easily understandable charts and interpretable sets of rules, which can give providers with an intuitive understanding of the knowledge generated, thus facilitating the deployment of such models in routine clinical workflows.

Methods

We performed a comprehensive, non-systematic review of the latest literature to define use cases of model-agnostic XAI frameworks in oncologic research. The examined database was PubMed/MEDLINE. The last search was run on May 1, 2022.

Key Content and Findings

In this review, we identified several fields in oncology research where ML models and XAI were utilized to improve interpretability, including prognostication, diagnosis, radiomics, pathology, treatment selection, radiation treatment workflows, and epidemiology. Within these fields, XAI facilitates determination of feature importance in the overall model, visualization of relationships and/or interactions, evaluation of how individual predictions are produced, feature selection, identification of prognostic and/or predictive thresholds, and overall confidence in the models, among other benefits. These examples provide a basis for future work to expand on, which can facilitate adoption in the clinic when the complexity of such modeling would otherwise be prohibitive.

Conclusions

Model-agnostic XAI frameworks offer an intuitive and effective means of describing oncology ML models, with applications including prognostication and determination of optimal treatment regimens. Using such frameworks presents an opportunity to improve understanding of ML models, which is a critical step to their adoption in the clinic.

Survival prediction by Bayesian network modeling for pseudomyxoma peritonei after cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy

OBJECTIVES

To establish a survival prognostic model for pseudomyxoma peritonei (PMP) treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC) based on Bayesian network (BN).

METHODS

453 PMP patients were included from the database at our center. The dataset was divided into a training set to establish BN model and a testing set to perform internal validation at a ratio of 8:2. From the training set, univariate and multivariate analyses were performed to identify independent prognostic factors for BN model construction. The confusion matrix, receiver operating characteristic (ROC) curve and the area under curve (AUC) were used to evaluate the performance of the BN model.

RESULTS

The univariate and multivariate analyses identified 7 independent prognostic factors: gender, previous operation history, histological grading, lymphatic metastasis, peritoneal cancer index, completeness of cytoreduction and splenectomy (all p < 0.05). Based on independent factors, the BN model of training set was established. After internal validation, the accuracy and AUC of the BN model were 70.3% and 73.5%, respectively.

CONCLUSION

The BN model provides a reasonable level of predictive performance for PMP patients undergoing CRS + HIPEC.