Can a Bayesian Belief Network Be Used to Estimate 1-year Survival in Patients With Bone Sarcomas?

Machine Learning-Assisted Decision Making in Orthopaedic Oncology

» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.

Identification of a novel MYC target gene set signature for predicting the prognosis of osteosarcoma patients

Osteosarcoma is a primary malignant tumor found mainly in teenagers and young adults. Patients have very little long-term survival. MYC controls tumor initiation and progression by regulating the expression of its target genes; thus, constructing a risk signature of osteosarcoma MYC target gene set will benefit the evaluation of both treatment and prognosis. In this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. Then, a risk signature consisting of 10 MYC target genes was developed using Cox regression analysis. The signature indicates that patients in the high-risk group performed poorly. After that, we verified it in the GSE21257 dataset. In addition, the difference in tumor immune function among the low- and high-risk populations was compared by single sample gene enrichment analysis. Immunotherapy and prediction of response to the anticancer drug have shown that the risk signature of the MYC target gene set was positively correlated with immune checkpoint response and drug sensitivity. Functional analysis has demonstrated that these genes are enriched in malignant tumors. Finally, STX10 was selected for functional experimentation. STX10 silence has limited osteosarcoma cell migration, invasion, and proliferation. Therefore, these findings indicated that the MYC target gene set risk signature could be used as a potential therapeutic target and prognostic indicator in patients with osteosarcoma.

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

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.

Comparisons of Forecasting for Survival Outcome for Head and Neck Squamous Cell Carcinoma by using Machine Learning Models based on Multi-omics

Background: Machine learning methods showed excellent predictive ability in a wide range of fields. For the survival of head and neck squamous cell carcinoma (HNSC), its multi-omics influence is crucial. This study attempts to establish a variety of machine learning multi-omics models to predict the survival of HNSC and find the most suitable machine learning prediction method. Methods: The HNSC clinical data and multi-omics data were downloaded from the TCGA database. The important variables were screened by the LASSO algorithm. We used a total of 12 supervised machine learning models to predict the outcome of HNSC survival and compared the results. In vitro qPCR was performed to verify core genes predicted by the random forest algorithm. Results: For omics of HNSC, the results of the twelve models showed that the performance of multi-omics was better than each single-omic alone. Results were presented, which showed that the Bayesian network(BN) model (area under the curve [AUC] 0.8250, F1 score=0.7917) and random forest(RF) model (area under the curve [AUC] 0.8002,F1 score=0.7839) played good prediction performance in HNSC multi-omics data. The results of in vitro qPCR were consistent with the RF algorithm. Conclusion: Machine learning methods could better forecast the survival outcome of HNSC. Meanwhile, this study found that the BN model and the RF model were the most superior. Moreover, the forecast result of multi-omics was better than single-omic alone in HNSC.

Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model

Background:

Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas. The purpose of this study is: (1) to externally validate the prior 1-year Bayesian belief network prediction model for survival in patients with bone sarcomas and (2) to develop a gradient boosting machine model using Nandra et al.’s cohort and evaluate whether the gradient boosting machine model outperforms the Bayesian belief network model when externally validated in an independent Danish population cohort.

Material and Methods:

The training cohort comprised 3493 patients newly diagnosed with bone sarcoma from the institutional prospectively maintained database at the Royal Orthopaedic Hospital, Birmingham, UK. The validation cohort comprised 771 patients with newly diagnosed bone sarcoma included from the Danish Sarcoma Registry during January 1, 2000–June 22, 2016. We performed area under receiver operator characteristic curve analysis, Brier score and decision curve analysis to evaluate the predictive performance of the models.

Results:

External validation of the Bayesian belief network 1-year prediction model demonstrated an area under receiver operator characteristic curve of 68% (95% confidence interval, 62%-73%). Area under receiver operator characteristic curve of the gradient boosting machine model demonstrated: 75% (95% confidence interval: 70%-80%), overall model performance by the Brier score was 0.09 (95% confidence interval: 0.077–0.11) and decision curve analysis demonstrated a positive net benefit for threshold probabilities above 0.5. External validation of the developed gradient boosting machine model demonstrated an area under receiver operator characteristic curve of 63% (95% confidence interval: 57%-68%), and the Brier score was 0.14 (95% confidence interval: 0.12–0.16).

Conclusion:

External validation of the 1-year Bayesian belief network survival model yielded a poor outcome based on a Danish population cohort validation. We successfully developed a gradient boosting machine 1-year survival model. The gradient boosting machine did not outperform the Bayesian belief network model based on external validation in a Danish population-based cohort.

A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.

Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.

Identifying biomolecules and constructing a prognostic risk prediction model for recurrence in osteosarcoma

INTRODUCTION

Osteosarcoma is a high-morbidity bone cancer with an unsatisfactory prognosis. The aim of this study is to develop novel potential prognostic biomarkers and construct a prognostic risk prediction model for recurrence in osteosarcoma.

METHODS

By analyzing microarray data, univariate and multivariate Cox regression analyses were performed to screen prognostic RNA signatures and to build a prognostic model. The RNA signatures were validated using Kaplan-Meier curves. Then, we developed and validated a nomogram combining age, recurrence, metastatic, and Prognostic score (PS) models to predict the individual's overall survival at the 3- and 5-year points. Pathway enrichment of RNA was conducted based on the significant co-expressed RNAs.

RESULTS

A total of 319 mRNAs and 14 lncRNAs were identified in the microarray data. One lncRNA (LINC00957) and six mRNAs (METL1, CA9, B3GALT4, ALDH1A1, LAMB3, and ITGB4) were identified as RNA signatures and showed good performances in survival prediction for both the training and validation cohorts. Cox regression analysis showed that the seven RNA signatures could independently predict overall survival. Furthermore, age, recurrence, metastatic, and PS models were identified as independent prognostic factors via univariate and multivariate Cox analyses (P < 0.05) and included in the prognostic nomogram. The C-index values for the 3- and 5-year overall survival predictions of the nomogram were 0.809 and 0.740, respectively.

CONCLUSIONS

The current study provides the novel potential of seven RNA candidates as prognostic biomarkers. Nomograms were constructed to provide accurate and individualized survival prediction for recurrence in osteosarcoma patients.

Prediction of mortality at one year after surgery for pertrochanteric fracture in the elderly via a Bayesian belief network

BACKGROUND

Pertrochanteric fractures in the elderly are common and associated with considerable mortality and disability. However, the predictors of the fracture mortality have been somewhat controversial. The aim of this study was to use univariate, multivariate analyses and a Bayesian belief network (BBN) model, which are graphic and intuitive to the clinician, to understand of the prognosis of pertrochanteric fractures.

METHODS

Records of patients undergoing surgery at our hospital between January 2013 and June 2018 were retrospectively reviewed. Univariate and multivariate regression as well as a machine-learned BBN model were used to estimate mortality at one year after surgery for pertrochanteric fracture in the elderly.

RESULTS

Complete data were available for 448 surgically treated patients who were followed up for 12 months (age ≥60 years). Multivariate regression analysis revealed that hypertension, diabetes mellitus, chronic obstructive pulmonary disease, albumin, serum potassium, blood urea nitrogen and blood lactate were independent risk factors for death in surgical treatment patients (P < 0.05). First-degree predictors of mortality following surgery were established: the number of comorbid diseases, serum albumin, blood lactate and blood urea nitrogen. Following cross-validation, the area under the ROC curve was 0.85 (95% CI: 0.76-0.91) for the one-year probability of postoperative mortality.

CONCLUSION

We believe cohesive models such as the Bayesian belief network can be useful as clinical decision-support tools and provide clinicians with information to the treatment of old pertrochanteric fracture. This method warrants further development and must be externally validated in other patient populations.

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics

Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.

Downregulation of RPN2 induces apoptosis and inhibits migration and invasion in colon carcinoma

The morbidity of colorectal cancer (CRC) increases annualy, which accounts to higher mortality worldwide. Therefore, it is important to study the pathogenesis of colon cancer. Ribophorin II (RPN2), part of the N-oligosaccharyltransferase complex, is highly expressed in CRC. In the present study, we investigated whether RPN2 can regulate apoptosis, migration and invasion by RNA interference in CRC and sought to clarify the molecular mechanism involved. Based on previous research, an abnormal high expression of RPN2 was observed in CRC tissues and cell lines by real-time (RT)-PCR, immunohistochemistry (IHC) and western blot analysis. RPN2 knockdown via small RNA interference (siRNA) strategy attenuated the expression of RPN2 at the mRNA and protein levels in vivo, leading to decreased cell viability and increased cell apoptosis. In addition, RNAi-RPN2 effectively arrested the cell cycle at the G0/G1-phase in SW1116 and SW480 cells. Furthermore, the Transwell assay demonstrated that cell migration and invasion abilities were significantly inhibited after cell transfection with RPN2 interference plasmid. The apoptosis-related protein (caspase-3) expression was increased and the cell cycle-related protein (cyclin D1) expression was decreased in the siRNA-RPN2 group. RT-PCR and western blot analysis results indicated that migration- and invasion-related proteins including E-cadherin, matrix metalloproteinases (MMP)-2 and TIMP-2 were markedly regulated by RPN2 siRNA. Phosphorylation levels of signal transducer and activator of transcription (STAT)3 and Janus kinase (JAK)2 were inhibited by RPN2 siRNA. These findings indicated a novel pathway of tumor-promoting activity by RPN2 in CRC, with significant implications for unraveling the tumorigenesis of CRC.