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

Abstract

Bone sarcomas are difficult to treat requiring multidisciplinary input to strategize management. An evidence-based survival prediction can be a powerful adjunctive to clinicians in this scenario. We believe the short-term predictions can be used to evaluate services, with 1-year mortality already being a quality indicator. Mortality predictors also can be incorporated in clinical trials, for example, to identify patients who are least likely to experience the side effects of experimental toxic chemotherapeutic agents.

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

“…Unhealthy lifestyle, anti-oncogene inactivation and oncogene mutations can cause cells to grow uncontrollably, and further lead preexisting diseases such as ulcerative colitis and colonic adenoma to develop into malignant tumor ( 3 – 6 ). Research has demonstrated that most patients die from tumor metastasis and recurrence ( 7 ). The essential characteristics of malignant tumors are excessive proliferation, differentiation failure and apoptosis disorder ( 8 ).…”

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

“…Although I agree with Nandra and colleagues [3] that an individual clinician may be inaccurate, I'm not sure I would defer prognosis to an algorithm. Statistical expertise, no matter how sophisticated, cannot and should not substitute for clinical knowledge.…”

“…Forsberg and colleagues [1,2] suggest that the Bayesian belief network's representation of conditional probabilities is more robust to missing data than alternatives such as artificial neural networks or logistic regression. Although the math can be complicated, the resulting probabilities make sense, as can be seen in Table 4 of the current study [3].…”

“…This pre-test probability might be the overall prevalence of infection or it might be based on the initial assessment of the patient. After diagnostic tests (such as white blood cell counts or bone scans) are performed, a ''post-test probability'' can calculated based on the pre-test probability, the test results, and the known The Network element is shown in Figure 1 of the current study [3], where each block may be directly or indirectly associated with the outcome of 1-year survival. In a regression model, each of the blocks would directly affect 1-year survival, and the resulting diagram would have predictors distributed like spokes around a hub.…”

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

“…As a complicated disease, osteosarcoma results from interactions between genetic and other factors. Numerous studies have demonstrated that a variety of factors may contribute to the development of osteosarcoma, including age, gender, ethnicity, or physical, chemical, and biological agents [8] , [9] , [10] . In addition, some studies showed that genetic factors may play a more important role in the pathogenesis of osteosarcoma [11] , [12] , [13] .…”