Prognostic Model for DLBCL of the Small Intestines and Colon

By Patrick Daly - Last Updated: February 9, 2022

According to an article in Translational Cancer Research, data on the clinical outcomes of patients with primary diffuse large B-cell lymphoma of the small intestine and colon (PIC-DLBCL) is lacing despite significant updates to treatment methods in past years. In a study, lead author Yang Wang and colleagues retrospectively analyzed the survival of patients with PIC-DLBCL and constructed a visual prognostic model that appeared to “estimate individual prognosis simply and correctly, including marital status and medical insurance for the first time.”

The researchers retrospectively enrolled a total of 1,613 patients from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided into a training and validation cohort. Cox regression analysis, Log-rank test, and the Kaplan-Meier method were used to identify the independent prognostic factors in the training cohort, which were then used to construct a visual nomogram and graphical web page prognostic model. C-index and calibration plots were used to verify the predictive strength of the model. The team used decision curve analysis (CDA) and receiver operative characteristic (ROC) curve to compare their model with the International Prognostic Index (IPI) scoring model, which is widely used to estimate patient prognosis.

Among all of the included cases, the five-year overall survival rate was 64.5%. The factors associated with patient prognosis and used to construct the model included: age at diagnosis (hazard ratio [HR] = 2.58; 95% confidence interval [CI], 2.29–2.91), Ann Arbor stage (HR = 1.34; 95% CI, 1.24–1.44), marital status of divorced or separated (HR = 1.21; 95% CI, 1.06–1.38), no insurance (HR = 1.32; 95% CI, 1.19–1.45) and involvement of the colon (HR = 1.23; 95% CI, 1.08–1.40). According to the report, both DCA and ROC curve demonstrated that the model had “better authentication capability than the International Prognostic Index scoring model (AUC 0.820 vs. 0.714).”

Overall, the calibration plots verified that the model was accurately predicting patient prognosis, and the authors advanced their visual model as feasible and effective. According to Wang, the accounting of patients’ clinical and social characteristics performed in the study could provide “a new way to explore the improving prognosis of PIC-DLBCL.”

Post Tags:DLBCL
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