Status and prognostic nomogram of patients with Burkitt lymphoma

Abstract

The purpose of the present study was to evaluate the newest status of patients diagnosed Burkitt lymphoma (BL), an aggressive lymphoma subset with a high cure rate. Furthermore, the study aimed to create prognostic nomograms to consider various prognostic factors and estimate patient survival, paving the way for clinical decision-making. A total of 4,600 patients diagnosed with BL between 1983 and 2015 were investigated, via data collected from the SEER database. The overall status of the patients was analyzed through several aspects, including incidence and survival analysis of the previous three decades using the log-rank test and the Kaplan-Meier method. In order to construct and validate the nomograms, the patient diagnosed during 2005-2015 were randomly assigned to the training cohort and validation cohort. Univariate and multivariate analyses were applied to identify independent factors that were further included in the nomograms, predicting 3- and 5-year overall survival (OS) and cancer-specific survival (CSS). The data of the training cohort were used for internal validation and validation cohort used to external validation. C-index and calibration plots were used to validate the nomograms, comparing predicted values with actual outcomes. The incidence of BL was gradually increased from 1984 and reached its peak in 2009, at a rate of 0.491 per 100,000 [95% confidence interval (CI), 0.412-0.581]. From 2009, the incidence slowly declined year by year and dropped to 0.280 per 100,000 (95% CI, 0.224-0.346). The OS and CSS rates of patients diagnosed between 2005 and 2015 were increased, in contrast with those of patients diagnosed from 1983-1993 and 1994-2004. A total of five variables, including age, race, chemotherapy, primary site and stage, proved to be the prognostic factors of BL and were used to construct the nomograms predicting 3- and 5-year OS and CSS. The internal and external calibration plots for the probability of 3- and 5-year OS and CSS were consistent between nomogram prediction and observed outcomes. The slow decline in incidence and the significantly improved cure rate make BL a disease that is no longer an urgent problem. Effective nomograms were developed to predict the OS and CSS of patients with BL.

Keywords: Burkitt lymphoma; incidence; nomogram; prediction model; survival analysis.

Figure 1.

Optimal cutoff values of age identified by X-tile analysis of the patients with BL. (A) The optimal cutoff points calculated and selected by X-tile software evaluating the χ² log-rank value. The x-axis represents all potential cut-points from low to high (left to right) that define a low subset, whereas the y-axis represents cut-points from high to low (top to bottom), that define a high subset. The arrows represent the direction in which the low subset (x-axis) and the high subset (y-axis) increase in size. Red coloration of cut-points indicates an inverse correlation with survival, whereas green coloration represents direct associations. The optimal cut-point occurs at the brightest pixel (green or red), which was recognized by computer software. The optimal cutoff point occurs at the brightest pixel with red coloration, indicating an inverse correlation with survival, highlighted by the black circle. (B) Histogram based on the optimal cutoff point. (C) Kaplan-Meier analysis based on the optimal cutoff points. The optimal cutoff points for age were 20.0, 40.0 and 56.0 years based on the survival analysis (P<0.001). BL, Burkitt lymphoma.

Figure 2.

Estimated incidence of Burkitt lymphoma during 1983–2015. Incidence gradually increased from 1983 and reached its peak in 2009, with a rate of 0.491 per 100,000 (95% CI, 0.412–0.581). From 2009, the incidence slowly declined year by year and dropped to 0.280 per 100,000 (95% CI, 0.224–0.346). CI, confidence interval.

Figure 3.

Kaplan-Meier overall survival of the independent validation set according to three groups as determined by diagnostic year. The results showed that Burkitt lymphoma patients in early diagnostic time group have poorer overall survival than those in late diagnostic time group (log-rank P<0.0001).

Figure 4.

Trends in 5-year survival rates for patients with BL from the SEER database between 1983 and 2015. Data are shown with different groups classified by different diagnostic years (1983–1993, 1994–2004 and 2005–2015). (A) The OS rates for patients with BL. (B) The CSS rates for patients with BL. OS, overall survival; CSS, cancer-specific survival; BL, Burkitt lymphoma.

Figure 5.

Nomogram predicting the 3- and 5-year OS of patients with BL. PI, Pacific islander; AI, American Indian; AN, Alaska Native; 1, lymph nodes; 2, digestive system; 3, oral cavity and pharynx; 4, other; 5, bone marrow and nervous system; OS, overall survival.

Figure 6.

Nomogram predicting the 3- and 5-year CSS of patients with BL. PI, Pacific islander; AI, American Indian; CSS, cancer-specific survival; AN, Alaska native; 1, lymph nodes; 2, digestive system; 3, oral cavity and pharynx; 4, other; 5, bone marrow and nervous system.

Figure 7.

Internal calibration and external calibration of the nomograms predicting 3 and 5-year OS for patients with BL. (A) Internal calibration for 3-year OS. (B) Internal calibration for 5-year OS. (C) External calibration for 3-year OS. (D) External calibration for 5-year OS. The x-axis of each plot represents the nomogram prediction and the y-axis represents the observed outcomes. Each cohort equality divided into 5 and the distance between the points was calculated; the diagonal lines on the plots indicate the consistency between predictions and observed outcomes. OS, overall survival; CSS, cancer-specific survival.

Figure 8.

Internal calibration and external calibration of the nomograms predicting 3 and 5-year CSS for patients with BL patients. (A) Internal calibration for 3-year CSS. (B) the Internal calibration for 5-year CSS. (C) External calibration for 3-year CSS. (D) External calibration for 5-year CSS. The x-axis of each plot represents the nomogram prediction and the y-axis represents the observed outcomes. Each cohort equality divided into 5 and the distance between the points was calculated; the diagonal lines on the plots indicate the consistency between predictions and observed outcomes. OS, overall survival; CSS, cancer-specific survival.