Construction and validation of a prognostic model for NK/T-cell lymphoma based on random survival forest algorithm

To investigate the prognostic factors affecting survival in patients with natural killer T-cell lymphoma (NKTL), and then develop a prognostic model for predicting their overall survival (OS) based on random survival forest (RSF) algorithm.

Demographic and clinical pathological data of NKTL patients were collected from the SEER database during 2000 and 2020. The patients were divided into a training cohort (n=471) and a validation cohort (n=203) in a 7:3 ratio. Cox regression analysis was performed to identify prognostic factors affecting OS, and a nomogram model was constructed based on the obtained factors. Meanwhile, RSF algorithm was used to determine prognostic factors affecting OS to build the RSF model. The models were evaluated using receiver operating characteristic (ROC) curve, calibration curve, decision curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI), and the predictive performances of the 2 models were compared. Risk scores for each patient were calculated using the 2 models. Then the patients were divided into high- and low-risk groups based on the median risk score, and survival curve was plotted for comparison.

Ann Arbor stage, age, radiotherapy, combined treatment, and type of disease were identified as significant prognostic variables associated with OS. In the validation cohort, the area under the ROC curve (AUC) for the nomogram model at 1, 3, and 5 years was 0.745, 0.771, and 0.748, respectively, while the AUC for the RSF model was 0.764, 0.792, and 0.761 at the same time points. ROC curve analysis indicated that both models demonstrated good accuracy and discrimination in predicting OS. Calibration curve analysis showed a strong consistency between the predicted and actual OS for both models. Both models effectively stratified the patients into poor and favorable prognosis groups, with the OS of patients in the poor prognosis group being significantly shorter than that of the favorable prognosis group (P<0.0001). Decision curve analysis revealed that the net benefit of the RSF model was superior to that of the nomogram model. Compared to the nomogram model, the NRI for the RSF model was 0.184 (95%CI: 0.098~0.267, P<0.01), and the IDI was 0.300 (95%CI: 0.241~0.359, P<0.01). Overall, the RSF model demonstrated superior predictive capability than the nomogram model.

Ann Arbor stage, age, radiotherapy, combined treatment, and type of disease are prognostic factors affecting the prognosis of NKTL patients. Our RSF model demonstrates strong predictive capability for the prognosis of NKTL patients and can effectively assess patient outcomes.