Validation of nomogram-revised risk index and comparison with other models for extranodal nasal-type NK/T-cell lymphoma in the modern chemotherapy era: indication for prognostication and clinical decision-making

Abstract

Derived from our original nomogram study by using the risk variables from multivariable analyses in the derivation cohort of 1383 patients with extranodal NK/T-cell lymphoma, nasal-type (ENKTCL) who were mostly treated with anthracycline-based chemotherapy, we propose an easily used nomogram-revised risk index (NRI), validated it and compared with Ann Arbor staging, the International Prognostic Index (IPI), Korean Prognostic Index (KPI), and prognostic index of natural killer lymphoma (PINK) for overall survival (OS) prediction by examining calibration, discrimination, and decision curve analysis in a validation cohort of 1582 patients primarily treated with non-anthracycline-based chemotherapy. The calibration of the NRI showed satisfactory for predicting 3- and 5-year OS in the validation cohort. The Harrell's C-index and integrated Brier score (IBS) of the NRI for OS prediction demonstrated a better performance than that of the Ann Arbor staging system, IPI, KPI, and PINK. Decision curve analysis of the NRI also showed a superior outcome. The NRI is a promising tool for stratifying patients with ENKTCL into risk groups for designing clinical trials and for selecting appropriate individualized treatment.

Fig. 1. Survival curves.

Overall survival (OS) and progression-free survival (PFS) in the whole validation cohort (a). OS stratified by the Ann Arbor staging system in the validation cohort (b). OS stratified by the nomogram-revised risk index (NRI) for all patients (c) and early-stage patients (d) in the derivation cohort, and for all patients (e) and early-stage patients (f) in the validation cohort.

Fig. 2. Overall survival (OS) stratified by prognostic models in the validation cohort.

a International prognostic index (IPI), b Korean Prognostic Index (KPI), and c prognostic index of natural killer lymphoma (PINK) for all patients. d IPI, e KPI, and f PINK for early-stage patients.

Fig. 3. Validation and comparison of the nomogram-revised risk index (NRI).

The NRI was validated and compared with the international prognostic index (IPI), Ann Arbor staging, Korean Prognostic Index (KPI), and prognostic index of natural killer lymphoma (PINK) models by the area under the curve (AUC) in the validation cohort. The AUC for predicting 5-year overall survival (OS) for all-stage (a) and early-stage (b) patients. The time-dependent AUC between 6 and 84 months for all-stage (c) and early-stage (d) patients.

Fig. 4. Time-dependent decision curve analysis.

Time-dependent decision curve analysis of risk models predicting the 3-year mortality by any cause for all-stage (a) and early-stage (b) patients in the validation cohort. The threshold probability represented the 3-year risk of mortality by any cause based on each prognostic model for recommending clinical intervention. The threshold defines the weight w for false-positive (FP, treat while patient survived) vs. true-positive (TP, treat a patient who died) classifications. The net benefit (NB) balanced the risk of real 3-year mortality with the potential harms of unnecessary intervention (including decision of treatment, work-up, or follow-up) for false prediction and was calculated as the true-positive rate minus the weighted false-positive rate. The clinical usefulness of a prediction model can be summarized as: NB = (TP − w FP)/N, where N is the total number of patients. Solid black line: Assume no patients need receive clinical intervention (no patients died), net benefit is zero (no true-positive and no false-positive classification). Gray line: Assume all patients need receive clinical intervention (all died). Dotted color lines: Patients received clinical intervention if predictions exceeded a threshold, with 3-year mortality risk predictions based on different prognostic models. In general, the prognostic model with the highest net benefit at any threshold is deemed to have the highest clinical application value.