Modification of Ki67 on Prognostic Model of Extranodal NK/T Cell Lymphoma

Abstract

Abstract Background: To explore the value of Ki67 in the prognostic evaluation of extranodal NK / T cell lymphoma and the modification effect of NK cell lymphoma prognostic risk index (PINK) and Nomogram modified risk index (NRI). Methods: A retrospective analysis of the clinical data of 106 patients with extranodal nasal NK/T cell lymphoma diagnosed in Yantai Yuhuangding Hospital and the affiliated Hospital of Nantong University from 2008 to 2020. Ki67 immunohistochemical staining and Quantitative Dot Blot (QDB) which is an absolute protein quantitative detection were performed on pathological paraffin specimens, and the R4.1.0 and SPSS were used to analyze the data and determine the threshold. Results: A total of 106 patients,75/106 (70.7%) patients were male and 31/106(29.3%) were female. The onset age of patients ranged from 14 to 86 years old, and the median onset age was 60 years old. As of September 10, 2020, 105 patients were followed up, 54/105(51.4%) patients were died, 51/105 (48.6%) patients were survived, and the median survival time was 330 days. Immunohistochemical results showed that patients with Ki67 proliferation index > 60% or MIB1 (Ki67) > 11.9 nmol/g had worse prognosis. The data were modified by Ki67 (IHC) and Ki67 (QDB), and the survival analysis of PINK and NRI models showed that there were statistically significant differences among the risk groups (P < 0.001). Compared with Ki67 (IHC), Ki67 (QDB) modified PINK and NRI models could significantly improve their predictive ability (AUCPINK, 0.67 vs 0.74; C-indexPINK, 0.68 vs 0.75; AUCNRI, 0.74 vs 0.80; C-indexNRI, 0.69 vs 0.71). Conclusions: The results of Ki67 by QDB could significantly enhance the predictive ability of PINK and NRI models. PINK model modified by Ki67 QDB can more accurately describe the prognosis of ENKTCL patients. The absolute quantitative detection technology of QDB protein is expected to be applied to clinical laboratory detection to achieve individualized and accurate diagnosis and treatment of tumors.