AUTHOR=Xiang Jun-Yi , Huang Xiao-Shan , Feng Na , Zheng Xiao-Zhong , Rao Qin-Pan , Xue Li-Ming , Ma Lin-Ying , Chen Ying , Xu Jian-Xia 

TITLE=A diagnostic scoring model of ENKTCL in the nose-Waldeyer’s ring based on logistic regression: Differential diagnosis from DLBCL

JOURNAL=Frontiers in Oncology

VOLUME=Volume 13 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1065440

DOI=10.3389/fonc.2023.1065440

ISSN=2234-943X

ABSTRACT=Objective: To establish a logistic regression model based on CT and MRI imaging features and Epstein-Barr virus nucleic acid to develop a diagnostic score model to differentiate extranodal NK/T nasal type(ENKTCL) from diffuse large B cell lymphoma(DLBCL). Methods: This retrospective study included 36 cases of ENKTCL and 53 cases of DLBCL confirmed by pathology in our hospital from January 2013 to May 2021, who underwent CT/MR enhanced examination and Epstein-Barr virus nucleic acid test within 2 weeks before surgery. Clinical features, imaging features, and Epstein-Barr virus nucleic acid results were analyzed. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors of ENKTCL and establish a predictive model. Independent predictors were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the predictive model and score model. Results: There were statistically significant differences in gender, clinical symptoms, Epstein-Barr virus nucleic acid, site of disease, edge of the lesion, involvement of sinus tract complex, bone destruction, tonsillar enlargement, cervical lymph node enlargement, CT plain scan, CT enhancement, T1WI signal, T2WI signal and gyrus changes between the two groups of patients with different pathological types (P < 0.05). The independent predictors for ENKTCL diagnosis in multivariate logistic regression analysis, including the site of disease (nose), edge of lesion (fuzzy), T2WI (high signal), gyrus changes, Epstein-Barr virus nucleic acid (positive), and the weighted score of regression coefficient was 2, 3, 4, 3, 4. The AUC of the prediction model and the score model were 0.949 and 0.948. Conclusion: The diagnostic score model of ENKTCL is based on the Logistic regression model which is combined with imaging features and Epstein-Barr virus nucleic acid. The scoring system was convenient, practical, and non-invasive and could significantly improve the diagnostic accuracy of ENKTCL and the differential diagnosis of ENKTCL from DLBCL.