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학술저널
저자정보
강소라 (서울아산병원) 조형우 (서울아산병원 종양내과) 김신 (서울아산병원) 이경민 (서울아산병원) 강은희 (서울아산병원) 박정선 (서울아산병원) 이윤세 (울산대학교) 박찬식 (울산대학교) 고현정 (울산대학교) 허주령 (울산대학교) 류진숙 (울산대학교) 이상욱 (서울아산병원) 김석진 (성균관대학교) 김원석 (성균관대학교) 윤상은 (삼성서울병원) 고영혜 (삼성서울병원) 서철원 (울산대학교)
저널정보
대한암학회 Cancer Research and Treatment Cancer Research and Treatment 제55권 제1호
발행연도
2023.1
수록면
314 - 324 (11page)
DOI
10.4143/crt.2022.015

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Purpose Prognostic Index for Natural Killer Lymphoma (PINK) is the most widely accepted prognostic model for patients withextranodal natural killer/T-cell lymphoma (ENKTL) treated with non-anthracycline–based therapy. We aimed to evaluate the prognostic implications of serum β-2 microglobulin (β2M) in the context of PINK and proposed a new prognostic model.Materials and Methods A total of 138 patients who were newly diagnosed with ENKTL and treated with non-anthracycline-based chemotherapy were identified. The cut-off value of high serum β2M was calculated by maximal-chi square methods (4.1 mg/L). A new prognostic model incorporating serum β2M into PINK was proposed and validated in an independent validation cohort (n=88).Results The patients’ median age was 53.5 years (range, 19 to 80 years). Patients with high serum β2M levels had significantly worse overall survival (OS) and progression-free survival (PFS). In multivariate analysis, high serum β2M was an independent adverse prognostic factor for OS. A new PINK-B (Prognostic Index for Natural Killer Lymphoma-serum β-2 microglobulin) model stratifiedpatients into three groups with distinct OS and PFS in the training cohort (3-year OS, 84.1% [95% confidence interval, 75.1 to 94.2], 46.8% [36.1 to 60.8] and 17.6% [6.3 to 49.2] for the low-, intermediate, and high-risk groups, respectively; 3-year PFS, 70.6% [59.4 to 83.8], 35.9% [25.9 to 49.8], and 7.35% [1.1 to 46.7] for the low-, intermediate-, and high-risk groups, respectively). The PINK-B model was further validated in an independent cohort.Conclusion Serum β2M is an independent prognostic factor for ENKTL patients. The new serum β2M-based prognostic model may be useful for identifying ultra-high-risk patients, and it can easily be adopted into daily clinical practice.

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