Evidence map›Paper›PMID 35116395›Full record

ArticleTranslational cancer research2021

New prognostic models for extranodal natural killer T-cell lymphoma, nasal-type using Cox regression and machine learning.

Jianbin Sun, Xiaoyan Ke, Mingzhi Zhang, Yuhui Wang, Fengyang An, Yulin Zhao, Li Zhu

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Article in Translational cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

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8citing papers in PubMed
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3 · Its place in the literature

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8 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jianbin SunDepartment of Otorhinolaryngology, Head and Neck Surgery, Peking University Third Hospital, Beijing, China.
Xiaoyan KeDepartment of Hematology, Peking University Third Hospital, Beijing, China.
Mingzhi ZhangDepartment of Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yuhui WangDepartment of Otorhinolaryngology, Head and Neck Surgery, Peking University Third Hospital, Beijing, China.
Fengyang AnDepartment of Otorhinolaryngology, Head and Neck Surgery, Peking University Third Hospital, Beijing, China.
Yulin ZhaoDepartment of Rhinology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Li ZhuDepartment of Otorhinolaryngology, Head and Neck Surgery, Peking University Third Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prognostic index of natural killer lymphoma (PINK) is recommended for use as a prognostic model for determining the best non-anthracycline-based treatment for extranodal natural killer T-cell lymphoma, nasal-type (ENKTL). However, this model does not provide an accurate individual risk estimation for patients; therefore, our retrospective study was conducted to determine this risk.

methodsClinical data from 250 patients with ENKTL treated with non-anthracycline-based regimens were analyzed. The statistically significant clinical characteristics were selected as the parameters for our models. The patient data from 250 patients were randomly divided into 5 groups for 5-fold cross validation before final models were established on all of the patients' data. A statistical model nomogram based on a Cox proportional hazards model, and a machine learning model based on the lightGBM algorithm, were constructed. Concordance index (C-index) and calibration curve, areas under the curve (AUC) values, and binary error were used to evaluate two models.

resultsFive variables [age, the Chinese Southwest Oncology Group and Asia Lymphoma Study Group ENKTL (CA) staging system, Eastern Cooperative Oncology Group (ECOG) score, B symptoms, and lactate dehydrogenase (LDH)] were significant and were selected as parameters for creating the statistical model nomogram, while lesion sites (anatomical regions, lymph nodes and primary lesion site) took place of CA staging system in machine learning model. During cross validation, the mean C-indices of training cohort and validation cohort for statistical model nomogram and PINK were 0.851±0.008, 0.843±0.029, 0.758±0.019 and 0.757±0.080, respectively, while the mean 3-year AUC for machine learning model were 0.920±0.010 and 0.865±0.035, respectively. The calibration curves and binary errors showed a good correlation between the predicted result and the reality.

conclusionsThese two models could provide ENKTL patients with an accurate individual risk estimation in the era of non-anthracycline-based treatment.

Indexed as

CA staging systemExtranodal natural killer T-cell lymphoma, nasal-type (ENKTL)machine learningnomogramnon-anthracycline-based treatment

Identifiers

PMID35116395
PMCPMC8798130

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