Evidence map›Paper›PMID 41774183›Full record

ArticleAnnals of hematology2026

An inflammation-integrated prognostic nomogram for advanced-stage extranodal NK/T-cell lymphoma: a multicenter retrospective study.

Yongle Xia, Yinjie Zhang, Renqin Li, Fenglan Zhou, Sheng Ye, Hongyu Zhang, HongQiang Guo, Xinggui Chen, Chaoyong Liang, Xingxiang Pu and 9 more

Abstract readMulticenter Study
In one paragraph

Article in Annals of hematology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Yongle Xia *Department of Medical Oncology, Sichuan Clinical ResearchCenter for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yinjie Zhang *Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, 610042, China.
Renqin Li *Department of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, 610042, China.
Fenglan ZhouDepartment of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, 610042, China.
Sheng YeDepartment of Medical Oncology, The First Affiliated Hospital of Sun Yat- Sen University, Guangzhou, China.
Hongyu ZhangDepartment of Medical Oncology, The Fifth Affiliated Hospital of Sun Yat- Sen University, Zhuhai, China.
HongQiang GuoDepartment of Medical Oncology, Henan Cancer Hospital, Zhengzhou, China.
Xinggui ChenDepartment of Medical Oncology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Chaoyong LiangDepartment of Medical Oncology, Guangxi Cancer Hospital, Nanning, China.
Xingxiang PuDepartment of Medical Oncology, Hunan Cancer Hospital, Changsha, China.
Yabing CaoDepartment of Medical Oncology, Kiang Wu Hospital, Macau, China.
Quanguang RenDepartment of Medical Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaoqian LiDepartment of Medical Oncology, Shandong Cancer Hospital, Jinan, China.
Linzhu ZhaiDepartment of Medical Oncology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
He HuangDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
Zongyao HuangDepartment of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Yang LiuDepartment of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
Huangming HongDepartment of Medical Oncology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, 610042, China. honghuangming@scszlyy.org.cn.
Xiaojie FangDepartment of Medical Oncology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China. fangxj@sysucc.org.cn.

Funding

he Fellowship of China Postdoctoral Science Foundation 2024M760362The National Natural Science Foundation of China 82270198, 82003196The National Natural Science Foundation of China 82300216the Sichuan Medical Association 2024HR01the Sichuan Provincial Foundation of Science and Technology 2025ZNSFSC0699
6 · The paper itself

Abstract

Extranodal natural killer/T-cell lymphoma (ENKTL) is a rare and aggressive malignancy with poor outcomes in advanced stages. Current prognostic indices fail to fully account for the heterogeneity of stage Ⅲ–Ⅳ ENKTL. We aimed to develop a nomogram integrating inflammatory and clinical parameters to improve prognostic prediction. We retrospectively analyzed 291 patients with stage III/IV ENKTL across eight hospitals for model development and 145 patients for validation. Optimal cut-off values for absolute lymphocyte count (ALC), absolute monocyte count (AMC), and lymphocyte-to-monocyte ratio (LMR) were determined. Cox regression yielded independent prognostic indicators, which were incorporated into a nomogram. The model was validated with the concordance index (C-index), receiver operating characteristic curves, area under the curve (AUC) and calibration analysis. Five factors for overall survival (OS) were identified: age > 60 years (HR: 1.749), AMC > 0.4 × 10⁹/L (HR: 1.423), LMR ≤ 1.47 (HR: 1.460), visceral organ involvement (HR: 1.979), and bone marrow involvement (HR: 1.559). These were integrated into a nomogram with a C-index of 0.644 (95% CI: 0.601–0.687). The nomogram showed better discriminatory ability compared to both the prognostic index of natural killer lymphoma (PINK) (AUC: 0.721 vs. 0.595, P = 0.0085) and nomogramrevised risk index (NRI) (AUC: 0.721 vs. 0.585, P = 0.0034). Patients were stratified into high- and low-risk groups based on median nomogram score, with the low-risk group showing significantly better OS ( High-risk vs. low-risk group, P < 0.0001) and progression-free survival (PFS) (High-risk vs. low-risk group, P < 0.0001). In the validation cohort, the C-index was 0.634 and the 3-year AUC for OS was 0.705. Both OS and PFS were significantly inferior in the high-risk group (OS: P = 0.0015; PFS: P = 0.02). We developed an inflammation-based prognostic nomogram for advanced-stage ENKTL with moderate predictive accuracy potentially aiding clinicians in individualized risk stratification. Prospective multicenter studies are warranted to further optimize and validate its clinical applicability.

Indexed as

InflammationLymphoma, Extranodal NK-T-CellNomogramsAdultAgedAged, 80 and overFemaleHumansLymphocyte CountMaleMiddle AgedMonocytesNeoplasm StagingPrognosisRetrospective StudiesYoung AdultAdvanced-stageExtranodal natural killer/T-cell lymphomaNomogramPrognostic model

Identifiers

PMID41774183
PMCPMC12956960

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.