Evidence map›Paper›PMID 31009392›Full record

ArticleChinese medical journal2019

Prognostic significance of locally invaded sites and tissue types in patients with nasal extranodal natural-killer/T-cell lymphoma: a single-center retrospective analysis.

Ge-Hong Dong, Yong Li, Ji-Yong Dong, Xue Li, Hong-Fei Wan, Lei Yang, Jing-Wen Wang, Li-Ping Gong, Yi-Hua Zhao, Hong Zhang and 2 more

Abstract read
In one paragraph

Article in Chinese medical journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Ge-Hong DongDepartment of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
Yong LiDepartment of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Ji-Yong DongDepartment of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Xue LiDepartment of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
Hong-Fei WanDepartment of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Lei YangDepartment of Hematology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Jing-Wen WangDepartment of Hematology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Li-Ping GongDepartment of Pathology, School of Basic Medical of Capital Medical University, Beijing 100069, China.
Yi-Hua ZhaoDepartment of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Hong ZhangDepartment of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Zi-Fen GaoDepartment of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
Hong-Gang LiuDepartment of Pathology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExtranodal natural killer/T-cell lymphoma (ENKTL), nasal type, is an aggressive entity within the World Health Organization classification of lymphoid tumors. The International Prognostic Index is reported to be prognostically meaningful for ENKTL, but lacks discriminatory power for stage I/II ENKTL with extensive local invasion. This study aimed to evaluate the prognostic effects of local invasion by site and tissue type in patients with ENKTL.

methodsWe retrospectively analyzed data of 86 patients who were diagnosed with ENKTL by the Department of Pathology of Beijing Tongren Hospital from June 2002 to April 2016, and ascertained tumor infiltration of adjacent structures (AS), bone, and soft tissue for each patient, using physical findings and imaging scans. We used univariate and multivariate analysis to assess the association of each involved tissue or site with patients' overall survival (OS).

resultsOf the 86 patients, 71 (82.6%) experienced invasion of AS, 22 (25.6%) of soft tissue, and 26 (30.2%) had bone involvement. Overall, patients with AS involvement did not show significantly shorter survival than those without AS involvement (Log rank χ = 1.177, P = 0.278); however, patients who had involved eyeballs or brains showed significantly lower 2-year OS rates than those without eyeball involvement (Log rank χ = 4.105, P = 0.043) or brain involvement (Log rank χ = 7.126, P = 0.008). Patients with involved local soft tissue or bones, respectively, showed lower 2-year OS rates than those without involved local soft tissue (Log rank χ = 10.390, P = 0.001) or bones (Log rank χ = 8.993, P = 0.003). Multivariate analysis showed that involvement of the cheek or facial muscles (hazard ratio, HR = 5.471, 95% confidence interval [CI]: 1.466-20.416, P = 0.011) and the maxilla bone (HR = 6.120, 95% CI: 1.517-24.694, P = 0.011) were significantly independent predictors of lower 2-year OS rates.

conclusionsImaging can accurately detect ENKTL invasion of AS, soft tissue, and bone. Involvement of local soft tissue or bone was significantly associated with lower 2-year OS rates. Involvements of the cheek or facial muscle, as well as maxilla bone, are independent predictors of lower 2-year OS rates in ENKTL patients.

Indexed as

AdolescentAdultAgedBrainFacial MusclesFemaleHumansKaplan-Meier EstimateKiller Cells, NaturalLymphoma, Extranodal NK-T-CellMaleMiddle AgedMultivariate AnalysisPositron Emission Tomography Computed TomographyPrognosisRetrospective Studies

Identifiers

PMID31009392
PMCPMC6629357

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.