Evidence map›Paper›PMID 38409300›Full record

ArticleJapanese journal of radiology2024

Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study.

Jingfeng Cheng, Wenzhe Su, Yuzhe Wang, Yang Zhan, Yin Wang, Shuyu Yan, Yuan Yuan, Lingxin Chen, Zixun Wei, Shengjian Zhang and 2 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. 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.

Jingfeng Cheng *Department of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China.
Wenzhe Su *Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Yuzhe WangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Yang ZhanDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Yin WangDepartment of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China.
Shuyu YanFudan University, Shanghai, 200032, China.
Yuan YuanFudan University, Shanghai, 200032, China.
Lingxin ChenFudan University, Shanghai, 200032, China.
Zixun WeiFudan University, Shanghai, 200032, China.
Shengjian ZhangDepartment of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Xin GaoShanghai Universal Medical Imaging Diagnostic Center, Shanghai, 200233, China. george.ssmu@163.com.
Zuohua TangDepartment of Radiology, Eye & ENT Hospital of Fudan University, Shanghai Medical School, Fudan University, 83 Fenyang Road, Shanghai, 200031, China. tzh518sunny@163.com.

Funding

"Excellent doctor-Excellent Clinical Researcher" Project of Eye and ENT Hospital, Fudan University SYA202007"Keqing-Deji" science and technology innovation project of Fudan University SCH6222206A/022
6 · The paper itself

Abstract

purposeTo investigate the role of magnetic resonance imaging (MRI) based on radiomics using T2-weighted imaging fat suppression (T2WI-FS) and contrast enhanced T1-weighted imaging (CE-T1WI) sequences in differentiating T1-category nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoid hyperplasia (NPH). MATERIALS AND

methodsThis study enrolled 614 patients (training dataset: n = 390, internal validation dataset: n = 98, and external validation dataset: n = 126) of T1-category NPC and NPH. Three feature selection methods were used, including analysis of variance, recursive feature elimination, and relief. The logistic regression classifier was performed to construct the radiomics signatures of T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to differentiate T1-category NPC from NPH. The performance of the optimal radiomics signature (T2WI-FS + CE-T1WI) was compared with those of three radiologists in the internal and external validation datasets.

resultsTwelve, 15, and 15 radiomics features were selected from T2WI-FS, CE-T1WI, and T2WI-FS + CE-T1WI to develop the three radiomics signatures, respectively. The area under the curve (AUC) values for radiomics signatures of T2WI-FS + CE-T1WI and CE-T1WI were significantly higher than that of T2WI-FS (AUCs = 0.940, 0.935, and 0.905, respectively) for distinguishing T1-category NPC and NPH in the training dataset (Ps all < 0.05). In the internal and external validation datasets, the radiomics signatures based on T2WI-FS + CE-T1WI and CE-T1WI outperformed T2WI-FS with no significant difference (AUCs = 0.938, 0.925, and 0.874 for internal validation dataset and 0.932, 0.918, and 0.882 for external validation dataset; Ps > 0.05). The radiomics signature of T2WI-FS + CE-T1WI significantly performed better than three radiologists in the internal and external validation datasets.

conclusionThe MRI-based radiomics signature is meaningful in differentiating T1-category NPC from NPH and potentially helps clinicians select suitable therapy strategies.

Indexed as

HyperplasiaMagnetic Resonance ImagingNasopharyngeal CarcinomaNasopharyngeal NeoplasmsAdolescentAdultAgedContrast MediaDiagnosis, DifferentialFemaleHumansMaleMiddle AgedNasopharynxRadiomicsReproducibility of ResultsContrast MediaLymphoid hyperplasiaMagnetic resonance imagingNasopharyngeal carcinomaRadiomics

Identifiers

PMID38409300

What OpenQuestion holds

Textmetadata
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.