Evidence map›Paper›PMID 35668195›Full record

ArticleAbdominal radiology (New York)2022

Primary malignant melanoma of the esophagus: differentiation from esophageal squamous cell carcinoma and leiomyoma using dynamic contrast-enhanced CT findings.

Yan-Jie Shi, Xin Yang, Shuo Yan, Xiao-Ting Li, Yi-Yuan Wei, Xiao-Yan Zhang, Ying-Shi Sun

Open access · hybridAbstract read
In one paragraph

Article in Abdominal radiology (New York), 2022. 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
0.8field-weighted citation impact, top 29% of its field
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, 5 citations in OpenAlex.

  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

7 authors at 1 institution in 2 countries.

Yan-Jie Shi *Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Xin Yang *Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital & Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Shuo Yan *Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Xiao-Ting LiKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Yi-Yuan WeiKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Xiao-Yan ZhangKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
Ying-Shi SunKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China. sys27@163.com.ORCID 0000-0001-9424-1910
Peking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to summarize the computed tomography (CT) findings of PMME and differentiate it from esophageal SCC and leiomyoma using CT analysis.

methodsThis was a retrospective study including 23 patients with PMME, 69 patients with SCC, and 21 patients with leiomyoma in our hospital. Qualitative CT morphological characteristics of each lesion included the location, tumor range, ulcer, enhanced pattern, and so on. For quantitative CT analysis, thickness, length and area of tumor, size of largest lymph node, number of metastatic lymph node, and CT value of tumor in plain, arterial, and delayed phases were measured. The associated factors for differentiating PMME from SCC and leiomyoma were examined with univariate and multivariate analysis. Receive operating characteristic curve (ROC) was used to determine the performance of CT models in discriminating PMME from SCC and leiomyoma.

resultsThe thickness, mean CT value in arterial phase, and range of tumor were the independent factors for diagnosing PMME from SCC. These parameters were used to establish a diagnostic CT model with area under the ROC (AUC) of 0.969, and accuracy of 90.2%. In pathology, interstitial vessels in PMME were more abundant than that of SCC, and the stromal fibrosis was more obvious in SCC. PMME commonly exhibited intraluminal expansively growth pattern and SCC often showed infiltrative pattern. The postcontrast attenuation difference in maximum CT attenuation value between plain and arterial phases was the independent factor for diagnosing PMME from leiomyoma. This parameter was applied to differentiate PMME from leiomyoma with AUC of 0.929 and accuracy of 86.4%.

conclusionThe qualitative and quantitative CT analysis had excellent performance for differentiating PMME from SCC and esophageal leiomyoma.

Indexed as

Esophageal NeoplasmsEsophageal Squamous Cell CarcinomaLeiomyomaMelanomaCutaneous Malignant MelanomaHumansRetrospective StudiesSkin NeoplasmsTomography, X-Ray ComputedEsophageal cancerEsophagusLeiomyomaMelanomaX-ray computed tomography

Identifiers

PMID35668195
PMCPMC9300547
OpenAlexW4281671227

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.