Evidence map›Paper›PMID 31880021›Full record

ArticleJournal of clinical laboratory analysis2020

Evaluation of clinical application of multi-slice computerized tomography in primary retroperitoneal tumors.

Yuanchun Feng, Wei Zhang, Chenghua Luo

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Journal of clinical laboratory analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact, top 71% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  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

3 authors at 1 institution in 1 country.

Yuanchun FengRadiology Department, Peking University International Hospital, Beijing, China.ORCID https://orcid.org/0000-0003-0274-5906
Wei ZhangRadiology Department, Peking University International Hospital, Beijing, China.
Chenghua LuoRetroperitoneal Tumor Center, Peking University International Hospital, Beijing, China.
Peking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo accurately identify primary retroperitoneal tumors by multi-slice spiral CT (MSCT) for better treatment. MATERIALS AND

methodsThe common types of 380 cases of primary retroperitoneal tumor, lesion sites, and MSCT features were compared with pathological results. Fisher's or chi-square test approaches have been applied in this study.

resultsMulti-slice computerized tomography multi-directional reconstruction has a high accuracy for primary retroperitoneal tumors (95.7%). Seventy-three liposarcoma cases were located in peri-renal space, accounting for 76.8% (13/95) of the tumors in this region. Meanwhile, 65 cases of neurogenic tumors were located in the paravertebral column, accounting for 90.3% (65/72) of the tumors in this region. MSCT examination could effectively distinguish benign from malignant of primary retroperitoneal tumor (sensitivity = 87.2%, specificity = 82.7%, accuracy rate = 84.5%). Malignant tumors showed more irregularity shape than benign tumor (χ

conclusionThe accuracy of MSCT scan for retroperitoneal tumors is high. Meanwhile, the coincidence rate of qualitative diagnosis before operation and/or before biopsy is also high.

Indexed as

AdolescentAdultAgedAged, 80 and overChildFemaleHumansLipomaLiposarcomaMaleMiddle AgedRetroperitoneal NeoplasmsTeratomaTomography, Spiral ComputedYoung Adultbenign and malignantliposarcomamulti-slice computerized tomographyneurogenic tumorprimary retroperitoneal tumor

Identifiers

PMID31880021
PMCPMC7246388
OpenAlexW2998706827

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.