Evidence map›Paper›PMID 32162020›Full record

ArticleAbdominal radiology (New York)2020

Differentiation of borderline tumors from type I ovarian epithelial cancers on CT and MR imaging.

Sihua Yang, Huan Tang, Fuxia Xiao, Jingqi Zhu, Ting Hua, Guangyu Tang

Abstract read
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In one paragraph

Article in Abdominal radiology (New York), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

10 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. What is your diagnosis?Journal of the Turkish German Gynecological Association · 2023
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Giant Cystic Mucinous Borderline Tumour of the Ovary in a Young Female: A Rare Case Report.Nigerian medical journal : journal of the Nigeria Medical Association
    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

6 authors at 3 institutions in 1 country.

Sihua Yang *Department of Radiology, Shanghai Tenth People's Hospital of Tongji University, School of Medicine, Shanghai, 200072, China.
Huan Tang *Department of Radiology, Huadong Hospital of Fudan University, Shanghai, 200040, China.
Fuxia XiaoDepartment of Radiology, Shanghai Tenth People's Hospital of Tongji University, School of Medicine, Shanghai, 200072, China.
Jingqi ZhuDepartment of Radiology, Shanghai Tenth People's Hospital of Tongji University, School of Medicine, Shanghai, 200072, China.
Ting HuaDepartment of Radiology, Shanghai Tenth People's Hospital of Tongji University, School of Medicine, Shanghai, 200072, China.
Guangyu TangDepartment of Radiology, Shanghai Tenth People's Hospital of Tongji University, School of Medicine, Shanghai, 200072, China. tgy17@tongji.edu.cn.
Shanghai Tenth People's Hospital · CNTongji University · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo investigate the value of CT and MR imaging features in differentiating borderline ovarian tumor (BOT) from type I ovarian epithelial cancer (OEC), which could be significant for suitable clinical treatment and assessment of the prognosis of the patient.

methodsThirty-three patients with BOTs and 35 patients with type I OECs proven by pathology were retrospectively evaluated. The clinico-pathological information (age, premenopausal status, CA-125, and Ki-67) and imaging characteristics were compared between two groups of ovarian tumors. The diagnostic performance of the imaging features was evaluated using receiver operating characteristic analysis. The best predictor variables for type I EOCs were recognized via multivariate analyses.

resultsBOTs are more likely to involve younger patients and frequently show lower CA-125 values and lower proliferation indices (Ki-67 < 15%) than type I OECs. Compared with type I OECs, BOTs were more often purely cystic (15/33, 45.45% vs. 1/35, 2.86%; p < 0.001) and displayed less frequent mural nodules (16/33, 48.48% vs. 28/35, 80.00%; p = 0.007), less frequently unclear margin (3/33, 9.09% vs. 11/35, 31.43%; p = 0.023), smaller solid portion (0.56 ± 2.66 vs. 4.51 ± 3.88; p < 0.001), and thinner walls (0.3 ± 0.17 vs. 0.55 ± 0.24; p < 0.001). The maximum wall thickness presented the largest area under the curve (AUC, 0.848). Multivariate analysis revealed that the solid portion size (OR 10.822, p = 0.002) and maximum wall thickness (OR 9.130, p = 0.001) were independent indicators for the differential diagnosis between the two groups of lesions.

conclusionThe solid portion size and maximum wall thickness significantly influenced the classification of the two groups of ovarian tumors.

Indexed as

Magnetic Resonance ImagingOvarian NeoplasmsCarcinoma, Ovarian EpithelialFemaleHumansRetrospective StudiesTomography, X-Ray ComputedMagnetic resonance imagingMultivariate analysesOvarian epithelial cancerROC analysis

Identifiers

PMID32162020
OpenAlexW3012396909

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Registered trials

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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.