Evidence map›Paper›PMID 36620168›Full record

ArticleQuantitative imaging in medicine and surgery2023

Grayscale ultrasound feature typing of metastatic ovarian tumors, particularly signet-ring cell carcinoma.

Junying Liu, Cai Chang, Haixian Zhang

Open access · diamondAbstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 40% of its field
1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 citations in OpenAlex.

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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.

Junying LiuDepartment of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.
Cai ChangDepartment of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.
Haixian ZhangDepartment of Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.
Shanghai Medical College of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To describe grayscale ultrasound (US) features of metastatic ovarian tumors (MOTs) based on origin of the primary tumor in a large sample size study. Methods: This retrospective cross-sectional single-center study included 112 patients with 190 histopathologically confirmed MOTs. Among the patients, 102 collectively had 144 masses, which were detected via US. The clinical data and static US images of MOTs were collected. Results: The MOTs were mostly bilateral (78.9%) but had a lower rate of bilaterality when detected by US (55.6%). Breast cancer metastasis had the highest nondetection rate (69.6%), because its focal metastasis could only be recognized using histology or immunohistochemistry. The stomach was the most common origin of metastasis (45.3% and 50.7% detected via pathology and US, respectively). The US images were classified into three subtypes: multilocular solid (Type A), purely solid (Type B), and solid with several round or oval cysts (Type C). The MOTs that originated from the colon mostly belonged to Type A (65.1%) and closely mimicked primary epithelial ovarian tumor morphologically. The MOTs that originated from the stomach predominantly belonged to Types B (31.5%) and C (57.5%). Signet-ring cell carcinoma (SRCC) corresponded to Types B and C regardless of origin. Conclusions: The developed novel typing method provides more vivid images for classifying MOTs compared with existing typing methods. Given that no specific sonographic parameters have been established to distinguish MOTs from primary invasive ovarian tumors, these images may be helpful in diagnosing these masses.

Indexed as

Metastatic ovarian tumor (MOT)signet-ring cell carcinoma (SRCC)subtypeultrasound

Identifiers

PMID36620168
PMCPMC9816721
OpenAlexW4310811569

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.