ArticleChinese journal of cancer research = Chung-kuo yen cheng yen chiu2018
Feasibility of differentiating T3 from T4a gastric cancer in different Lauren classification by determining serosa invasion: Diagnostic performance of high enhanced serosa sign.
Article in Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Multiscale Spatial Fusion Feature-Driven Characterization of Gastric Cancer Invasive Margins: A Multicenter Cohort Study for Preoperative Accurate Differentiation Between T4a and T4b Subtypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Usefulness of histologic differences and perivascular infiltration for preoperative T staging of advanced gastric cancer using computed tomography.Japanese journal of radiology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo study the value of high enhanced serosa sign on contrast-enhanced computed tomography (CT) in differentiating T3 from T4a gastric cancer in different Lauren classification.
methodsThis study included 276 consecutive patients with surgically confirmed pT3 or pT4a gastric cancers. The pre-operative CT images were reviewed by two radiologists blinded. The demonstration of the high enhanced serosa on CT between T3 and T4a was compared with chi-square test. The diagnostic performance of this sign on CT in the differentiation of T4a from T3 in different Lauren classification was calculated.
resultsThe accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the judgement of serosa invasion using the high enhanced serosa sign on CT was 74.6%, 63.7%, 83.6%, 76.0% and 73.8% by one radiologist and 76.4%, 66.1%, 84.9%, 78.1% and 75.4% by the other radiologist. Compared to the intestinal-type, the sensitivity of the judgement of serosa invasion using the high enhanced serosa sign on CT in diffuse-type was significant higher (80% in both readers), while the specificity trended to be lower (65.9% and 80.5%, respectively). There is no significant difference in the accuracy of diagnosis between intestinal-type and diffuse-type of gastric cancers (the P-values of two radiologists were 0.968, 0.591, respectively). The combination of the high enhanced serosa sign with conventional CT signs is significant different in diagnosis of T3 and T4a (P<0.001). The diagnostic accuracy was increased in both radiologists after the combination. The two readers achieved substantial agreement, with Kappa coefficient of 0.63, P<0.001.
conclusionsThe high enhanced serosa sign on CT is associated with serosa involvement. The sensitivity of the judgement of serosa invasion using this sign on CT in diffuse-type was significant higher than that in intestinal-type.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.