ArticleCureus2024
Evaluation of Uterine Carcinosarcoma and Uterine Endometrial Carcinoma Using Magnetic Resonance Imaging Findings and Texture Features.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim This study aimed to evaluate the diagnostic feasibility of magnetic resonance imaging (MRI) findings and texture features (TFs) for differentiating uterine endometrial carcinoma from uterine carcinosarcoma. Methods This retrospective study included 102 patients who were histopathologically diagnosed after surgery with uterine endometrial carcinoma (n=68) or uterine carcinosarcoma (n=34) between January 2008 and December 2021. We assessed conventional MRI findings and measurements (cMRFMs) and TFs on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) map, as well as their combinations, in differentiating between uterine endometrial carcinoma and uterine carcinosarcoma. The least absolute shrinkage and selection operator (LASSO) was used to select three features with the highest absolute value of the LASSO regression coefficient for each model and construct a discriminative model. Binary logistic regression analysis was used to analyze the disease models and conduct receiver operating characteristic analyses on the cMRFMs, T2WI-TFs, ADC-TFs, and their combined model to compare the two diseases. Results A total of four models were constructed from each of the three selected features. The area under the curve (AUC) of the discriminative model using these features was 0.772, 0.878, 0.748, and 0.915 for the cMRFMs, T2WI-TFs, ADC-TFs, and a combined model of cMRFMs and TFs, respectively. The combined model showed a higher AUC than the other models, with a high diagnostic performance (AUC=0.915). Conclusion A combined model using cMRFMs and TFs might be helpful for the differential diagnosis of uterine endometrial carcinoma and uterine carcinosarcoma.
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.