ArticleVeterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association2022
Fall time may be a reliable discriminator between neoplastic and non-neoplastic urinary bladder lesions in dogs undergoing contrast-enhanced ultrasound: a pilot study.
Article in Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 2022. 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, 3 citations in OpenAlex.
- Use of New Ultrasonography Methods for Detecting Neoplasms in Dogs and Cats: A Review.Animals : an open access journal from MDPI · 2024Review
- Fall time may be a reliable discriminator between neoplastic and non-neoplastic urinary bladder lesions in dogs undergoing contrast-enhanced ultrasound: a pilot study.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Contrast-enhanced ultrasound (CEUS) can provide quantitative information on enhancement patterns and perfusion of lesions, based on time-intensity curves (TICs). No published studies have compared CEUS parameters in neoplastic and non-neoplastic urinary bladder lesions in dogs. The aim of the current prospective, pilot study was to quantitatively characterize the CEUS pattern of neoplastic and non-neoplastic urinary bladder lesions in dogs, assessing the influence of contrast arrival time (CAT) on the final appearance of the curves. Fourteen dogs with cyto-histopathological diagnoses were included (seven malignant and seven inflammatory lesions). B-mode ultrasound was performed followed by CEUS examination after an intravenous bolus injection of 0.04 mL/kg of contrast medium, and TICs were elaborated by dedicated software. Receiver operating characteristic curves (ROC) for each TIC parameter were obtained. Neoplastic lesions had subjectively shorter rise time (RT), time to peak (TTP) and fall time (FT) than inflammatory lesions. Based on ROC curve analyses, fall time ≥ 10.49 s was the most reliable parameter for diagnosing non-neoplastic disease in this small sample of dogs (area under the curve [AUC] 0.75, sensitivity 83.33%, specificity 66.67%). No difference was found between ROCs calculated for each parameter of TICs by adding or removing CAT. Results of the current study provide background for future, larger scale studies evaluating use of a CEUS FT threshold of 10.49 s as a possible discriminator for urinary bladder neoplastic lesions in dogs.
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.