ArticleRadiology. Artificial intelligence2021
Artificial Intelligence for Classification of Soft-Tissue Masses at US.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 34 citations in OpenAlex.
- Liver fibrosis stage classification in stacked microvascular images based on deep learning.BMC medical imaging · 2025Article
- Artificial intelligence and machine learning applications for the imaging of bone and soft tissue tumors.Frontiers in radiology · 2024Review
- Development of ultrasound-based clinical, radiomics and deep learning fusion models for the diagnosis of benign and malignant soft tissue tumors.Frontiers in oncology · 2024Article
- Article
- Application and prospects of AI-based radiomics in ultrasound diagnosis.Visual computing for industry, biomedicine, and art · 2023Review
- Musculoskeletal ultrasound: a technical and historical perspective.Journal of ultrasonography · 2023Article
- A multiparametric clinic-ultrasomics nomogram for predicting extremity soft-tissue tumor malignancy: a combined retrospective and prospective bicentric study.La Radiologia medica · 2023Article
- Popular deep learning algorithms for disease prediction: a review.Cluster computing · 2023Article
- Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review.European radiology · 2022Article
- Diagnostic performance of CT lung severity score and quantitative chest CT for stratification of COVID-19 patients.La Radiologia medica · 2022Article
- A proposed lung ultrasound and phenotypic algorithm for the care of COVID-19 patients with acute respiratory failure.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses. MATERIALS AND
methodsIn this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years ± 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision (
resultsThe model trained to classify malignant and benign masses demonstrated an accuracy of 79% (95% CI: 68, 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.84, 0.98), matching the performance of two expert readers. Performance of the model distinguishing three benign masses was lower, with an accuracy of 71% (95% CI: 61, 80) on the test data.
conclusionThe trained CNN was capable of differentiating between benign and malignant soft-tissue masses depicted on US images, with performance matching that of two experienced musculoskeletal radiologists.© RSNA, 2020.
Identifiers
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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.