Cargando…

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

OBJECTIVE: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the var...

Descripción completa

Detalles Bibliográficos
Autores principales:	Sultan, Laith R., Bouzghar, Ghizlane, Levenback, Benjamin J., Faizi, Nauroze A., Venkatesh, Santosh S., Conant, Emily F., Sehgal, Chandra M.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	2015
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8298005/ https://www.ncbi.nlm.nih.gov/pubmed/34306838 http://dx.doi.org/10.4236/abcr.2015.41001

Ejemplares similares

Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer
por: Moustafa, Afaf F., et al.
Publicado: (2020)

A Review of Early Experience in Lung Ultrasound in the Diagnosis and Management of COVID-19
por: Sultan, Laith R., et al.
Publicado: (2020)

Quantitative pleural line characterization outperforms traditional lung texture ultrasound features in detection of COVID‐19
por: Sultan, Laith R., et al.
Publicado: (2021)

Interobserver concordance in the BI-RADS classification of breast ultrasound exams
por: Calas, Maria Julia G, et al.
Publicado: (2012)

Photoacoustic monitoring of oxygenation changes induced by therapeutic ultrasound in murine hepatocellular carcinoma
por: Karmacharya, Mrigendra B., et al.
Publicado: (2021)

Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound
por: Sultan, Laith R., et al.
Publicado: (2023)

Can Sequential Images from the Same Object Be Used for Training Machine Learning Models? A Case Study for Detecting Liver Disease by Ultrasound Radiomics
por: Sultan, Laith R., et al.
Publicado: (2022)

Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images
por: Fleury, Eduardo, et al.
Publicado: (2019)

Hydralazine augmented ultrasound hyperthermia for the treatment of hepatocellular carcinoma
por: Karmacharya, Mrigendra B., et al.
Publicado: (2021)

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms
por: Boumaraf, Said, et al.
Publicado: (2020)

A National Chinese Survey on Ultrasound Feature Interpretation and Risk Assessment of Breast Masses Under ACR BI-RADS
por: Wen, Wen, et al.
Publicado: (2021)

Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images
por: Huang, Yunzhi, et al.
Publicado: (2019)

Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS
por: Luo, Wei-quan, et al.
Publicado: (2019)

2167 Beyond diagnosis: Using ultrasound to affect tumor vasculature for hepatocellular carcinoma (HCC) therapy
por: D’Souza, Julia, et al.
Publicado: (2018)

Prospective evaluation of contrast-enhanced ultrasound of breast BI-RADS 3–5 lesions
por: Janu, Eva, et al.
Publicado: (2020)

Association between ultrasound BI-RADS signs and molecular typing of invasive breast cancer
por: Pan, Qiao-Hong, et al.
Publicado: (2023)

Ultrasound Radiomics for the Detection of Early-Stage Liver Fibrosis
por: Al-Hasani, Maryam, et al.
Publicado: (2022)

Subsequent Ultrasound Vascular Targeting Therapy of Hepatocellular Carcinoma Improves the Treatment Efficacy
por: Sultan, Laith R., et al.
Publicado: (2021)

Ultrasound real-time elastography can predict malignancy in BI-RADS(®)-US 3 lesions
por: Wojcinski, Sebastian, et al.
Publicado: (2013)

Non-mass lesions on breast ultrasound: why does not the ACR BI-RADS breast ultrasound lexicon add the terminology?
por: Uematsu, Takayoshi
Publicado: (2023)

Variability in Observer Performance Between Faculty Members and Residents Using Breast Imaging Reporting and Data System (BI-RADS)-Ultrasound, Fifth Edition (2013)
por: Lee, Youn Joo, et al.
Publicado: (2016)

BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations
por: ZHANG, BOYU, et al.
Publicado: (2023)

4267 Noninvasive hybrid ultrasound and photoacoustic imaging for the assessment of liver fibrosis
por: Sultan, Laith Riyadh, et al.
Publicado: (2020)

Practical and illustrated summary of updated BI-RADS for ultrasonography
por: Lee, Jiyon
Publicado: (2017)

Comparison of (99m)Tc-MIBI scintigraphy, ultrasound, and mammography for the diagnosis of BI-RADS 4 category lesions
por: Liu, Hongbiao, et al.
Publicado: (2020)

Agreement in breast lesion assessment and final BI-RADS classification between radial and meander-like breast ultrasound
por: Brasier-Lutz, Pascale, et al.
Publicado: (2021)

Evaluation of diagnostic efficacy of multimode ultrasound in BI-RADS 4 breast neoplasms and establishment of a predictive model
por: Chen, Yunhao, et al.
Publicado: (2022)

Ultrasound classification of non-mass breast lesions following BI-RADS presents high positive predictive value
por: Lin, Mingnan, et al.
Publicado: (2022)

Interobserver variability and likelihood of malignancy for fifth edition BI-RADS MRI descriptors in non-mass breast lesions
por: Lunkiewicz, Magdalena, et al.
Publicado: (2019)

Diagnostic Performance of Prototype Handheld Ultrasound According to the Fifth Edition of BI-RADS for Breast Ultrasound Compared with Automated Breast Ultrasound among Females with Positive Lumps
por: Ibraheem, Shahad A., et al.
Publicado: (2023)

Observer Agreement Using the ACR Breast Imaging Reporting and Data System (BI-RADS)-Ultrasound, First Edition (2003)
por: Park, Chang Suk, et al.
Publicado: (2007)

Reliability of Breast Ultrasound BI-RADS Final Assessment in Mammographically Negative Patients with Nipple Discharge and Radiologic Predictors of Malignancy
por: Park, Chae Jung, et al.
Publicado: (2016)

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A
por: Niu, Sihua, et al.
Publicado: (2020)

Downgrade BI-RADS 4A Patients Using Nomogram Based on Breast Magnetic Resonance Imaging, Ultrasound, and Mammography
por: Xie, Yamie, et al.
Publicado: (2022)

Novel approach in the evaluation of ultrasound BI-RADS 3 & 4 breast masses with a combination method of elastography & Doppler
por: Reghunath, Anjuna, et al.
Publicado: (2021)

Analysis of the accuracy of ultrasound elastography and BI-RADS classification of breast masses located within the superficial fat layer of the glands
por: Xue, Nianyu, et al.
Publicado: (2022)

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics
por: Qiu, Shunmin, et al.
Publicado: (2023)

Comparison of Danish dichotomous and BI-RADS classifications of mammographic density
por: Hodge, Rebecca, et al.
Publicado: (2014)

MRI for the assessment of malignancy in BI-RADS 4 mammographic microcalcifications
por: Bennani-Baiti, Barbara, et al.
Publicado: (2017)

BI-RADS 3: Current and Future Use of Probably Benign
por: Lee, Karen A., et al.
Publicado: (2018)

Cannot write session to /tmp/vufind_sessions/sess_2lafqdn9335uo77l4mejl0qsph