Cargando…

An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions

OBJECTIVES: The probability of Breast Imaging Reporting and Data Systems (BI-RADS) 4 lesions being malignant is 2%–95%, which shows the difficulty to make a diagnosis. Radiomics models based on magnetic resonance imaging (MRI) can replace clinicopathological diagnosis with high performance. In the p...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhang, Renzhi, Wei, Wei, Li, Rang, Li, Jing, Zhou, Zhuhuang, Ma, Menghang, Zhao, Rui, Zhao, Xinming
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2022
Materias:	Oncology
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8833233/ https://www.ncbi.nlm.nih.gov/pubmed/35155178 http://dx.doi.org/10.3389/fonc.2021.733260

Ejemplares similares

Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination
por: Liu, Jinrui, et al.
Publicado: (2023)

Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment
por: Hao, Wen, et al.
Publicado: (2020)

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning
por: Zhou, Jiejie, et al.
Publicado: (2021)

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification
por: Liu, Xinmiao, et al.
Publicado: (2022)

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies
por: Lyu, Yidong, et al.
Publicado: (2023)

Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions
por: Wu, Xinxin, et al.
Publicado: (2021)

Radiomics Based on DCE-MRI Improved Diagnostic Performance Compared to BI-RADS Analysis in Identifying Sclerosing Adenosis of the Breast
por: Ruan, Mei, et al.
Publicado: (2022)

Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions
por: Sun, Shi Yun, et al.
Publicado: (2021)

MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions
por: Zhao, Yanjie, et al.
Publicado: (2021)

Nomograms for prediction of breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A single-center retrospective study based on radiomics features
por: Hong, Zhi-Liang, et al.
Publicado: (2022)

Ultrasound-based radiomics analysis for differentiating benign and malignant breast lesions: From static images to CEUS video analysis
por: Zhu, Jun-Yan, et al.
Publicado: (2022)

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram
por: Qi, Jinbo, et al.
Publicado: (2022)

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation
por: Piludu, Francesca, et al.
Publicado: (2021)

Combined Use of Shear Wave Elastography, Microvascular Doppler Ultrasound Technique, and BI-RADS for the Differentiation of Benign and Malignant Breast Masses
por: Wang, Bin, et al.
Publicado: (2022)

An Approach Based on Mammographic Imaging and Radiomics for Distinguishing Male Benign and Malignant Lesions: A Preliminary Study
por: Huang, Yan, et al.
Publicado: (2021)

An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses
por: Li, Chunxiang, et al.
Publicado: (2022)

A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions
por: Liang, Lei, et al.
Publicado: (2021)

Corrigendum: An ultrasonic-based radiomics nomogram for distinguishing between benign and malignant solid renal masses
por: Li, Chunxiang, et al.
Publicado: (2023)

Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for Identifying Benign and Malignant Breast Lesions of Sub-1 cm
por: Lin, Fan, et al.
Publicado: (2020)

A Radiomics Nomogram for Distinguishing Benign From Malignant Round-Like Breast Tumors
por: Wang, Lanyun, et al.
Publicado: (2022)

MRI Based Radiomics Compared With the PI-RADS V2.1 in the Prediction of Clinically Significant Prostate Cancer: Biparametric vs Multiparametric MRI
por: Chen, Tong, et al.
Publicado: (2022)

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)

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)

Identification of the Benignity and Malignancy of BI-RADS 4 Breast Lesions Based on a Combined Quantitative Model of Dynamic Contrast-Enhanced MRI and Intravoxel Incoherent Motion
por: Xu, Wenjuan, et al.
Publicado: (2022)

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors
por: Yu, Qiang, et al.
Publicado: (2022)

The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams
por: Milos, Ruxandra Iulia, et al.
Publicado: (2020)

A Comparative Assessment of MR BI-RADS 4 Breast Lesions With Kaiser Score and Apparent Diffusion Coefficient Value
por: Meng, Lingsong, et al.
Publicado: (2021)

Development and Validation of a Radiomics Nomogram for Predicting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions
por: Li, Tianping, et al.
Publicado: (2022)

Clinical features combined with ultrasound-based radiomics nomogram for discrimination between benign and malignant lesions in ultrasound suspected supraclavicular lymphadenectasis
por: Luo, Jieli, et al.
Publicado: (2023)

Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis
por: Cui, Qian, et al.
Publicado: (2021)

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)

Utility of Clinical–Radiomic Model to Identify Clinically Significant Prostate Cancer in Biparametric MRI PI-RADS V2.1 Category 3 Lesions
por: Jin, Pengfei, et al.
Publicado: (2022)

Value of the BI-RADS classification in MR-Mammography for diagnosis of benign and malignant breast tumors
por: Sohns, Christian, et al.
Publicado: (2011)

Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions
por: Li, Linghao, et al.
Publicado: (2022)

Radiomics Nomograms Based on Multi-Parametric MRI for Preoperative Differential Diagnosis of Malignant and Benign Sinonasal Tumors: A Two-Centre Study
por: Bi, Shu-cheng, et al.
Publicado: (2021)

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

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)

Validation of Molecular Typing for Endometrial Screening Test That Predicts Benign and Malignant Lesions
por: Tuo, Xiaoqian, et al.
Publicado: (2019)

Different multiparametric MRI-based radiomics models for differentiating stage IA endometrial cancer from benign endometrial lesions: A multicenter study
por: Bi, Qiu, et al.
Publicado: (2022)

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

Cannot write session to /tmp/vufind_sessions/sess_ogb0d4eldh5s0pgmai43epbv57