Cargando…

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

PURPOSE: To develop nomograms for predicting breast malignancy in BI-RADS ultrasound (US) category 4 or 5 lesions based on radiomics features. METHODS: Between January 2020 and January 2022, we prospectively collected and retrospectively analyzed the medical records of 496 patients pathologically pr...

Descripción completa

Detalles Bibliográficos
Autores principales:	Hong, Zhi-Liang, Chen, Sheng, Peng, Xiao-Rui, Li, Jian-Wei, Yang, Jian-Chuan, Wu, Song-Song
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/PMC9538156/ https://www.ncbi.nlm.nih.gov/pubmed/36212503 http://dx.doi.org/10.3389/fonc.2022.894476

Ejemplares similares

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)

An MRI-Based Radiomics Model for Predicting the Benignity and Malignancy of BI-RADS 4 Breast Lesions
por: Zhang, Renzhi, et al.
Publicado: (2022)

Risk-predicted dual nomograms consisting of clinical and ultrasound factors for downgrading BI-RADS category 4a breast lesions - A multiple centre study
por: Niu, Zihan, et al.
Publicado: (2021)

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

CEUS Helps to Rerate Small Breast Tumors of BI-RADS Category 3 and Category 4
por: Zhang, Jian-xing, et al.
Publicado: (2014)

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

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)

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

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)

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions
por: Shiyan, Guo, et al.
Publicado: (2023)

Radiomic Applications on Digital Breast Tomosynthesis of BI-RADS Category 4 Calcifications Sent for Vacuum-Assisted Breast Biopsy
por: Favati, Benedetta, 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)

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)

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)

A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses
por: Interlenghi, Matteo, et al.
Publicado: (2022)

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)

Subdividing BI-RADS category 4 breast lesions observed on magnetic resonance imaging: Is it feasible?
por: Bitencourt, Almir Galvão Vieira
Publicado: (2016)

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)

Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma
por: Wu, Jiangfeng, et al.
Publicado: (2022)

Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma
por: Wang, Fei, et al.
Publicado: (2019)

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)

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions
por: Wang, Shi-Jie, et al.
Publicado: (2022)

Prediction for Breast Cancer in BI-RADS Category 4 Lesion Categorized by Age and Breast Composition of Women in Songklanagarind Hospital
por: Noonpradej, Seechad, et al.
Publicado: (2021)

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study
por: Zhang, Huiting, 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)

Dependability of Automated Breast Ultrasound (ABUS) in Assessing Breast Imaging Reporting and Data System (BI-RADS) Category and Size of Malignant Breast Lesions Compared with Handheld Ultrasound (HHUS) and Mammography (MG)
por: Chen, He, 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)

Predicting Axillary Lymph Node Status With a Nomogram Based on Breast Lesion Ultrasound Features: Performance in N1 Breast Cancer Patients
por: Luo, Yanwen, et al.
Publicado: (2020)

The breast lesion excision system procedure: An optimal solution for the management of indeterminate BI-RADS category 3 breast lesions in women with severe anxiety
por: Hot, Semih, et al.
Publicado: (2018)

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

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)

Development of an ultrasound-based radiomics nomogram to preoperatively predict Ki-67 expression level in patients with breast cancer
por: Liu, Jinjin, et al.
Publicado: (2022)

An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast
por: Wang, Hui, et al.
Publicado: (2023)

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

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)

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)

Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma
por: Wu, Jiangfeng, et al.
Publicado: (2022)

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

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

Cannot write session to /tmp/vufind_sessions/sess_4nbtdh64osrh75l8vq9jke82fe