AIMC Topic: Fibroadenoma
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Deep Learning for Distinguishing Mucinous Breast Carcinoma From Fibroadenoma on Ultrasound.
Clinical breast cancer
Sep 4, 2024
PURPOSE: Mucinous breast carcinoma (MBC) tends to be misdiagnosed as fibroadenomas (FA) due to its benign imaging characteristics. We aimed to develop a deep learning (DL) model to differentiate MBC and FA based on ultrasound (US) images. The model c...
Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.
Sensors (Basel, Switzerland)
May 26, 2023
The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgical treatment. Although several imaging modalities are available, reliable differentiation between PT ...
Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning.
Asian Pacific journal of cancer prevention : APJCP
Feb 1, 2023
OBJECTIVE: Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep...
Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions.
Journal of cancer research and clinical oncology
Jun 30, 2022
PURPOSE: To investigate the value of the combined diagnosis of multiparametric MRI-based deep learning models to differentiate triple-negative breast cancer (TNBC) from fibroadenoma magnetic resonance Breast Imaging-Reporting and Data System category...
Artificial intelligence modelling in differentiating core biopsies of fibroadenoma from phyllodes tumor.
Laboratory investigation; a journal of technical methods and pathology
Nov 24, 2021
Breast fibroepithelial lesions (FEL) are biphasic tumors which consist of benign fibroadenomas (FAs) and the rarer phyllodes tumors (PTs). FAs and PTs have overlapping features, but have different clinical management, which makes correct core biopsy ...
Impact of radiomics on the breast ultrasound radiologist's clinical practice: From lumpologist to data wrangler.
European journal of radiology
Jul 30, 2020
OBJECTIVE: The study aims to assess the impact of radiomics in the clinical practice of breast ultrasound, to determine which lesions are undetermined by the software, and to discuss the future of the radiologist's role.
Robust phase-based texture descriptor for classification of breast ultrasound images.
Biomedical engineering online
Mar 24, 2015
BACKGROUND: Classification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers t...
Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study.
Cancer imaging : the official publication of the International Cancer Imaging Society
May 8, 2025
OBJECTIVE: Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical...
Deep learning-assisted distinguishing breast phyllodes tumours from fibroadenomas based on ultrasound images: a diagnostic study.
The British journal of radiology
Nov 1, 2024
OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences.
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