Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis

Liu, Junjie; Lei, Jiangjie; Ou, Yuhang; Zhao, Yilong; Tuo, Xiaofeng; Zhang, Baoming; Shen, Mingwang

doi:10.1007/s10238-022-00895-0

Cited by 13 publications

(6 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The detection, segmentation and classification of image lesions were achieved with the help of deep learning. Additionally, better diagnostic performance was achieved with applications in mammography, ultrasound, and magnetic resonance imaging (MRI) (20)(21)(22)(23)(24). This result is consistent with the deep learning model for mammography in this study.…”

supporting

confidence: 87%

Diagnostic value of mammography density of breast masses by using deep learning

Chen

Lin

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

ObjectiveIn order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density.MethodsThis retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5 mm, including the lesion). We then obtained the mammary glands’ density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity.ResultsIn total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC = 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5 mm ROI model, we obtained the highest AUC (AUC = 0.877 P < 0.001).ConclusionsDeep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future.

show abstract

supporting

confidence: 87%

Diagnostic value of mammography density of breast masses by using deep learning

Chen

Lin

et al. 2023

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…The sensitivity of mammography can be improved considerably by harnessing the potential of Artificial Intelligence. The capacity of AI to analyze images comprehensively, learn and adapt continually, deliver decision support, and reduce human errors adds to earlier diagnosis and greater accuracy in diagnosing BC by lowering false positive rates, ultimately leading to better patient outcomes (Mahoro and Akhloufi, 2022;Liu et al, 2022;Zizaan and Idri, 2023).…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

Amin,

Acharya,

et al. 2024

Preprint

View full text Add to dashboard Cite

Breast cancer (BC) is a leading cause of mortality in women worldwide, and its incidence and prognosis vary greatly, especially in low-income areas such as Asia and Africa, compared to America and Europe. This review examines the critical role of computer-aided diagnostic systems utilizing Deep Learning (DL) techniques in improving the precision of BC detection, which can help researchers and practitioners better understand the obstacles and emerging trends in the field. The comprehensive analysis and synthesis of the published articles is provided by the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Following several searches, 287 articles were found eligible for the assessment followed by data extraction and synthesis. This work provides insight into the pathology of BC and its corresponding mammographic appearance, a comprehensive analysis of publication trends, important contributors, and emerging themes using bibliometric techniques in addition to a thorough review of research articles. Focus maps were also created to identify and shed light on the body of knowledge. In contrast to other research, this review sheds light on the subtle identification of breast density, mass, and calcification from mammography images by emphasizing the critical evaluation of image pre- processing, augmentation, detection, segmentation, and classification techniques used from 2018 to 2023, further highlighting articles related to the Asian dataset. Furthermore, the review examines contributions made by authors, countries, and experimental datasets, providing insights into various approaches. Prospero registration: CRD42022478896

show abstract

“…These systems typically involve a series of image processing steps, feature extraction techniques, and classi cation algorithms to analyze digital mammography images and distinguish between benign and malignant masses 10 . Various computer-based methods have been proposed for mass detection and classi cation, including traditional machine learning approaches such as support vector machines and arti cial neural networks [11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Leveraging Multi-Task Transfer Learning for Enhanced Breast Mass Classification in Digital Mammography

Wu,

Zhou,

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

Purpose Accurate breast mass classification is crucial for early breast cancer diagnosis. Deep learning shows promise in computer-aided diagnosis but faces challenges due to limited annotated data and lesion complexity. We propose a novel multi-task transfer learning framework to improve mass classification performance and provide a well-performed framework for medical image analysis. Methods The proposed framework comprises a transfer learning backbone and multi-task-specific branches. Pretraining weights from natural image datasets are leveraged to finetune the backbone network, enhancing the ability to extract breast mass characteristics. The classification branches include the primary task for breast mass classification and auxiliary tasks for BI-RADS evaluation, guiding the model to focus on relevant diagnostic features. Result Three models were compared using ResNet50/InceptionV3 as backbones. The Multi-Task Transfer Learning Framework (MTL + TL) achieved the highest AUC values of 0.852 ± 0.019 / 0.824 ± 0.021, outperforming other models in accuracy (0.7654 ± 0.0218 / 0.7667 ± 0.0224), precision (0.6842 ± 0.0379 / 0.7179 ± 0.0379), and F1-score (0.6842 ± 0.0379 / 0.7179 ± 0.0379). Grad-CAM heatmaps confirmed its effectiveness and ability to capture breast mass characteristics. Conclusion Our proposed framework significantly improved breast mass classification, addressing limited annotated data and providing an effective solution to address the limited availability of annotated data. The proposed framework enhanced feature recognition and overall performance by simulating a clinician's observation and decision-making of imaging features through the auxiliary task. This approach can be extended to other breast lesion classifications and provide valuable guidance for medical imaging analysis.

show abstract

Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis

Cited by 13 publications

References 67 publications

Diagnostic value of mammography density of breast masses by using deep learning

Diagnostic value of mammography density of breast masses by using deep learning

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

Leveraging Multi-Task Transfer Learning for Enhanced Breast Mass Classification in Digital Mammography

Contact Info

Product

Resources

About