Automated Classification of Benign and Malignant Breast Lesions in Dynamic Contrast-Enhanced MRI Using 3D Dynamic and Radiomic Features

Core Concepts

Leveraging 3D dynamic and radiomic features from DCE-MRI can effectively distinguish between benign and malignant breast lesions.

Abstract

This study proposes an automated 3D classification framework for identifying benign and malignant breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The key aspects are: Automated tumor segmentation: An automated tumor segmentation algorithm is used to extract the 3D region-of-interest (ROI) from the breast DCE-MRI scans. Feature extraction: Dynamic features: 3D kinetic curve features are extracted, including ratios of different enhancement types (slow, medium, fast, persistent, plateau, washout) as well as conventional dynamic features like peak enhancement, signal enhancement ratio, and functional tumor volume. Radiomic features: 800 3D radiomic features are extracted, covering diagnostic, first-order, shape, texture, and other categories, from the original and filtered DCE-MRI images. Feature selection: Least absolute shrinkage and selection operator (LASSO) is used to select the most relevant 10 dynamic features and 58 radiomic features. Classification: The selected dynamic and radiomic features are combined and used to train a linear discriminant analysis (LDA) classifier for distinguishing between benign and malignant breast lesions. The proposed method is evaluated on an in-house dataset of 200 DCE-MRI scans with 298 breast tumors (172 benign, 126 malignant). The classification results demonstrate that simultaneously considering 3D dynamic and radiomic features can achieve favorable performance, with an area under the curve (AUC) of 0.9476, outperforming the use of either feature type alone.

Stats

The dataset includes 200 DCE-MRI scans with 298 breast tumors, of which 172 are benign and 126 are malignant. The DCE-MRI scans have a dimension of (336-432) × (336-432) × 300 and a spatial resolution of (0.926-0.949) × (0.926-0.949) × 0.500 mm³.

Quotes

"By simultaneously considering the dynamic and radiomic features, it is beneficial to effectively distinguish between benign and malignant breast lesions." "The experimental results on an in-house DCE-MRI dataset show the efficacy of the proposed method."

Key Insights Distilled From

Exploring Kinetic Curves Features for the Classification of Benign and Malignant Breast Lesions in DCE-MRI

by Zixian Li,Yu... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13929.pdf

Deeper Inquiries

How can the proposed framework be extended to provide additional clinical insights beyond binary classification, such as predicting tumor grade or treatment response?

The proposed framework can be extended to provide additional clinical insights by incorporating more advanced machine learning techniques and expanding the feature set. To predict tumor grade, features related to tumor characteristics such as size, shape irregularity, and enhancement patterns can be included. Machine learning models like support vector machines or random forests can be trained on these features to classify tumors into different grades. For predicting treatment response, temporal features can be added to capture changes in tumor characteristics over time. By analyzing how features evolve during and after treatment, the model can predict the likelihood of response or recurrence. Additionally, integrating genomic data with imaging features can enhance the predictive power of the model, enabling personalized treatment strategies based on genetic profiles.

What are the potential limitations of the current approach, and how could it be improved to handle more challenging cases, such as small or atypical lesions?

One potential limitation of the current approach is the reliance on manual segmentation for tumor ROI extraction, which may introduce variability and errors, especially in cases of small or atypical lesions. To address this, automated segmentation algorithms tailored for detecting small or atypical lesions can be developed. Utilizing deep learning techniques like U-Net or Mask R-CNN can improve the accuracy and robustness of tumor segmentation, even in challenging cases. Moreover, the current feature set may not capture all the nuances of small or atypical lesions. Including features that focus on subtle changes in texture, intensity, or shape specific to these types of lesions can enhance the model's ability to differentiate between benign and malignant cases. Augmenting the dataset with more examples of small or atypical lesions can also improve the model's performance in handling such challenging cases.

Given the importance of early breast cancer detection, how could this work be leveraged to develop more accessible and affordable screening tools for underserved populations?

To develop more accessible and affordable screening tools for underserved populations, the work can be translated into a user-friendly software application that can be integrated into existing healthcare systems. By simplifying the user interface and automating the analysis process, healthcare providers in underserved areas can easily upload DCE-MRI scans and receive automated classification results. Additionally, leveraging cloud computing resources can enable remote access to the screening tool, eliminating the need for expensive hardware infrastructure at local healthcare facilities. This cloud-based approach can also facilitate collaboration between healthcare professionals for second opinions and consultations, improving diagnostic accuracy. Furthermore, partnerships with non-profit organizations, government agencies, or international health initiatives can help distribute the screening tool to underserved populations at reduced costs or for free. Implementing telemedicine services alongside the screening tool can further enhance access to timely diagnosis and treatment for individuals in remote or underserved areas.