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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation.

Wen-Yi Yue, Hong-Tao Zhang¹, Shen Gao¹

¹From the Fifth Medical Center of Chinese PLA General Hospital.

Journal of Computer Assisted Tomography

|September 14, 2023

Summary

Radiomics using automatic segmentation accurately predicts breast cancer molecular subtypes from MRI scans. This noninvasive method shows potential for large-scale clinical application in breast cancer subtyping.

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Area of Science:

Medical Imaging
Oncology
Artificial Intelligence

Background:

Accurate breast cancer molecular subtyping is crucial for guiding treatment decisions.
Current methods for subtyping can be invasive or time-consuming.
There is a need for noninvasive, efficient methods to predict molecular subtypes.

Purpose of the Study:

To evaluate the feasibility of radiomics, utilizing automatic segmentation, for predicting breast cancer molecular subtypes.
To assess the performance of radiomics models in classifying different molecular subtypes.

Main Methods:

A retrospective study of 516 breast cancer patients was conducted.
An automatic 3D UNet-based Convolutional Neural Network was used for region of interest segmentation.
1316 radiomics features were extracted, and 18 radiomics methods were employed for model selection and performance assessment using AUC, accuracy, sensitivity, and specificity.

Main Results:

The automatic segmentation achieved an average Dice similarity coefficient of 0.89.
Radiomics models successfully predicted 4 molecular subtypes with an average AUC of 0.8623.
High performance was observed for specific subtype predictions, including triple-negative breast cancer (AUC = 0.9335).

Conclusions:

Radiomics based on automatic MRI segmentation offers a noninvasive approach for predicting breast cancer molecular subtypes.
This method demonstrates potential for application in large patient cohorts for improved breast cancer management.