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Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm.

Richard Ha¹, Simukayi Mutasa², Jenika Karcich²

¹Department of Radiology, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA. rh2616@columbia.edu.

Journal of Digital Imaging

|February 2, 2019

Summary

This summary is machine-generated.

A novel deep learning algorithm using magnetic resonance imaging (MRI) can predict breast cancer molecular subtypes. This convolutional neural network (CNN) approach aids in classifying breast tumors for improved treatment strategies.

Keywords:

Breast MRI CNN Molecular subtype

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Area of Science:

Radiology
Oncology
Artificial Intelligence

Background:

Accurate breast cancer molecular subtyping is crucial for effective treatment planning.
Magnetic resonance imaging (MRI) offers detailed anatomical and functional information.
Predicting molecular subtypes non-invasively can streamline diagnostic pathways.

Purpose of the Study:

To develop and evaluate a convolutional neural network (CNN) algorithm for predicting breast cancer molecular subtypes using MRI features.
To assess the diagnostic performance of the developed CNN model.

Main Methods:

A 14-layer CNN architecture with residual and inception-style layers was designed.
3D segmentation of MRI images was performed using 3D Slicer.
Extensive regularization techniques and class imbalance handling were implemented.
The model was trained and validated on 216 patient datasets and tested on a holdout set of 40 patients.

Main Results:

The CNN model achieved a testing set accuracy of 70%.
The class-normalized macro area under the receiver operating curve (ROC) was 0.853.
Micro-aggregated AUC was 0.871, indicating strong discriminatory power for Luminal A and B subtypes.
Aggregate sensitivity and specificity were 0.603 and 0.958, respectively.

Conclusions:

A novel CNN algorithm applied to MRI data can effectively predict breast cancer molecular subtypes.
This AI-driven approach shows promise for non-invasive tumor classification.
Further validation with larger datasets is expected to enhance model performance.