Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results

TL;DR

This study introduces a multiparametric deep learning network that effectively segments and classifies breast tissue in MRI images, achieving high accuracy in differentiating malignant from benign lesions.

Contribution

The paper presents a novel deep learning approach using stacked autoencoders for multiparametric MRI analysis, enhancing lesion segmentation and classification in breast cancer detection.

Findings

High sensitivity (90%) and specificity (85%) in lesion classification

AUC of 0.93 indicating excellent diagnostic performance

Accurate segmentation and tissue classification in breast MRI

Abstract

A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by computer systems of different lesion types to help the radiologist in defining disease. For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images. The MPDL network was constructed from stacked sparse autoencoders with inputs from mpMRI. Evaluation of MPDL consisted of cross-validation, sensitivity, and specificity. Dice similarity between MPDL and post-DCE lesions were evaluated. We demonstrate high sensitivity and specificity for differentiation of malignant from benign lesions of 90% and 85% respectively with an AUC of 0.93. The Integrated MPDL method accurately segmented and classified different breast tissue from multiparametric breast MRI using deep leaning tissue signatures.