Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Breast Cancer Mass Detection in DCE-MRI Using Deep-Learning Features Followed by Discrimination of Infiltrative vs In Situ Carcinoma through A Machine-learning Approach

Version 1 : Received: 6 August 2020 / Approved: 7 August 2020 / Online: 7 August 2020 (09:29:33 CEST)

A peer-reviewed article of this Preprint also exists.

Conte, L.; Tafuri, B.; Portaluri, M.; Galiano, A.; Maggiulli, E.; De Nunzio, G. Breast Cancer Mass Detection in DCE–MRI Using Deep-Learning Features Followed by Discrimination of Infiltrative vs. In Situ Carcinoma through a Machine-Learning Approach. Appl. Sci. 2020, 10, 6109. Conte, L.; Tafuri, B.; Portaluri, M.; Galiano, A.; Maggiulli, E.; De Nunzio, G. Breast Cancer Mass Detection in DCE–MRI Using Deep-Learning Features Followed by Discrimination of Infiltrative vs. In Situ Carcinoma through a Machine-Learning Approach. Appl. Sci. 2020, 10, 6109.

Journal reference: Appl. Sci. 2020, 10, 6109
DOI: 10.3390/app10176109

Abstract

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumour can be categorized into two main groups: in situ and infiltrative, with the latter being the most common malignant lesions. The current use of Magnetic Resonance Imaging (MRI) was shown to provide highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a Computer-Aided Detection/Diagnosis (CAD) system that could provide valuable assistance to the radiologist for the discrimination between in situ and infiltrating tumours. The system consists of two main processing levels: 1) Localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter) followed by a deep-feature extraction and classification method for false-positive rejection; 2) Characterization of the selected ROIs and discrimination between in situ and invasive tumour, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE-MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70.

Subject Areas

Breast Cancer; Radiomics; Machine Learning; Deep Learning; Segmentation; In Situ Breast Cancer; Infiltrative Breast Cancer

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