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

Applied Sciences

Giorgio De Nunzio¹  Benedetta Tafuri¹  Luana Conte¹  Eleonora Maggiulli²  Alessandro Galiano³  Maurizio Portaluri⁴ 

[1] Laboratory of Biomedical Physics and Environment, Department of Mathematics and Physics “E. De Giorgi”, University of Salento, 73100 Lecce, Italy;Operative Unit of Medical Physics, ASL (Local Health Authority), Brindisi, and ‘Di Summa-Perrino’ Hospital, 72100 Brindisi, Italy;Operative Unit of Radiodiagnostics, ASL (Local Health Authority), Brindisi, and ‘Di Summa-Perrino’ Hospital, 72100 Brindisi, Italy;Operative Unit of Radiotherapy, ASL (Local Health Authority), Brindisi, and ‘Di Summa-Perrino’ Hospital, 72100 Brindisi, Italy;

关键词: breast cancer;    Radiomics;    machine learning;    deep learning;    segmentation;    in situ breast cancer;   

DOI  :  10.3390/app10176109

来源: DOAJ

【 摘 要 】

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumor 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 the 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 radiologists for discrimination between in situ and infiltrating tumors. 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; and (2) characterization of the selected ROIs and discrimination between in situ and invasive tumor, 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.

【 授权许可】

Unknown