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Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography

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Abstract

The objective of the study was to determine if the pathology depicted on a mammogram is either benign or malignant (ductal or non-ductal carcinoma) using deep learning and artificial intelligence techniques. A total of 559 patients underwent breast ultrasound, mammography, and ultrasound-guided breast biopsy. Based on the histopathological results, the patients were divided into three categories: benign, ductal carcinomas, and non-ductal carcinomas. The mammograms in the cranio-caudal view underwent pre-processing and segmentation. Given the large variability of the areola, an algorithm was used to remove it and the adjacent skin. Therefore, patients with breast lesions close to the skin were removed. The remaining breast image was resized on the Y axis to a square image and then resized to 512 × 512 pixels. A variable square of 322,622 pixels was searched inside every image to identify the lesion. Each image was rotated with no information loss. For data augmentation, each image was rotated 360 times and a crop of 227 × 227 pixels was saved, resulting in a total of 201,240 images. The reason why our images were cropped at this size is because the deep learning algorithm transfer learning used from AlexNet network has an input image size of 227 × 227. The mean accuracy was 95.8344% ± 6.3720% and mean AUC 0.9910% ± 0.0366%, computed on 100 runs of the algorithm. Based on the results, the proposed solution can be used as a non-invasive and highly accurate computer-aided system based on deep learning that can classify breast lesions based on changes identified on mammograms in the cranio-caudal view.

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Availability of Data and Material

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly.

Code Availability

The code used in this study is available from the corresponding author, MSS, upon reasonable request.

Abbreviations

ACC:

Accuracy

DL:

Deep learning

ML:

Machine learning

AI:

Artificial intelligence

CNN:

Convolutional neural network

MM:

Mammography

MLO:

Medio-lateral oblique

CC:

Cranio-caudal

BI-RADS:

Breast Imaging-Reporting and Data System

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Author information

Authors and Affiliations

  1. Doctoral School, University of Medicine and Pharmacy of Craiova, 2-4 Petru Rareș Street, 200349, Craiova, Romania

    Raluca-Elena Nica

  2. Department of Radiology and Medical Imaging, Emergency Clinical County Hospital of Craiova, 1 Tabaci Street, 200642, Craiova, Romania

    Raluca-Elena Nica, Lucian-Mihai Florescu, Georgiana-Cristiana Camen & Ioana-Andreea Gheonea

  3. Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, 2-4 Petru Rareș Street, 200349, Craiova, Romania

    Mircea-Sebastian Șerbănescu

  4. Department of Radiology and Medical Imaging, University of Medicine and Pharmacy of Craiova, 2-4 Petru Rareș Street, 200349, Craiova, Romania

    Lucian-Mihai Florescu, Georgiana-Cristiana Camen & Ioana-Andreea Gheonea

  5. Department of Research Methodology, University of Medicine and Pharmacy of Craiova, 2-4 Petru Rareș Street, 200349, Craiova, Romania

    Costin Teodor Streba

  6. Department of Pulmonology, University of Medicine and Pharmacy of Craiova, 2-4 Petru Rareș Street, 200349, Craiova, Romania

    Costin Teodor Streba

Authors
  1. Raluca-Elena Nica
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  2. Mircea-Sebastian Șerbănescu
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  3. Lucian-Mihai Florescu
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  4. Georgiana-Cristiana Camen
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  5. Costin Teodor Streba
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  6. Ioana-Andreea Gheonea
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Contributions

REN, MSS, and IAG share main authorship due to conceiving the main conceptual ideas and designing the study. GCC and REN performed all the imaging techniques and ultrasound-guided breast biopsies. MSS designed the model and the computational framework and with support from CTS and LMF carried out the implementation and worked out all the technical details. IAG supervised the study and together with the rest of the authors provided critical feedback and helped shape the research, analysis, and manuscript.

Corresponding author

Correspondence to Mircea-Sebastian Șerbănescu.

Ethics declarations

Ethics Approval

Institutional review board approval was obtained (University of Medicine and Pharmacy of Craiova, Committee of Ethics and Academic and Scientific Deontology approval 45/17.06.2020).

Consent to Participate

Written informed consent was obtained from all subjects (patients) in this study.

Conflict of Interest

The authors declare no competing interests.

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Key Points

1. Deep learning computer-aided diagnosis of breast pathology on a digital mammogram helps clinicians classify lesions either benign or malignant.

2. Deep learning algorithms may also be used as an objective first or second reader and as a support tool to accelerate radiologists’ time to process examinations.

3. The patients can benefit from a more appropriate and a less invasive management and treatment of the breast lesions.

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Nica, RE., Șerbănescu, MS., Florescu, LM. et al. Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography. J Digit Imaging 34, 1190–1198 (2021). https://doi.org/10.1007/s10278-021-00508-4

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  • DOI: https://doi.org/10.1007/s10278-021-00508-4

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