Radiomics in Breast Imaging: Future Development

Author:

Panico Alessandra1,Gatta Gianluca1ORCID,Salvia Antonio1,Grezia Graziella Di2ORCID,Fico Noemi3ORCID,Cuccurullo Vincenzo4ORCID

Affiliation:

1. Radiology Division, Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80138 Naples, Italy

2. Radiology Division, ASL Avellino, 83100 Avellino, Italy

3. Department of Physics “Ettore Pancini”, Università di Napoli Federico II, 80126 Naples, Italy

4. Nuclear Medicine Unit, Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, 80138 Naples, Italy

Abstract

Breast cancer is the most common and most commonly diagnosed non-skin cancer in women. There are several risk factors related to habits and heredity, and screening is essential to reduce the incidence of mortality. Thanks to screening and increased awareness among women, most breast cancers are diagnosed at an early stage, increasing the chances of cure and survival. Regular screening is essential. Mammography is currently the gold standard for breast cancer diagnosis. In mammography, we can encounter problems with the sensitivity of the instrument; in fact, in the case of a high density of glands, the ability to detect small masses is reduced. In fact, in some cases, the lesion may not be particularly evident, it may be hidden, and it is possible to incur false negatives as partial details that may escape the radiologist’s eye. The problem is, therefore, substantial, and it makes sense to look for techniques that can increase the quality of diagnosis. In recent years, innovative techniques based on artificial intelligence have been used in this regard, which are able to see where the human eye cannot reach. In this paper, we can see the application of radiomics in mammography.

Publisher

MDPI AG

Subject

Medicine (miscellaneous)

Reference66 articles.

1. Aiom (2023, May 15). Linee Guida Neoplasie Della Mammella. AIOM Edizione 2018. Aggiornamento 28 October 2018. Available online: https://www.aiom.it/wp-content/uploads/2018/11/2018_LG_AIOM_Mammella.pdf.

2. Airtum, W.G. (2019). L’incidenza dei tumori in Italia, AIOM.

3. Breast ultrasound in the management of gynecomastia in Peutz-Jeghers syndrome in monozygotic twins: Two case reports;Romano;J. Med. Case Rep.,2014

4. The 2019 World Health Organization classification of tumours of the breast;Tan;Histopathology,2020

5. Underestimation of atypical lobular hyperplasia and lobular carcinoma in situ at stereotaxic 11-gauge vacuum-assisted breast biopsy;Gatta;Eur. J. Inflamm.,2013

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