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      Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future

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          Abstract

          Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.

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          Most cited references130

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Deep learning.

            Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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              Radiomics: the bridge between medical imaging and personalized medicine

              Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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                Author and article information

                Journal
                Curr Oncol
                Curr Oncol
                curroncol
                Current Oncology
                MDPI
                1198-0052
                1718-7729
                25 June 2021
                August 2021
                : 28
                : 4
                : 2351-2372
                Affiliations
                [1 ]Division of Breast Radiology, IRCSS, IEO European Institute of Oncology, 20141 Milan, Italy; anna.rotili@ 123456ieo.it (A.R.); silvia.penco@ 123456ieo.it (S.P.); valeria.dominelli@ 123456ieo.it (V.D.); enrico.cassano@ 123456ieo.it (E.C.)
                [2 ]Department of Radiology, University of Brescia, Piazzale Spedali Civili 1, 25123 Brescia, Italy; giorgiomaria.agazzi@ 123456gmail.com
                [3 ]Medical Physics Unit, IRCSS, IEO European Institute of Oncology, 20141 Milan, Italy; francesca.botta@ 123456ieo.it
                [4 ]Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, IRCSS, IEO European Institute of Oncology, 20139 Milan, Italy; sara.raimondi@ 123456ieo.it
                [5 ]Radiation Research Unit, IRCSS, IEO European Institute of Oncology, 20141 Milan, Italy; marta.cremonesi@ 123456ieo.it
                [6 ]Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy; barbara.jereczek@ 123456ieo.it
                [7 ]Division of Radiotherapy, IRCCS, IEO European Institute of Oncology, 20141 Milan, Italy
                [8 ]Radiology Unit, Foundation IRCCS Cà Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy; gianpaolo.carrafiello@ 123456unimi.it
                [9 ]Radiology Department of Health Sciences, University of Milano, 20122 Milan, Italy
                Author notes
                [* ]Correspondence: filippo.pesapane@ 123456ieo.it ; Tel.: +39-02-57489-1
                Author information
                https://orcid.org/0000-0002-0374-5054
                Article
                curroncol-28-00217
                10.3390/curroncol28040217
                8293249
                34202321
                441a69e5-ff18-4bf5-adcf-f6063769851c
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 12 May 2021
                : 21 June 2021
                Categories
                Review

                Oncology & Radiotherapy
                radiomics,breast cancer,radiology,oncology,medical physics,radiotherapy,artificial intelligence

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