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      Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography.

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          Abstract

          To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer.

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          Regression Shrinkage and Selection Via the Lasso

          Robert Tibshirani (1996)
          Article 0 comments Cited 3596 times – based on 0 reviews     Review now
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            Radiomics: extracting more information from medical images using advanced feature analysis.

            Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar … (2012)
            Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              The lasso method for variable selection in the Cox model.

              ROBERT TIBSHIRANI (1997)
              I propose a new method for variable selection and shrinkage in Cox's proportional hazards model. My proposal minimizes the log partial likelihood subject to the sum of the absolute values of the parameters being bounded by a constant. Because of the nature of this constraint, it shrinks coefficients and produces some coefficients that are exactly zero. As a result it reduces the estimation variance while providing an interpretable final model. The method is a variation of the 'lasso' proposal of Tibshirani, designed for the linear regression context. Simulations indicate that the lasso can be more accurate than stepwise selection in this setting.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Cancers (Basel)
                Title: Cancers
                Publisher: MDPI AG
                ISSN: 2072-6694
                ISSN (Print): 2072-6694
                Publication date (Electronic): Apr 25 2022
                Volume: 14
                Issue: 9
                Affiliations
                [1 ] Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy.
                [2 ] Medical Oncology Division, Igea SpA, 80013 Naples, Italy.
                [3 ] Pathology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131 Naples, Italy.
                [4 ] Direzione Scientifica-IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124 Bari, Italy.
                [5 ] SSD Fisica Sanitaria-IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124 Bari, Italy.
                [6 ] Obstetrics and Gynecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari Aldo Moro, Piazza Giulio Cesare 11, 70124 Bari, Italy.
                [7 ] Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy.
                [8 ] Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II, Via Orazio Flacco 65, 70124 Bari, Italy.
                Article
                Publisher Item ID: cancers14092132
                DOI: 10.3390/cancers14092132
                PMC ID: 9102628
                PubMed ID: 35565261
                SO-VID: 8a0ef7be-2916-49ae-9054-f4068d9ba56e
                History

                Keywords: radiomics,artificial intelligence,Dynamic Contrast Magnetic Resonance Imaging (DCE-MRI),Contrast-Enhanced Mammography (CEM)
                Data availability:
                Keywords: radiomics, artificial intelligence, Dynamic Contrast Magnetic Resonance Imaging (DCE-MRI), Contrast-Enhanced Mammography (CEM)

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