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      Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer ☆

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.

          Highlights

          • •

            Artificial intelligence is used in target delineation and treatment planning.

          • •

            Benefits are expected from individualising dose based on recurrence patterns.

          • •

            Collaboration between different expertises is essential to generate models.

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          Most cited references83

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          Twenty-Year Follow-up of a Randomized Trial Comparing Total Mastectomy, Lumpectomy, and Lumpectomy plus Irradiation for the Treatment of Invasive Breast Cancer

          Bernard Fisher, Stewart Anderson, John Bryant … (2002)
          New England Journal of Medicine, 347(16), 1233-1241
          Article 0 comments Cited 1188 times – based on 0 reviews     Review now
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            MR-based synthetic CT generation using a deep convolutional neural network method.

            Xiao Han (2017)
            Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images.
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              PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer

              Gordon C Wishart, Elizabeth Azzato, David Greenberg … (2010)
              Introduction The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK. Methods Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation. Results Differences in overall actual and predicted mortality were <1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were <1% at eight years for ECRIC (12.9% vs. 13.5%) and <1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75). Conclusions We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.
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                Author and article information

                Contributors
                Philip M.P. Poortmans
                Journal
                Journal ID (nlm-ta): Breast
                Journal ID (iso-abbrev): Breast
                Title: The Breast : official journal of the European Society of Mastology
                Publisher: Elsevier
                ISSN (Print): 0960-9776
                ISSN (Electronic): 1532-3080
                Publication date PMC-release: 26 November 2019
                Publication date Collection: February 2020
                Publication date (Electronic): 26 November 2019
                Volume: 49
                Pages: 194-200
                Affiliations
                [a ]Paris Sciences & Lettres - PSL University, Paris, France
                [b ]Institut Curie, Department of Radiation Oncology, Paris, France
                [c ]Department of Radiation Oncology – Hospital Sírio-Libanês, Brazil
                [d ]Department of Radiology and Oncology – Radiation Oncology, Instituto Do Câncer Do Estado de São Paulo (ICESP), Faculdade de Medicina da Universidade de São Paulo, Brazil
                [e ]Department of Experimental and Clinical Biomedical Sciences “M. Serio”, University of Florence, Florence, Italy
                [f ]Radiation Oncology Unit, Oncology Department, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
                [g ]Radiation Oncology Unit, Breast Radiation Unit, Sheba Tel Ha’shomer, Ramat Gan, Israel
                Author notes
                [∗ ]Corresponding author. Paris Sciences & Lettres - PSL University, Paris, France. philip.poortmans@ 123456telenet.be
                Article
                Publisher ID: S0960-9776(19)31103-8
                DOI: 10.1016/j.breast.2019.11.011
                PMC ID: 7375562
                PubMed ID: 31931265
                SO-VID: 0d64f2db-01ce-4cfc-8387-77fe4229a07a
                Copyright © © 2019 Published by Elsevier Ltd.
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 22 July 2019
                Date revision received : 16 November 2019
                Date accepted : 20 November 2019
                Categories
                Subject: Virtual special issue: Artificial Intelligence in Breast Cancer Care; Edited by Nehmat Houssami, Maria João Cardoso, Giuseppe Pozzi and Brigitte Seroussi

                ScienceOpen disciplines: Obstetrics & Gynecology
                Keywords: artificial intelligence,breast cancer,radiation therapy,neural network,deep learning,auto-segmentation
                Data availability:
                ScienceOpen disciplines: Obstetrics & Gynecology
                Keywords: artificial intelligence, breast cancer, radiation therapy, neural network, deep learning, auto-segmentation

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