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      MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer

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          Abstract

          Background

          To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC).

          Methods

          Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models’ development and validation.

          Results

          We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance.

          Conclusions

          The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC’s true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.

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          Most cited references55

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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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            Radiomics: extracting more information from medical images using advanced feature analysis.

            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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              Decision curve analysis: a novel method for evaluating prediction models.

              Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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                Author and article information

                Contributors
                wangweihu88@163.com
                Journal
                Br J Cancer
                Br J Cancer
                British Journal of Cancer
                Nature Publishing Group UK (London )
                0007-0920
                1532-1827
                2 April 2022
                2 April 2022
                20 July 2022
                : 127
                : 2
                : 249-257
                Affiliations
                [1 ]GRID grid.412474.0, ISNI 0000 0001 0027 0586, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, , Peking University Cancer Hospital and Institute, ; Beijing, China
                [2 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Department of Radiation Oncology, Peking Union Medical College Hospital, , Chinese Academy of Medical Sciences & Peking Union Medical College, ; Beijing, China
                [3 ]GRID grid.417031.0, ISNI 0000 0004 1799 2675, Department of Oncology, , Tianjin Union Medical Center, ; Tianjin, China
                [4 ]Blot Info & Tech (Beijing) Co. Ltd, Beijing, China
                [5 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Radiation Oncology and Molecular Radiation Sciences, , Johns Hopkins University School of Medicine, ; Baltimore, MD USA
                [6 ]GRID grid.488525.6, Department of Radiation Oncology, Department of Medical Engineering, Guangdong Institute of Gastroenterology, , The Sixth Affiliated Hospital of Sun Yat-sen University, ; Guangzhou, China
                Author information
                http://orcid.org/0000-0001-9952-3618
                http://orcid.org/0000-0001-9437-6188
                http://orcid.org/0000-0003-4969-398X
                Article
                1786
                10.1038/s41416-022-01786-7
                9296479
                35368044
                bdfab9d1-da01-4c6d-90cd-ffd2d8f04f15
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 April 2021
                : 29 January 2022
                : 8 March 2022
                Funding
                Funded by: National Natural Science Foundation (no.32071156).
                Funded by: Capital’s Funds for Health Improvement and Research (no. 2020-1-4021)
                Funded by: Capital’s Funds for Health Improvement and Research (no.2020-2-1027)
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2022

                Oncology & Radiotherapy
                rectal cancer,surgical oncology,translational research,cancer models,cancer imaging

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