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      Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

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          Abstract

          Purpose

          The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.

          Methods

          Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.

          Results

          Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.

          Conclusions

          The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.

          Electronic supplementary material

          The online version of this article (10.1007/s00259-019-04372-x) contains supplementary material, which is available to authorized users.

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          Most cited references142

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          Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

          To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).
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            Automated Identification of Diabetic Retinopathy Using Deep Learning

            Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses.
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              Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

              Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. (©) RSNA, 2016 Online supplemental material is available for this article.
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                Author and article information

                Contributors
                +39-02-8224-6621 , margarita.kirienko@icloud.com
                Journal
                Eur J Nucl Med Mol Imaging
                Eur. J. Nucl. Med. Mol. Imaging
                European Journal of Nuclear Medicine and Molecular Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1619-7070
                1619-7089
                18 June 2019
                18 June 2019
                2019
                : 46
                : 13
                : 2656-2672
                Affiliations
                [1 ]GRID grid.452490.e, Department of Biomedical Sciences, , Humanitas University, ; Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Milan, Italy
                [2 ]GRID grid.417728.f, ISNI 0000 0004 1756 8807, Nuclear Medicine, , Humanitas Clinical and Research Center IRCCS, ; Rozzano, Milan, Italy
                Author information
                http://orcid.org/0000-0003-2214-6492
                http://orcid.org/0000-0003-1832-1083
                http://orcid.org/0000-0002-5806-1856
                http://orcid.org/0000-0002-3923-1151
                Article
                4372
                10.1007/s00259-019-04372-x
                6879445
                31214791
                19a9bfdd-a261-4d23-8a15-5161d357176e
                © The Author(s) 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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.

                History
                : 7 May 2019
                : 23 May 2019
                Categories
                Review Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2019

                Radiology & Imaging
                radiomics,artificial intelligence,texture analysis,imaging,systematic review,trial phases

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