3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment

      research-article

      Read this article at

      Bookmark
          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

          Purpose

          Human papillomavirus (HPV) status assessment is crucial for decision making in oropharyngeal cancer patients. In last years, several articles have been published investigating the possible role of radiomics in distinguishing HPV-positive from HPV-negative neoplasms. Aim of this review was to perform a systematic quality assessment of radiomic studies published on this topic.

          Methods

          Radiomics studies on HPV status prediction in oropharyngeal cancer patients were selected. The Radiomic Quality Score (RQS) was assessed by three readers to evaluate their methodological quality. In addition, possible correlations between RQS% and journal type, year of publication, impact factor, and journal rank were investigated.

          Results

          After the literature search, 19 articles were selected whose RQS median was 33% (range 0–42%). Overall, 16/19 studies included a well-documented imaging protocol, 13/19 demonstrated phenotypic differences, and all were compared with the current gold standard. No study included a public protocol, phantom study, or imaging at multiple time points. More than half (13/19) included feature selection and only 2 were comprehensive of non-radiomic features. Mean RQS was significantly higher in clinical journals.

          Conclusion

          Radiomics has been proposed for oropharyngeal cancer HPV status assessment, with promising results. However, these are supported by low methodological quality investigations. Further studies with higher methodological quality, appropriate standardization, and greater attention to validation are necessary prior to clinical adoption.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00234-022-02959-0.

          Related collections

          Most cited references55

          • Record: found
          • Abstract: found
          • Article: not found

          Radiomics: Images Are More than Pictures, They Are Data

          This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Radiomics: the process and the challenges.

              "Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer. Copyright © 2012 Elsevier Inc. All rights reserved.
                Bookmark

                Author and article information

                Contributors
                lorenzo.ugga@gmail.com , lorenzo.ugga@unina.it
                Journal
                Neuroradiology
                Neuroradiology
                Neuroradiology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0028-3940
                1432-1920
                23 April 2022
                23 April 2022
                2022
                : 64
                : 8
                : 1639-1647
                Affiliations
                [1 ]GRID grid.4691.a, ISNI 0000 0001 0790 385X, Department of Advanced Biomedical Sciences, , University of Naples “Federico II”, ; Via Sergio Pansini 5, 80131 Naples, Italy
                [2 ]GRID grid.417893.0, ISNI 0000 0001 0807 2568, Department of Radiology, , Fondazione IRCCS Istituto Nazionale Dei Tumori, ; Via Giacomo Venezian 1, 20133 Milan, Italy
                [3 ]GRID grid.4691.a, ISNI 0000 0001 0790 385X, Department of Clinical Medicine and Surgery, , University of Naples “Federico II”, ; Via Sergio Pansini 5, 80131 Naples, Italy
                [4 ]GRID grid.4691.a, ISNI 0000 0001 0790 385X, Interdepartmental Research Center on Management and Innovation in Healthcare—CIRMIS, , University of Naples “Federico II”, ; Via Sergio Pansini 5, 80131 Naples, Italy
                Author information
                http://orcid.org/0000-0001-7811-4612
                Article
                2959
                10.1007/s00234-022-02959-0
                9271107
                35459957
                83b64a91-2f86-46f5-abd8-d2fbd90d114d
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 December 2021
                : 7 April 2022
                Categories
                Head-Neck-ENT Radiology
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2022

                Radiology & Imaging
                systematic review,radiomics,oropharyngeal neoplasms,human papillomavirus,machine learning

                Comments

                Comment on this article