Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models
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Le résumé fourni par la source
Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent external validation that is decisive for their clinical application. Therefore, in this manuscript we present two complementary studies. In our modelling study, we developed and validated different radiomics signatures for outcome prediction after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) based on computed tomography (CT) and T2-weighted (T2w) magnetic resonance (MR) imaging datasets of 4 independent institutions (training: 122, validation 68 patients). We compared different feature classes extracted from the gross tumour volume for the prognosis of tumour response and freedom from distant metastases (FFDM): morphological and first order (MFO) features, second order texture (SOT) features, and Laplacian of Gaussian (LoG) transformed intensity features. Analyses were performed for CT and MRI separately and combined. Model performance was assessed by the area under the curve (AUC) and the concordance index (CI) for tumour response and FFDM, respectively. Overall, intensity features of LoG transformed CT and MR imaging combined with clinical T stage (cT) showed the best performance for tumour response prediction, while SOT features showed good performance for FFDM in independent validation (AUC = 0.70, CI = 0.69). In our external validation study, we aimed to validate previously published radiomics signatures on our multicentre cohort. We identified relevant publications on comparable patient datasets through a literature search and applied the reported radiomics models to our dataset. Only one of the identified studies could be validated, indicating an overall lack of reproducibility and the need of further standardization of radiomics before clinical application.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models
- Date Crossref
- 17/06/2022
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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German Cancer Research Center German Cancer Consortium (DKTK) partner site Dresden pays non établi dans la noticeStructure de recherche
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Heidelberg University pays non établi dans la noticeUniversité ou école supérieure
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Helmholtz-Zentrum Dresden-Rossendorf pays non établi dans la noticeStructure de recherche
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OncoRay Helmholtz-Zentrum Dresden-Rossendorf pays non établi dans la noticeÉtablissement de santé
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University Hospital Carl Gustav Carus OncoRay-National Center for Radiation Research in Oncology pays non établi dans la noticeÉtablissement de santé
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German Cancer Consortium (DKTK) Dresden pays non établi dans la noticeStructure de recherche
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Technische Universität Dresden pays non établi dans la noticeUniversité ou école supérieure
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National Center for Tumor Diseases pays non établi dans la noticeOrganisme public
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Nationales Centrum für Tumorerkrankungen Dresden pays non établi dans la noticeÉtablissement de santé
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TUM Klinikum pays non établi dans la noticeÉtablissement de santé
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Helmholtz Munich pays non établi dans la noticeStructure de recherche
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Technical University of Munich pays non établi dans la noticeUniversité ou école supérieure
German Cancer Consortium (DKTK) partner site Dresden — German Cancer Research Center, Heidelberg University et Helmholtz-Zentrum Dresden-Rossendorf, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.