Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma
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Le résumé fourni par la source
BACKGROUND: To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS: We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS: For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION: To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.
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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma
- Date Crossref
- 08/05/2024
- 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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Qingdao University Department of Radiology pays non établi dans la noticeUniversité ou école supérieure
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Affiliated Hospital of Qingdao University pays non établi dans la noticeÉtablissement de santé
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Qingdao Women and Children's Hospital pays non établi dans la noticeÉtablissement de santé
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Shandong Provincial Hospital Department of Radiology pays non établi dans la noticeÉtablissement de santé
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Shandong First Medical University pays non établi dans la noticeUniversité ou école supérieure
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Hebei Medical University Department of Radiology pays non établi dans la noticeUniversité ou école supérieure
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Third Hospital of Hebei Medical University pays non établi dans la noticeÉtablissement de santé
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Ltd Department of Research Collaboration pays non établi dans la noticeEntreprise
Department of Radiology — Qingdao University, Affiliated Hospital of Qingdao University et Qingdao Women and Children's Hospital, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.