Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma
| Title: | Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma |
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| Authors: | Yuan Yu, Hongwei Guo, Meng Zhang, Feng Hou, Shifeng Yang, Chencui Huang, Lisha Duan, Hexiang Wang |
| Source: | Cancer Imaging, Vol 24, Iss 1, Pp 1-12 (2024) |
| Publisher Information: | BMC, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Medical physics. Medical radiology. Nuclear medicine LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| Subject Terms: | Radiomics, Soft tissue sarcoma, Disease Progression, Progression-free survival, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| More Details: | Abstract 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1470-7330 64111342 |
| Relation: | https://doaj.org/toc/1470-7330 |
| DOI: | 10.1186/s40644-024-00705-8 |
| Access URL: | https://doaj.org/article/818c6e64111342b784b8d848d92dc014 |
| Accession Number: | edsdoj.818c6e64111342b784b8d848d92dc014 |
| Database: | Directory of Open Access Journals |
| ISSN: | 14707330 64111342 |
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| DOI: | 10.1186/s40644-024-00705-8 |
| Published in: | Cancer Imaging |
| Language: | English |