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A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA
- Source :
- Kostakoglu, L, Dalmasso, F, Berchialla, P, Pierce, L A, Vitolo, U, Martelli, M, Sehn, L H, Trněný, M, Nielsen, T G, Bolen, C R, Sahin, D, Lee, C, El-Galaly, T C, Mattiello, F, Kinahan, P E & Chauvie, S 2022, ' A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA ', EJHaem, vol. 3, no. 2, pp. 406-414 . https://doi.org/10.1002/jha2.421
- Publication Year :
- 2021
-
Abstract
- Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT)-derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression-free survival (PFS) and overall survival (OS) predictions. Baseline FDG-PET scans were available for 1263 patients, 832 patients of these were cell-of-origin (COO)-evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low-, intermediate- and high-risk groups. The random forest model with COO subgroups identified a clearer high-risk population (45% 2-year PFS [95% confidence interval (CI) 40%-52%]; 65% 2-year OS [95% CI 59%-71%]) than the IPI (58% 2-year PFS [95% CI 50%-67%]; 69% 2-year OS [95% CI 62%-77%]). This study confirms that standard clinical risk factors can be combined with PET-derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.
Details
- ISSN :
- 26886146
- Volume :
- 3
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- EJHaem
- Accession number :
- edsair.doi.dedup.....052c33bc95a3c8bb577245b0b48fce52