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• W4380086043 abstract "Valvular Heart Disease (VHD) is a known late complication of radiotherapy for childhood cancer (CC), and identifying high-risk survivors correctly remains a challenge. This paper focuses on the distribution of the radiation dose absorbed by heart tissues. We propose that a dosiomics signature could provide insight into the spatial characteristics of the heart dose associated with a VHD, beyond the already-established risk induced by high doses. We analyzed data from the 7670 survivors of the French Childhood Cancer Survivors' Study (FCCSS), 3902 of whom were treated with radiotherapy. In all, 63 (1.6%) survivors that had been treated with radiotherapy experienced a VHD, and 57 of them had heterogeneous heart doses. From the heart-dose distribution of each survivor, we extracted 93 first-order and spatial dosiomics features. We trained random forest algorithms adapted for imbalanced classification and evaluated their predictive performance compared to the performance of standard mean heart dose (MHD)-based models. Sensitivity analyses were also conducted for sub-populations of survivors with spatially heterogeneous heart doses. Our results suggest that MHD and dosiomics-based models performed equally well globally in our cohort and that, when considering the sub-population having received a spatially heterogeneous dose distribution, the predictive capability of the models is significantly improved by the use of the dosiomics features. If these findings are further validated, the dosiomics signature may be incorporated into machine learning algorithms for radiation-induced VHD risk assessment and, in turn, into the personalized refinement of follow-up guidelines." @default.
• W4380086043 created "2023-06-10" @default.
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• W4380086043 date "2023-06-08" @default.
• W4380086043 modified "2023-10-04" @default.
• W4380086043 title "Dosiomics-Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer" @default.
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• W4380086043 doi "https://doi.org/10.3390/cancers15123107" @default.
• W4380086043 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37370717" @default.
• W4380086043 hasPublicationYear "2023" @default.
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