Simple SummaryChildhood cancer survivors are often prone to experiencing late effects due to treatment complications. Valvular Heart Disease is a known iatrogenic effect of radiation leakage to the heart during radiotherapy and is often linked with the occurrence of other cardiac diseases like heart failure. Early identification and treatment of survivors prone to develop valvular heart disease is an important public health issue that remains challenging. In the FCCSS, a voxel-scaled reconstruction of radiation dose to the heart is available for patients that had been treated with radiotherapy. This type of uncommon data allows us to take into consideration information on the dose level that was absorbed by the cardiac tissues, as well as on the spatial characteristics of radiation dose distribution to the heart. With the help of machine learning algorithms, we attempted to train models capable of accurately predicting survivors high at risk of experiencing a late valvular heart disease after radiotherapy for childhood cancer. We suggest that there is an underlying association of the radiation dose with the occurrence of a valvular heart disease that goes beyond the mean dose to the heart and can be explained by the combination of spatial and descriptive features of the dose.AbstractValvular 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.