{"doi":"10.1016/j.radonc.2020.10.017","title":"Development and validation of an age-scalable cardiac model with substructures for dosimetry in late-effects studies of childhood cancer survivors","abstract":"BACKGROUND AND PURPOSE: Radiation therapy is a risk factor for late cardiac disease in childhood cancer survivors. Several pediatric cohort studies have established whole heart dose and dose-volume response models. Emerging data suggest that dose to cardiac substructures may be more predictive than whole heart metrics. In order to develop substructure dose-response models, the heart model previously used for pediatric cohort dosimetry needed enhancement and substructure delineation. METHODS: To enhance our heart model, we combined the age-scalable capability of our computational phantom with the anatomically-delineated (with substructures) heart models from an international humanoid phantom series. We examined cardiac volume similarity/overlap between registered age-scaled phantoms (1, 5, 10, and 15 years) with the enhanced heart model and the reference phantoms of the same age; dice similarity coefficient (DSC) and overlap coefficient (OC) were calculated for each matched pair. To assess the accuracy of our enhanced heart model, we compared doses from computed tomography-based planning (ground truth) with reconstructed heart doses. We also compared doses calculated with the prior and enhanced heart models for a cohort of nearly 5000 childhood cancer survivors. RESULTS: We developed a realistic cardiac model with 14-substructures, scalable across a broad age range (1-15 years); average DSC and OC were 0.84 ± 0.05 and 0.90 ± 0.05, respectively. The average percent difference between reconstructed and ground truth mean heart doses was 4.2%. In the cohort dosimetry analysis, dose and dose-volume metrics were approximately 10% lower on average when the enhanced heart model was used for dose reconstructions. CONCLUSION: We successfully developed and validated an anatomically realistic age-scalable cardiac model that can be used to establish substructure dose-response models for late cardiac disease in childhood cancer survivor cohorts.","journal":"Radiotherapy and Oncology","year":2020,"id":83715,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9523,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":430415,"name":"Aashish C. Gupta","orcid":"0000-0002-0178-0921","position":1,"is_corresponding":false},{"id":430416,"name":"James E. Bates","orcid":"0000-0001-7060-7156","position":2,"is_corresponding":false},{"id":430417,"name":"Choonsik Lee","orcid":"0000-0003-4289-9870","position":3,"is_corresponding":false},{"id":430418,"name":"Constance A. Owens","orcid":"0000-0001-7208-394X","position":4,"is_corresponding":false},{"id":430419,"name":"Bradford S. Hoppe","orcid":"0000-0002-2312-5418","position":5,"is_corresponding":false},{"id":430420,"name":"Louis S. Constine","orcid":"0000-0003-0742-1740","position":6,"is_corresponding":false},{"id":310247,"name":"Susan A. Smith","orcid":"0000-0002-6367-3080","position":7,"is_corresponding":false},{"id":430421,"name":"Ying Qiao","orcid":"0000-0003-4308-5633","position":8,"is_corresponding":false},{"id":430422,"name":"Rita E. Weathers","orcid":"0000-0003-4300-0468","position":9,"is_corresponding":false},{"id":255628,"name":"Yutaka Yasui","orcid":"0000-0002-7717-8638","position":10,"is_corresponding":false},{"id":299202,"name":"Laurence E. Court","orcid":"0000-0002-3241-6145","position":11,"is_corresponding":false},{"id":288903,"name":"Arnold C. Paulino","orcid":"0000-0002-0269-3045","position":12,"is_corresponding":false},{"id":388064,"name":"Chelsea C. Pinnix","orcid":"0000-0003-3982-3664","position":13,"is_corresponding":false},{"id":341613,"name":"Stephen F. Kry","orcid":"0000-0001-6899-197X","position":14,"is_corresponding":false},{"id":341612,"name":"D Followill","orcid":"0000-0001-6744-0439","position":15,"is_corresponding":false},{"id":230934,"name":"Gregory T. Armstrong","orcid":"0000-0001-8722-4207","position":16,"is_corresponding":false},{"id":310246,"name":"Rebecca M. Howell","orcid":"0000-0002-0803-071X","position":17,"is_corresponding":false},{"id":430414,"name":"Suman Shrestha","orcid":"0000-0002-2365-2592","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-18T21:54:32.422022Z","pmid":"33075392","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}