{"paper":{"title":"Voxel-aware oxygen kinetics resolves radiation-induced DNA damage retention across LET-oxygen conditions","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"Voxel-aware oxygen kinetics enables particle-specific modeling of DNA damage retention across LET and oxygen conditions at clinical planning speeds.","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Ramon Jose C. Bagunu, Renato III Fernan Bolo","submitted_at":"2026-05-11T15:00:31Z","abstract_excerpt":"Objective. Hypoxic tumor subvolumes resist radiation through elevated oxygen enhancement ratios (OER), yet no computational OER model is simultaneously particle-specific, mechanistically grounded, and fast enough for voxel-scale treatment planning. We present the VOxel-Aware Oxygen Model (VOxA) to address all three requirements.\n  Approach. An Oxygen Model (OM) encodes particle-specific LET-OER dependence through dual sigmoidal transitions constrained to increase monotonically with atomic number Z, combined with Michaelis-Menten oxygen kinetics. A Voxel-Aware (VA) extension resolves per-DSB lo"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"VOxA is the first particle-specific OER model to reproduce Z-ordering analytically at clinical planning speed, validated on the largest OER calibration dataset for this model class.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The dual sigmoidal LET-OER transitions constrained to increase monotonically with atomic number Z, combined with Michaelis-Menten kinetics and a single calibrated particle-specific sensitivity parameter, sufficiently capture the underlying biology across the full range of LET and oxygen conditions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"VOxA achieves R²=0.719 on 233 OER observations across 10 particle types and outperforms clinical standards by 28.4% in survival OER MAE while running over 10^6 times faster than Monte Carlo.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Voxel-aware oxygen kinetics enables particle-specific modeling of DNA damage retention across LET and oxygen conditions at clinical planning speeds.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1704d374824a32736cb358b7d87b70f7f40764ddab2c03965d49d4f4122ab6f9"},"source":{"id":"2605.12558","kind":"arxiv","version":2},"verdict":{"id":"b5e74848-023d-4239-8a96-1161b329f35e","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:18:51.550815Z","strongest_claim":"VOxA is the first particle-specific OER model to reproduce Z-ordering analytically at clinical planning speed, validated on the largest OER calibration dataset for this model class.","one_line_summary":"VOxA achieves R²=0.719 on 233 OER observations across 10 particle types and outperforms clinical standards by 28.4% in survival OER MAE while running over 10^6 times faster than Monte Carlo.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The dual sigmoidal LET-OER transitions constrained to increase monotonically with atomic number Z, combined with Michaelis-Menten kinetics and a single calibrated particle-specific sensitivity parameter, sufficiently capture the underlying biology across the full range of LET and oxygen conditions.","pith_extraction_headline":"Voxel-aware oxygen kinetics enables particle-specific modeling of DNA damage retention across LET and oxygen conditions at clinical planning speeds."},"references":{"count":60,"sample":[{"doi":"","year":null,"title":"Alper, T. and Howard-Flanders, P. , title =. Nature , year =","work_id":"9847906a-240b-4a5c-8f19-2c8b3521e215","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Arnould, C. and Rocher, V. and Finoux, A.-L. and others , title =. Nature , year =","work_id":"3dafca2d-e642-42c9-92db-89bfbc517a90","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"and Paganetti, Harald and Schuemann, Jan , title =","work_id":"89b5c8aa-733d-445b-9239-ade940b3195f","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Cancers , year =","work_id":"75356189-62a3-4770-b9ea-7b65607dfb5b","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1088/1361-6560/ad87a7","year":null,"title":"D-Kondo, Naoki and Masilela, Thongchai A. M. and Shin, Wook-Geun and Faddegon, Bruce and LaVerne, Jay and Schuemann, Jan and Ramos-Mendez, Jose , title =. Physics in Medicine and Biology , year =. doi","work_id":"26f710d5-19ec-42b1-955f-1307dcfe6a80","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":60,"snapshot_sha256":"c501afdd8b5b97fbfa6642574831400213a48f108d4ef2e4fc1caf6387f5f214","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"f74f05e706d233fb061d12825563c13453b08c1a52e214605f8a824d1865bf4b"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}