{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DKZ2IKYYUYPCH2V2NRJ3WFV3G4","short_pith_number":"pith:DKZ2IKYY","schema_version":"1.0","canonical_sha256":"1ab3a42b18a61e23eaba6c53bb16bb370fafa027d33892abf8e8b499ac0540e1","source":{"kind":"arxiv","id":"2509.09610","version":1},"attestation_state":"computed","paper":{"title":"Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Andreas Rauschecker, Catherine R. Jutzeler, Daria Laslo, Efthymios Georgiou, Marius George Linguraru, Sabine Muller, Sarah Bruningk","submitted_at":"2025-09-11T16:52:09Z","abstract_excerpt":"Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthes"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2509.09610","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-09-11T16:52:09Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d3080dcf69c4ab32652bac5964ec628a032cb1bea35e7d32159206b67fab80d6","abstract_canon_sha256":"2437fb73130787d177d50a9f56347aa1f06fef07dbeb565a60a69e31d7dffe1f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:39.317329Z","signature_b64":"BX46DFSZ4UxizGLqlvvaVEhMBTcAxh0vw/2alOV161dJfLpIQKHuVSfAVpsu9sq5NwzJuxqfxib5+xdwDktwBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ab3a42b18a61e23eaba6c53bb16bb370fafa027d33892abf8e8b499ac0540e1","last_reissued_at":"2026-07-05T12:09:39.316758Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:39.316758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Andreas Rauschecker, Catherine R. Jutzeler, Daria Laslo, Efthymios Georgiou, Marius George Linguraru, Sabine Muller, Sarah Bruningk","submitted_at":"2025-09-11T16:52:09Z","abstract_excerpt":"Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthes"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.09610","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2509.09610/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2509.09610","created_at":"2026-07-05T12:09:39.316830+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.09610v1","created_at":"2026-07-05T12:09:39.316830+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.09610","created_at":"2026-07-05T12:09:39.316830+00:00"},{"alias_kind":"pith_short_12","alias_value":"DKZ2IKYYUYPC","created_at":"2026-07-05T12:09:39.316830+00:00"},{"alias_kind":"pith_short_16","alias_value":"DKZ2IKYYUYPCH2V2","created_at":"2026-07-05T12:09:39.316830+00:00"},{"alias_kind":"pith_short_8","alias_value":"DKZ2IKYY","created_at":"2026-07-05T12:09:39.316830+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06094","citing_title":"Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming","ref_index":121,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4","json":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4.json","graph_json":"https://pith.science/api/pith-number/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/graph.json","events_json":"https://pith.science/api/pith-number/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/events.json","paper":"https://pith.science/paper/DKZ2IKYY"},"agent_actions":{"view_html":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4","download_json":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4.json","view_paper":"https://pith.science/paper/DKZ2IKYY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.09610&json=true","fetch_graph":"https://pith.science/api/pith-number/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/graph.json","fetch_events":"https://pith.science/api/pith-number/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/action/storage_attestation","attest_author":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/action/author_attestation","sign_citation":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/action/citation_signature","submit_replication":"https://pith.science/pith/DKZ2IKYYUYPCH2V2NRJ3WFV3G4/action/replication_record"}},"created_at":"2026-07-05T12:09:39.316830+00:00","updated_at":"2026-07-05T12:09:39.316830+00:00"}