{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:WKF47VB5LXRZHWNACLBAGIRV5F","short_pith_number":"pith:WKF47VB5","canonical_record":{"source":{"id":"1709.09233","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2017-09-26T19:43:29Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"1ac74a866130f8dbb9483ee401f6f16f2ec6c9476e6768bda5dc08e85ec437d3","abstract_canon_sha256":"c9a0972cfeac6d2c39ea9b0e827fe449dec4ea99147c49f8eb6a3b714f15b2d2"},"schema_version":"1.0"},"canonical_sha256":"b28bcfd43d5de393d9a012c2032235e95ec452853950fb3a41c4e671fd1b57c2","source":{"kind":"arxiv","id":"1709.09233","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.09233","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"arxiv_version","alias_value":"1709.09233v4","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.09233","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"pith_short_12","alias_value":"WKF47VB5LXRZ","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"WKF47VB5LXRZHWNA","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"WKF47VB5","created_at":"2026-05-18T12:31:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:WKF47VB5LXRZHWNACLBAGIRV5F","target":"record","payload":{"canonical_record":{"source":{"id":"1709.09233","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2017-09-26T19:43:29Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"1ac74a866130f8dbb9483ee401f6f16f2ec6c9476e6768bda5dc08e85ec437d3","abstract_canon_sha256":"c9a0972cfeac6d2c39ea9b0e827fe449dec4ea99147c49f8eb6a3b714f15b2d2"},"schema_version":"1.0"},"canonical_sha256":"b28bcfd43d5de393d9a012c2032235e95ec452853950fb3a41c4e671fd1b57c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:33.155942Z","signature_b64":"XHiE+8VdD60f6UjlrNQEJ9ulzQUFB4zh/4whENQV8XMQZByzXjAQhBgWVueUss4FbY3NHKcCpuiWkjvrY9c2AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b28bcfd43d5de393d9a012c2032235e95ec452853950fb3a41c4e671fd1b57c2","last_reissued_at":"2026-05-17T23:59:33.155165Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:33.155165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.09233","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3oaTWvtx0m82H7/kahRGV+EvqXSFwRvcgR7nC4kaMU8QG1tJwmGnMTClw+fJxtstsOvRWFrzoFkAyBX75g12BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:12:34.631859Z"},"content_sha256":"b2c0f24aaaacab9b376522bd4c20f84e22d573f4cb6682881891cd70505cd6d8","schema_version":"1.0","event_id":"sha256:b2c0f24aaaacab9b376522bd4c20f84e22d573f4cb6682881891cd70505cd6d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:WKF47VB5LXRZHWNACLBAGIRV5F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"physics.med-ph","authors_text":"Dan Nguyen, Steve Jiang, Troy Long, Weiguo Lu, Xuejun Gu, Xun Jia, Zohaib Iqbal","submitted_at":"2017-09-26T19:43:29Z","abstract_excerpt":"With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.09233","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NQl2u/lE8MOpEdarhrvqinWvCA5994bj29pCLnljm82/O3VTCYJPsb4l6B7dG67hWKC+zLdP9LH3RNBPBlOjBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:12:34.632413Z"},"content_sha256":"417c04dfed37dfac44b2307891daf482bbf7ccce319f2023cd09c22ebdb34fdd","schema_version":"1.0","event_id":"sha256:417c04dfed37dfac44b2307891daf482bbf7ccce319f2023cd09c22ebdb34fdd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WKF47VB5LXRZHWNACLBAGIRV5F/bundle.json","state_url":"https://pith.science/pith/WKF47VB5LXRZHWNACLBAGIRV5F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WKF47VB5LXRZHWNACLBAGIRV5F/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T19:12:34Z","links":{"resolver":"https://pith.science/pith/WKF47VB5LXRZHWNACLBAGIRV5F","bundle":"https://pith.science/pith/WKF47VB5LXRZHWNACLBAGIRV5F/bundle.json","state":"https://pith.science/pith/WKF47VB5LXRZHWNACLBAGIRV5F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WKF47VB5LXRZHWNACLBAGIRV5F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:WKF47VB5LXRZHWNACLBAGIRV5F","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c9a0972cfeac6d2c39ea9b0e827fe449dec4ea99147c49f8eb6a3b714f15b2d2","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2017-09-26T19:43:29Z","title_canon_sha256":"1ac74a866130f8dbb9483ee401f6f16f2ec6c9476e6768bda5dc08e85ec437d3"},"schema_version":"1.0","source":{"id":"1709.09233","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.09233","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"arxiv_version","alias_value":"1709.09233v4","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.09233","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"pith_short_12","alias_value":"WKF47VB5LXRZ","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"WKF47VB5LXRZHWNA","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"WKF47VB5","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:417c04dfed37dfac44b2307891daf482bbf7ccce319f2023cd09c22ebdb34fdd","target":"graph","created_at":"2026-05-17T23:59:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able","authors_text":"Dan Nguyen, Steve Jiang, Troy Long, Weiguo Lu, Xuejun Gu, Xun Jia, Zohaib Iqbal","cross_cats":["cs.AI","cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2017-09-26T19:43:29Z","title":"A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.09233","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b2c0f24aaaacab9b376522bd4c20f84e22d573f4cb6682881891cd70505cd6d8","target":"record","created_at":"2026-05-17T23:59:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c9a0972cfeac6d2c39ea9b0e827fe449dec4ea99147c49f8eb6a3b714f15b2d2","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2017-09-26T19:43:29Z","title_canon_sha256":"1ac74a866130f8dbb9483ee401f6f16f2ec6c9476e6768bda5dc08e85ec437d3"},"schema_version":"1.0","source":{"id":"1709.09233","kind":"arxiv","version":4}},"canonical_sha256":"b28bcfd43d5de393d9a012c2032235e95ec452853950fb3a41c4e671fd1b57c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b28bcfd43d5de393d9a012c2032235e95ec452853950fb3a41c4e671fd1b57c2","first_computed_at":"2026-05-17T23:59:33.155165Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:33.155165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XHiE+8VdD60f6UjlrNQEJ9ulzQUFB4zh/4whENQV8XMQZByzXjAQhBgWVueUss4FbY3NHKcCpuiWkjvrY9c2AA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:33.155942Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.09233","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b2c0f24aaaacab9b376522bd4c20f84e22d573f4cb6682881891cd70505cd6d8","sha256:417c04dfed37dfac44b2307891daf482bbf7ccce319f2023cd09c22ebdb34fdd"],"state_sha256":"30531acba3687ed16d56eda0cf06c2cb776bbae53e868219c79823e62a3f9d6a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TmVtP6KqnnxJPzg8m8r65EEn8PbZHnIWUPv57GniK0kxb9lWdlNyeg0fUEL6WazGs7MjIw0Eq1EMpVzXpr1cDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T19:12:34.636740Z","bundle_sha256":"ae24b9c5117bfecad0731965488e375fb4532db4d84aec24e2f7e19b97a5917f"}}