{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AAMURNX5H5VAH3BD5URR56M4VO","short_pith_number":"pith:AAMURNX5","schema_version":"1.0","canonical_sha256":"001948b6fd3f6a03ec23ed231ef99cabb5546b7513ccbbea6c172cb3f8e59d42","source":{"kind":"arxiv","id":"2507.08189","version":2},"attestation_state":"computed","paper":{"title":"Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.med-ph","authors_text":"Ali Fathi Jouzdani, Amir Hossein Pouria, Arman Rahmim, Ilker Hacihaliloglu, Mehdi Maghsudi, Mehrdad Oveisi, Mohammad R. Salmanpour, Shahram Taeb, Somayeh Sadat Mehrnia, Sonia Falahati","submitted_at":"2025-07-10T21:57:15Z","abstract_excerpt":"Background: CT imaging is vital for lung cancer management, offering detailed visualization for AI-based prognosis. However, supervised learning SL models require large labeled datasets, limiting their real-world application in settings with scarce annotations.\n  Methods: We analyzed CT scans from 977 patients across 12 datasets extracting 1218 radiomics features using Laplacian of Gaussian and wavelet filters via PyRadiomics Dimensionality reduction was applied with 56 feature selection and extraction algorithms and 27 classifiers were benchmarked A semi supervised learning SSL framework with"},"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":"2507.08189","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.med-ph","submitted_at":"2025-07-10T21:57:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"cd78f14e007a22d3345e6264c19cebe7f50c74dc2dc7e800361dc17d1d8cccfb","abstract_canon_sha256":"9d5cf822e196512dae8949f55819bfd944bf2a30754e259867d4dfcbb3dabfb2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:37:25.885762Z","signature_b64":"m8b3LCxrdGBdUrUGtdCDrwOacnp1v+oeyjiqkUm5cEQTsyrLlIyMA/MtesdnJoVf+SsWC3bMFLP94Vhr34s2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"001948b6fd3f6a03ec23ed231ef99cabb5546b7513ccbbea6c172cb3f8e59d42","last_reissued_at":"2026-07-05T11:37:25.885243Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:37:25.885243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.med-ph","authors_text":"Ali Fathi Jouzdani, Amir Hossein Pouria, Arman Rahmim, Ilker Hacihaliloglu, Mehdi Maghsudi, Mehrdad Oveisi, Mohammad R. Salmanpour, Shahram Taeb, Somayeh Sadat Mehrnia, Sonia Falahati","submitted_at":"2025-07-10T21:57:15Z","abstract_excerpt":"Background: CT imaging is vital for lung cancer management, offering detailed visualization for AI-based prognosis. However, supervised learning SL models require large labeled datasets, limiting their real-world application in settings with scarce annotations.\n  Methods: We analyzed CT scans from 977 patients across 12 datasets extracting 1218 radiomics features using Laplacian of Gaussian and wavelet filters via PyRadiomics Dimensionality reduction was applied with 56 feature selection and extraction algorithms and 27 classifiers were benchmarked A semi supervised learning SSL framework with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.08189","kind":"arxiv","version":2},"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/2507.08189/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":"2507.08189","created_at":"2026-07-05T11:37:25.885301+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.08189v2","created_at":"2026-07-05T11:37:25.885301+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.08189","created_at":"2026-07-05T11:37:25.885301+00:00"},{"alias_kind":"pith_short_12","alias_value":"AAMURNX5H5VA","created_at":"2026-07-05T11:37:25.885301+00:00"},{"alias_kind":"pith_short_16","alias_value":"AAMURNX5H5VAH3BD","created_at":"2026-07-05T11:37:25.885301+00:00"},{"alias_kind":"pith_short_8","alias_value":"AAMURNX5","created_at":"2026-07-05T11:37:25.885301+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO","json":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO.json","graph_json":"https://pith.science/api/pith-number/AAMURNX5H5VAH3BD5URR56M4VO/graph.json","events_json":"https://pith.science/api/pith-number/AAMURNX5H5VAH3BD5URR56M4VO/events.json","paper":"https://pith.science/paper/AAMURNX5"},"agent_actions":{"view_html":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO","download_json":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO.json","view_paper":"https://pith.science/paper/AAMURNX5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.08189&json=true","fetch_graph":"https://pith.science/api/pith-number/AAMURNX5H5VAH3BD5URR56M4VO/graph.json","fetch_events":"https://pith.science/api/pith-number/AAMURNX5H5VAH3BD5URR56M4VO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO/action/storage_attestation","attest_author":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO/action/author_attestation","sign_citation":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO/action/citation_signature","submit_replication":"https://pith.science/pith/AAMURNX5H5VAH3BD5URR56M4VO/action/replication_record"}},"created_at":"2026-07-05T11:37:25.885301+00:00","updated_at":"2026-07-05T11:37:25.885301+00:00"}