{"id":"idc-nsclc-radiogenomics","title":"NSCLC Radiogenomics","short_title":"nsclc_radiogenomics","aliases":["nsclc_radiogenomics"],"summary":"Radiogenomics needs the same patients measured both ways, and almost no public resource provides it. This collection does: 211 patients imaged with thin-section CT and whole-body PET/CT before surgical excision, with a subset carrying matched microarray expression data deposited separately in the Gene Expression Omnibus.\nThe link between the two is recorded in the collection description as prose, not as a structured cross-reference, which is why the pairing is easy to miss. We extract that accession automatically and surface it here. Everything is openly downloadable with no account.","one_liner":"211 non-small cell lung cancers with pre-surgical CT and PET/CT, semantic annotations, segmentations and matched gene expression - a genuinely paired imaging-genomics cohort, fully open, and described only in prose.","identifiers":[{"scheme":"idc_collection_id","value":"nsclc_radiogenomics","url":"https://portal.imaging.datacommons.cancer.gov/explore/?collection_id=nsclc_radiogenomics","is_primary":true,"evidence":[{"method":"api","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC v3 API /collections","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":null,"locator":null,"confidence":"high","reviewer":null,"reviewed_at":null,"note":null}]},{"scheme":"tcia_collection","value":"NSCLC Radiogenomics","url":"https://www.cancerimagingarchive.net/collections/","is_primary":false,"evidence":[{"method":"api","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC v3 API /collections","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":null,"locator":null,"confidence":"high","reviewer":null,"reviewed_at":null,"note":null}]},{"scheme":"geo_series","value":"GSE28827","url":null,"is_primary":false,"evidence":[{"method":"fulltext","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC collection description","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":"ised samples, which is available on the NCBI Gene Expression Omnibus), where DICOM patient names are identical to micro","locator":"accession extracted from description prose","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Cross-repository link recovered from prose; verify before relying on it."}]},{"scheme":"doi","value":"10.7937/K9/TCIA.2017.7hs46erv","url":null,"is_primary":false,"evidence":[{"method":"fulltext","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC collection description","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":"where DICOM patient names are identical to microarray sample names. Please see the NSCLC Radiogenomics wiki page to learn more about the images and to obtain any supporting metadata for this c","locator":"accession extracted from description prose","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Cross-repository link recovered from prose; verify before relying on it."}]}],"repository":{"name":"NCI Imaging Data Commons","short_name":"IDC","url":"https://portal.imaging.datacommons.cancer.gov","is_nci_crdc_node":true,"is_nih_repository":true},"additional_repositories":[],"program_name":"NCI Imaging Data Commons / The Cancer Imaging Archive","program_url":"https://imaging.datacommons.cancer.gov/","nci_program":"IDC","generating_institutions":[],"data_generation_funding_role":"unknown","version":null,"release_date":"2012-01-01","last_upstream_update":null,"retrieved_at":"2026-09-18T02:44:36.389375Z","landing_page_url":"https://portal.imaging.datacommons.cancer.gov/explore/?collection_id=nsclc_radiogenomics","cancer_types":[{"label":"Non-small Cell Lung Cancer","ontology":null,"code":null,"url":null}],"primary_sites":["Chest"],"subject":{"scope":"single","tissues":["LUNG"],"evidence":[{"method":"derived","source_url":null,"source_label":"cds.subjects","retrieved_at":null,"snippet":null,"locator":"cancer_types: Non-small Cell Lung Cancer; primary_sites: Chest","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Repository disease labels or primary sites mapped to pinned tissues."}]},"is_pediatric":null,"model_systems":[],"cohort":{"n_cases":211,"n_samples":null,"n_aliquots":null,"n_files":null,"total_bytes":null,"n_tumor_cases":null,"n_normal_cases":null,"has_matched_normal":null,"demographics":{"sex":{},"race":{},"ethnicity":{},"age_at_diagnosis_years":{},"vital_status":{},"country_or_region":{},"evidence":[]},"evidence":[{"method":"api","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC v3 API /collections","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":null,"locator":null,"confidence":"high","reviewer":null,"reviewed_at":null,"note":null}]},"assays":[{"modality":"radiology","label":"DICOM medical imaging","platform":null,"n_cases":211,"n_samples":null,"n_files":null,"data_levels":["DICOM series"],"access_tier":"open","evidence":[{"method":"api","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC v3 API /collections","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":null,"locator":null,"confidence":"high","reviewer":null,"reviewed_at":null,"note":null}]},{"modality":"clinical","label":"Clinical (accompanying data, per IDC supporting_data)","platform":null,"n_cases":null,"n_samples":null,"n_files":null,"data_levels":[],"access_tier":null,"evidence":[{"method":"api","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC v3 API /collections","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":null,"locator":"field: supporting_data","confidence":"medium","reviewer":null,"reviewed_at":null,"note":null}]},{"modality":"wgs","label":"Genomics (accompanying data, per IDC supporting_data)","platform":null,"n_cases":null,"n_samples":null,"n_files":null,"data_levels":[],"access_tier":null,"evidence":[{"method":"api","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC v3 API /collections","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":null,"locator":"field: supporting_data","confidence":"medium","reviewer":null,"reviewed_at":null,"note":null}]}],"clinical_variables":[],"longitudinal":{"has_followup":null,"has_survival_endpoint":null,"survival_endpoints":[],"median_followup_months":null,"max_followup_months":null,"n_cases_with_followup":null,"has_serial_samples":null,"timepoints":[],"n_cases_with_serial":null,"has_treatment_response":null,"response_criteria":[],"evidence":[]},"access":{"tier":"open","open_components":[],"controlled_components":[],"mechanism":"Public DICOM download from the IDC portal, its API, or the idc-index Python package; no account required for public collections.","dua_url":null,"data_access_committee":null,"typical_turnaround":null,"embargo_until":null,"login_required":false,"citation_requirement":"Cite the collection DOI and the IDC/TCIA data-citation guidance.","license":"Per-collection license, typically CC BY 3.0/4.0 or CC BY-NC","publication_policy_url":"https://www.cancerimagingarchive.net/data-usage-policies-and-restrictions/","evidence":[{"method":"api","source_url":"https://api.imaging.datacommons.cancer.gov/v3/collections/nsclc_radiogenomics","source_label":"IDC v3 API /collections","retrieved_at":"2026-09-18T02:44:36.389375Z","snippet":null,"locator":null,"confidence":"high","reviewer":null,"reviewed_at":null,"note":null}]},"useful_for":[{"question":"Do quantitative CT or PET features predict gene expression programs in lung adenocarcinoma?","rationale":"This is the defining radiogenomics question and it needs paired data on the same patients, which this collection provides for the subset with matched microarrays. Imaging is available for all 211.","required_fields":[],"required_modalities":["radiology","rna_seq"],"feasibility":"needs_linkage","approx_n":26,"statistical_note":"The matched-expression subset described in the collection is around 26 patients, which supports hypothesis generation and feature-level correlation but not a validated predictive model. Confirm the exact overlap before designing.","exemplar_pmid":"30325352","topics":["radiogenomics","imaging","lung"],"evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"question":"How reproducible are radiomic features across CT and PET acquisitions of the same tumor?","rationale":"Both modalities were acquired for the same patients pre-surgically, so feature stability across modality and acquisition can be assessed directly - a prerequisite for any radiomics claim and rarely testable.","required_fields":[],"required_modalities":["radiology"],"feasibility":"direct","approx_n":211,"statistical_note":"Reproducibility is estimable at this sample size; report intraclass correlation.","exemplar_pmid":null,"topics":["radiomics","reproducibility","methods-benchmarking"],"evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"question":"Can models trained on this cohort's segmentations generalize to other lung imaging collections in the same repository?","rationale":"Expert segmentations and semantic annotations are provided, and the Imaging Data Commons holds several other lung collections that can serve as external validation within one access framework.","required_fields":[],"required_modalities":["radiology"],"feasibility":"needs_linkage","approx_n":211,"statistical_note":null,"exemplar_pmid":null,"topics":["segmentation","generalization","lung"],"evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]}],"limitations":[{"kind":"statistical_power","statement":"The matched imaging-and-expression subset is a small fraction of the 211 imaged patients - the collection describes roughly 26 cases with microarray data. The cohort is imaging-rich and genomics-poor.","severity":"blocking","affected_analyses":["Predictive radiogenomic modeling","Genome-wide imaging-expression association"],"mitigation":"Treat radiogenomic analyses as hypothesis-generating and validate elsewhere. Use the full 211 for imaging-only questions.","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"kind":"harmonization","statement":"The link between imaging and expression is documented in free text in the collection description rather than as a structured cross-reference, and depends on DICOM patient names matching microarray sample names.","severity":"major","affected_analyses":["Any paired analysis"],"mitigation":"Verify the join on a handful of cases before building on it, and report how many patients actually matched.","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"kind":"platform_obsolescence","statement":"The matched expression data are microarray, not RNA sequencing, which constrains integration with contemporary transcriptomic cohorts.","severity":"minor","affected_analyses":["Cross-cohort expression integration"],"mitigation":"Restrict to genes measurable on both platforms; use rank-based methods.","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"kind":"missing_data","statement":"Outcome and treatment annotation are not distributed through the imaging repository metadata, so survival questions require the source publication's supplementary data.","severity":"major","affected_analyses":["Prognostic imaging models"],"mitigation":"Retrieve clinical variables from the Scientific Data paper's supplement.","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]}],"inappropriate_uses":[{"statement":"Training a deep radiogenomic model end to end.","reason":"Around 26 paired cases is far too few; the result would not generalize.","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"statement":"Population-level claims about lung cancer imaging phenotypes.","reason":"Single-institution surgical series with no sampling frame.","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]}],"primary_publications":[{"pmid":"30325352","pmcid":null,"doi":null,"title":"A radiogenomic dataset of non-small cell lung cancer","journal":"Scientific Data","year":2018,"authors_short":null,"n_authors":null,"url":"https://pubmed.ncbi.nlm.nih.gov/30325352/","is_open_access":null,"citation_count":null,"evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]}],"reuse":[{"publication":{"pmid":"22710433","pmcid":"PMC3596510","doi":"10.1158/0008-5472.can-11-3943","title":"Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer.","journal":"Cancer research","year":2012,"authors_short":"Nair et al.","n_authors":null,"url":"https://pubmed.ncbi.nlm.nih.gov/22710433/","is_open_access":false,"citation_count":94,"evidence":[]},"tier":"t3_analyzed","kind":"secondary_analysis","independent_of_generators":false,"what_they_analyzed":null,"what_they_found":null,"methods_summary":null,"modalities_used":[],"accession_snippet":null,"accession_locator":"ACCESSION_ID, METHODS","author_keys":["davidzon g","gevaert o","graves e","hoang c","nair v","napel s","plevritis s","quon a","rubin d","shrager j"],"nci_funded_reuse":true,"linked_grants":["U54CA149145","U01CA142555","R01CA160251","P30CA124435"],"adjudicated_by":null,"adjudicated_at":null,"evidence":[{"method":"fulltext","source_url":"https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=ACCESSION_ID:\"GSE28827\"","source_label":"Europe PMC section search (ACCESSION_ID)","retrieved_at":"2026-09-18T03:20:44.515497Z","snippet":null,"locator":"accession \"GSE28827\" indexed in ACCESSION_ID","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Search strategy epmc-sectioned-v1. Section placement is used to grade the strength of reuse evidence, not merely to confirm a citation."}]},{"publication":{"pmid":"29556367","pmcid":"PMC5858511","doi":"10.7150/thno.23767","title":"Integrative analysis of imaging and transcriptomic data of the immune landscape associated with tumor metabolism in lung adenocarcinoma: Clinical and prognostic implications.","journal":"Theranostics","year":2018,"authors_short":"Choi et al.","n_authors":null,"url":"https://pubmed.ncbi.nlm.nih.gov/29556367/","is_open_access":true,"citation_count":42,"evidence":[]},"tier":"t3_analyzed","kind":"secondary_analysis","independent_of_generators":true,"what_they_analyzed":null,"what_they_found":null,"methods_summary":null,"modalities_used":[],"accession_snippet":null,"accession_locator":"ACCESSION_ID, METHODS","author_keys":["choi h"],"nci_funded_reuse":false,"linked_grants":[],"adjudicated_by":null,"adjudicated_at":null,"evidence":[{"method":"fulltext","source_url":"https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=ACCESSION_ID:\"GSE28827\"","source_label":"Europe PMC section search (ACCESSION_ID)","retrieved_at":"2026-09-18T03:20:44.515497Z","snippet":null,"locator":"accession \"GSE28827\" indexed in ACCESSION_ID","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Search strategy epmc-sectioned-v1. Section placement is used to grade the strength of reuse evidence, not merely to confirm a citation."}]},{"publication":{"pmid":"36832225","pmcid":"PMC9955510","doi":"10.3390/diagnostics13040738","title":"Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study.","journal":"Diagnostics (Basel, Switzerland)","year":2023,"authors_short":"Dovrou et al.","n_authors":null,"url":"https://pubmed.ncbi.nlm.nih.gov/36832225/","is_open_access":true,"citation_count":12,"evidence":[]},"tier":"t3_analyzed","kind":"secondary_analysis","independent_of_generators":true,"what_they_analyzed":null,"what_they_found":null,"methods_summary":null,"modalities_used":[],"accession_snippet":null,"accession_locator":"ACCESSION_ID, METHODS","author_keys":["bei e","dovrou a","marias k","papanikolaou n","sfakianakis s","zervakis m"],"nci_funded_reuse":false,"linked_grants":[],"adjudicated_by":null,"adjudicated_at":null,"evidence":[{"method":"fulltext","source_url":"https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=ACCESSION_ID:\"GSE28827\"","source_label":"Europe PMC section search (ACCESSION_ID)","retrieved_at":"2026-09-18T03:20:44.515497Z","snippet":null,"locator":"accession \"GSE28827\" indexed in ACCESSION_ID","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Search strategy epmc-sectioned-v1. Section placement is used to grade the strength of reuse evidence, not merely to confirm a citation."}]},{"publication":{"pmid":"32582396","pmcid":"PMC7295928","doi":"10.1007/s13139-020-00642-x","title":"Radiogenomics Based on PET Imaging.","journal":"Nuclear medicine and molecular imaging","year":2020,"authors_short":"Park et al.","n_authors":null,"url":"https://pubmed.ncbi.nlm.nih.gov/32582396/","is_open_access":false,"citation_count":5,"evidence":[]},"tier":"t3_analyzed","kind":"secondary_analysis","independent_of_generators":true,"what_they_analyzed":null,"what_they_found":null,"methods_summary":null,"modalities_used":[],"accession_snippet":null,"accession_locator":"ACCESSION_ID, TABLE","author_keys":["moon s","park y","shin m"],"nci_funded_reuse":false,"linked_grants":[],"adjudicated_by":null,"adjudicated_at":null,"evidence":[{"method":"fulltext","source_url":"https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=ACCESSION_ID:\"GSE28827\"","source_label":"Europe PMC section search (ACCESSION_ID)","retrieved_at":"2026-09-18T03:20:44.515497Z","snippet":null,"locator":"accession \"GSE28827\" indexed in ACCESSION_ID","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Search strategy epmc-sectioned-v1. Section placement is used to grade the strength of reuse evidence, not merely to confirm a citation."}]}],"reuse_metrics":{"n_candidates_screened":4,"n_by_tier":{"t3_analyzed":3,"t2_declared":0,"t1_accession":4,"t0_mention":1},"n_by_tier_raw":{"t3_analyzed":3,"t2_declared":0,"t1_accession":4,"t0_mention":1},"accession_precision":{"token":"GSE28827","query":"METHODS:\"GSE28827\"","strategy_id":"epmc-literal-precision-v1","needs_correction":false,"precision":1.0,"precision_low":1.0,"precision_high":1.0,"n_sampled":0,"n_checked":0,"n_literal":0,"note":"This accession is a single indexed term, so Europe PMC matches it exactly and the hit count needs no correction."},"n_citations_to_primary_publication":202,"citation_to_reuse_ratio":67.3,"has_citable_accession":true,"n_verified_reuse":3,"n_reuse_examined":4,"n_independent_reuse":3,"n_nci_funded_reuse":1,"first_reuse_year":2012,"latest_reuse_year":2023,"years_since_release":14.72,"expected_reuse":172.82,"reuse_gap_index":-5.441,"reuse_gap_percentile":0.8,"search_strategy_id":"epmc-index-v3","searched_at":"2026-09-20T18:07:48.296208Z","no_reuse_identified":false,"evidence":[{"method":"derived","source_url":"https://www.ebi.ac.uk/europepmc/webservices/rest/search","source_label":"Europe PMC section-scoped hit counts","retrieved_at":"2026-09-20T18:07:48.296208Z","snippet":null,"locator":"tokens: GSE28827; queries: METHODS:\"GSE28827\"; DATA_AVAILABILITY:\"GSE28827\"; ACCESSION_ID:\"GSE28827\"; REF:\"GSE28827\"","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Index strategy epmc-index-v3. 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This is a lower bound on availability."}]},"underexplored":{"is_underexplored":true,"basis":["3 article(s) reference this dataset's accession in a methods section, against 172.8 predicted for datasets of comparable size, age, modality breadth and access tier (reuse gap index -5.44 on a log2 scale, 1st percentile of the assessed corpus).","Its publications are cited 202 times, about 67 citations for every article that analyzed the data - the dataset is known but little used."],"index_value":-5.441,"percentile":0.8,"comparator_set":"132 datasets in this corpus with a citable accession and sufficient metadata","evidence":[{"method":"derived","source_url":null,"source_label":"Reuse Gap Index (rgi-huber-v2)","retrieved_at":"2026-09-21T02:11:22.976067Z","snippet":null,"locator":"Huber robust regression of log2(analyzing articles + 1) on cohort size, years available, modality breadth and access tier","confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Model, covariates and coefficients are published with the dataset export so the label can be recomputed or contested."}]},"grants":[{"core_project_num":"U54CA149145","project_num":null,"title":"MODELING THE ROLE OF DIFFERENTIATION IN AML","pi_names":["Garry P Nolan","SYLVIA KATINA PLEVRITIS","RONALD LEVY","DEAN W FELSHER"],"agency_ic":"NCI","activity_code":"U54","fiscal_years":[2010,2011,2012,2013,2014],"award_amount_usd":24274196,"org_name":"STANFORD UNIVERSITY","reporter_url":"https://reporter.nih.gov/search/results?text_criteria=U54CA149145","role":"infrastructure","evidence":[{"method":"api","source_url":"https://api.reporter.nih.gov/v2/publications/search","source_label":"NIH RePORTER","retrieved_at":"2026-09-20T17:49:03.146364Z","snippet":null,"locator":"PMID 22710433 reported under U54CA149145","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Funding role inferred from whether the award appears on the dataset's own publication or on a downstream study."}]},{"core_project_num":"U01CA142555","project_num":null,"title":"Computerized Quantitative Imaging Assessment of Tumor Burden","pi_names":["Daniel L Rubin"],"agency_ic":"NCI","activity_code":"U01","fiscal_years":[2010,2011,2012,2013,2014],"award_amount_usd":2887111,"org_name":"STANFORD UNIVERSITY","reporter_url":"https://reporter.nih.gov/search/results?text_criteria=U01CA142555","role":"generation","evidence":[{"method":"api","source_url":"https://api.reporter.nih.gov/v2/publications/search","source_label":"NIH RePORTER","retrieved_at":"2026-09-20T17:49:03.146364Z","snippet":null,"locator":"PMID 22710433 reported under U01CA142555","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Funding role inferred from whether the award appears on the dataset's own publication or on a downstream study."},{"method":"api","source_url":"https://api.reporter.nih.gov/v2/publications/search","source_label":"NIH RePORTER","retrieved_at":"2026-09-20T17:21:14.041075Z","snippet":null,"locator":"PMID 30325352 (this dataset's marker paper) reported under U01CA142555","confidence":"high","reviewer":null,"reviewed_at":null,"note":"The award is credited on the dataset's own marker paper, so it paid to produce these data rather than to analyse them. Resolved from RePORTER's publication index; only a repository-supplied or reviewer-supplied marker paper is used, never one this pipeline nominated itself."}]},{"core_project_num":"R01CA160251","project_num":null,"title":"Tools for Linking and Mining image and Genomic Data in Non-Small Cell Lung Cancer","pi_names":["SANDY A. NAPEL","SYLVIA KATINA PLEVRITIS"],"agency_ic":"NCI","activity_code":"R01","fiscal_years":[2011,2012,2013,2014,2015],"award_amount_usd":2944495,"org_name":"STANFORD UNIVERSITY","reporter_url":"https://reporter.nih.gov/search/results?text_criteria=R01CA160251","role":"generation","evidence":[{"method":"api","source_url":"https://api.reporter.nih.gov/v2/publications/search","source_label":"NIH RePORTER","retrieved_at":"2026-09-20T17:49:03.146364Z","snippet":null,"locator":"PMID 22710433 reported under R01CA160251","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Funding role inferred from whether the award appears on the dataset's own publication or on a downstream study."},{"method":"api","source_url":"https://api.reporter.nih.gov/v2/publications/search","source_label":"NIH RePORTER","retrieved_at":"2026-09-20T17:21:14.041075Z","snippet":null,"locator":"PMID 30325352 (this dataset's marker paper) reported under R01CA160251","confidence":"high","reviewer":null,"reviewed_at":null,"note":"The award is credited on the dataset's own marker paper, so it paid to produce these data rather than to analyse them. Resolved from RePORTER's publication index; only a repository-supplied or reviewer-supplied marker paper is used, never one this pipeline nominated itself."}]},{"core_project_num":"P30CA124435","project_num":null,"title":"Leadership, Planning and Evaluation","pi_names":["STEVEN E ARTANDI","MANISHA DESAI","Holden T. Maecker","Ryan D. Leib","Brooke E Howitt","LAURA D ATTARDI","Heike Elizabeth Daldrup-Link","NATHANAEL Schiander GRAY","Lawrence D Recht","Lisa A Nichols","Pauline Funchain","Quynh-Thu Xuan Le","John S. Witte","Alyce Sophia Adams","Melinda L. Telli","Summer S Han","DAVID Edward SOLOW-CORDERO","MICHAEL KENNEY","Crystal Mackall","Vittorio Sebastiano","frezghi habte","GORDON X WANG","Michael N. Mindrinos","JAMES M. FORD","Allis Soe-Lang Chien","Laura Adams","RONALD LEVY","Elizabeth Anderson","JONATHAN W MULHOLLAND","ESTHER M. JOHN","Jason Thanh Lee","Daniel L Rubin","John Coller","David Rubenson","ROELAND NUSSE","Mark Daniel Pegram","SANJIV S GAMBHIR","Robert S Negrin","Marty Bigos","PHILIP WILLIAM LAVORI","miriam bischoff","ROBERT William HAILE","Teri A Longacre","MICHAEL L CLEARY","Amato J. Giaccia","Beverly S. Mitchell","Timothy Charles Doyle","Michael F Clarke","EDGAR G. ENGLEMAN","ALICE S WHITTEMORE","Marcia L. Stefanick","CHRISTOPHER H CONTAG","MIRIAM B BISCHOFF","SUSAN MITCHELL","Dean Felsher","Ruby Yanru Chen-Tsai","JON MULHOLLAND","JONATHAN R POLLACK","BRANIMIR I SIKIC","Irving L. Weissman","Gavin J Sherlock","Susan J. Knox","TIMOTHY C DOYLE","Alice Whittemore","Michael Clarke","MANUEL A GARCIA","DAVID R. PARKS","EVELYN W WANG","Joanne Murphy"],"agency_ic":"NCI","activity_code":"P30","fiscal_years":[2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026],"award_amount_usd":125519822,"org_name":"STANFORD UNIVERSITY","reporter_url":"https://reporter.nih.gov/search/results?text_criteria=P30CA124435","role":"infrastructure","evidence":[{"method":"api","source_url":"https://api.reporter.nih.gov/v2/publications/search","source_label":"NIH RePORTER","retrieved_at":"2026-09-20T17:49:03.146364Z","snippet":null,"locator":"PMID 22710433 reported under P30CA124435","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Funding role inferred from whether the award appears on the dataset's own publication or on a downstream study."}]},{"core_project_num":"U01CA190214","project_num":null,"title":"Qualification and Deployment of Imaging Biomarkers of Cancer Treatment Response","pi_names":["Daniel L Rubin"],"agency_ic":"NCI","activity_code":"U01","fiscal_years":[2015,2017,2018,2019,2020],"award_amount_usd":3108335,"org_name":"STANFORD UNIVERSITY","reporter_url":"https://reporter.nih.gov/search/results?text_criteria=U01CA190214","role":"generation","evidence":[{"method":"api","source_url":"https://api.reporter.nih.gov/v2/publications/search","source_label":"NIH RePORTER","retrieved_at":"2026-09-20T17:21:14.041075Z","snippet":null,"locator":"PMID 30325352 (this dataset's marker paper) reported under U01CA190214","confidence":"high","reviewer":null,"reviewed_at":null,"note":"The award is credited on the dataset's own marker paper, so it paid to produce these data rather than to analyse them. Resolved from RePORTER's publication index; only a repository-supplied or reviewer-supplied marker paper is used, never one this pipeline nominated itself."}]},{"core_project_num":"U01CA187947","project_num":null,"title":"Computing, Optimizing, and Evaluating Quantitative Cancer Imaging Biomarkers","pi_names":["SANDY A. NAPEL","Daniel L Rubin"],"agency_ic":"NCI","activity_code":"U01","fiscal_years":[2015,2016,2017,2018,2019],"award_amount_usd":2927436,"org_name":"STANFORD UNIVERSITY","reporter_url":"https://reporter.nih.gov/search/results?text_criteria=U01CA187947","role":"generation","evidence":[{"method":"api","source_url":"https://api.reporter.nih.gov/v2/publications/search","source_label":"NIH RePORTER","retrieved_at":"2026-09-20T17:21:14.041075Z","snippet":null,"locator":"PMID 30325352 (this dataset's marker paper) reported under U01CA187947","confidence":"high","reviewer":null,"reviewed_at":null,"note":"The award is credited on the dataset's own marker paper, so it paid to produce these data rather than to analyse them. Resolved from RePORTER's publication index; only a repository-supplied or reviewer-supplied marker paper is used, never one this pipeline nominated itself."}]}],"access_steps":[{"order":1,"action":"Explore the collection in the Imaging Data Commons","detail":"Fully open. No account, no data access request.","url":"https://portal.imaging.datacommons.cancer.gov/explore/?collection_id=nsclc_radiogenomics","requires":[],"est_time":"30 minutes","cli_snippet":null,"audience":"human","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"order":2,"action":"Pull DICOM with the idc-index Python package","detail":null,"url":null,"requires":[],"est_time":"1-2 hours","cli_snippet":"pip install idc-index\npython -c \"\nfrom idc_index import IDCClient\nc = IDCClient()\ns = c.index[c.index['collection_id'] == 'nsclc_radiogenomics']\nprint(len(s), 'series', round(s['series_size_MB'].sum() / 1024, 1), 'GB')\nc.download_from_selection(collection_id='nsclc_radiogenomics', downloadDir='.')\n\"\n","audience":"human","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"order":3,"action":"Retrieve the matched expression series from GEO and verify the identifier join","detail":null,"url":"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28827","requires":[],"est_time":"2 hours","cli_snippet":null,"audience":"human","evidence":[{"method":"curated","source_url":null,"source_label":"Expert review","retrieved_at":"2026-09-21T12:30:56.908866Z","snippet":null,"locator":null,"confidence":"high","reviewer":"CD2S project curation","reviewed_at":"2026-09-18","note":"Written by a named reviewer after reading the source documentation and the measured field coverage. Not machine-generated."}]},{"order":4,"action":"List the series before pulling pixels","detail":"List modality and size first so an agent can choose what to fetch.","url":"https://learn.canceridc.dev/","requires":[],"est_time":null,"cli_snippet":"python - <<'PY'\nfrom idc_index import IDCClient\ni = IDCClient().index\ns = i[i[\"collection_id\"] == \"nsclc_radiogenomics\"]\nprint(len(s), \"series\", s[\"PatientID\"].nunique(), \"patients\",\n      round(s[\"series_size_MB\"].sum() / 1024, 1), \"GB\")\nprint(s[\"Modality\"].value_counts().to_dict())\nPY","audience":"agent","evidence":[{"method":"derived","source_url":"https://learn.canceridc.dev/","source_label":"IDC access documentation","retrieved_at":"2026-09-21T12:30:59.431700Z","snippet":null,"locator":null,"confidence":"medium","reviewer":null,"reviewed_at":null,"note":"Generated by applying the repository's documented access policy to this dataset's identifiers. It is not a reviewer's account of this dataset."}]}],"analysis_examples":[{"level":"advanced","title":"Join the same patients across two NCI repositories","question":"Which cohorts have both imaging and molecular data on the same patients, and how many patients actually carry both?","problem":"Molecular and imaging data for the same cohort live in separate NCI repositories.","lesson":"Join shared patient identifiers and quantify the real multimodal overlap, not the advertised totals.","figure":"Paired horizontal bars per cohort comparing GDC molecular case counts with IDC imaging patient counts, where the shorter bar is the ceiling on a multimodal study of that cohort.","featured":true,"inputs":["Open GDC and IDC APIs, no account required","pandas"],"outputs":["Patient-level intersection between a GDC project and an IDC collection","Counts of molecular-only, imaging-only and joined patients","A map of every cohort present in both repositories","A chart comparing molecular and imaging patient counts per linkable cohort"],"steps":["Retrieve patient identifiers from the GDC project","Query IDC's public SQL endpoint for the collection's patient identifiers","Intersect on the shared barcode and quantify the overlap","Sweep every GDC project against every IDC collection"],"findings":["All 1,098 TCGA-BRCA patients appear in both the GDC and the IDC, a 100% join with nothing imaging-only.","39 cohorts exist in both repositories, covering 15,761 patients.","HCMI-CMDC holds 805 molecular cases but 382 imaging patients, so its multimodal ceiling is 382, not 805."],"contributor":null,"contributor_url":null,"language":"python","est_runtime":"about two minutes","est_compute":null,"workbook_path":"workbooks/python/05_cross_repository_linkage.py","workbook_url":null,"notebook_download_url":"/data/notebooks/05_cross_repository_linkage.ipynb","notebook_preview_url":"/data/notebooks/05_cross_repository_linkage.png","notebook_preview_width":1045,"notebook_preview_height":1045,"colab_url":null,"binder_url":null,"receipt":{"executed":true,"executed_at":"2026-09-21T00:43:01.297366Z","runtime_seconds":4.98,"executor":"nbclient / python 3.12.13 / Darwin 25.5.0 arm64","package_versions":{"python":"3.12.13","pandas":"3.0.6","numpy":"2.5.3","matplotlib":"3.11.2","lifelines":"0.30.0","scipy":"1.18.1","requests":"2.34.2","httpx":"0.28.1"},"n_cells":6,"n_cells_executed":6,"output_hash":"fe7a493665d23ae04c207c4b406923c9","data_retrieved_at":null,"error":null},"template_source":"05_cross_repository_linkage","evidence":[{"method":"curated","source_url":null,"source_label":"CD2S workbook","retrieved_at":"2026-09-21T12:30:59.450361Z","snippet":null,"locator":"workbooks/python/05_cross_repository_linkage.py","confidence":"high","reviewer":null,"reviewed_at":null,"note":"Executed end to end against the live public APIs; the receipt records when, with which package versions and how long it took."}]}],"agent_package":{"json_url":"/data/datasets/idc-nsclc-radiogenomics.json","jsonld_url":"/data/jsonld/idc-nsclc-radiogenomics.jsonld","croissant_url":"/data/croissant/idc-nsclc-radiogenomics.json","instructions_url":"/data/agent/idc-nsclc-radiogenomics.md","bundle_url":null,"mcp_resource_uri":null},"related_dataset_ids":[],"related_underexplored_ids":[],"review":{"status":"project_curated","reviewer":"CD2S project curation","reviewer_affiliation":null,"reviewed_at":"2026-09-18","sections_reviewed":["useful_for","limitations","inappropriate_uses","access_steps","primary_publications"],"open_questions":["The collection description states that 26 of the cases have matched microarray data in GEO series GSE28827, with DICOM patient names matching microarray sample names. Confirm the join works as described before designing around it."],"notes":null},"verification":{"last_verified_at":null,"link_checks":[],"n_links_ok":0,"n_links_broken":0,"upstream_version_seen":null,"upstream_version_changed":false,"upstream_counts_seen":{},"upstream_drift":{},"notes":[]},"is_showcase":true,"completeness_score":94.6,"tags":["has-clinical-table","has-derived-annotations","idc","imaging","imaging-genomics","lung","multimodal","open-access","underexplored-candidate"],"search_text":null}