Q (sonnet): is there any imaging data? ▶ read_guide { "uri": "cbioportal://external-resources-guide" } ◀ result # External Resources Guide Use this guide when the user asks about data that may be linked from cBioPortal rather than stored directly in molecular or clinical tables. ## Routing Triggers Read this guide before answering or refusing questions that mention: - imaging, radiology, CT, MRI, pathology slides, histology, Minerva, viewer - external portal, external resource, image data, spatial data - HTAN studies or study-specific linked viewers ## Core Rule Do not say cBioPortal has no imaging or external-resource data until you have checked: - `resource_definition` - `resource_study` - `resource_sample` - `resource_patient` cBioPortal may store links to external viewers or portals even when it does not store raw images. ## Which Studies Have Imaging Data For "which studies have imaging / pathology slides / CT" or "how many samples have images", read `cancer_study.resource_sample_counts` — per-study sample counts keyed by resource display name, the same numbers as the portal's "Data type" filter. One query, no joins: ```sql SELECT cancer_study_identifier, name, resource_sample_counts['Slide Microscopy'] AS slide_microscopy_samples FROM cancer_study WHERE resource_sample_counts['Slide Microscopy'] > 0 ORDER BY slide_microscopy_samples DESC; ``` List the resource names that exist with `SELECT DISTINCT arrayJoin(mapKeys(resource_sample_counts)) FROM cancer_study` (e.g. `'Slide Microscopy'`, `'Computed Tomography'`, `'Magnetic Resonance'`, `'H&E Slide'`, `'MxIF Image'`). The map counts sample- and patient-level resources only. For study-level links (`resource_study`) and for the URLs themselves, use the queries below. ## Discovery Query Start with table and column validation, then use this pattern: ```sql SELECT rd.resource_id, rd.display_name, rd.description, rd.resource_type, rs.cancer_study_identifier, rs.url FROM resource_study rs JOIN resource_definition rd ON rs.resource_id = rd.resource_id WHERE lower(rd.display_name) LIKE '%minerva%' OR lower(rd.description) LIKE '%minerva%' OR lower(rd.display_name) LIKE '%image%' OR lower(rd.description) LIKE '%image%' OR lower(rd.display_name) LIKE '%pathology%' OR lower(rd.description) LIKE '%pathology%' OR lower(rd.display_name) LIKE '%histology%' OR lower(rd.description) LIKE '%histology%' ORDER BY rs.cancer_study_identifier, rd.display_name LIMIT 100; ``` If no study-level rows appear, check sample- and patient-level resource links: ```sql SELECT rd.resource_id, rd.display_name, rd.description, rs.cancer_study_identifier, rs.sample_unique_id, rs.url FROM resource_sample rs JOIN resource_definition rd ON rs.resource_id = rd.resource_id WHERE lower(rd.display_name) LIKE '%minerva%' OR lower(rd.description) LIKE '%minerva%' OR lower(rd.display_name) LIKE '%image%' OR lower(rd.description) LIKE '%image%' OR lower(rd.display_name) LIKE '%pathology%' OR lower(rd.description) LIKE '%pathology%' … (981 more chars) ▶ clickhouse_run_select_query { "query": "SELECT DISTINCT arrayJoin(mapKeys(resource_sample_counts)) AS resource_name FROM cancer_study" } ◀ result {"rows":[{"resource_name":"MxIF Image"},{"resource_name":"H&E Slide"},{"resource_name":"CyCIF"},{"resource_name":"Slide Microscopy"},{"resource_name":"Computed Tomography"},{"resource_name":"Magnetic Resonance"},{"resource_name":"Positron Emission Tomography"},{"resource_name":"Nuclear Medicine"},{"resource_name":"Computed Radiography"},{"resource_name":"Mammography"},{"resource_name":"Digital Radiography"}]} ▶ clickhouse_run_select_query { "query": " SELECT cancer_study_identifier, name, resource_sample_counts['Slide Microscopy'] AS slide_microscopy, resource_sample_counts['H&E Slide'] AS he_slide, resource_sample_counts['MxIF Image'] AS mxif, resource_sample_counts['CyCIF'] AS cycif, resource_sample_counts['Computed Tomography'] AS ct, resource_sample_counts['Magnetic Resonance'] AS mri, resource_sample_counts['Positron Emission Tomography'] AS pet, resource_sample_counts['Nuclear Medicine'] AS nuc_med, resource_sample_counts['Computed Radiography'] AS comp_radio, resource_sample_counts['Mammography'] AS mammo, resource_sample_counts['Digital Radiography'] AS digital_radio FROM cancer_study WHERE resource_sample_counts['Slide Microscopy'] > 0 OR resource_sample_counts['H&E Slide'] > 0 OR resource_sample_counts['MxIF Image'] > 0 OR resource_sample_counts['CyCIF'] > 0 OR resource_sample_counts['Computed Tomography'] > 0 OR resource_sample_counts['Magnetic Resonance'] > 0 OR resource_sample_counts['Positron Emission Tomography'] > 0 OR resource_sample_counts['Nuclear Medicine'] > 0 OR resource_sample_counts['Computed Radiography'] > 0 OR resource_sample_counts['Mammography'] > 0 OR resource_sample_counts['Digital Radiography'] > 0 ORDER BY slide_microscopy DESC ", "max_rows": 100 } ◀ result {"rows":[{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","slide_microscopy":1084,"he_slide":0,"mxif":0,"cycif":0,"ct":0,"mri":136,"pet":0,"nuc_med":0,"comp_radio":0,"mammo":5,"digital_radio":0},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","slide_microscopy":594,"he_slide":0,"mxif":0,"cycif":0,"ct":21,"mri":0,"pet":0,"nuc_med":0,"comp_radio":0,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","slide_microscopy":592,"he_slide":0,"mxif":0,"cycif":0,"ct":0,"mri":0,"pet":0,"nuc_med":0,"comp_radio":0,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","slide_microscopy":575,"he_slide":0,"mxif":0,"cycif":0,"ct":140,"mri":1,"pet":0,"nuc_med":0,"comp_radio":0,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","slide_microscopy":529,"he_slide":0,"mxif":0,"cycif":0,"ct":65,"mri":8,"pet":5,"nuc_med":0,"comp_radio":1,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"hnsc_tcga_pan_can_atlas_2018","name":"Head and Neck Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","slide_microscopy":518,"he_slide":0,"mxif":0,"cycif":0,"ct":0,"mri":0,"pet":0,"nuc_med":0,"comp_radio":0,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","slide_microscopy":514,"he_slide":0,"mxif":0,"cycif":0,"ct":0,"mri":0,"pet":0,"nuc_med":0,"comp_radio":0,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","slide_microscopy":514,"he_slide":0,"mxif":0,"cycif":0,"ct":60,"mri":0,"pet":23,"nuc_med":2,"comp_radio":0,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018","name":"Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)","slide_microscopy":512,"he_slide":0,"mxif":0,"cycif":0,"ct":221,"mri":60,"pet":0,"nuc_med":0,"comp_radio":1,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"thca_tcga_pan_can_atlas_2018","name":"Thyroid Carcinoma (TCGA, PanCancer Atlas)","slide_microscopy":500,"he_slide":0,"mxif":0,"cycif":0,"ct":6,"mri":0,"pet":1,"nuc_med":0,"comp_radio":0,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018","name":"Prostate Adenocarcinoma (TCGA, PanCancer Atlas)","slide_microscopy":494,"he_slide":0,"mxif":0,"cycif":0,"ct":4,"mri":10,"pet":3,"nuc_med":0,"comp_radio":0,"mammo":0,"digital_radio":0},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","slide_microscopy":487,"he_slide":0,"mxif":0,"cycif":0,"ct":33,"mri":0,"pet": … (6477 more chars) ═ answer (success) Yes — cBioPortal has substantial imaging data linked at the sample/patient level across many studies, spanning several modalities: **Radiology (linked to TCGA imaging via TCIA)** - Computed Tomography (CT), Magnetic Resonance (MRI), Positron Emission Tomography (PET), Nuclear Medicine, Computed Radiography, Mammography, Digital Radiography - Notable examples: KIRC TCGA (221 CT, 60 MRI), OV TCGA (140 CT), BLCA TCGA (107 CT, 20 MRI, 7 mammography, 7 digital radiography), UCEC TCGA (65 CT, 8 MRI, 5 PET) **Digital Pathology / Slide Microscopy** - Nearly every TCGA PanCancer Atlas study has whole-slide microscopy images (H&E), ranging from ~36 samples (cholangiocarcinoma) up to 1,084 (breast invasive carcinoma) **Multiplexed/spatial imaging** - **CyCIF**: `ovary_geomx_gray_foundation_2024` — 567 samples - **MxIF**: `crc_hta8_htan_2024` (58), `crc_orion_2024` (40), `crc_hta11_htan_2021` (25), `msk_spectrum_tme_2022` (8) - **H&E Slide** (separate from Slide Microscopy resource type): `brca_aurora_2023` (142), `msk_spectrum_tme_2022` (37), `crc_hta11_htan_2021` (26), `difg_glass` (10) These are external resource links (e.g., to TCIA or study-specific viewers) rather than raw pixel data stored in cBioPortal's molecular/clinical tables. If you'd like, I can pull the actual resource URLs for a specific study, or navigate you to one of these cohorts in StudyView. Which study or modality are you most interested in?