From 85dc47e070a14df47bfc375f52b274f06ba7dcb9 Mon Sep 17 00:00:00 2001 From: rmadupuri Date: Thu, 22 Jan 2026 16:03:14 -0500 Subject: [PATCH 1/5] add imaging data --- .../acc_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/acc_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../blca_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/blca_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../brca_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/brca_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../cesc_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/cesc_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../chol_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/chol_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../data_resource_definition.txt | 4 ++-- .../data_resource_patient.txt | 4 ++-- .../dlbc_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/dlbc_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../esca_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/esca_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../gbm_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/gbm_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../hnsc_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/hnsc_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../kich_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/kich_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../kirc_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/kirc_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../kirp_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/kirp_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- public/laml_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../lgg_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/lgg_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../lihc_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/lihc_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../luad_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/luad_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../lusc_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/lusc_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../meso_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/meso_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../ov_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/ov_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../paad_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/paad_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../pcpg_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/pcpg_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../prad_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/prad_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../sarc_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/sarc_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../skcm_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/skcm_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../stad_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/stad_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../tgct_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/tgct_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../thca_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/thca_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../thym_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/thym_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../ucec_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/ucec_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../ucs_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/ucs_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- .../uvm_tcga_pan_can_atlas_2018/data_resource_definition.txt | 4 ++-- public/uvm_tcga_pan_can_atlas_2018/data_resource_patient.txt | 4 ++-- 63 files changed, 126 insertions(+), 126 deletions(-) diff --git a/public/acc_tcga_pan_can_atlas_2018/data_resource_definition.txt b/public/acc_tcga_pan_can_atlas_2018/data_resource_definition.txt index 91a018aa54..27b4b49339 100644 --- a/public/acc_tcga_pan_can_atlas_2018/data_resource_definition.txt +++ 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b/public/uvm_tcga_pan_can_atlas_2018/data_resource_patient.txt index cb11fe86ae..7afd3c6b88 100644 --- a/public/uvm_tcga_pan_can_atlas_2018/data_resource_patient.txt +++ b/public/uvm_tcga_pan_can_atlas_2018/data_resource_patient.txt @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:23a15230b2c7646a371f0588f528208c062f60baea537e7fb11606e853f3f787 -size 9838 +oid sha256:a7dbea3da8c5f4fa918329d20c09369340ae56b5dec0db41a7eed822d3351545 +size 10078 From 7fbde945dd7c62c07a7a1e6ae37a7d188d596c1c Mon Sep 17 00:00:00 2001 From: rmadupuri Date: Thu, 22 Jan 2026 16:35:02 -0500 Subject: [PATCH 2/5] update docs --- docs/tcga_pan_can_atlas/ohif-viewer.md | 165 +++++++++++++++++++++++-- 1 file changed, 153 insertions(+), 12 deletions(-) diff --git a/docs/tcga_pan_can_atlas/ohif-viewer.md b/docs/tcga_pan_can_atlas/ohif-viewer.md index 976a6e431e..0d38233494 100644 --- a/docs/tcga_pan_can_atlas/ohif-viewer.md +++ b/docs/tcga_pan_can_atlas/ohif-viewer.md @@ -1,21 +1,162 @@ -# OHIF Viewer for TCGA CT Scans -OHIF URLs for CT Scans were obtained from the Imaging Data Commons and added as -resource data to all tcga_pan_can_2018 studies, +# TCGA Imaging Data Integration with cBioPortal + +This document describes the process of acquiring TCGA imaging studies from the NCI Imaging Data Commons (IDC) and integrating them as patient resources in cBioPortal's TCGA Pan-Cancer Atlas 2018 studies. The images can be viewed using OHIF (radiology) and SLIM (pathology) viewers within the portal. + +## Data Structure in IDC + +IDC organizes imaging data hierarchically: + +``` +Collections → Patients → Studies → Series → Instances +``` + +- **Study**: One complete imaging exam for a patient, regardless of modality (e.g., "CT Chest with Contrast", "MRI Brain"). One patient could have undergone multiple imaging studies over time. +- **Series**: Individual image batches within a study (e.g., different CT scan sections or sequences) + +## Available Imaging Modalities + +| Code | Modality | Viewer | Resource ID | +|------|----------|--------|-------------| +| CR | Computed Radiography | OHIF | IDC_OHIF_CR | +| CT | Computed Tomography | OHIF | IDC_OHIF_CT | +| DX | Digital Radiography | OHIF | IDC_OHIF_DX | +| MG | Mammography | OHIF | IDC_OHIF_MG | +| MR | Magnetic Resonance | OHIF | IDC_OHIF_MR | +| NM | Nuclear Medicine | OHIF | IDC_OHIF_NM | +| PT | Positron Emission Tomography | OHIF | IDC_OHIF_PT | +| SM | Slide Microscopy (H&E) | SLIM | IDC_SLIM | + +**Note**: Annotation (ANN), Segmentation (SEG), Structured Report (SR) modalities are excluded as they are not directly viewable. And Other (OT) modality is also excluded. + +## Implementation Steps -## Steps ### 1. Download Data from IDC -Go to IDC and select TCGA -> CT Scans. Download a manifest with all the links to ~/Downloads/idc_ohif.tsv. +[idc-index](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/getting_started/part2_searching_basics.ipynb) package was used to programmatically download UID's for all tcga from IDC. +```python +from idc_index import IDCClient + +client = IDCClient() + +client.sql_query("SELECT * FROM index WHERE collection_id LIKE '%tcga%'").to_csv("idc_tcga.txt", sep='\t', index=None) +``` + +### 2. Generate data -### 2. Curate coadread_tcga semi-manually +```python +import pandas as pd +import numpy as np +import os -Add all *resource* files for coad and read data and link patients to samples. This was done semi-manually. +df_tcga = pd.read_csv("idc_tcga.txt", sep='\t', dtype=str) -### 3. Do the rest using a command one liner +# Remove modalities that cannot directly be viewed along with Other type +df_tcga = df_tcga[~df_tcga['Modality'].isin(['ANN', 'SEG', 'SR', 'OT'])] + +# Group the patients by collection_id, PatientID, StudyInstanceUID, Modality +cols = ['PatientID', 'StudyInstanceUID', 'Modality'] +resource_df = df_tcga[cols].drop_duplicates().reset_index(drop=True) + +# Map modalities +modality_map = { + 'CR': 'IDC_OHIF_CR', + 'CT': 'IDC_OHIF_CT', + 'DX': 'IDC_OHIF_DX', + 'MG': 'IDC_OHIF_MG', + 'MR': 'IDC_OHIF_MR', + 'NM': 'IDC_OHIF_NM', + 'PT': 'IDC_OHIF_PT', + 'SM': 'IDC_SLIM' +} +resource_df['Modality'] = resource_df['Modality'].replace(modality_map) + +# Add StudyInstanceUID URL +resource_df['StudyInstanceUID'] = np.where( + resource_df['Modality'] == 'IDC_SLIM', + 'https://viewer.imaging.datacommons.cancer.gov/slim/studies/' + resource_df['StudyInstanceUID'], + 'https://viewer.imaging.datacommons.cancer.gov/viewer/' + resource_df['StudyInstanceUID'] +) + +# Rename columns +resource_df = resource_df[['PatientID', 'Modality', 'StudyInstanceUID']] +resource_df.columns = ['PATIENT_ID', 'RESOURCE_ID', 'URL'] + +# Write filtered resource files per study +dh_files_path = "/Users/madupurr/Github/datahub/public" + +# resource defenition file map +definition_map = { + 'IDC_OHIF_CR': 'Computed Radiography', + 'IDC_OHIF_CT': 'CT Scan', + 'IDC_OHIF_DX': 'Digital Radiography', + 'IDC_OHIF_MG': 'Mammography', + 'IDC_OHIF_MR': 'Magnetic Resonance', + 'IDC_OHIF_NM': 'Nuclear Medicine', + 'IDC_OHIF_PT': 'PET Scan', + 'IDC_SLIM': 'H&E Slide' +} + +for st in os.listdir(dh_files_path): + if 'tcga_pan_can_atlas_2018' in st: + patient_file = os.path.join(dh_files_path, st, 'data_clinical_patient.txt') + resource_file = os.path.join(dh_files_path, st, 'data_resource_patient.txt') + resource_def_file = os.path.join(dh_files_path, st, 'data_resource_definition.txt') + + clinical_df = pd.read_csv(patient_file, sep='\t', skiprows=4, dtype=str) + patient_ids = clinical_df['PATIENT_ID'].unique() + + # Filter resource_df for only matching patients + filtered_resource_df = resource_df[resource_df['PATIENT_ID'].isin(patient_ids)] + + # Write to tab-separated file + filtered_resource_df = filtered_resource_df.sort_values(by='PATIENT_ID').reset_index(drop=True) + filtered_resource_df.to_csv(resource_file, sep='\t', index=False) + print(f"Written {len(filtered_resource_df)} resources to {resource_file}") + + # Get unique RESOURCE_IDs for this study that are in definition_map + resource_ids_in_study = filtered_resource_df['RESOURCE_ID'].unique() + + # Build resource definition DataFrame only if there are RESOURCE_IDs + if len(resource_ids_in_study) > 0: + resource_def_df = pd.DataFrame({ + 'RESOURCE_ID': resource_ids_in_study, + 'DISPLAY_NAME': [definition_map[rid] for rid in resource_ids_in_study], + 'RESOURCE_TYPE': 'PATIENT', + 'DESCRIPTION': [definition_map[rid] for rid in resource_ids_in_study], + 'OPEN_BY_DEFAULT': 'TRUE', + 'PRIORITY': 1 + }) + + # Write to data_resource_definition.txt file + resource_def_df.to_csv(resource_def_file, sep='\t', index=False) + print(f"Written definitions to {resource_def_file}") + -Generate them for the rest: -```bash -for f in $(cut -f2 ~/Downloads/idc_ohif.tsv | gsort | uniq | grep tcga_ | grep -v Filters | grep -v coad | grep -v read); do (head -1 coadread_tcga_pan_can_atlas_2018/data_resource_patient.txt; cut -f1,2,4 ~/Downloads/idc_ohif.tsv | tail -n +9 | grep $f | cut -f1,3 | awk -vFS='\t' -vOFS='\t' '{$1=substr($1,0,12); $3="https://viewer.imaging.datacommons.cancer.gov/viewer/"$2; $2="IDC_OHIF_V2"; print $0}' | gsort -k1,1 | uniq | rev | uniq -f2 | rev; ) > ${f/tcga_/}_tcga_pan_can_atlas_2018/*data_resource*patient*; done ``` -Note: there are a few patients that have multiple CT Scans. Not entirely sure what the difference is, the above command just selects the first one +## Output Files + +For each TCGA Pan-Cancer Atlas 2018 study, the script generates two files: + +1. **`data_resource_patient.txt`**: Links patients to their imaging studies + - Columns: `PATIENT_ID`, `RESOURCE_ID`, `URL` + +2. **`data_resource_definition.txt`**: Defines the resource types available in the study + - Columns: `RESOURCE_ID`, `DISPLAY_NAME`, `RESOURCE_TYPE`, `DESCRIPTION`, `OPEN_BY_DEFAULT`, `PRIORITY` + +## Viewer URLs + +- **OHIF Viewer** (for radiology imaging): `https://viewer.imaging.datacommons.cancer.gov/viewer/{StudyInstanceUID}` +- **SLIM Viewer** (for slide microscopy): `https://viewer.imaging.datacommons.cancer.gov/slim/studies/{StudyInstanceUID}` + +## Dependencies + +- pandas +- numpy +- idc-index + +## Notes + +- Only patients already present in the clinical data files are included in the resource files +- All resource files are sorted by `PATIENT_ID` for consistency +- Resources are set to open by default in cBioPortal with priority level 1 From dcdffc421e43040f67db71ee059c7215a1bdccce Mon Sep 17 00:00:00 2001 From: rmadupuri Date: Fri, 23 Jan 2026 10:18:12 -0500 Subject: [PATCH 3/5] update doc --- docs/tcga_pan_can_atlas/ohif-viewer.md | 21 +++++++++------------ 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/docs/tcga_pan_can_atlas/ohif-viewer.md b/docs/tcga_pan_can_atlas/ohif-viewer.md index 0d38233494..9fff853955 100644 --- a/docs/tcga_pan_can_atlas/ohif-viewer.md +++ b/docs/tcga_pan_can_atlas/ohif-viewer.md @@ -1,6 +1,6 @@ # TCGA Imaging Data Integration with cBioPortal -This document describes the process of acquiring TCGA imaging studies from the NCI Imaging Data Commons (IDC) and integrating them as patient resources in cBioPortal's TCGA Pan-Cancer Atlas 2018 studies. The images can be viewed using OHIF (radiology) and SLIM (pathology) viewers within the portal. +This document describes the process of acquiring TCGA imaging studies from the NCI Imaging Data Commons (IDC) and integrating them as patient resources in cBioPortal's TCGA Pan Cancer Atlas 2018 studies. The images can be viewed using OHIF (radiology) and SLIM (pathology) viewers within the portal. ## Data Structure in IDC @@ -13,6 +13,8 @@ Collections → Patients → Studies → Series → Instances - **Study**: One complete imaging exam for a patient, regardless of modality (e.g., "CT Chest with Contrast", "MRI Brain"). One patient could have undergone multiple imaging studies over time. - **Series**: Individual image batches within a study (e.g., different CT scan sections or sequences) +**Note**: While a single study may contain multiple imaging modalities, data was extracted at the study level and then split by modality for cBioPortal integration. This modality-level organization enables allows users to selectively access specific imaging types (e.g., CT scans vs. H&E slides) for each patient. + ## Available Imaging Modalities | Code | Modality | Viewer | Resource ID | @@ -26,7 +28,7 @@ Collections → Patients → Studies → Series → Instances | PT | Positron Emission Tomography | OHIF | IDC_OHIF_PT | | SM | Slide Microscopy (H&E) | SLIM | IDC_SLIM | -**Note**: Annotation (ANN), Segmentation (SEG), Structured Report (SR) modalities are excluded as they are not directly viewable. And Other (OT) modality is also excluded. +**Note**: Annotation (ANN), Segmentation (SEG) and Structured Report (SR) are excluded as standalone resources since they are automatically loaded with their parent imaging studies in OHIF. Other (OT) modality is also excluded. ## Implementation Steps @@ -43,6 +45,8 @@ client.sql_query("SELECT * FROM index WHERE collection_id LIKE '%tcga%'").to_csv ### 2. Generate data +Generate patient-level resource files for each TCGA Pan Cancer study by linking patients to their imaging studies via OHIF and SLIM viewer URLs. + ```python import pandas as pd import numpy as np @@ -134,7 +138,7 @@ for st in os.listdir(dh_files_path): ``` -## Output Files +#### Output Files For each TCGA Pan-Cancer Atlas 2018 study, the script generates two files: @@ -144,19 +148,12 @@ For each TCGA Pan-Cancer Atlas 2018 study, the script generates two files: 2. **`data_resource_definition.txt`**: Defines the resource types available in the study - Columns: `RESOURCE_ID`, `DISPLAY_NAME`, `RESOURCE_TYPE`, `DESCRIPTION`, `OPEN_BY_DEFAULT`, `PRIORITY` -## Viewer URLs +#### Viewer URLs - **OHIF Viewer** (for radiology imaging): `https://viewer.imaging.datacommons.cancer.gov/viewer/{StudyInstanceUID}` - **SLIM Viewer** (for slide microscopy): `https://viewer.imaging.datacommons.cancer.gov/slim/studies/{StudyInstanceUID}` -## Dependencies - -- pandas -- numpy -- idc-index - -## Notes +#### Notes - Only patients already present in the clinical data files are included in the resource files - All resource files are sorted by `PATIENT_ID` for consistency -- Resources are set to open by default in cBioPortal with priority level 1 From f063ce1591cf487bea9523f4b69a5c3a1ba74f69 Mon Sep 17 00:00:00 2001 From: rmadupuri Date: Fri, 23 Jan 2026 10:22:44 -0500 Subject: [PATCH 4/5] update doc --- docs/tcga_pan_can_atlas/ohif-viewer.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/docs/tcga_pan_can_atlas/ohif-viewer.md b/docs/tcga_pan_can_atlas/ohif-viewer.md index 9fff853955..463bf88884 100644 --- a/docs/tcga_pan_can_atlas/ohif-viewer.md +++ b/docs/tcga_pan_can_atlas/ohif-viewer.md @@ -17,16 +17,16 @@ Collections → Patients → Studies → Series → Instances ## Available Imaging Modalities -| Code | Modality | Viewer | Resource ID | -|------|----------|--------|-------------| -| CR | Computed Radiography | OHIF | IDC_OHIF_CR | -| CT | Computed Tomography | OHIF | IDC_OHIF_CT | -| DX | Digital Radiography | OHIF | IDC_OHIF_DX | -| MG | Mammography | OHIF | IDC_OHIF_MG | -| MR | Magnetic Resonance | OHIF | IDC_OHIF_MR | -| NM | Nuclear Medicine | OHIF | IDC_OHIF_NM | -| PT | Positron Emission Tomography | OHIF | IDC_OHIF_PT | -| SM | Slide Microscopy (H&E) | SLIM | IDC_SLIM | +| Code | Modality | Viewer | Resource ID | Resource Tab Name | +|------|----------|--------|-------------|-------------------| +| CR | Computed Radiography | OHIF | IDC_OHIF_CR | Computed Radiography | +| CT | Computed Tomography | OHIF | IDC_OHIF_CT | CT Scan | +| DX | Digital Radiography | OHIF | IDC_OHIF_DX | Digital Radiography | +| MG | Mammography | OHIF | IDC_OHIF_MG | Mammography | +| MR | Magnetic Resonance | OHIF | IDC_OHIF_MR | Magnetic Resonance | +| NM | Nuclear Medicine | OHIF | IDC_OHIF_NM | Nuclear Medicine | +| PT | Positron Emission Tomography | OHIF | IDC_OHIF_PT | PET Scan | +| SM | Slide Microscopy (H&E) | SLIM | IDC_SLIM | H&E Slide | **Note**: Annotation (ANN), Segmentation (SEG) and Structured Report (SR) are excluded as standalone resources since they are automatically loaded with their parent imaging studies in OHIF. Other (OT) modality is also excluded. From 2615fc6c90fb0e30994d43a09c13d1f2abb81122 Mon Sep 17 00:00:00 2001 From: rmadupuri Date: Fri, 23 Jan 2026 16:11:48 -0500 Subject: [PATCH 5/5] update doc --- docs/tcga_pan_can_atlas/ohif-viewer.md | 121 ++----------------------- 1 file changed, 9 insertions(+), 112 deletions(-) diff --git a/docs/tcga_pan_can_atlas/ohif-viewer.md b/docs/tcga_pan_can_atlas/ohif-viewer.md index 463bf88884..cf31904138 100644 --- a/docs/tcga_pan_can_atlas/ohif-viewer.md +++ b/docs/tcga_pan_can_atlas/ohif-viewer.md @@ -13,7 +13,7 @@ Collections → Patients → Studies → Series → Instances - **Study**: One complete imaging exam for a patient, regardless of modality (e.g., "CT Chest with Contrast", "MRI Brain"). One patient could have undergone multiple imaging studies over time. - **Series**: Individual image batches within a study (e.g., different CT scan sections or sequences) -**Note**: While a single study may contain multiple imaging modalities, data was extracted at the study level and then split by modality for cBioPortal integration. This modality-level organization enables allows users to selectively access specific imaging types (e.g., CT scans vs. H&E slides) for each patient. +**Note**: While a single study may contain multiple imaging modalities, data was extracted at the study level and then split by modality for cBioPortal integration. This modality-level organization allows users to selectively access specific imaging types (e.g., CT scans vs. H&E slides) for each patient. ## Available Imaging Modalities @@ -32,115 +32,17 @@ Collections → Patients → Studies → Series → Instances ## Implementation Steps -### 1. Download Data from IDC +### Generate Resource Files -[idc-index](https://github.com/ImagingDataCommons/IDC-Tutorials/blob/master/notebooks/getting_started/part2_searching_basics.ipynb) package was used to programmatically download UID's for all tcga from IDC. -```python -from idc_index import IDCClient +Patient-level resource files are generated for each TCGA Pan Cancer study by: +1. Downloading TCGA imaging metadata from IDC using the idc-index package +2. Linking patients to their imaging studies via OHIF and SLIM viewer URLs -client = IDCClient() +**Script**: [generate_imaging_resources.py](https://github.com/cBioPortal/datahub-study-curation-tools/tree/master/generate_imaging_resources) -client.sql_query("SELECT * FROM index WHERE collection_id LIKE '%tcga%'").to_csv("idc_tcga.txt", sep='\t', index=None) -``` - -### 2. Generate data - -Generate patient-level resource files for each TCGA Pan Cancer study by linking patients to their imaging studies via OHIF and SLIM viewer URLs. - -```python -import pandas as pd -import numpy as np -import os - -df_tcga = pd.read_csv("idc_tcga.txt", sep='\t', dtype=str) - -# Remove modalities that cannot directly be viewed along with Other type -df_tcga = df_tcga[~df_tcga['Modality'].isin(['ANN', 'SEG', 'SR', 'OT'])] - -# Group the patients by collection_id, PatientID, StudyInstanceUID, Modality -cols = ['PatientID', 'StudyInstanceUID', 'Modality'] -resource_df = df_tcga[cols].drop_duplicates().reset_index(drop=True) - -# Map modalities -modality_map = { - 'CR': 'IDC_OHIF_CR', - 'CT': 'IDC_OHIF_CT', - 'DX': 'IDC_OHIF_DX', - 'MG': 'IDC_OHIF_MG', - 'MR': 'IDC_OHIF_MR', - 'NM': 'IDC_OHIF_NM', - 'PT': 'IDC_OHIF_PT', - 'SM': 'IDC_SLIM' -} -resource_df['Modality'] = resource_df['Modality'].replace(modality_map) - -# Add StudyInstanceUID URL -resource_df['StudyInstanceUID'] = np.where( - resource_df['Modality'] == 'IDC_SLIM', - 'https://viewer.imaging.datacommons.cancer.gov/slim/studies/' + resource_df['StudyInstanceUID'], - 'https://viewer.imaging.datacommons.cancer.gov/viewer/' + resource_df['StudyInstanceUID'] -) - -# Rename columns -resource_df = resource_df[['PatientID', 'Modality', 'StudyInstanceUID']] -resource_df.columns = ['PATIENT_ID', 'RESOURCE_ID', 'URL'] - -# Write filtered resource files per study -dh_files_path = "/Users/madupurr/Github/datahub/public" - -# resource defenition file map -definition_map = { - 'IDC_OHIF_CR': 'Computed Radiography', - 'IDC_OHIF_CT': 'CT Scan', - 'IDC_OHIF_DX': 'Digital Radiography', - 'IDC_OHIF_MG': 'Mammography', - 'IDC_OHIF_MR': 'Magnetic Resonance', - 'IDC_OHIF_NM': 'Nuclear Medicine', - 'IDC_OHIF_PT': 'PET Scan', - 'IDC_SLIM': 'H&E Slide' -} - -for st in os.listdir(dh_files_path): - if 'tcga_pan_can_atlas_2018' in st: - patient_file = os.path.join(dh_files_path, st, 'data_clinical_patient.txt') - resource_file = os.path.join(dh_files_path, st, 'data_resource_patient.txt') - resource_def_file = os.path.join(dh_files_path, st, 'data_resource_definition.txt') - - clinical_df = pd.read_csv(patient_file, sep='\t', skiprows=4, dtype=str) - patient_ids = clinical_df['PATIENT_ID'].unique() - - # Filter resource_df for only matching patients - filtered_resource_df = resource_df[resource_df['PATIENT_ID'].isin(patient_ids)] - - # Write to tab-separated file - filtered_resource_df = filtered_resource_df.sort_values(by='PATIENT_ID').reset_index(drop=True) - filtered_resource_df.to_csv(resource_file, sep='\t', index=False) - print(f"Written {len(filtered_resource_df)} resources to {resource_file}") - - # Get unique RESOURCE_IDs for this study that are in definition_map - resource_ids_in_study = filtered_resource_df['RESOURCE_ID'].unique() - - # Build resource definition DataFrame only if there are RESOURCE_IDs - if len(resource_ids_in_study) > 0: - resource_def_df = pd.DataFrame({ - 'RESOURCE_ID': resource_ids_in_study, - 'DISPLAY_NAME': [definition_map[rid] for rid in resource_ids_in_study], - 'RESOURCE_TYPE': 'PATIENT', - 'DESCRIPTION': [definition_map[rid] for rid in resource_ids_in_study], - 'OPEN_BY_DEFAULT': 'TRUE', - 'PRIORITY': 1 - }) - - # Write to data_resource_definition.txt file - resource_def_df.to_csv(resource_def_file, sep='\t', index=False) - print(f"Written definitions to {resource_def_file}") - - -``` +## Output Files -#### Output Files - -For each TCGA Pan-Cancer Atlas 2018 study, the script generates two files: +For each TCGA Pan-Cancer Atlas 2018 study, two files are generated: 1. **`data_resource_patient.txt`**: Links patients to their imaging studies - Columns: `PATIENT_ID`, `RESOURCE_ID`, `URL` @@ -148,12 +50,7 @@ For each TCGA Pan-Cancer Atlas 2018 study, the script generates two files: 2. **`data_resource_definition.txt`**: Defines the resource types available in the study - Columns: `RESOURCE_ID`, `DISPLAY_NAME`, `RESOURCE_TYPE`, `DESCRIPTION`, `OPEN_BY_DEFAULT`, `PRIORITY` -#### Viewer URLs +## Viewer URLs - **OHIF Viewer** (for radiology imaging): `https://viewer.imaging.datacommons.cancer.gov/viewer/{StudyInstanceUID}` - **SLIM Viewer** (for slide microscopy): `https://viewer.imaging.datacommons.cancer.gov/slim/studies/{StudyInstanceUID}` - -#### Notes - -- Only patients already present in the clinical data files are included in the resource files -- All resource files are sorted by `PATIENT_ID` for consistency