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MINDS is a framework designed to integrate multimodal oncology data. It queries and integrates data from multiple sources, including clinical data, genomic data, and imaging data from the NIH NCI CRDC and TCIA portals.

Note

We are currently updating MINDS to include more data sources and improve the user experience. If you have any suggestions or would like to contribute, please feel free to reach out to us. Here is a list of the projects to be included in MINDS (115,974 total patients).

Projects in MINDS
Project Name Cases Clinical Radiology Histopathology Molecular
Foundation Medicine (FM) 18,004 ✓ ✓
The Cancer Genome Atlas (TCGA) 11,428 ✓ ✓ ✓ ✓
Therapeutically Applicable Research to Generate Effective Treatments (TARGET) 6,543 ✓ ✓
Clinical Proteomic Tumor Analysis Consortium (CPTAC) 1,656 ✓ ✓ ✓
The Molecular Profiling to Predict Response to Treatment (MP2PRT) 1,562 ✓ ✓
Multiple Myeloma Research Foundation (MMRF) 995 ✓ ✓
BEATAML1.0 882 ✓ ✓
Cancer Genome Characterization Initiatives (CGCI) 645 ✓ ✓ ✓
NCI Center for Cancer Research (NCICCR) 489 ✓ ✓
REBC 449 ✓ ✓
MATCH 448 ✓ ✓
Ukrainian National Research Center for Radiation Medicine Trio Study (TRIO) 339 ✓ ✓
Count Me In (CMI) 299 ✓ ✓
Human Cancer Model Initiative (HCMI) 278 ✓ ✓ ✓
West Coast Prostrate Cancer Dream Team (WCDT) 101 ✓ ✓
Oregon Health and Science University (OHSU) 176 ✓ ✓
Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) 87 ✓ ✓
EXCEPTIONAL RESPONDERS 84 ✓ ✓
Environment And Genetics in Lung Cancer Etiology (EAGLE) 50 ✓ ✓
ORGANOID 70 ✓ ✓
Clinical Trials Sequencing Project (CTSP) 45 ✓ ✓
VA Research Precision Oncology Program (VAREPOP) 7 ✓ ✓
4D-Lung 20 ✓
A091105 83 ✓
AAPM-RT-MAC 55 ✓
ACNS0332 85 ✓
ACRIN-6698 385 ✓
ACRIN-Contralateral-Breast-MR 984 ✓
ACRIN-DSC-MR-Brain 123 ✓
ACRIN-FLT-Breast 83 ✓ ✓
ACRIN-FMISO-Brain 45 ✓
ACRIN-HNSCC-FDG-PET-CT 260 ✓
ACRIN-NSCLC-FDG-PET 242 ✓
Adrenal-ACC-Ki67-Seg 53 ✓ ✓
Advanced-MRI-Breast-Lesions 632 ✓ ✓ ✓
AHEP0731 80 ✓
AHOD0831 165 ✓
AML-Cytomorphology_LMU 200 ✓
AML-Cytomorphology_MLL_Helmholtz 189 ✓
Anti-PD-1_Lung 46 ✓
Anti-PD-1_MELANOMA 47 ✓
APOLLO-5 414 ✓
ARAR0331 108 ✓
AREN0532 544 ✓
AREN0533 294 ✓
AREN0534 239 ✓
B-mode-and-CEUS-Liver 120 ✓
Bone-Marrow-Cytomorphology_MLL_Helmholtz_Fraunhofer 945 ✓
Brain-TR-GammaKnife 47 ✓
Brain-Tumor-Progression 20 ✓
Breast-Cancer-Screening-DBT 5,060 ✓
BREAST-DIAGNOSIS 88 ✓
Breast-Lesions-USG 256 ✓
Breast-MRI-NACT-Pilot 64 ✓
Burdenko-GBM-Progression 180 ✓
C-NMC 2019 118 ✓
C4KC-KiTS 210 ✓
CALGB50303 155 ✓
CBIS-DDSM 1,566 ✓
CC-Radiomics-Phantom 17 ✓
CC-Radiomics-Phantom-2 251 ✓
CC-Tumor-Heterogeneity 23 ✓
CDD-CESM 326 ✓
CMB-AML 8 ✓ ✓
CMB-CRC 49 ✓ ✓
CMB-GEC 7 ✓ ✓
CMB-LCA 61 ✓ ✓
CMB-MEL 44 ✓ ✓
CMB-MML 64 ✓ ✓
CMB-PCA 12 ✓ ✓
CMMD 1,775 ✓ ✓ ✓
CODEX imaging of HCC 15 ✓
Colorectal-Liver-Metastases 197 ✓
COVID-19-AR 105 ✓
COVID-19-NY-SBU 1,384 ✓
CRC_FFPE-CODEX_CellNeighs 35 ✓
CT COLONOGRAPHY 825 ✓ ✓
CT Images in COVID-19 661 ✓
CT Lymph Nodes 176 ✓
CT-ORG 140 ✓
CT-Phantom4Radiomics 1 ✓
CT-vs-PET-Ventilation-Imaging 20 ✓
CTpred-Sunitinib-panNET 38 ✓
DFCI-BCH-BWH-PEDs-HGG 61 ✓
DLBCL-Morphology 209 ✓
DRO-Toolkit 32 ✓
Duke-Breast-Cancer-MRI 922 ✓
EA1141 500 ✓
ExACT 30 ✓
FDG-PET-CT-Lesions 900 ✓
GammaKnife-Hippocampal 390 ✓
GBM-DSC-MRI-DRO 3 ✓
GLIS-RT 230 ✓
HCC-TACE-Seg 105 ✓
HE-vs-MPM 12 ✓
Head-Neck Cetuximab 111 ✓
Head-Neck-PET-CT 298 ✓
HEAD-NECK-RADIOMICS-HN1 137 ✓
Healthy-Total-Body-CTs 30 ✓
HER2 tumor ROIs 273 ✓
HistologyHSI-GB 13 ✓
HNC-IMRT-70-33 211 ✓
HNSCC 627 ✓
HNSCC-3DCT-RT 31 ✓
HNSCC-mIF-mIHC-comparison 8 ✓
Hungarian-Colorectal-Screening 200 ✓
ISPY1 222 ✓
ISPY2 719 ✓
IvyGAP 39 ✓
LCTSC 60 ✓
LDCT-and-Projection-data 299 ✓
LGG-1p19qDeletion 159 ✓
LIDC-IDRI 1,010 ✓
Lung Phantom 1 ✓
Lung-Fused-CT-Pathology 6 ✓
Lung-PET-CT-Dx 355 ✓
LungCT-Diagnosis 61 ✓
Meningioma-SEG-CLASS 96 ✓
MIDRC-RICORD-1A 110 ✓
MIDRC-RICORD-1B 117 ✓
MIDRC-RICORD-1C 361 ✓
MiMM_SBILab 5 ✓
NADT-Prostate 37 ✓
NaF PROSTATE 9 ✓
NLST 26,254 ✓ ✓
NRG-1308 12 ✓
NSCLC Radiogenomics 211 ✓
NSCLC-Cetuximab 490 ✓
NSCLC-Radiomics 422 ✓
NSCLC-Radiomics-Genomics 89 ✓
NSCLC-Radiomics-Interobserver1 22 ✓
OPC-Radiomics 606 ✓
Osteosarcoma-Tumor-Assessment 4 ✓
Ovarian Bevacizumab Response 78 ✓
Pancreas-CT 82 ✓
Pancreatic-CT-CBCT-SEG 40 ✓
PCa_Bx_3Dpathology 50 ✓ ✓
Pediatric-CT-SEG 359 ✓
Pelvic-Reference-Data 58 ✓
Phantom FDA 7 ✓
Post-NAT-BRCA 64 ✓
Pretreat-MetsToBrain-Masks 200 ✓ ✓
Prostate Fused-MRI-Pathology 28 ✓
Prostate-3T 64 ✓
Prostate-Anatomical-Edge-Cases 131 ✓
PROSTATE-DIAGNOSIS 92 ✓
PROSTATE-MRI 26 ✓
Prostate-MRI-US-Biopsy 1,151 ✓
PROSTATEx 346 ✓
Pseudo-PHI-DICOM-Data 21 ✓
PTRC-HGSOC 174 ✓
QIBA CT-1C 1 ✓
QIBA-CT-Liver-Phantom 3 ✓
QIN Breast DCE-MRI 10 ✓
QIN GBM Treatment Response 54 ✓
QIN LUNG CT 47 ✓
QIN PET Phantom 2 ✓
QIN PROSTATE 22 ✓
QIN-BRAIN-DSC-MRI 49 ✓
QIN-BREAST 67 ✓
QIN-BREAST-02 13 ✓
QIN-HEADNECK 279 ✓
QIN-PROSTATE-Repeatability 15 ✓
QIN-SARCOMA 15 ✓
RADCURE 3,346 ✓ ✓
REMBRANDT 130 ✓
ReMIND 114 ✓
RHUH-GBM 40 ✓
RIDER Breast MRI 5 ✓
RIDER Lung CT 32 ✓
RIDER Lung PET-CT 244 ✓
RIDER NEURO MRI 19 ✓
RIDER PHANTOM MRI 10 ✓
RIDER PHANTOM PET-CT 20 ✓
RIDER Pilot 8 ✓
S0819 1,299 ✓
SLN-Breast 78 ✓
SN-AM 60 ✓
Soft-tissue-Sarcoma 51 ✓
SPIE-AAPM Lung CT Challenge 70 ✓
StageII-Colorectal-CT 230 ✓
UCSF-PDGM 495 ✓
UPENN-GBM 630 ✓
Vestibular-Schwannoma-MC-RC 124 ✓
Vestibular-Schwannoma-SEG 242 ✓
VICTRE 2,994 ✓

Installation

Currently the cloud version of MINDS is in closed beta, but, you can still recreate the MINDS database locally. To get the local version of the MINDS database running, you will need to setup a MySQL database and populate it with the MINDS schema. This can be easily done using a docker container. First, you will need to install docker. You can find the installation instructions for your operating system here. Next, you will need to pull the MySQL docker image and run a container with the following command.

Note

Please replace my-secret-pw with your desired password and port with the port you want to use to access the database. The default port for MySQL is 3306. The following command will not work until you replace port with a valid port number.

docker run -d --name minds -e MYSQL_ROOT_PASSWORD=my-secret-pw -e MYSQL_DATABASE=minds -p port:3306 mysql

Finally, to install the MINDS python package use the following pip command:

pip install git+https://github.com/lab-rasool/MINDS.git

After installing the package, please create a .env file in the root directory of the project with the following variables:

HOST=127.0.0.1
PORT=3306
DB_USER=root
PASSWORD=my-secret-pw
DATABASE=minds

Usage

Initial setup and automated updates

If you have locally setup the MINDS database, then you will need to populate it with data. To do this, or to update the database with the latest data, you can use the following command:

# Import the minds package
import minds

# Update the database with the latest data
minds.update()

Querying the MINDS database

The MINDS python package provides a python interface to the MINDS database. You can use this interface to query the database and return the results as a pandas dataframe.

import minds

# get a list of all the tables in the database
tables = minds.get_tables()

# get a list of all the columns in a table
columns = minds.get_columns("clinical")

# Query the database directly
query = "SELECT * FROM minds.clinical WHERE project_id = 'TCGA-LUAD' LIMIT 10"
df = minds.query(query)

Building the cohort and downloading the data

# Generate a cohort to download from query
query_cohort = minds.build_cohort(query=query, output_dir="./data")

# or you can now directly supply a cohort from GDC
gdc_cohort = minds.build_cohort(gdc_cohort="cohort_Unsaved_Cohort.2024-02-12.tsv", output_dir="./data")

# to get the cohort details
gdc_cohort.stats()

# to download the data from the cohort to the output directory specified
# you can also specify the number of threads to use and the modalities to exclude or include
gdc_cohort.download(threads=12, exclude=["Slide Image"])

Please cite our work

@Article{s24051634,
    AUTHOR = {Tripathi, Aakash and Waqas, Asim and Venkatesan, Kavya and Yilmaz, Yasin and Rasool, Ghulam},
    TITLE = {Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets},
    JOURNAL = {Sensors},
    VOLUME = {24},
    YEAR = {2024},
    NUMBER = {5},
    ARTICLE-NUMBER = {1634},
    URL = {https://www.mdpi.com/1424-8220/24/5/1634},
    ISSN = {1424-8220},
    DOI = {10.3390/s24051634}
}

Contributing

We welcome contributions from the community. If you would like to contribute to the MINDS project, please read our contributing guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.