Welcome to the MINDS Database Documentation

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 PostgreSQL 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 PostgreSQL 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 PostgreSQL is 5432. The following command will not work until you replace port with a valid port number.

docker run -d --name minds -e POSTGRES_PASSWORD=my-secret-pw -e POSTGRES_DB=minds -p port:5432 postgres

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

pip install med-minds

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=5432
DB_USER=postgres
PASSWORD=my-secret-pw
DATABASE=minds   

PostgreSQL Migration (v0.0.6)

Version 0.0.6 introduces a migration from MySQL to PostgreSQL as the database backend, offering:

• Better performance for complex queries
• Advanced data types and indexing options
• More robust transaction support
• Better standards compliance

If you're upgrading from a previous version that used MySQL, please ensure your database environment is updated to use PostgreSQL 12 or later.

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.