-- DBMS: snowflake
create or replace TABLE ORIGINAL_COLLECTIONS_METADATA (
	"collection_name" VARCHAR(16777216),
	"collection_id" VARCHAR(16777216),
	"Program" VARCHAR(16777216),
	"Status" VARCHAR(16777216),
	"Updated" DATE,
	"Access" VARIANT,
	"ImageTypes" VARCHAR(16777216),
	"Subjects" NUMBER(38,0),
	DOI VARCHAR(16777216),
	URL VARCHAR(16777216),
	"CancerType" VARCHAR(16777216),
	"SupportingData" VARCHAR(16777216),
	"Species" VARCHAR(16777216),
	"Location" VARCHAR(16777216),
	"licenses" VARIANT,
	"Description" VARCHAR(16777216),
	"idc_webapp_collection_id" VARCHAR(16777216),
	"tcia_api_collection_id" VARCHAR(16777216),
	"tcia_wiki_collection_id" VARCHAR(16777216)
);

INSERT INTO "IDC"."IDC_V17"."ORIGINAL_COLLECTIONS_METADATA" (CancerType, Access, ImageTypes, DOI, idc_webapp_collection_id, tcia_api_collection_id, Subjects, tcia_wiki_collection_id, Status, Species, Updated, Location, Description, collection_name, URL, SupportingData, licenses, collection_id, Program) VALUES ('Breast Cancer', '[
  "Public"
]', 'MG', '10.7937/tcia.2019.ho23nxaw', 'victre', 'VICTRE', 2994, 'VICTRE', 'Complete', 'Human', '2019-03-08', 'Breast', '<p >	The VICTRE Trial: Open-Source, In-Silico Clinical Trial For Evaluating Digital Breast Tomosynthesis</p>
<p>
	<span>A total of 2986 subjects, with breast sizes and radiographic densities representative of a screening population and compressed thicknesses from 3.5 to 6 cm, were simulated and imaged on in-silico versions of DM and DBT systems using fast Monte Carlo x-ray transport. Images were interpreted by a computational reader detecting the presence of lesions. The in-silico trial (VICTRE) was designed to replicate a comparative trial from a previous regulatory submission. The endpoint was the difference in area under the receiver-operating-characteristic curve between modalities (delta-AUC) for lesion detection.</span></p>
<p>
	See the <a href="" url="http://doi.org/10.7937/TCIA.2019.ho23nxaw" data-toggle="modal" data-target="#external-web-warning" class="external-link">VICTRE Collection <i class="fa-solid fa-external-link external-link-icon" aria-hidden="true"></i></a> wiki page to learn more about the images and to obtain any supporting metadata for this collection.</p>
', 'VICTRE', 'https://doi.org/10.7937/tcia.2019.ho23nxaw', '', '[
  {
    "license_long_name": "Creative Commons Attribution 3.0 Unported License",
    "license_short_name": "CC BY 3.0",
    "license_url": "https://creativecommons.org/licenses/by/3.0/"
  }
]', 'victre', 'FDA'), ('Breast Cancer', '[
  "Public"
]', 'KO, MR', '10.7937/k9/tcia.2014.a2n1ixox', 'qin_breast_dce_mri', 'QIN Breast DCE-MRI', 10, 'QIN Breast DCE-MRI', 'Complete', 'Human', '2019-04-18', 'Breast', '<p>
	This collection of breast dynamic contrast-enhanced (DCE) MRI data contains images from a longitudinal study to assess breast cancer response to neoadjuvant chemotherapy. Images were acquired at four time points: prior to the start of treatment (Visit 1, V1), after the first cycle of treatment (Visit 2, V2), at midpoint of treatment course (Visit 3, V3), and after completion of treatment (prior to surgery) (Visit 4, V4). The value of this collection is to provide clinical imaging data for the development and validation of quantitative imaging methods for assessment of breast cancer response to treatment.</p>
<p>
	Please see the <a href="" url="https://doi.org/10.7937/k9/tcia.2014.a2n1ixox" data-toggle="modal" data-target="#external-web-warning" class="external-link">QIN Breast DCE-MRI <i class="fa-solid fa-external-link external-link-icon" aria-hidden="true"></i></a> page to learn more about the images and to obtain any supporting metadata for this collection.</p>
', 'QIN Breast DCE-MRI', 'https://doi.org/10.7937/k9/tcia.2014.a2n1ixox', 'Clinical, Image Analyses', '[
  {
    "license_long_name": "Creative Commons Attribution 3.0 Unported License",
    "license_short_name": "CC BY 3.0",
    "license_url": "https://creativecommons.org/licenses/by/3.0/"
  }
]', 'qin_breast_dce_mri', 'QIN'), ('Phantom', '[
  "Public"
]', 'CT, SEG', '10.7937/k9/tcia.2015.08a1ixoo', 'lung_phantom', 'Lung Phantom', 1, 'Lung Phantom', 'Complete', 'Human', '2014-06-19', 'Lung Phantom', '<p>
	The FDA anthropomorphic thorax phantom with 12 phantom lesions of different sizes (10 and 20 mm in effective diameter), shapes (spherical, elliptical, lobulated, and spiculated), and densities (&minus;630,&minus;10, and +100 HU) was scanned at Columbia University Medical Center on a 64-detector row scanner (LightSpeed VCT, GE Healthcare, Milwaukee, WI). The CT scanning parameters were 120 kVp, 100 mAs, 64x0.625 collimation, and pitch of 1.375. The images were reconstructed with the lung kernel using 1.25 mm slice thickness.</p>
<p>
	Please see the <a href="" url="https://doi.org/10.7937/K9/TCIA.2015.08A1IXOO" data-toggle="modal" data-target="#external-web-warning" class="external-link">Lung Phantom <i class="fa-solid fa-external-link external-link-icon" aria-hidden="true"></i></a> wiki page to learn more about the images and to obtain any supporting metadata for this collection.</p>
', 'Lung Phantom', 'https://doi.org/10.7937/k9/tcia.2015.08a1ixoo', 'Image Analyses', '[
  {
    "license_long_name": "Creative Commons Attribution 3.0 Unported License",
    "license_short_name": "CC BY 3.0",
    "license_url": "https://creativecommons.org/licenses/by/3.0/"
  }
]', 'lung_phantom', 'QIN'), ('Bladder Cancer', '[
  "Public"
]', 'MR', '10.7937/tcia.2019.b6u7wmqw', 'pdmr_bl0293_f563', 'PDMR-BL0293-F563', 19, 'PDMR-BL0293-F563', 'Complete', 'Mouse', '2019-10-02', 'Bladder', '<span><p>We identified and characterized a patient-derived xenograft model with metastatic potential, bladder xenograft BL0293-F563. In this study we performed a detailed imaging characterization of this model, which develops spontaneous liver and bone metastases.</span></p>
<p>	<span>Please see the <a href="" url="https://doi.org/10.7937/tcia.2019.b6u7wmqw" data-toggle="modal" data-target="#external-web-warning" class="external-link">PDMR-BL0293-F563 <i class="fa-solid fa-external-link external-link-icon" aria-hidden="true"></i></a> wiki page to learn more about the images and to obtain any supporting metadata for this collection.
</span></p>', 'PDMR-BL0293-F563', 'https://doi.org/10.7937/tcia.2019.b6u7wmqw', 'Clinical', '[
  {
    "license_long_name": "Creative Commons Attribution 3.0 Unported License",
    "license_short_name": "CC BY 3.0",
    "license_url": "https://creativecommons.org/licenses/by/3.0/"
  }
]', 'pdmr_bl0293_f563', 'PDMR'), ('Sarcomas', '[
  "Public"
]', 'CT, MR, SM', '10.7937/k9/tcia.2016.cx6ylsux', 'tcga_sarc', 'TCGA-SARC', 261, 'TCGA-SARC', 'Complete', 'Human', '2020-05-29', 'Chest-Abdomen-Pelvis, Leg, TSpine', '<p>
	The Cancer Genome Atlas-Sarcoma Cancer (TCGA-SARC) data collection is part of a larger effort to enhance the TCGA http://cancergenome.nih.gov/ data set with characterized radiological images. The Cancer Imaging Program (CIP) with the cooperation of several of the TCGA tissue-contributing institutions are working to archive a large portion of the radiological images of the genetically-analyzed SARC cases.</p><br />
<p>
	Please see the <a href="" url="https://doi.org/10.7937/K9/TCIA.2016.CX6YLSUX" data-toggle="modal" data-target="#external-web-warning" class="external-link">TCGA-SARC <i class="fa-solid fa-external-link external-link-icon" aria-hidden="true"></i></a> page to learn more about the images and to obtain any supporting metadata for this collection.</p>
', 'TCGA-SARC', 'https://doi.org/10.7937/k9/tcia.2016.cx6ylsux', 'Clinical, Genomics', '[
  {
    "license_long_name": "Creative Commons Attribution 3.0 Unported License",
    "license_short_name": "CC BY 3.0",
    "license_url": "https://creativecommons.org/licenses/by/3.0/"
  }
]', 'tcga_sarc', 'TCGA');