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162 results for “msi”
Histological image tiles for TCGA-CRC-DX, color-normalized, sorted by MSI status, train/test split
<p>These are histological images of colorectal cancer, derived from the TCGA database at https://portal.cdc.cancer.gov. Tumor tissue was outlined manually and the tumor region was cut into tiles of 256 µm edge length, saved as 512 px images (effective magnification 0.5 µm/px). All image tiles were color-normalized with the Macenko method. Patients were split into training and test set in a 2:1 ratio. For all patients, MSI status was acquired (patients with MSI-H = MSIH; patients with MSI-L and MSS = NonMSIH) and all tiles inherited the label of the parent patient. Then, tiles in the training set were randomly undersampled to equalize classes. The test set was not undersampled. Further info: www.kather.ai</p>
Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples
<p>This repository contains 411,890 unique image patches derived from histological images of colorectal cancer and gastric cancer patients in the TCGA cohort (original whole slide SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this repository are derived from formalin-fixed paraffin-embedded (FFPE) diagnostic slides ("DX" at the GDC data portal). This is explained well in this blog: http://www.andrewjanowczyk.com/download-tcga-digital-pathology-images-ffpe/</p> <p><strong>Preprocessing</strong></p> <p>All SVS slides were preprocessed as follows</p> <p>1. automatic detection of tumor</p> <p>2. resizing to 224 px x 224 px at a resolution of 0.5 µm/px</p> <p>4. color normalization with the Macenko method (Macenko et al., 2009, http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf)</p> <p>5. assignment of patients to either "MSS" (microsatellite stable) or "MSIMUT" (microsatellite instable or highly mutated)</p> <p>6. randomization of patients to training and testing sets (~70% and ~30%). Randomization was done on a patient level rather than on a slide or tile level</p> <p>7. equilibration of training sets by undersampling (removing excess tiles in MSS class in a random way)</p> <p><strong>File description</strong></p> <p>1. STAD_TRAIN_MSS - training images (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 50285 unique image patches; FFPE samples</p> <p>2. STAD_TRAIN_MSIMUT - training images ( (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 50285 unique image patches; FFPE samples</p> <p>3. STAD_TEST_MSS - test images (~30% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 90104 unique image patches; FFPE samples</p> <p>4. STAD_TEST_MSIMUT - test images ( ~30% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 27904 unique image patches; FFPE samples</p> <p>5. CRC_DX_TEST_MSIMUT - test images (~30% of all patients) for colorectal cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 29335 unique image patches; FFPE samples</p> <p>6. CRC_DX_TEST_MSS - test images (~30% of all patients) for colorectal cancer TCGA patients with MSS (microsatellite stable) tumors, 70569 unique image patches; FFPE samples</p> <p>7. CRC_DX_TRAIN_MSIMUT - training images (~70% of all patients) for colorectal cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 46704 unique image patches; FFPE samples</p> <p>8. CRC_DX_TRAIN_MSS - training images (~70% of all patients) for colorectal cancer TCGA patients with MSS (microsatellite stable) tumors, 46704 unique image patches; FFPE samples</p>
Histological images for MSI vs. MSS classification in gastrointestinal cancer, snap-frozen samples
<p>This repository contains 218,578 unique image patches derived from histological images of colorectal cancer patients in the TCGA cohort (original whole slide SVS images are freely available at https://portal.gdc.cancer.gov/). All images in this repository are derived from snap-frozen tissue slides ("TS" or "BS" at the GDC data portal).</p> <p><strong>Preprocessing</strong></p> <p>All SVS slides were preprocessed as follows</p> <p>1. automatic detection of tumor</p> <p>2. resizing to 224 px x 224 px at a resolution of 0.5 µm/px</p> <p>4. color normalization with the Macenko method (Macenko et al., 2009, http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf)</p> <p>5. assignment of patients to either "MSS" (microsatellite stable) or "MSIMUT" (microsatellite unstable or hypermutated)</p> <p>6. randomization of patients to training and testing sets (~70% and ~30%). Randomization was done on a patient level rather than on a slide or tile level</p> <p>7. equilibration of training sets by undersampling (removing excess tiles in MSS class in a random way)</p> <p><strong>File description</strong></p> <p>1. STAD_TRAIN_MSS - training images (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 50285 unique image patches; FFPE samples</p> <p>2. STAD_TRAIN_MSIMUT - training images ( (~70% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 50285 unique image patches; FFPE samples</p> <p>3. STAD_TEST_MSS - test images (~30% of all patients) for gastric (stomach) cancer TCGA patients with MSS (microsatellite stable) tumors, 90104 unique image patches; FFPE samples</p> <p>4. STAD_TEST_MSIMUT - test images ( ~30% of all patients) for gastric (stomach) cancer TCGA patients with MSI (microsatellite instable) or highly mutated tumors, 27904 unique image patches; FFPE samples</p>
Raw data for MSI prediction from HE
<ul> <li><strong>Network_comparison.xlsx</strong> contains raw data for the comparison of different neural network models on the tumor detection benchmark task. This is related to Extended Data Figure 1 in our manuscript "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer"</li> <li><strong>TCGA_GI_Test_set_predictions.xlsx</strong> contains predicted MSIness for test set patients in TCGA-CRC-KR, TCGA-CRC-DX, TCGA-STAD. This is related to Extended Data Figure 2 in our manuscript "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer"</li> <li><strong>DACHS_predictions.xlsx</strong> contains predicted MSIness for DACHS patients. This is related to Extended Data Figure 2 in our manuscript "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer"</li> <li><strong>DACHS_MSIness_in_geneticMSS.xlsx </strong>contains MSIness raw data for genetic MSS DACHS patients. This is related to Extended Data Figure 2 in our manuscript "Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer"</li> </ul> <p>in all files, each line is one patient.</p>
Guinea Pig Skin & Tick Bite Site - MSI Raw Data Files
<p>Ticks: nymphal <em>Ixodes scapularis</em>. Skin: Hartley Guinea Pigs. Raw MALDI-FTICR mass spectrometry imaging (MSI) data. File labeled "1" is negative ion mode. File labeled "2" is positive ion mode.</p>
MSI-78 Topical Cream vs. Oral Ofloxacin in the Treatment of Infected Diabetic Ulcers
ClinicalTrials.gov study NCT00563394. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Avelumab Plus 5-FU Based Chemotherapy as Adjuvant Treatment for Stage 3 MSI-High or POLE Mutant Colon Cancer
ClinicalTrials.gov study NCT03827044. IPD Sharing: NO. Countries: 1. Publications: 1.
QL1604 Monotherapy for dMMR or MSI-H Advanced Solid Tumors
ClinicalTrials.gov study NCT04326829. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Standard Chemotherapy vs Immunotherapie in 2nd Line Treatment of MSI Colorectal Mestastatic Cancer
ClinicalTrials.gov study NCT03186326. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Sintilimab Plus Hypofractionated Radiotherapy for MSI-H/dMMR Rectal Cancer
ClinicalTrials.gov study NCT04636008. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Toripalimab Plus Bevacizumab and Chemotherapy as Neoadjuvant Therapy in Advanced MSI-H or dMMR Colorectal Cancer
ClinicalTrials.gov study NCT04988191. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Nivolumab and Ipilimumab and Radiation Therapy in MSS and MSI High Colorectal and Pancreatic Cancer
ClinicalTrials.gov study NCT03104439. IPD Sharing: NO. Countries: 1. Publications: 1.
Vaccination Against MSI Colorectal Cancer
ClinicalTrials.gov study NCT01461148. IPD Sharing: Not stated. Countries: 1. Publications: 2.
TremelImumab aNd Durvalumab For the Non-operatIve Management (NOM) of MSI-high Resectable GC/GEJC.
ClinicalTrials.gov study NCT04817826. IPD Sharing: NO. Countries: 1. Publications: 1.
MSI-78 Topical Cream vs. Oral Ofloxacin in the Treatment of Infected Diabetic Ulcers
ClinicalTrials.gov study NCT00563433. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Oncometabolome and MALDI-MSI in Upper GI Carcinomas - Chemosensitivity in Esophageal Carcinoma
ClinicalTrials.gov study NCT06642506. IPD Sharing: NO. Countries: 1. Publications: 28.
Study of Pembrolizumab (MK-3475) vs Standard Therapy in Participants With Microsatellite Instability-High (MSI-H) or Mismatch Repair Deficient (dMMR) Stage IV Colorectal Carcinoma (MK-3475-177/KEYNOTE
ClinicalTrials.gov study NCT02563002. IPD Sharing: YES. Countries: 0. Publications: 4.
DETERMINE Trial Treatment Arm 02: Atezolizumab in Adult, Paediatric and Teenage/Young Adult Patients With Cancers With High Tumour Mutational Burden (TMB) or Microsatellite Instability-high (MSI-high)
ClinicalTrials.gov study NCT05770102. IPD Sharing: YES. Countries: 1. Publications: 0.
PD-1 Antibody Combined With COX Inhibitor in MSI-H/dMMR or High TMB Colorectal Cancer
ClinicalTrials.gov study NCT03638297. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Add-On Study of MSI-195 (S-Adenosyl-L-Methionine, SAMe) for Patients With Major Depressive Disorder (MDD)
ClinicalTrials.gov study NCT01912196. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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OpenNeuro
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