Skip to Main Content

Paper Details

Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm.
Clin Cancer Res
58
2019
Author NameAffiliation
Joost W SchoutenErasmus MC-University Medical Centre Rotterdam
Pim J FrenchErasmus MC Cancer Institute
  • 1 - 2

Datasets

Dataset
Description
Source Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
The Cancer Imaging ArchiveTCIA is a service which de-identifies and publishes medical image datasets to study cancer. The data are organized as ⿿Collections⿝, typically subjects related by a common disease (e.g. lung cancer), image modality (MRI, CT, etc) or research focus. DICOM is the primary file format used by TCIA for radiology image storage. Supporting data related to the images such as patient outcomes, treatment details, genomics, pathology, and image analyses are also provided when available.Link
Release 1.2