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Learners can view the images, annotations, and case reports together in the OHIF viewer, making training more efficient and accessible.","img":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","backgroundColor":"#080808","images":{"fallback":{"src":"/static/31184bb275ab319251262bb0a2ba61f7/ea9df/qipcm-01.png","srcSet":"/static/31184bb275ab319251262bb0a2ba61f7/f6273/qipcm-01.png 750w,\n/static/31184bb275ab319251262bb0a2ba61f7/efb70/qipcm-01.png 1080w,\n/static/31184bb275ab319251262bb0a2ba61f7/ea9df/qipcm-01.png 1254w","sizes":"(min-width: 1254px) 1254px, 100vw"},"sources":[{"srcSet":"/static/31184bb275ab319251262bb0a2ba61f7/6566f/qipcm-01.webp 750w,\n/static/31184bb275ab319251262bb0a2ba61f7/d3f2a/qipcm-01.webp 1080w,\n/static/31184bb275ab319251262bb0a2ba61f7/5bf0c/qipcm-01.webp 1254w","type":"image/webp","sizes":"(min-width: 1254px) 1254px, 100vw"}]},"width":1254,"height":847}}},"video":null},{"caption":"QIPCM curated medical image for the OCTANE trial, whose mission is to expand the capacity of next-generation sequencing (NGS) testing for advanced solid tumor patients across Ontario, while creating a repository of blood, tumor samples and medical images from patients for future research. As shown here, the data repository is organized in cBioPortal to enable visualization and analysis. Patient imaging studies are arranged as a time series relative to the date of diagnosis, and at each time point QIPCM provides a link to the OHIF viewer for accessing and reviewing the imaging study.","img":{"childImageSharp":{"gatsbyImageData":{"layout":"constrained","backgroundColor":"#f8f8f8","images":{"fallback":{"src":"/static/a530873961a3c0bf80114a0fe0c4a8f3/ea9df/qipcm-02.png","srcSet":"/static/a530873961a3c0bf80114a0fe0c4a8f3/f6273/qipcm-02.png 750w,\n/static/a530873961a3c0bf80114a0fe0c4a8f3/efb70/qipcm-02.png 1080w,\n/static/a530873961a3c0bf80114a0fe0c4a8f3/ea9df/qipcm-02.png 1254w","sizes":"(min-width: 1254px) 1254px, 100vw"},"sources":[{"srcSet":"/static/a530873961a3c0bf80114a0fe0c4a8f3/6566f/qipcm-02.webp 750w,\n/static/a530873961a3c0bf80114a0fe0c4a8f3/d3f2a/qipcm-02.webp 1080w,\n/static/a530873961a3c0bf80114a0fe0c4a8f3/5bf0c/qipcm-02.webp 1254w","type":"image/webp","sizes":"(min-width: 1254px) 1254px, 100vw"}]},"width":1254,"height":847}}},"video":null}],"overview":"The Quantitative Imaging for Personalized Cancer Medicine (QIPCM) program is a Canadian imaging core lab at the University Health Network in Toronto. We provide end-to-end support for clinical trials, ensuring imaging data is curated consistently and reliably. Our data ingestion pipeline securely transfers images from health centres to our central repository, where they undergo de-identification, quality assurance, and final storage. Within our hospital network, we also have the capability for batch extraction from clinical imaging PACS. We provide remote permission-based access to enable our clinical investigators and industry sponsors from around the world to collaborate seamlessly on their data. Our web-based imaging tools include RT Review and the OHIF viewer, and for maximum flexibility we offer virtual machines and GPU access for AI model development. Our research interests include theranostics and federated data sharing. \n\n Since its inception in 2013, QIPCM has supported more than 90 clinical trials and imaging research studies across 65 hospitals worldwide. Funded through grant support from The Ontario Institute for Cancer Research, fee for service contracts and philanthropic funding from The Princess Margaret Cancer Foundation, we continue to expand our tools and services to better support clinical trials in Canada and around the world.","quote":{"author":"Julia Publicover, MSc.","text":"With OHIF's lightweight, web-based viewer, our users gain easy, secure access to imaging data, delivering a practical, scalable solution that enhances collaboration and efficiency.","position":"Director - Translational Research and Innovation, UHN"},"title":"Quantitative Imaging for Personalized Cancer Medicine (QIPCM)"}},"pageContext":{"id":"QIPCM","prev":{"id":"Pixilib","overview":"Pixilib (https://www.gaelo.fr) is an imaging CRO promoting decisional image based biomarkers for patient management in clinical trials. Our main topic (but not limited) is PET/CT imaging in lymphoma. We are implementing a PET/CT viewer built on the top of OHIF, this viewer is integrating custom and personalized PET/CT visualization workflow and advanced quantification of prognostic biomarkers (SUV, SUL, TMTV, Dmax …). The client side rendering architecture of OHIF is a game changing approach for clinical research allowing users to have a portable viewer available everywhere with no installation with high performance as all image processing relies on the client hardware with same performances as a locally installed viewer. This way our expert physicians panel can work from any computer connected to the internet either in a hospital or from home making centralized decisions available a few hours after image reception. OHIF flexibility allows us to fully connect the viewer with our data management backend, filling our e-CRF automatically from the viewer and tomorrow integrating AI aided diagnosis for researchers. Pixilib is an active contributor to OHIF ecosystem and aims to change the paradigm of viewer in clinical research by making an extensible viewer to fit each clinical trial need and build personalized medicine of tomorrow.","shortDescription":"Pixilib utilizes OHIF for a portable PET/CT viewer, integrating it with a backend for data management. It aids clinical trials with advanced quantification of biomarkers and future AI diagnosis tools."},"next":{"id":"TCIA","overview":"The Cancer Imaging Archive (TCIA) is a service which de-identifies and publishes medical images of cancer.  TCIA is funded by the Cancer Imaging Program (CIP), a part of the United States  National Cancer Institute (NCI), and is managed by the Frederick National Laboratory for Cancer Research (FNLCR). \n The imaging data are organized as “collections” defined by a common disease (e.g. lung cancer), image modality or type (MRI, CT, digital histopathology, etc) or research focus. TCIA currently hosts over 200 collections consisting of imaging data from more than 70,000 subjects.  An emphasis is made to provide supporting data related to the images such as patient outcomes, treatment details, genomics and expert analyses.","shortDescription":"The Cancer Imaging Archive (TCIA), funded by the US National Cancer Institute, publishes de-identified cancer medical images. Hosting 200+ collections with 70,000+ subjects, it provides supporting data like patient outcomes and treatment details."}}},
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