The Cancer Imaging Archive Updates : August 2024

[The Cancer Imaging Program]

The Cancer Imaging Archive Updates : August 2024 Addressing demographic disparities in TCIA datasets CT image from "CT-RTSTRUCT-RTDOSE-RTPLAN Sets of Head and Neck Cancers Treated with Identical Prescriptions using IMRT: An Open Dataset for Deep Learning in Treatment Planning (HNC-IMRT-70-33)." TCIA leadership recently published a letter to the editors of Radiology: Imaging Cancer that outlines plans to  better represent the at-large population in TCIA datasets.  This includes a call to the community to pay careful attention to the demographic distribution of newly proposed datasets, additional emphasis on collecting key demographic data points, and a renewed commitment to making it easier to find demographic information in TCIA search interfaces.  Frederick National Lab released a companion news article reinforcing  the commitment to reducing disparities in data sets to improve applicability of resulting analysis to the general population. Glioblastoma Awareness Glioblastoma is the most common and deadliest type of brain cancer. Researchers seeking to improve outcomes for these patients can leverage 14 unique datasets on TCIA including radiology & pathology imaging, analyses, & clinical data for ~2,000 subjects. In recognition of Glioblastoma Awareness Day (observed July 17) TCIA published or updated several new datasets to combine for a total of 14 unique datasets including radiology & pathology imaging, analyses, and clinical data from around 2,000 subjects which can be used to study this disease.  Read on to learn about our recently published glioblastoma datasets. Additional NCI Clinical Proteomic Tumor Analysis Consortium subjects were added to CPTAC-GBM and a new related dataset was published, "Multi-scale signaling and tumor evolution in high-grade gliomas (CPTAC-Glioblastoma-CODEX)".  Both datasets are described in the recent Cancer Cell publication, "Multi-scale signaling and tumor evolution in high-grade gliomas"  which discusses  how CPTAC researchers are helping illuminate key signaling pathways and tumor evolution in high-grade glioma (HGG). The group leveraged 14 proteogenomic and metabolomic platforms to characterize 228 HGG samples, plus 18 normal brain samples and 14 brain metastases as comparators, to generate and investigate a rich multi-omic dataset.   TCIA also published a new dataset, "MR imaging of pediatric subjects with high-grade gliomas (DFCI-BCH-BWH-PEDs-HGG)" which reflects the extension of the well-known Brain Tumor Segmentation (BraTS) Challenge series to address pediatric cancer, and adds to the growing number of TCIA datasets which help support the mission of NCI's Childhood Cancer Dataset Initiative.  Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children and the five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations.  This dataset includes a retrospective multi-institutional cohort of conventional/structural magnetic resonance imaging (MRI) sequences from Boston’s Children Hospital and Dana-Farber Cancer Institute, and Brigham and Women's Hospital. MR imaging of pediatric subjects with high-grade gliomas (DFCI-BCH-BWH-PEDs-HGG) The "Histological Hyperspectral Glioblastoma Dataset (HistologyHSI-GB)" is now available on TCIA. The database is composed of 469 annotated hyperspectral images derived from 13 subjects’ histological slides (482 total images).  Hyperspectral imaging technology combines the main features of two existing technologies: conventional imaging and spectroscopy. Hyperspectral cameras make it possible to analyze, at the same time and in a non-contact way, the morphological features and chemical composition of the objects captured. From: Histological Hyperspectral Glioblastoma Dataset (HistologyHSI-GB) - https://www.nature.com/articles/s41597-024-03510-x Stay Connected with The Cancer Imaging Archive  Manage Subscriptions | Help The Cancer imaging Archive is funded in part by Frederick Natational Lab for Cancer Research.

 

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