Mapping Pituitary Neuroendocrine Tumors: An Annotated MRI Dataset Profiling Tumor and Carotid Characteristics

Pituitary neuroendocrine tumors remain one of the most common intracranial tumors. While radiomic research related to pituitary tumors is progressing, public data sets for external validation remain scarce. We introduce an open dataset comprising high-resolution T1 contrast-enhanced MR scans of 136...

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Bibliographic Details
Published in:Scientific data 2025-01, Vol.12 (1), p.80-5, Article 80
Main Authors: Pandit, A. S., Keenlyside, A., Khan, D. Z., Reischer, G., Kamal, M. A., Yoh, N., Jaunmuktane, Z., Borg, A., Dorward, N. L., Baldeweg, S. E., Davagnanam, I., Hyare, H., Nachev, P., Marcus, H. J.
Format: Article
Language:English
Subjects:
631/67/1922
692/163/2743/2742/1738
692/699/2743/2742/1738
Brain cancer
Brain tumors
Carotid Arteries - diagnostic imaging
Carotid artery
Data Descriptor
Humanities and Social Sciences
Humans
Magnetic Resonance Imaging
multidisciplinary
Neuroendocrine tumors
Neuroendocrine Tumors - diagnostic imaging
Pituitary
Pituitary gland
Pituitary Neoplasms - diagnostic imaging
Radiomics
Science
Science (multidisciplinary)
Segmentation
Tumors
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Description
Summary:Pituitary neuroendocrine tumors remain one of the most common intracranial tumors. While radiomic research related to pituitary tumors is progressing, public data sets for external validation remain scarce. We introduce an open dataset comprising high-resolution T1 contrast-enhanced MR scans of 136 patients with pituitary tumors, annotated for tumor segmentation and accompanied by clinical, radiological and pathological metadata. This diverse dataset captures variations in tumor size, location, and pathological activity, essential for understanding this complex condition. Expert annotations of both the tumor and adjacent carotid arteries ensure precise delineation, facilitating the development of automated segmentation algorithms. Our initiative addresses the need for standardized data in pituitary oncology, fosters collaboration and innovation, and enables the development and benchmarking of workflows that utilize pituitary radiomics for treatment planning and outcome prediction.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-04218-8