MAGIC-CT: Multiorgan Annotation and Grounded Image Captioning in CT for Cancer

MAGIC-CT: Multiorgan Annotation and Grounded Image Captioning in CT for Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF data-descriptor MAGIC-CT: Multiorgan Annotation and Grounded Image Captioning in CT for Cancer Maxim Popov, Zangir Iklassov, Zhanas Baimagambet, Murat Jakipov, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8777425/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Computed Tomography (CT) imaging is a cornerstone of abdominal oncology, offering critical insights into tumor morphology and spread. While artificial intelligence (AI) holds promise for automating lesion detection, segmentation, and reporting, progress is hindered by the scarcity of multimodal datasets that pair 3D anatomical annotations with expert-curated clinical descriptions. We present MAGIC-CT, a contrast-enhanced CT dataset of 562 patients with abdominal tumors (liver cysts/cancer, lung metastases, lung cancer, kidney cysts, renal cancer, pancreatic cancer). All 562 patients have CT scans and 3D lesion/organ masks; a subset of 492 patients also have organ-wise, radiologist-authored reports (RU/KZ/EN) totaling 4,937 organ descriptions. The dataset spans 8 pathologies across 4 organs, with about 1,250 annotated lesions and about 500 lesion-linked textual findings, enabling training of multimodal systems that connect volumetric localization to clinical language. MAGIC-CT uniquely integrates volumetric lesion localization, quantitative metrics (e.g., tumor volume, angular involvement of vasculature), and rich semantic context (e.g., "cuff-like encasement of the celiac trunk"), addressing the lack of resources bridging radiological imaging, segmentation, and clinical language. This dataset is expected to enable advancements in AI-driven tumor characterization, automated report generation, and metastasis tracking, with implications for precision oncology. This dataset is openly available at Zenodo: \href{ https://doi.org/10.5281/zenodo.18389015}{10.5281/zenodo.18389015} . Computed Tomography Abdominal Oncology Multiorgan Segmentation Grounded Image Captioning Radiology Reports 3D Medical Imaging Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8777425","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":631201262,"identity":"5e719bf2-a019-485d-a22a-75cb519fd5d5","order_by":0,"name":"Maxim 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While artificial intelligence (AI) holds promise for automating lesion detection, segmentation, and reporting, progress is hindered by the scarcity of multimodal datasets that pair 3D anatomical annotations with expert-curated clinical descriptions. We present MAGIC-CT, a contrast-enhanced CT dataset of 562 patients with abdominal tumors (liver cysts/cancer, lung metastases, lung cancer, kidney cysts, renal cancer, pancreatic cancer). All 562 patients have CT scans and 3D lesion/organ masks; a subset of 492 patients also have organ-wise, radiologist-authored reports (RU/KZ/EN) totaling 4,937 organ descriptions. The dataset spans 8 pathologies across 4 organs, with about 1,250 annotated lesions and about 500 lesion-linked textual findings, enabling training of multimodal systems that connect volumetric localization to clinical language. MAGIC-CT uniquely integrates volumetric lesion localization, quantitative metrics (e.g., tumor volume, angular involvement of vasculature), and rich semantic context (e.g., \"cuff-like encasement of the celiac trunk\"), addressing the lack of resources bridging radiological imaging, segmentation, and clinical language. This dataset is expected to enable advancements in AI-driven tumor characterization, automated report generation, and metastasis tracking, with implications for precision oncology. 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