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Radiological Investigation III: Pulmonary Angiogram and PET Scan01:13

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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
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PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT Dataset with Corresponding Radiology Reports.

Le Xue1,2, Gang Feng3,4, Wenbo Zhang5,6

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This summary is machine-generated.

We introduce PETWB-REP, a novel dataset of whole-body PET/CT scans and radiology reports for 490 cancer patients. This resource supports AI development and multimodal research in medical imaging.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Large-scale medical imaging datasets are vital for AI development and clinical research.
  • Multimodal datasets integrating functional/anatomical imaging and radiology reports for diverse cancers are scarce.

Purpose of the Study:

  • To present PETWB-REP, a curated dataset of whole-body 18F-Fluorodeoxyglucose (FDG) Positron Emission Tomography/Computed Tomography (PET/CT) scans and radiology reports.
  • To provide a standardized, multimodal resource for advancing AI in oncology.

Main Methods:

  • Curated dataset of 490 patients with diverse malignancies (lung, liver, breast, prostate, ovarian).
  • Included whole-body PET/CT scans and corresponding radiology reports.
  • Organized data using the Brain Imaging Data Structure (BIDS) standard, including raw and processed data, bilingual reports, and clinical metadata.

Main Results:

  • The PETWB-REP dataset contains 490 patient cases with comprehensive imaging and reporting data.
  • Data is de-identified and structured according to BIDS, with bilingual reports and processed derivatives.
  • The dataset covers a wide range of malignancies, enhancing its utility for diverse research.

Conclusions:

  • PETWB-REP addresses the scarcity of multimodal oncology imaging datasets.
  • The dataset facilitates multi-center validation and cross-disciplinary research in medical imaging, radiomics, and AI.
  • It is poised to advance automated report generation and multimodal representation learning.