Dataset Information

A Dual Reporter Iodinated Labeling Reagent for Cancer Positron Emission Tomography Imaging and Fluorescence-Guided Surgery.

ABSTRACT: The combination of early diagnosis and complete surgical resection offers the greatest prospect of curative cancer treatment. An iodine-124/fluorescein-based dual-modality labeling reagent, 124I-Green, constitutes a generic tool for one-step installation of a positron emission tomography (PET) and a fluorescent reporter to any cancer-specific antibody. The resulting antibody conjugate would allow both cancer PET imaging and intraoperative fluorescence-guided surgery. 124I-Green was synthesized in excellent radiochemical yields of 92 ± 5% (n = 4) determined by HPLC with an improved one-pot three-component radioiodination reaction. The A5B7 carcinoembryonic antigen (CEA)-specific antibody was conjugated to 124I-Green. High tumor uptake of the dual-labeled A5B7 of 20.21 ± 2.70, 13.31 ± 0.73, and 10.64 ± 1.86%ID/g was observed in CEA-expressing SW1222 xenograft mouse model (n = 3) at 24, 48, and 72 h post intravenous injection, respectively. The xenografts were clearly visualized by both PET/CT and ex vivo fluorescence imaging. These encouraging results warrant the further translational development of 124I-Green for cancer PET imaging and fluorescence-guided surgery.

SUBMITTER: Lu Z

PROVIDER: S-EPMC5847118 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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A Dual Reporter Iodinated Labeling Reagent for Cancer Positron Emission Tomography Imaging and Fluorescence-Guided Surgery.

Lu Zhi Z Pham Truc Thuy TT Rajkumar Vineeth V Yu Zilin Z Pedley R Barbara RB Årstad Erik E Maher John J Yan Ran R

Journal of medicinal chemistry 20180206 4

The combination of early diagnosis and complete surgical resection offers the greatest prospect of curative cancer treatment. An iodine-124/fluorescein-based dual-modality labeling reagent, <sup>124</sup>I-Green, constitutes a generic tool for one-step installation of a positron emission tomography (PET) and a fluorescent reporter to any cancer-specific antibody. The resulting antibody conjugate would allow both cancer PET imaging and intraoperative fluorescence-guided surgery. <sup>124</sup>I-G ...[more]

PMID: 29388770

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Project description:BackgroundDeep learning has recently shown great potential in medical image reconstruction tasks. For positron emission tomography (PET) images, the direct reconstruction from raw data to radioactivity images using deep learning without any constraint may lead to the production of nonexistent structures. The aim of this study was to specifically develop and test a flexibly deep learning-based reconstruction network guided by any form of prior knowledge to achieve high quality and high reliability reconstruction.MethodsWe developed a novel prior information-guided reconstruction network (PIGRN) with a dual-channel generator and a 2-scale discriminator based on a conditional generative adversarial network (cGAN). Besides the raw data channel, an additional channel is provided in the generator for prior information (PI) to guide the training phase. The PI can be reconstructed images obtained via conventional methods, nuclear medical images from other modalities, attenuation correction maps from time-of-flight-PET (TOF-PET) data, or any other physical parameters. For this study, the reconstructed images generated by filtered back projection (FBP) were chosen as the input of the additional channel. To improve the image quality, a 2-scale discriminator was adopted which can focus on both the coarse and fine field of the reconstruction images. Experiments were carried out on both a simulation dataset and a real Sprague Dawley (SD) rat dataset.ResultsTwo classic deep learning-based reconstruction networks, including U-Net and Deep-PET, were compared in our study. Compared with these two methods, our method could provide much higher quality PET image reconstruction in the study of the simulation dataset. The peak signal-to-noise ratio (PSNR) value reached 31.8498, and the structure similarity index measure (SSIM) value reached 0.9754. The real study on SD rats indicated that the proposed network also has strong generalization ability.ConclusionsThe flexible PIGRN based on cGAN for PET images combines both raw data and PI. The results of comparison experiments and a generalization experiment based on simulation and SD rat datasets demonstrated that the proposed PIGRN has the ability to improve image quality and has strong generalization ability.

Dataset Information

A Dual Reporter Iodinated Labeling Reagent for Cancer Positron Emission Tomography Imaging and Fluorescence-Guided Surgery.

Publications

A Dual Reporter Iodinated Labeling Reagent for Cancer Positron Emission Tomography Imaging and Fluorescence-Guided Surgery.

Similar Datasets

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