Dataset Information

Ultra-sensitive Nanoprobe Modified with Tumor Cell Membrane for UCL/MRI/PET Multimodality Precise Imaging of Triple-Negative Breast Cancer.

ABSTRACT: Triple-negative breast cancer (TNBC) is a subtype of breast cancer in which the estrogen receptor and progesterone receptor are not expressed, and human epidermal growth factor receptor 2 is not amplified or overexpressed either, which make the clinical diagnosis and treatment very challenging. Molecular imaging can provide an effective way to diagnose TNBC. Upconversion nanoparticles (UCNPs), are a promising new generation of molecular imaging probes. However, UCNPs still need to be improved for tumor-targeting ability and biocompatibility. This study describes a novel probe based on cancer cell membrane-coated upconversion nanoparticles (CCm-UCNPs), owing to the low immunogenicity and homologous-targeting ability of cancer cell membranes, and modified multifunctional UCNPs. This probe exhibits excellent performance in breast cancer molecular classification and TNBC diagnosis through UCL/MRI/PET tri-modality imaging in vivo. By using this probe, MDA-MB-231 was successfully differentiated between MCF-7 tumor models in vivo. Based on the tumor imaging and molecular classification results, the probe is also expected to be modified for drug delivery in the future, contributing to the treatment of TNBC. The combination of nanoparticles with biomimetic cell membranes has the potential for multiple clinical applications.

SUBMITTER: Fang H

PROVIDER: S-EPMC7770711 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Ultra-sensitive Nanoprobe Modified with Tumor Cell Membrane for UCL/MRI/PET Multimodality Precise Imaging of Triple-Negative Breast Cancer.

Fang Hanyi H Li Mengting M Liu Qingyao Q Gai Yongkang Y Yuan Lujie L Wang Sheng S Zhang Xiao X Ye Min M Zhang Yongxue Y Gao Mingyuan M Hou Yi Y Lan Xiaoli X

Nano-micro letters 20200222 1

Triple-negative breast cancer (TNBC) is a subtype of breast cancer in which the estrogen receptor and progesterone receptor are not expressed, and human epidermal growth factor receptor 2 is not amplified or overexpressed either, which make the clinical diagnosis and treatment very challenging. Molecular imaging can provide an effective way to diagnose TNBC. Upconversion nanoparticles (UCNPs), are a promising new generation of molecular imaging probes. However, UCNPs still need to be improved fo ...[more]

PMID: 34138297

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Project description:BackgroundLowering the dose for positron emission tomography (PET) imaging reduces patients' radiation burden but decreases the image quality by increasing noise and reducing imaging detail and quantifications. This paper introduces a method for acquiring high-quality PET images from an ultra-low-dose state to achieve both high-quality images and a low radiation burden.MethodsWe developed a two-task-based end-to-end generative adversarial network, named bi-c-GAN, that incorporated the advantages of PET and magnetic resonance imaging (MRI) modalities to synthesize high-quality PET images from an ultra-low-dose input. Moreover, a combined loss, including the mean absolute error, structural loss, and bias loss, was created to improve the trained model's performance. Real integrated PET/MRI data from 67 patients' axial heads (each with 161 slices) were used for training and validation purposes. Synthesized images were quantified by the peak signal-to-noise ratio (PSNR), normalized mean square error (NMSE), structural similarity (SSIM), and contrast noise ratio (CNR). The improvement ratios of these four selected quantitative metrics were used to compare the images produced by bi-c-GAN with other methods.ResultsIn the four-fold cross-validation, the proposed bi-c-GAN outperformed the other three selected methods (U-net, c-GAN, and multiple input c-GAN). With the bi-c-GAN, in a 5% low-dose PET, the image quality was higher than that of the other three methods by at least 6.7% in the PSNR, 0.6% in the SSIM, 1.3% in the NMSE, and 8% in the CNR. In the hold-out validation, bi-c-GAN improved the image quality compared to U-net and c-GAN in both 2.5% and 10% low-dose PET. For example, the PSNR using bi-C-GAN was at least 4.46% in the 2.5% low-dose PET and at most 14.88% in the 10% low-dose PET. Visual examples also showed a higher quality of images generated from the proposed method, demonstrating the denoising and improving ability of bi-c-GAN.ConclusionsBy taking advantage of integrated PET/MR images and multitask deep learning (MDL), the proposed bi-c-GAN can efficiently improve the image quality of ultra-low-dose PET and reduce radiation exposure.

Dataset Information

Ultra-sensitive Nanoprobe Modified with Tumor Cell Membrane for UCL/MRI/PET Multimodality Precise Imaging of Triple-Negative Breast Cancer.

Publications

Ultra-sensitive Nanoprobe Modified with Tumor Cell Membrane for UCL/MRI/PET Multimodality Precise Imaging of Triple-Negative Breast Cancer.

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