Charged Gold Nanoparticles for Target Identification–Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma
Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification–alignment and auto...
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Published in | Nano letters Vol. 24; no. 34; pp. 10614 - 10623 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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American Chemical Society
28.08.2024
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Abstract | Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification–alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART. |
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AbstractList | Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART. Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification–alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable in vitro aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an in situ sHCC mouse model was established for in vivo CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART. |
Author | Liu, Pengyuan Liu, Jiping Wu, Yajun Hu, Haili Cheng, Weiyi Ren, Weiye Luo, Zhizeng Wu, Zhibing Piao, Ji-Gang Jiang, Hao Lai, Jianjun He, Li |
AuthorAffiliation | Zhejiang Hospital Department of Radiation Oncology Department of Radiation Physics Department of Pharmacy Instiute of Intelligent Control and Robotics School of Pharmaceutical Sciences |
AuthorAffiliation_xml | – name: School of Pharmaceutical Sciences – name: Department of Radiation Physics – name: Department of Pharmacy – name: Department of Radiation Oncology – name: Zhejiang Hospital – name: Instiute of Intelligent Control and Robotics |
Author_xml | – sequence: 1 givenname: Jianjun orcidid: 0000-0002-2440-7919 surname: Lai fullname: Lai, Jianjun organization: Instiute of Intelligent Control and Robotics – sequence: 2 givenname: Zhizeng surname: Luo fullname: Luo, Zhizeng organization: Instiute of Intelligent Control and Robotics – sequence: 3 givenname: Jiping surname: Liu fullname: Liu, Jiping organization: Department of Radiation Physics – sequence: 4 givenname: Haili surname: Hu fullname: Hu, Haili organization: Department of Radiation Oncology – sequence: 5 givenname: Hao surname: Jiang fullname: Jiang, Hao organization: Department of Radiation Oncology – sequence: 6 givenname: Pengyuan surname: Liu fullname: Liu, Pengyuan organization: Department of Radiation Oncology – sequence: 7 givenname: Li surname: He fullname: He, Li organization: School of Pharmaceutical Sciences – sequence: 8 givenname: Weiyi surname: Cheng fullname: Cheng, Weiyi organization: School of Pharmaceutical Sciences – sequence: 9 givenname: Weiye surname: Ren fullname: Ren, Weiye organization: School of Pharmaceutical Sciences – sequence: 10 givenname: Yajun surname: Wu fullname: Wu, Yajun email: 546026929@qq.com organization: Zhejiang Hospital – sequence: 11 givenname: Ji-Gang orcidid: 0000-0002-4280-0634 surname: Piao fullname: Piao, Ji-Gang email: jgpiao@zcmu.edu.cn organization: School of Pharmaceutical Sciences – sequence: 12 givenname: Zhibing surname: Wu fullname: Wu, Zhibing email: wuzhibing@zju.edu.cn organization: Department of Radiation Oncology |
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Title | Charged Gold Nanoparticles for Target Identification–Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma |
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