Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study

Shengnan Ai; Ping Xue; Chengming Wang; Wenxin Zhang; Jui Cheng Hsieh; Zhengyu Chen; Bin He; Xiao Zhang; Ning Zhang; Ying Gu

doi:10.1109/ACCESS.2020.2972275

Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study

Shengnan Ai, Ping Xue^*, Chengming Wang, Wenxin Zhang, Jui Cheng Hsieh, Zhengyu Chen, Bin He, Xiao Zhang, Ning Zhang, Ying Gu

^*此作品的通讯作者

医学技术学院

科研成果: 期刊稿件 › 文章 › 同行评审

2 引用（Scopus）

摘要

In this paper, we report a fully automatic method for the prediction of the treatment efficacy of photodynamic therapy during the clinical treatment in port-wine stains. Histogram of oriented gradients (HOG) features were extracted from optical coherence tomography images. Isolation forest (iForest) was used to build classifier based on these features, achieving a sensitivity of 84% and specificity of 91%. Our dataset consists of 336 PWS lesions of 121 patients. We aim to build a comprehensive computational model for the patients who respond positively to the photodynamic therapy, which could be used to sort and identify patients who respond poorly to photodynamic therapy before treatment and prevent them from unnecessary treatment.

源语言	英语
文章编号	8986618
页（从-至）	31227-31233
页数	7
期刊	IEEE Access
卷	8
DOI	https://doi.org/10.1109/ACCESS.2020.2972275
出版状态	已出版 - 2020

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Ai, S., Xue, P., Wang, C., Zhang, W., Hsieh, J. C., Chen, Z., He, B., Zhang, X., Zhang, N., & Gu, Y. (2020). Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study. IEEE Access, 8, 31227-31233. 文章 8986618. https://doi.org/10.1109/ACCESS.2020.2972275

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title = "Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study",

abstract = "In this paper, we report a fully automatic method for the prediction of the treatment efficacy of photodynamic therapy during the clinical treatment in port-wine stains. Histogram of oriented gradients (HOG) features were extracted from optical coherence tomography images. Isolation forest (iForest) was used to build classifier based on these features, achieving a sensitivity of 84% and specificity of 91%. Our dataset consists of 336 PWS lesions of 121 patients. We aim to build a comprehensive computational model for the patients who respond positively to the photodynamic therapy, which could be used to sort and identify patients who respond poorly to photodynamic therapy before treatment and prevent them from unnecessary treatment.",

keywords = "Anomaly detection, machine learning, optical coherence tomography, port-wine stain, support vector data description",

author = "Shengnan Ai and Ping Xue and Chengming Wang and Wenxin Zhang and Hsieh, {Jui Cheng} and Zhengyu Chen and Bin He and Xiao Zhang and Ning Zhang and Ying Gu",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2020",

doi = "10.1109/ACCESS.2020.2972275",

language = "English",

volume = "8",

pages = "31227--31233",

journal = "IEEE Access",

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T1 - Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment

T2 - A Pilot Study

AU - Ai, Shengnan

AU - Xue, Ping

AU - Wang, Chengming

AU - Zhang, Wenxin

AU - Hsieh, Jui Cheng

AU - Chen, Zhengyu

AU - He, Bin

AU - Zhang, Xiao

AU - Zhang, Ning

AU - Gu, Ying

PY - 2020

Y1 - 2020

N2 - In this paper, we report a fully automatic method for the prediction of the treatment efficacy of photodynamic therapy during the clinical treatment in port-wine stains. Histogram of oriented gradients (HOG) features were extracted from optical coherence tomography images. Isolation forest (iForest) was used to build classifier based on these features, achieving a sensitivity of 84% and specificity of 91%. Our dataset consists of 336 PWS lesions of 121 patients. We aim to build a comprehensive computational model for the patients who respond positively to the photodynamic therapy, which could be used to sort and identify patients who respond poorly to photodynamic therapy before treatment and prevent them from unnecessary treatment.

AB - In this paper, we report a fully automatic method for the prediction of the treatment efficacy of photodynamic therapy during the clinical treatment in port-wine stains. Histogram of oriented gradients (HOG) features were extracted from optical coherence tomography images. Isolation forest (iForest) was used to build classifier based on these features, achieving a sensitivity of 84% and specificity of 91%. Our dataset consists of 336 PWS lesions of 121 patients. We aim to build a comprehensive computational model for the patients who respond positively to the photodynamic therapy, which could be used to sort and identify patients who respond poorly to photodynamic therapy before treatment and prevent them from unnecessary treatment.

KW - Anomaly detection

KW - machine learning

KW - optical coherence tomography

KW - port-wine stain

KW - support vector data description

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Fully Automatic Prediction for Efficacy of Photodynamic Therapy in Clinical Port-Wine Stains Treatment: A Pilot Study

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