Cargando…

Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus

Oral lichen planus (OLP) is a chronic inflammatory disease, and the common management focuses on controlling inflammation with immunosuppressive therapy. While the response to the immunosuppressive therapy is heterogeneous, exploring the mechanism and prediction of the response gain greater importan...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Ziang, Han, Qi, Yang, Dan, Li, Yijun, Shang, Qianhui, Liu, Jiaxin, Li, Weiqi, Xu, Hao, Chen, Qianming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9260599/
https://www.ncbi.nlm.nih.gov/pubmed/35812391
http://dx.doi.org/10.3389/fimmu.2022.942945
_version_ 1784742074688471040
author Xu, Ziang
Han, Qi
Yang, Dan
Li, Yijun
Shang, Qianhui
Liu, Jiaxin
Li, Weiqi
Xu, Hao
Chen, Qianming
author_facet Xu, Ziang
Han, Qi
Yang, Dan
Li, Yijun
Shang, Qianhui
Liu, Jiaxin
Li, Weiqi
Xu, Hao
Chen, Qianming
author_sort Xu, Ziang
collection PubMed
description Oral lichen planus (OLP) is a chronic inflammatory disease, and the common management focuses on controlling inflammation with immunosuppressive therapy. While the response to the immunosuppressive therapy is heterogeneous, exploring the mechanism and prediction of the response gain greater importance. Here, we developed a workflow for prediction of immunosuppressive therapy response prediction in OLP, which could automatically acquire image-based features. First, 38 features were acquired from 208 OLP pathological images, and 6 features were subsequently obtained which had a significant impact on the effect of OLP immunosuppressive therapy. By observing microscopic structure and integrated with the corresponding transcriptome, the biological implications of the 6 features were uncovered. Though the pathway enrichment analysis, three image-based features which advantageous to therapy indicated the different lymphocytes infiltration, and the other three image-based features which bad for therapy respectively indicated the nicotinamide adenine dinucleotide (NADH) metabolic pathway, response to potassium ion pathway and adenosine monophosphate (AMP) activated protein kinase pathway. In addition, prediction models for the response to immunosuppressive therapy, were constructed with above image-based features. The best performance prediction model built by logistic regression showed an accuracy of 90% and the area under the receiver operating characteristic curve (AUROC) reached 0.947. This study provided a novel approach to automatically obtain biological meaningful image-based features from unannotated pathological images, which could indicate the immunosuppressive therapy in OLP. Besides, the novel and accurate prediction model may be useful for the OLP clinical management.
format Online
Article
Text
id pubmed-9260599
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92605992022-07-08 Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus Xu, Ziang Han, Qi Yang, Dan Li, Yijun Shang, Qianhui Liu, Jiaxin Li, Weiqi Xu, Hao Chen, Qianming Front Immunol Immunology Oral lichen planus (OLP) is a chronic inflammatory disease, and the common management focuses on controlling inflammation with immunosuppressive therapy. While the response to the immunosuppressive therapy is heterogeneous, exploring the mechanism and prediction of the response gain greater importance. Here, we developed a workflow for prediction of immunosuppressive therapy response prediction in OLP, which could automatically acquire image-based features. First, 38 features were acquired from 208 OLP pathological images, and 6 features were subsequently obtained which had a significant impact on the effect of OLP immunosuppressive therapy. By observing microscopic structure and integrated with the corresponding transcriptome, the biological implications of the 6 features were uncovered. Though the pathway enrichment analysis, three image-based features which advantageous to therapy indicated the different lymphocytes infiltration, and the other three image-based features which bad for therapy respectively indicated the nicotinamide adenine dinucleotide (NADH) metabolic pathway, response to potassium ion pathway and adenosine monophosphate (AMP) activated protein kinase pathway. In addition, prediction models for the response to immunosuppressive therapy, were constructed with above image-based features. The best performance prediction model built by logistic regression showed an accuracy of 90% and the area under the receiver operating characteristic curve (AUROC) reached 0.947. This study provided a novel approach to automatically obtain biological meaningful image-based features from unannotated pathological images, which could indicate the immunosuppressive therapy in OLP. Besides, the novel and accurate prediction model may be useful for the OLP clinical management. Frontiers Media S.A. 2022-06-23 /pmc/articles/PMC9260599/ /pubmed/35812391 http://dx.doi.org/10.3389/fimmu.2022.942945 Text en Copyright © 2022 Xu, Han, Yang, Li, Shang, Liu, Li, Xu and Chen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Xu, Ziang
Han, Qi
Yang, Dan
Li, Yijun
Shang, Qianhui
Liu, Jiaxin
Li, Weiqi
Xu, Hao
Chen, Qianming
Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus
title Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus
title_full Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus
title_fullStr Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus
title_full_unstemmed Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus
title_short Automatic Detection of Image-Based Features for Immunosuppressive Therapy Response Prediction in Oral Lichen Planus
title_sort automatic detection of image-based features for immunosuppressive therapy response prediction in oral lichen planus
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9260599/
https://www.ncbi.nlm.nih.gov/pubmed/35812391
http://dx.doi.org/10.3389/fimmu.2022.942945
work_keys_str_mv AT xuziang automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus
AT hanqi automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus
AT yangdan automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus
AT liyijun automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus
AT shangqianhui automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus
AT liujiaxin automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus
AT liweiqi automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus
AT xuhao automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus
AT chenqianming automaticdetectionofimagebasedfeaturesforimmunosuppressivetherapyresponsepredictioninorallichenplanus