Cargando…
Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma
In this study, we aim to develop and validate a radiomics model for pretreatment prediction of RPS6K expression in hepatocellular carcinoma (HCC) patients, thus helping clinical decision-making of mTOR-inhibitor (mTORi) therapy. We retrospectively enrolled 147 HCC patients, who underwent curative he...
Autores principales: | , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363521/ https://www.ncbi.nlm.nih.gov/pubmed/37482600 http://dx.doi.org/10.1186/s43556-023-00133-3 |
_version_ | 1785076645515755520 |
---|---|
author | Yang, Fan Wan, Yidong Shen, Xiaoyong Wu, Yichao Xu, Lei Meng, Jinwen Wang, Jianguo Liu, Zhikun Chen, Jun Lu, Di Wen, Xue Zheng, Shusen Niu, Tianye Xu, Xiao |
author_facet | Yang, Fan Wan, Yidong Shen, Xiaoyong Wu, Yichao Xu, Lei Meng, Jinwen Wang, Jianguo Liu, Zhikun Chen, Jun Lu, Di Wen, Xue Zheng, Shusen Niu, Tianye Xu, Xiao |
author_sort | Yang, Fan |
collection | PubMed |
description | In this study, we aim to develop and validate a radiomics model for pretreatment prediction of RPS6K expression in hepatocellular carcinoma (HCC) patients, thus helping clinical decision-making of mTOR-inhibitor (mTORi) therapy. We retrospectively enrolled 147 HCC patients, who underwent curative hepatic resection at First Affiliated Hospital Zhejiang University School of Medicine. RPS6K expression was determined with immunohistochemistry staining. Patients were randomly split into training or validation cohorts on a 7:3 ratio. Radiomics features were extracted from T2-weighted and diffusion-weighted images. Machine learning algorithms including multiple logistic regression (MLR), supporting vector machine (SVM), random forest (RF), and artificial neural network (ANN) were applied to construct the predictive model. A nomogram was further built to visualize the possibility of RPS6K expression. The area under the receiver operating characteristic (AUC) was used to evaluate the performance of diagnostic models. 174 radiomics features were confirmed correlated with RPS6K expression. Amongst all built models, the ANN-based hybrid model exhibited best predictive ability with AUC of 0.887 and 0.826 in training and validation cohorts. ALB was identified as the key clinical index, and the nomogram displayed further improved ability with AUC of 0.917 and 0.845. In this study, we proved MRI-based radiomics model and nomogram can accurately predict RPS6K expression non-invasively, thus providing help for clinical decision making for mTORi therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43556-023-00133-3. |
format | Online Article Text |
id | pubmed-10363521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-103635212023-07-25 Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma Yang, Fan Wan, Yidong Shen, Xiaoyong Wu, Yichao Xu, Lei Meng, Jinwen Wang, Jianguo Liu, Zhikun Chen, Jun Lu, Di Wen, Xue Zheng, Shusen Niu, Tianye Xu, Xiao Mol Biomed Research In this study, we aim to develop and validate a radiomics model for pretreatment prediction of RPS6K expression in hepatocellular carcinoma (HCC) patients, thus helping clinical decision-making of mTOR-inhibitor (mTORi) therapy. We retrospectively enrolled 147 HCC patients, who underwent curative hepatic resection at First Affiliated Hospital Zhejiang University School of Medicine. RPS6K expression was determined with immunohistochemistry staining. Patients were randomly split into training or validation cohorts on a 7:3 ratio. Radiomics features were extracted from T2-weighted and diffusion-weighted images. Machine learning algorithms including multiple logistic regression (MLR), supporting vector machine (SVM), random forest (RF), and artificial neural network (ANN) were applied to construct the predictive model. A nomogram was further built to visualize the possibility of RPS6K expression. The area under the receiver operating characteristic (AUC) was used to evaluate the performance of diagnostic models. 174 radiomics features were confirmed correlated with RPS6K expression. Amongst all built models, the ANN-based hybrid model exhibited best predictive ability with AUC of 0.887 and 0.826 in training and validation cohorts. ALB was identified as the key clinical index, and the nomogram displayed further improved ability with AUC of 0.917 and 0.845. In this study, we proved MRI-based radiomics model and nomogram can accurately predict RPS6K expression non-invasively, thus providing help for clinical decision making for mTORi therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s43556-023-00133-3. Springer Nature Singapore 2023-07-24 /pmc/articles/PMC10363521/ /pubmed/37482600 http://dx.doi.org/10.1186/s43556-023-00133-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Yang, Fan Wan, Yidong Shen, Xiaoyong Wu, Yichao Xu, Lei Meng, Jinwen Wang, Jianguo Liu, Zhikun Chen, Jun Lu, Di Wen, Xue Zheng, Shusen Niu, Tianye Xu, Xiao Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma |
title | Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma |
title_full | Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma |
title_fullStr | Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma |
title_full_unstemmed | Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma |
title_short | Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma |
title_sort | application of multi-modality mri-based radiomics in the pre-treatment prediction of rps6k expression in hepatocellular carcinoma |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363521/ https://www.ncbi.nlm.nih.gov/pubmed/37482600 http://dx.doi.org/10.1186/s43556-023-00133-3 |
work_keys_str_mv | AT yangfan applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT wanyidong applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT shenxiaoyong applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT wuyichao applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT xulei applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT mengjinwen applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT wangjianguo applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT liuzhikun applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT chenjun applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT ludi applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT wenxue applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT zhengshusen applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT niutianye applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma AT xuxiao applicationofmultimodalitymribasedradiomicsinthepretreatmentpredictionofrps6kexpressioninhepatocellularcarcinoma |