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Yang F, Wan Y, Shen X, Wu Y, Xu L, Meng J, Wang J, Liu Z, Chen J, Lu D, Wen X, Zheng S, Niu T, Xu X. Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma. Mol Biomed 2023; 4:22. [PMID: 37482600 PMCID: PMC10363521 DOI: 10.1186/s43556-023-00133-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/20/2023] [Indexed: 07/25/2023] Open
Abstract
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.
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Affiliation(s)
- Fan Yang
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Yidong Wan
- Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310020, Zhejiang, China
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
| | - Xiaoyong Shen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qinchun Road, Hangzhou, 310003, China
| | - Yichao Wu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Lei Xu
- Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, 310020, Zhejiang, China
- Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, Zhejiang, China
| | - Jinwen Meng
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Jianguo Wang
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Zhikun Liu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Jun Chen
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Di Lu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
| | - Xue Wen
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qinchun Road, Hangzhou, 310003, China
| | - Shusen Zheng
- NHC Key Laboratory of Combined Multi-Organ Transplantation, Hangzhou, 310003, China
- Institute of Organ Transplantation, Zhejiang University, Hangzhou, 310003, China
- Department of Hepatobiliary and Pancreatic Surgery, Shulan Health Hangzhou Hospital, Hangzhou, 310004, Zhejiang, China
| | - Tianye Niu
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China.
| | - Xiao Xu
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
- NHC Key Laboratory of Combined Multi-Organ Transplantation, Hangzhou, 310003, China.
- Institute of Organ Transplantation, Zhejiang University, Hangzhou, 310003, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, China.
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