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Xiao L, Zhao H, Liu S, Dong W, Gao Y, Wang L, Huang B, Li Z. Staging liver fibrosis: comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease. Abdom Radiol (NY) 2024; 49:1165-1174. [PMID: 38219254 DOI: 10.1007/s00261-023-04142-2] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 01/16/2024]
Abstract
OBJECTIVES To develop and compare radiomics model and fusion model based on multiple MR parameters for staging liver fibrosis in patients with chronic liver disease. MATERIALS AND METHODS Patients with chronic liver disease who underwent multiparametric abdominal MRI were included in this retrospective study. Multiparametric MR images were imported into 3D-Slicer software for drawing bounding boxes on MR images. By using a 3D-Slicer extension of SlicerRadiomics, radiomics features were extracted from these MR images. The z-score normalization method was used for post-processing radiomics features. The least absolute shrinkage and selection operator method (LASSO) was performed for selecting significant radiomics features. The logistic regression analysis was used for building the radiomics model. A fusion model was built by integrating serum fibrosis biomarkers of aspartate transaminase-to-platelet ratio index (APRI) and the fibrosis-4 index (FIB-4) with radiomics signatures. RESULTS In the training cohort, AUCs of radiomics and fusion model were 0.707-0.842 and 0.718-0.854 for differentiating different groups. In the testing cohort, AUCs were 0.514-0.724 and 0.609-0.728. For the training cohort, there was no significant difference of AUCs between radiomics and fusion model (p > 0.05). For the testing cohort, AUCs of fusion model were higher than those of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 (p = 0.011 & 0.042). CONCLUSIONS Radiomics model and fusion model based on multiparametric MRI exhibited the feasibility for staging liver fibrosis in patients with CLD, and APRI and FIB-4 could improve the diagnostic performance of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4.
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Affiliation(s)
- Longyang Xiao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Haichen Zhao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Shunli Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Wenlu Dong
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Yuanxiang Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China
| | - Lili Wang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Baoxiang Huang
- College of Computer Science and Technology of Qingdao University, No.308 Ningxia Road, Qingdao, 266071, China.
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, No.59 Haier Road, Qingdao, 266000, China.
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Li Y, Wu X, Yan Y, Zhou P. Automated breast volume scanner based Radiomics for non-invasively prediction of lymphovascular invasion status in breast cancer. BMC Cancer 2023; 23:813. [PMID: 37648970 PMCID: PMC10466688 DOI: 10.1186/s12885-023-11336-w] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023] Open
Abstract
PURPOSE Lymphovascular invasion (LVI) indicates resistance to preoperative adjuvant chemotherapy and a poor prognosis and can only be diagnosed by postoperative pathological examinations in breast cancer. Thus, a technique for preoperative diagnosis of LVI is urgently needed. We aim to explore the ability of an automated breast volume scanner (ABVS)-based radiomics model to noninvasively predict the LVI status in breast cancer. METHODS We conducted a retrospective analysis of data from 335 patients diagnosed with T1-3 breast cancer between October 2019 and September 2022. The patients were divided into training cohort and validation cohort with a ratio of 7:3. For each patient, 5901 radiomics features were extracted from ABVS images. Feature selection was performed using LASSO method. We created machine learning models for different feature sets with support vector machine algorithm to predict LVI. And significant clinicopathologic factors were identified by univariate and multivariate logistic regression to combine with three radiomics signatures as to develop a fusion model. RESULTS The three SVM-based prediction models, demonstrated relatively high efficacy in identifying LVI of breast cancer, with AUCs of 79.00%, 80.00% and 79.40% and an accuracy of 71.00%, 80.00% and 75.00% in the validation cohort for AP, SP and CP plane image. The fusion model achieved the highest AUC of 87.90% and an accuracy of 85.00% in the validation cohort. CONCLUSIONS The combination of radiomics features from ABVS images and an SVM prediction model showed promising performance for preoperative noninvasive prediction of LVI in breast cancer.
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Affiliation(s)
- Yue Li
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - Xiaomin Wu
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - Yueqiong Yan
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
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Zhuo X, Zhao H, Chen M, Mu Y, Li Y, Cai J, Li H, Xu Y, Tang Y. A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma. Radiat Oncol 2023; 18:43. [PMID: 36859353 PMCID: PMC9979431 DOI: 10.1186/s13014-023-02235-2] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 02/20/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND Methylprednisolone is recommended as the front-line therapy for radiation-induced brain necrosis (RN) after radiotherapy for nasopharyngeal carcinoma. However, some patients fail to benefit from methylprednisolone or even progress. This study aimed to develop and validate a radiomic model to predict the response to methylprednisolone in RN. METHODS Sixty-six patients receiving methylprednisolone were enrolled. In total, 961 radiomic features were extracted from the pre-treatment magnetic resonance imagings of the brain. Least absolute shrinkage and selection operator regression was then applied to construct the radiomics signature. Combined with independent clinical predictors, a radiomics model was built with multivariate logistic regression analysis. Discrimination, calibration and clinical usefulness of the model were assessed. The model was internally validated using 10-fold cross-validation. RESULTS The radiomics signature consisted of 16 selected features and achieved favorable discrimination performance. The radiomics model incorporating the radiomics signature and the duration between radiotherapy and RN diagnosis, yielded an AUC of 0.966 and an optimism-corrected AUC of 0.967 via 10-fold cross-validation, which also revealed good discrimination. Calibration curves showed good agreement. Decision curve analysis confirmed the clinical utility of the model. CONCLUSIONS The presented radiomics model can be conveniently used to facilitate individualized prediction of the response to methylprednisolone in patients with RN.
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Affiliation(s)
- Xiaohuang Zhuo
- grid.12981.330000 0001 2360 039XDepartment of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, NO.107 Yan Jiang Xi Road, Guangzhou, Guangdong Province People’s Republic of China
| | - Huiying Zhao
- grid.12981.330000 0001 2360 039XDepartment of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province People’s Republic of China ,grid.12981.330000 0001 2360 039XGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province People’s Republic of China
| | - Meiwei Chen
- grid.12981.330000 0001 2360 039XDepartment of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Youqing Mu
- grid.12981.330000 0001 2360 039XSchool of Life Sciences, Sun Yat-Sen University, Guangzhou, People’s Republic of China
| | - Yi Li
- grid.12981.330000 0001 2360 039XDepartment of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, NO.107 Yan Jiang Xi Road, Guangzhou, Guangdong Province People’s Republic of China
| | - Jinhua Cai
- grid.12981.330000 0001 2360 039XDepartment of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, NO.107 Yan Jiang Xi Road, Guangzhou, Guangdong Province People’s Republic of China
| | - Honghong Li
- grid.12981.330000 0001 2360 039XDepartment of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, NO.107 Yan Jiang Xi Road, Guangzhou, Guangdong Province People’s Republic of China
| | - Yongteng Xu
- grid.12981.330000 0001 2360 039XDepartment of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, NO.107 Yan Jiang Xi Road, Guangzhou, Guangdong Province People’s Republic of China
| | - Yamei Tang
- Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, NO.107 Yan Jiang Xi Road, Guangzhou, Guangdong Province, People's Republic of China. .,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, People's Republic of China. .,Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, People's Republic of China.
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Wang JC, Fu R, Tao XW, Mao YF, Wang F, Zhang ZC, Yu WW, Chen J, He J, Sun BC. A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data. Biomark Res 2020; 8:47. [PMID: 32963787 PMCID: PMC7499912 DOI: 10.1186/s40364-020-00219-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 08/20/2020] [Indexed: 02/08/2023] Open
Abstract
Background To establish and validate a radiomics-based model for predicting liver cirrhosis in patients with hepatitis B virus (HBV) by using non-contrast computed tomography (CT). Methods This retrospective study developed a radiomics-based model in a training cohort of 144 HBV-infected patients. Radiomic features were extracted from abdominal non-contrast CT scans. Features selection was performed with the least absolute shrinkage and operator (LASSO) method based on highly reproducible features. Support vector machine (SVM) was adopted to build a radiomics signature. Multivariate logistic regression analysis was used to establish a radiomics-based nomogram that integrated radiomics signature and other independent clinical predictors. Performance of models was evaluated through discrimination ability, calibration and clinical benefits. An internal validation was conducted in 150 consecutive patients. Results The radiomics signature comprised 25 cirrhosis-related features and showed significant differences between cirrhosis and non-cirrhosis cohorts (P < 0.001). A radiomics-based nomogram that integrates radiomics signature, alanine transaminase, aspartate aminotransferase, globulin and international normalized ratio showed great calibration and discrimination ability in the training cohort (area under the curve [AUC]: 0.915) and the validation cohort (AUC: 0.872). Decision curve analysis confirmed the most clinical benefits can be provided by the nomogram compared with other methods. Conclusions Our developed radiomics-based nomogram can successfully diagnose the status of cirrhosis in HBV-infected patients, that may help clinical decision-making.
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Affiliation(s)
- Jin-Cheng Wang
- Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China.,Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
| | - Rao Fu
- Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China.,Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
| | - Xue-Wen Tao
- Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China.,Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
| | - Ying-Fan Mao
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
| | - Fei Wang
- Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China.,Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
| | - Ze-Chuan Zhang
- Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China.,Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
| | - Wei-Wei Yu
- Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Jun Chen
- Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
| | - Jian He
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
| | - Bei-Cheng Sun
- Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China.,Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
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