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O'Sullivan NJ, Temperley HC, Horan MT, Kamran W, Corr A, O'Gorman C, Saadeh F, Meaney JM, Kelly ME. Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review. Abdom Radiol (NY) 2024:10.1007/s00261-024-04330-8. [PMID: 38744703 DOI: 10.1007/s00261-024-04330-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 05/16/2024]
Abstract
Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77-0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies.
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Affiliation(s)
- Niall J O'Sullivan
- Department of Radiology, St. James's Hospital, Dublin, Ireland.
- School of Medicine, Trinity College Dublin, Dublin, Ireland.
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland.
| | | | - Michelle T Horan
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Waseem Kamran
- Department of Gynaecology, St. James's Hospital, Dublin, Ireland
| | - Alison Corr
- Department of Radiology, St. James's Hospital, Dublin, Ireland
| | | | - Feras Saadeh
- Department of Gynaecology, St. James's Hospital, Dublin, Ireland
| | - James M Meaney
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- School of Medicine, Trinity College Dublin, Dublin, Ireland
- The National Centre for Advanced Medical Imaging (CAMI), St. James's Hospital, Dublin, Ireland
| | - Michael E Kelly
- Department of Radiology, St. James's Hospital, Dublin, Ireland
- Department of Surgery, St. James's Hospital, Dublin, Ireland
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Mao B, Ren Y, Yu X, Liang X, Ding X. Preoperative prediction for early recurrence of hepatocellular carcinoma using machine learning-based radiomics. Front Oncol 2024; 14:1346124. [PMID: 38559563 PMCID: PMC10978579 DOI: 10.3389/fonc.2024.1346124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Objective To develop a contrast-enhanced computed tomography (CECT) based radiomics model using machine learning method and assess its ability of preoperative prediction for the early recurrence of hepatocellular carcinoma (HCC). Methods A total of 297 patients confirmed with HCC were assigned to the training dataset and test dataset based on the 8:2 ratio, and the follow-up period of the patients was from May 2012 to July 2017. The lesion sites were manually segmented using ITK-SNAP, and the pyradiomics platform was applied to extract radiomic features. We established the machine learning model to predict the early recurrence of HCC. The accuracy, AUC, standard deviation, specificity, and sensitivity were applied to evaluate the model performance. Results 1,688 features were extracted from the arterial phase and venous phase images, respectively. When arterial phase and venous phase images were employed correlated with clinical factors to train a prediction model, it achieved the best performance (AUC with 95% CI 0.8300(0.7560-0.9040), sensitivity 89.45%, specificity 79.07%, accuracy 82.67%, p value 0.0064). Conclusion The CECT-based radiomics may be helpful to non-invasively reveal the potential connection between CECT images and early recurrence of HCC. The combination of radiomics and clinical factors could boost model performance.
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Affiliation(s)
- Bing Mao
- Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital; Henan University People’s Hospital, Zhengzhou, Henan, China
| | - Yajun Ren
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xuan Yu
- Department of Medical Imaging, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinliang Liang
- Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital; Henan University People’s Hospital, Zhengzhou, Henan, China
| | - Xiangming Ding
- Department of Gastroenterology, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Sheng L, Yang C, Chen Y, Song B. Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors. Biomedicines 2023; 12:58. [PMID: 38255165 PMCID: PMC10813632 DOI: 10.3390/biomedicines12010058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.
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Affiliation(s)
- Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
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Zheng C, Lu F, Chen B, Yang J, Yu H, Wang D, Xie H, Chen K, Xie Y, Li J, Bo Z, Wang Y, Chen G, Deng T. Gut microbiome as a biomarker for predicting early recurrence of HBV-related hepatocellular carcinoma. Cancer Sci 2023; 114:4717-4731. [PMID: 37778742 PMCID: PMC10728007 DOI: 10.1111/cas.15983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/17/2023] [Accepted: 09/11/2023] [Indexed: 10/03/2023] Open
Abstract
To investigate the potential of the gut microbiome as a biomarker for predicting the early recurrence of HBV-related hepatocellular carcinoma (HCC), we enrolled 124 patients diagnosed with HBV-associated HCC and 82 HBV-related hepatitis, and 86 healthy volunteers in our study, collecting 292 stool samples for 16S rRNA sequencing and 35 tumor tissue samples for targeted metabolomics. We performed an integrated bioinformatics analysis of gut microbiome and tissue metabolome data to explore the gut microbial-liver metabolite axis associated with the early recurrence of HCC. We constructed a predictive model based on the gut microbiota and validated its efficacy in the temporal validation cohort. Dialister, Veillonella, the Eubacterium coprostanoligenes group, and Lactobacillus genera, as well as the Streptococcus pneumoniae and Bifidobacterium faecale species, were associated with an early recurrence of HCC. We also found that 23 metabolites, including acetic acid, glutamate, and arachidonic acid, were associated with the early recurrence of HCC. A comprehensive analysis of the gut microbiome and tissue metabolome revealed that the entry of gut microbe-derived acetic acid into the liver to supply energy for tumor growth and proliferation may be a potential mechanism for the recurrence of HCC mediated by gut microbe. We constructed a nomogram to predict early recurrence by combining differential microbial species and clinical indicators, achieving an AUC of 78.0%. Our study suggested that gut microbes may serve as effective biomarkers for predicting early recurrence of HCC, and the gut microbial-tumor metabolite axis may explain the potential mechanism by which gut microbes promote the early recurrence of HCC.
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Affiliation(s)
- Chongming Zheng
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Fei Lu
- Wenzhou Medical UniversityWenzhouChina
| | - Bo Chen
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Jinhuan Yang
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Haitao Yu
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Daojie Wang
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Haonan Xie
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Kaiwen Chen
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Yitong Xie
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Jiacheng Li
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Zhiyuan Bo
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and ManagementWenzhou Medical UniversityWenzhouChina
| | - Gang Chen
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Key Laboratory of Diagnosis and Treatment of Severe Hepato‐Pancreatic Diseases of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- The First Affiliated Hospital of Wenzhou Medical UniversityZhejiang‐Germany Interdisciplinary Joint Laboratory of Hepatobiliary‐Pancreatic Tumor and BioengineeringWenzhouChina
| | - Tuo Deng
- Department of Hepatobiliary SurgeryThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- Hepatobiliary Pancreatic Tumor Bioengineering Cross International Joint Laboratory of Zhejiang ProvinceThe First Affiliated Hospital of Wenzhou Medical UniversityWenzhouChina
- The First Affiliated Hospital of Wenzhou Medical UniversityZhejiang‐Germany Interdisciplinary Joint Laboratory of Hepatobiliary‐Pancreatic Tumor and BioengineeringWenzhouChina
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Zhang YB, Yang G, Bu Y, Lei P, Zhang W, Zhang DY. Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma. World J Gastroenterol 2023; 29:5804-5817. [PMID: 38074914 PMCID: PMC10701309 DOI: 10.3748/wjg.v29.i43.5804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 10/07/2023] [Accepted: 11/03/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Surgical resection is the primary treatment for hepatocellular carcinoma (HCC). However, studies indicate that nearly 70% of patients experience HCC recurrence within five years following hepatectomy. The earlier the recurrence, the worse the prognosis. Current studies on postoperative recurrence primarily rely on postoperative pathology and patient clinical data, which are lagging. Hence, developing a new pre-operative prediction model for postoperative recurrence is crucial for guiding individualized treatment of HCC patients and enhancing their prognosis. AIM To identify key variables in pre-operative clinical and imaging data using machine learning algorithms to construct multiple risk prediction models for early postoperative recurrence of HCC. METHODS The demographic and clinical data of 371 HCC patients were collected for this retrospective study. These data were randomly divided into training and test sets at a ratio of 8:2. The training set was analyzed, and key feature variables with predictive value for early HCC recurrence were selected to construct six different machine learning prediction models. Each model was evaluated, and the best-performing model was selected for interpreting the importance of each variable. Finally, an online calculator based on the model was generated for daily clinical practice. RESULTS Following machine learning analysis, eight key feature variables (age, intratumoral arteries, alpha-fetoprotein, pre-operative blood glucose, number of tumors, glucose-to-lymphocyte ratio, liver cirrhosis, and pre-operative platelets) were selected to construct six different prediction models. The XGBoost model outperformed other models, with the area under the receiver operating characteristic curve in the training, validation, and test datasets being 0.993 (95% confidence interval: 0.982-1.000), 0.734 (0.601-0.867), and 0.706 (0.585-0.827), respectively. Calibration curve and decision curve analysis indicated that the XGBoost model also had good predictive performance and clinical application value. CONCLUSION The XGBoost model exhibits superior performance and is a reliable tool for predicting early postoperative HCC recurrence. This model may guide surgical strategies and postoperative individualized medicine.
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Affiliation(s)
- Yu-Bo Zhang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Gang Yang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Yang Bu
- Department of Hepatobiliary Surgery, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Peng Lei
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Wei Zhang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
| | - Dan-Yang Zhang
- Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University, Yinchuan 750003, Ningxia Hui Autonomous Region, China
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Tian H, Xie Y, Wang Z. Radiomics for preoperative prediction of early recurrence in hepatocellular carcinoma: a meta-analysis. Front Oncol 2023; 13:1114983. [PMID: 37350952 PMCID: PMC10282764 DOI: 10.3389/fonc.2023.1114983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 05/18/2023] [Indexed: 06/24/2023] Open
Abstract
Background/Objective Early recurrence (ER) affects the long-term survival prognosis of patients with hepatocellular carcinoma (HCC). Many previous studies have utilized CT/MRI-based radiomics to predict ER after radical treatment, achieving high predictive value. However, the diagnostic performance of radiomics for the preoperative identification of ER remains uncertain. Therefore, we aimed to perform a meta-analysis to investigate the predictive performance of radiomics for ER in HCC. Methods A systematic literature search was conducted in PubMed, Web of Science (including MEDLINE), EMBASE and the Cochrane Central Register of Controlled Trials to identify studies that utilized radiomics methods to assess ER in HCC. Data were extracted and quality assessed for retrieved studies. Statistical analyses included pooled data, tests for heterogeneity, and publication bias. Meta-regression and subgroup analyses were performed to investigate potential sources of heterogeneity. Results The analysis included fifteen studies involving 3,281 patients focusing on preoperative CT/MRI-based radiomics for the prediction of ER in HCC. The combined sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic were 75% (95% CI: 65-82), 78% (95% CI: 68-85), and 83% (95% CI: 79-86), respectively. The combined positive likelihood ratio, negative likelihood ratio, diagnostic score, and diagnostic odds ratio were 3.35 (95% CI: 2.41-4.65), 0.33 (95% CI: 0.25-0.43), 2.33 (95% CI: 1.91-2.75), and 10.29 (95% CI: 6.79-15.61), respectively. Substantial heterogeneity was observed among the studies (I²=99%; 95% CI: 99-100). Meta-regression showed imaging equipment contributed to the heterogeneity of specificity in subgroup analysis (P= 0.03). Conclusion Preoperative CT/MRI-based radiomics appears to be a promising and non-invasive predictive approach with moderate ER recognition performance.
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Affiliation(s)
- Huan Tian
- Department of Radiology, Aerospace Center Hospital, Beijing, China
| | - Yong Xie
- Department of Interventional Radiology and Vascular Surgery, Peking University First Hospital, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing, China
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Zhang H, Huo F. Prediction of early recurrence of HCC after hepatectomy by contrast-enhanced ultrasound-based deep learning radiomics. Front Oncol 2022; 12:930458. [PMID: 36248986 PMCID: PMC9554932 DOI: 10.3389/fonc.2022.930458] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/07/2022] [Indexed: 12/07/2022] Open
Abstract
Objective This study aims to evaluate the predictive model based on deep learning (DL) and radiomics features from contrast-enhanced ultrasound (CEUS) to predict early recurrence (ER) in patients with hepatocellular carcinoma (HCC). Methods One hundred seventy-two patients with HCC who underwent hepatectomy and followed up for at least 1 year were included in this retrospective study. The data were divided according to the 7:3 ratios of training and test data. The ResNet-50 architecture, CEUS-based radiomics, and the combined model were used to predict the early recurrence of HCC after hepatectomy. The receiver operating characteristic (ROC) curve and calibration curve were drawn to evaluate its diagnostic efficiency. Results The CEUS-based radiomics ROCs of the “training set” and “test set” were 0.774 and 0.763, respectively. The DL model showed increased prognostic value, the ROCs of the “training set” and “test set” were 0.885 and 0.834, respectively. The combined model ROCs of the “training set” and “test set” were 0.943 and 0.882, respectively. Conclusion The deep learning radiomics model integrating DL and radiomics features from CEUS was used to predict ER and achieve satisfactory performance. Its diagnostic efficiency is significantly better than that of the single model.
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Affiliation(s)
- Hui Zhang
- Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nanchong, Sichuan, China
| | - Fanding Huo
- Department of Medical Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
- *Correspondence: Fanding Huo,
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