101
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Lee IC, Huang JY, Chen TC, Yen CH, Chiu NC, Hwang HE, Huang JG, Liu CA, Chau GY, Lee RC, Hung YP, Chao Y, Ho SY, Huang YH. Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection. Liver Cancer 2021; 10:572-582. [PMID: 34950180 PMCID: PMC8647074 DOI: 10.1159/000518728] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 07/24/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND AND AIMS Current prediction models for early recurrence of hepatocellular carcinoma (HCC) after surgical resection remain unsatisfactory. The aim of this study was to develop evolutionary learning-derived prediction models with interpretability using both clinical and radiomic features to predict early recurrence of HCC after surgical resection. METHODS Consecutive 517 HCC patients receiving surgical resection with available contrast-enhanced computed tomography (CECT) images before resection were retrospectively enrolled. Patients were randomly assigned to a training set (n = 362) and a test set (n = 155) in a ratio of 7:3. Tumor segmentation of all CECT images including noncontrast phase, arterial phase, and portal venous phase was manually performed for radiomic feature extraction. A novel evolutionary learning-derived method called genetic algorithm for predicting recurrence after surgery of liver cancer (GARSL) was proposed to design prediction models for early recurrence of HCC within 2 years after surgery. RESULTS A total of 143 features, including 26 preoperative clinical features, 5 postoperative pathological features, and 112 radiomic features were used to develop GARSL preoperative and postoperative models. The area under the receiver operating characteristic curves (AUCs) for early recurrence of HCC within 2 years were 0.781 and 0.767, respectively, in the training set, and 0.739 and 0.741, respectively, in the test set. The accuracy of GARSL models derived from the evolutionary learning method was significantly better than models derived from other well-known machine learning methods or the early recurrence after surgery for liver tumor (ERASL) preoperative (AUC = 0.687, p < 0.001 vs. GARSL preoperative) and ERASL postoperative (AUC = 0.688, p < 0.001 vs. GARSL postoperative) models using clinical features only. CONCLUSION The GARSL models using both clinical and radiomic features significantly improved the accuracy to predict early recurrence of HCC after surgical resection, which was significantly better than other well-known machine learning-derived models and currently available clinical models.
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Affiliation(s)
- I-Cheng Lee
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan,Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan
| | - Jo-Yu Huang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Ting-Chun Chen
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chia-Heng Yen
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan,Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Nai-Chi Chiu
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hsuen-En Hwang
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Jia-Guan Huang
- National Taiwan University School of Medicine, Taipei, Taiwan
| | - Chien-An Liu
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Gar-Yang Chau
- Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Rheun-Chuan Lee
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yi-Ping Hung
- Cancer Center, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yee Chao
- Cancer Center, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Shinn-Ying Ho
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan,Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan,Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan,College of Health Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan,*Shinn-Ying Ho,
| | - Yi-Hsiang Huang
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan,Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan,Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan,**Yi-Hsiang Huang,
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102
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Xu H, Zou X, Zhao Y, Zhang T, Tang Y, Zheng A, Zhou X, Ma X. Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography. Technol Cancer Res Treat 2021; 20:15330338211039125. [PMID: 34499018 PMCID: PMC8435928 DOI: 10.1177/15330338211039125] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Purpose: This study aimed to explore the ability of texture parameters combining with machine learning methods in distinguishing intrahepatic cholangiocarcinoma (ICCA) and hepatic lymphoma (HL). Method: A total of 28 patients with HL and 101 patients with ICCA were included. A total of 45 texture features were extracted by the software LifeX from contrast-enhanced computer tomography (CECT) images and 38 of them were eligible. A total of 5 feature selection methods and 9 feature classification methods were used to build the best diagnostic models, combining with the 10-fold cross-validation to assess the accuracy of these models. The discriminative ability of each model was evaluated by receiver operating characteristic analysis. Result: A total of 45 predictive models were built by the cross combination of each selection and classification method to differentiate ICCA from HL. According to the results of test group, most of the models performed well with a large area under the curve (AUC) (>0.85) and high accuracy (>0.85). Random Forest (RF)_Linear Discriminant Analysis (LDA) (AUC = 0.997, accuracy = 0.969) was the best model among all the 45 models. Conclusion: Combining texture parameters from CECT with multiple machine learning models can differentiate ICCA and HL effectively, and RF_LDA performed the best in this process.
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Affiliation(s)
- Hanyue Xu
- Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, PR China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Xiuhe Zou
- Department of Thyroid Surgery, West China Hospital of Sichuan University, Chengdu 610041, PR China
| | - Yunuo Zhao
- Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, PR China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Tao Zhang
- Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, PR China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Youyin Tang
- Department of Liver surgery, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Aiping Zheng
- Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, PR China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Xianghong Zhou
- Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, PR China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, PR China
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103
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Chen F, Li Y, Ye G, Zhou L, Bian X, Liu J. Development and Validation of a Prognostic Model for Cognitive Impairment in Parkinson's Disease With REM Sleep Behavior Disorder. Front Aging Neurosci 2021; 13:703158. [PMID: 34322014 PMCID: PMC8311737 DOI: 10.3389/fnagi.2021.703158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 06/21/2021] [Indexed: 12/29/2022] Open
Abstract
The presentation and progression of Parkinson’s disease (PD) are not uniform, but the presence of rapid eye movement sleep behavior disorder (RBD) in PD patients may indicate a worse prognosis than isolated PD. Increasing evidence suggests that patients with comorbid PD and RBD (PD-RBD) are more likely to develop cognitive impairment (CI) than those with isolated PD; however, the predictors of CI in PD-RBD patients are not well understood. This study aimed to develop a prognostic model for predicting mild cognitive impairment (MCI) in PD-RBD patients. The data of PD-RBD patients were extracted from the Parkinson’s Progression Markers Initiative study (PPMI), and the sample was randomly divided into a training set (n = 96) and a validation set (n = 24). PD-MCI as defined by the level II Movement Disorder Society (MDS) diagnostic criteria was the outcome of interest. The demographic features, clinical assessments, dopamine transporter (DAT) imaging data, cerebrospinal fluid (CSF) analyses and genetic data of PD patients were considered candidate predictors. We found that performance on the University of Pennsylvania Smell Identification Test (UPSIT), the mean signal and asymmetry index of the putamen on DAT imaging, p-tau/α-syn and p-tau in CSF, and rs55785911 genotype were predictors of PD-MCI in PD-RBD patients. A C-index of 0.81 was obtained with this model, and a C-index of 0.73 was obtained in the validation set. Favorable results of calibrations and decision curve analysis demonstrated the efficacy and feasibility of this model. In conclusion, we developed a prognostic model for predicting MCI in PD-RBD patients; the model displayed good discrimination and calibration and may be a convenient tool for clinical application. Larger samples and external validation sets are needed to validate this model.
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Affiliation(s)
- Fangzheng Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanyuan Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guanyu Ye
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liche Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaolan Bian
- Department of Pharmacy, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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104
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Castaldo A, De Lucia DR, Pontillo G, Gatti M, Cocozza S, Ugga L, Cuocolo R. State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma. Diagnostics (Basel) 2021; 11:1194. [PMID: 34209197 PMCID: PMC8307071 DOI: 10.3390/diagnostics11071194] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.
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Affiliation(s)
- Anna Castaldo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Davide Raffaele De Lucia
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Marco Gatti
- Radiology Unit, Department of Surgical Sciences, University of Turin, 10124 Turin, Italy;
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Lorenzo Ugga
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (A.C.); (D.R.D.L.); (G.P.); (S.C.); (L.U.)
| | - Renato Cuocolo
- Department of Clinical Medicine and Surgery, University of Naples “Federico II”, 80131 Naples, Italy
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105
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Ding Z, Lin K, Fu J, Huang Q, Fang G, Tang Y, You W, Lin Z, Lin Z, Pan X, Zeng Y. An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver. World J Surg Oncol 2021; 19:181. [PMID: 34154624 PMCID: PMC8215802 DOI: 10.1186/s12957-021-02266-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/18/2021] [Indexed: 12/23/2022] Open
Abstract
Purpose We aimed to develop and validate a radiomics model for differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI). Methods We retrospectively enrolled 149 HCC and 75 FNH patients treated between May 2015 and May 2019 at our center. Patients were randomly allocated to a training (n=156) and validation set (n=68). In total, 2260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forest, least absolute shrinkage, and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the models was compared. Results Eight radiomics features were chosen for the radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen for the clinical model. A combined model was built using the factors from the previous models. The classification accuracy of the combined model differentiated HCC from FNH in both the training and validation sets (0.956 and 0.941, respectively). The area under the receiver operating characteristic curve of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p=0.002) and validation (0.972 vs. 0.903, p=0.032) sets. Conclusions The combined model provided a non-invasive quantitative method for differentiating HCC from FNH in non-cirrhotic liver with high accuracy. Our model may assist clinicians in the clinical decision-making process.
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Affiliation(s)
- Zongren Ding
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, China.,The Big Data Institute of Southeast Hepatobiliary Health Information, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Kongying Lin
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, China.,The Big Data Institute of Southeast Hepatobiliary Health Information, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Jun Fu
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, China.,The Big Data Institute of Southeast Hepatobiliary Health Information, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Qizhen Huang
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, China.,The Big Data Institute of Southeast Hepatobiliary Health Information, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Guoxu Fang
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, China.,The Big Data Institute of Southeast Hepatobiliary Health Information, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Yanyan Tang
- Department of Radiology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Wuyi You
- Department of Radiology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Zhaowang Lin
- Department of Radiology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Zhan Lin
- Department of Radiology, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Xingxi Pan
- Department of Oncology, Nanhai Hospital Affiliated to Southern Medical University, Foshan, 528000, China
| | - Yongyi Zeng
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Xihong Road 312, Fuzhou, 350025, China.
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106
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Chong H, Gong Y, Pan X, Liu A, Chen L, Yang C, Zeng M. Peritumoral Dilation Radiomics of Gadoxetate Disodium-Enhanced MRI Excellently Predicts Early Recurrence of Hepatocellular Carcinoma without Macrovascular Invasion After Hepatectomy. J Hepatocell Carcinoma 2021; 8:545-563. [PMID: 34136422 PMCID: PMC8200148 DOI: 10.2147/jhc.s309570] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/06/2021] [Indexed: 01/27/2023] Open
Abstract
Background Whether peritumoral dilation radiomics can excellently predict early recrudescence (≤2 years) in hepatocellular carcinoma (HCC) remains unclear. Methods Between March 2012 and June 2018, 323 pathologically confirmed HCC patients without macrovascular invasion, who underwent liver resection and preoperative gadoxetate disodium (Gd-EOB-DTPA) MRI, were consecutively recruited into this study. Multivariate logistic regression identified independent clinicoradiologic predictors of 2-year recrudescence. Peritumoral dilation (tumor and peritumoral zones within 1cm) radiomics extracted features from 7-sequence images for modeling and achieved average but robust predictive performance through 5-fold cross validation. Independent clinicoradiologic predictors were then incorporated with the radiomics model for constructing a comprehensive nomogram. The predictive discrimination was quantified with the area under the receiver operating characteristic curve (AUC) and net reclassification improvement (NRI). Results With the median recurrence-free survival (RFS) reaching 60.43 months, 28.2% (91/323) and 16.4% (53/323) patients suffered from early and delay relapse, respectively. Microvascular invasion, tumor size >5 cm, alanine aminotransferase >50 U/L, γ-glutamyltransferase >60 U/L, prealbumin ≤250 mg/L, and peritumoral enhancement independently impaired 2-year RFS in the clinicoradiologic model with AUC of 0.694 (95% CI 0.628–0.760). Nevertheless, these indexes were paucity of robustness (P >0.05) when integrating with 38 most recurrence-related radiomics signatures for developing the comprehensive nomogram. The peritumoral dilation radiomics—the ultimate prediction model yielded satisfactory mean AUCs (training cohort: 0.939, 95% CI 0.908–0.973; validation cohort: 0.842, 95% CI 0.736–0.951) after 5-fold cross validation and fitted well with the actual relapse status in the calibration curve. Besides, our radiomics model obtained the best clinical net benefits, with significant improvements of NRI (35.9%-66.1%, P <0.001) versus five clinical algorithms: the clinicoradiologic model, the tumor-node-metastasis classification, the Barcelona Clinic Liver Cancer stage, the preoperative and postoperative risks of Early Recurrence After Surgery for Liver tumor. Conclusion Gd-EOB-DTPA MRI-based peritumoral dilation radiomics is a potential preoperative biomarker for early recurrence of HCC patients without macrovascular invasion.
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Affiliation(s)
- Huanhuan Chong
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China.,Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China
| | - Yuda Gong
- Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianpan Pan
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, People's Republic of China
| | - Aie Liu
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, People's Republic of China
| | - Lei Chen
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, 200232, People's Republic of China
| | - Chun Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China.,Shanghai Institute of Medical Imaging, Shanghai, 200032, People's Republic of China.,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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107
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Lang Q, Zhong C, Liang Z, Zhang Y, Wu B, Xu F, Cong L, Wu S, Tian Y. Six application scenarios of artificial intelligence in the precise diagnosis and treatment of liver cancer. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10023-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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108
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Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021; 13:cancers13112522. [PMID: 34063937 PMCID: PMC8196591 DOI: 10.3390/cancers13112522] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Part I is an overview aimed to investigate some technical principles and the main fields of radiomic application in gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy in gastrointestinal cancers, describing mostly the results for each pre-eminent tumor. In particular, this paper provides a general description of the main radiomic drawbacks and future challenges, which limit radiomic application in clinical setting as routine. Further investigations need to standardize and validate the Radiomics as a helpful tool in management of oncologic patients. In that context, Radiomics has been playing a relevant role and could be considered as a future imaging landscape. Abstract Radiomics has been playing a pivotal role in oncological translational imaging, particularly in cancer diagnosis, prediction prognosis, and therapy response assessment. Recently, promising results were achieved in management of cancer patients by extracting mineable high-dimensional data from medical images, supporting clinicians in decision-making process in the new era of target therapy and personalized medicine. Radiomics could provide quantitative data, extracted from medical images, that could reflect microenvironmental tumor heterogeneity, which might be a useful information for treatment tailoring. Thus, it could be helpful to overcome the main limitations of traditional tumor biopsy, often affected by bias in tumor sampling, lack of repeatability and possible procedure complications. This quantitative approach has been widely investigated as a non-invasive and an objective imaging biomarker in cancer patients; however, it is not applied as a clinical routine due to several limitations related to lack of standardization and validation of images acquisition protocols, features segmentation, extraction, processing, and data analysis. This field is in continuous evolution in each type of cancer, and results support the idea that in the future Radiomics might be a reliable application in oncologic imaging. The first part of this review aimed to describe some radiomic technical principles and clinical applications to gastrointestinal oncologic imaging (CT and MRI) with a focus on diagnosis, prediction prognosis, and assessment of response to therapy.
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Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome-Umberto I University Hospital, Viale del Policlinico, 155, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-063-377-5285
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Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021; 12:31. [PMID: 33675433 PMCID: PMC7936998 DOI: 10.1186/s13244-021-00977-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 02/15/2021] [Indexed: 12/24/2022] Open
Abstract
With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.
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Affiliation(s)
- Zhi-Min Zou
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China
| | - De-Hua Chang
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Hui Liu
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China
| | - Yu-Dong Xiao
- Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China.
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Zhang Y, Shu Z, Ye Q, Chen J, Zhong J, Jiang H, Wu C, Yu T, Pang P, Ma T, Lin C. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Multi-Parametric MRI Radiomics. Front Oncol 2021; 11:633596. [PMID: 33747956 PMCID: PMC7968223 DOI: 10.3389/fonc.2021.633596] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 01/29/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives To systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model. Methods A total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined. Multivariate logistic regression analyses were undertaken to construct clinical model, respective single-sequence radiomics models, fusion radiomics models based on different sequences and combined model. The accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of different models. Results Among nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model. Conclusions The combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.
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Affiliation(s)
- Yang Zhang
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Qin Ye
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Junfa Chen
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Jianguo Zhong
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Hongyang Jiang
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Cuiyun Wu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Taihen Yu
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Peipei Pang
- Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, China
| | - Tianshi Ma
- Department of Pathology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Chunmiao Lin
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
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Sato M, Tateishi R, Yatomi Y, Koike K. Artificial intelligence in the diagnosis and management of hepatocellular carcinoma. J Gastroenterol Hepatol 2021; 36:551-560. [PMID: 33709610 DOI: 10.1111/jgh.15413] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/07/2021] [Accepted: 01/15/2021] [Indexed: 02/06/2023]
Abstract
Despite recent improvements in therapeutic interventions, hepatocellular carcinoma is still associated with a poor prognosis in patients with an advanced disease at diagnosis. Recently, significant progress has been made in image recognition through advances in the field of artificial intelligence (AI) (or machine learning), especially deep learning. AI is a multidisciplinary field that draws on the fields of computer science and mathematics for developing and implementing computer algorithms capable of maximizing the predictive accuracy from static or dynamic data sources using analytic or probabilistic models. Because of the multifactorial and complex nature of liver diseases, the machine learning approach to integrate multiple factors would appear to be an advantageous approach to improve the likelihood of making a precise diagnosis and predicting the response of treatment and prognosis of liver diseases. In this review, we attempted to summarize the potential use of AI in the diagnosis and management of liver diseases, especially hepatocellular carcinoma.
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Affiliation(s)
- Masaya Sato
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryosuke Tateishi
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yutaka Yatomi
- Department of Clinical Laboratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuhiko Koike
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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112
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Maruyama H, Yamaguchi T, Nagamatsu H, Shiina S. AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound. Diagnostics (Basel) 2021; 11:diagnostics11020292. [PMID: 33673229 PMCID: PMC7918339 DOI: 10.3390/diagnostics11020292] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 02/03/2021] [Accepted: 02/10/2021] [Indexed: 02/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.
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Affiliation(s)
- Hitoshi Maruyama
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
- Correspondence: ; Tel.: +81-3-38133111; Fax: +81-3-56845960
| | - Tadashi Yamaguchi
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoicho, Inage, Chiba 263-8522, Japan;
| | - Hiroaki Nagamatsu
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
| | - Shuichiro Shiina
- Department of Gastroenterology, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; (H.N.); (S.S.)
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Moldogazieva NT, Mokhosoev IM, Zavadskiy SP, Terentiev AA. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines 2021; 9:biomedicines9020159. [PMID: 33562077 PMCID: PMC7914649 DOI: 10.3390/biomedicines9020159] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 01/27/2021] [Accepted: 02/02/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.
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Affiliation(s)
- Nurbubu T. Moldogazieva
- Laboratory of Bioinformatics, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
- Correspondence: or
| | - Innokenty M. Mokhosoev
- Department of Biochemistry and Molecular Biology, N.I. Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (I.M.M.); (A.A.T.)
| | - Sergey P. Zavadskiy
- Department of Pharmacology, A.P. Nelyubin Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia;
| | - Alexander A. Terentiev
- Department of Biochemistry and Molecular Biology, N.I. Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (I.M.M.); (A.A.T.)
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Zhou Y, Ma XL, Zhang T, Wang J, Zhang T, Tian R. Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging 2021; 48:2904-2913. [PMID: 33547553 DOI: 10.1007/s00259-021-05220-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 01/25/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE This study was designed and performed to assess the ability of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomics features combined with machine learning methods to differentiate between primary and metastatic lung lesions and to classify histological subtypes. Moreover, we identified the optimal machine learning method. METHODS A total of 769 patients pathologically diagnosed with primary or metastatic lung cancers were enrolled. We used the LIFEx package to extract radiological features from semiautomatically segmented PET and CT images within the same volume of interest. Patients were randomly distributed in training and validation sets. Through the evaluation of five feature selection methods and nine classification methods, discriminant models were established. The robustness of the procedure was controlled by tenfold cross-validation. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS Based on the radiomics features extracted from PET and CT images, forty-five discriminative models were established. Combined with appropriate feature selection methods, most classifiers showed excellent discriminative ability with AUCs greater than 0.75. In the differentiation between primary and metastatic lung lesions, the feature selection method gradient boosting decision tree (GBDT) combined with the classifier GBDT achieved the highest classification AUC of 0.983 in the PET dataset. In contrast, the feature selection method eXtreme gradient boosting combined with the classifier random forest (RF) achieved the highest AUC of 0.828 in the CT dataset. In the discrimination between squamous cell carcinoma and adenocarcinoma, the combination of GBDT feature selection method with GBDT classification had the highest AUC of 0.897 in the PET dataset. In contrast, the combination of the GBDT feature selection method with the RF classification had the highest AUC of 0.839 in the CT dataset. Most of the decision tree (DT)-based models were overfitted, suggesting that the classification method was not appropriate for practical application. CONCLUSION 18F-FDG PET/CT radiomics features combined with machine learning methods can distinguish between primary and metastatic lung lesions and identify histological subtypes in lung cancer. GBDT and RF were considered optimal classification methods for the PET and CT datasets, respectively, and GBDT was considered the optimal feature selection method in our analysis.
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Affiliation(s)
- Yi Zhou
- Department of Nuclear Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Xue-Lei Ma
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Ting Zhang
- West China School of Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Jian Wang
- School of Computer Science, Nanjing University of Science and Technology, No. 200, Xiaolinwei Road, Nanjing, 210094, China
| | - Tao Zhang
- West China School of Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, 37# GuoXueLane, Chengdu, 610041, China.
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Chong HH, Yang L, Sheng RF, Yu YL, Wu DJ, Rao SX, Yang C, Zeng MS. Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm. Eur Radiol 2021; 31:4824-4838. [PMID: 33447861 PMCID: PMC8213553 DOI: 10.1007/s00330-020-07601-2] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 10/28/2020] [Accepted: 12/03/2020] [Indexed: 02/06/2023]
Abstract
Objectives To develop radiomics-based nomograms for preoperative microvascular invasion (MVI) and recurrence-free survival (RFS) prediction in patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm. Methods Between March 2012 and September 2019, 356 patients with pathologically confirmed solitary HCC ≤ 5 cm who underwent preoperative gadoxetate disodium–enhanced MRI were retrospectively enrolled. MVI was graded as M0, M1, or M2 according to the number and distribution of invaded vessels. Radiomics features were extracted from DWI, arterial, portal venous, and hepatobiliary phase images in regions of the entire tumor, peritumoral area ≤ 10 mm, and randomly selected liver tissue. Multivariate analysis identified the independent predictors for MVI and RFS, with nomogram visualized the ultimately predictive models. Results Elevated alpha-fetoprotein, total bilirubin and radiomics values, peritumoral enhancement, and incomplete or absent capsule enhancement were independent risk factors for MVI. The AUCs of MVI nomogram reached 0.920 (95% CI: 0.861–0.979) using random forest and 0.879 (95% CI: 0.820–0.938) using logistic regression analysis in validation cohort (n = 106). With the 5-year RFS rate of 68.4%, the median RFS of MVI-positive (M2 and M1) and MVI-negative (M0) patients were 30.5 (11.9 and 40.9) and > 96.9 months (p < 0.001), respectively. Age, histologic MVI, alkaline phosphatase, and alanine aminotransferase independently predicted recurrence, yielding AUC of 0.654 (95% CI: 0.538–0.769, n = 99) in RFS validation cohort. Instead of histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest achieved comparable accuracy in MVI stratification and RFS prediction. Conclusions Preoperative radiomics-based nomogram using random forest is a potential biomarker of MVI and RFS prediction for solitary HCC ≤ 5 cm. Key Points • The radiomics score was the predominant independent predictor of MVI which was the primary independent risk factor for postoperative recurrence. • The radiomics-based nomogram using either random forest or logistic regression analysis has obtained the best preoperative prediction of MVI in HCC patients so far. • As an excellent substitute for the invasive histologic MVI, the preoperatively predicted MVI by MVI nomogram using random forest (MVI-RF) achieved comparable accuracy in MVI stratification and outcome, reinforcing the radiologic understanding of HCC angioinvasion and progression. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-020-07601-2.
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Affiliation(s)
- Huan-Huan Chong
- Shanghai Institute of Medical Imaging, 180 Fenglin Road, Shanghai, China.,Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Li Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Ruo-Fan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Yang-Li Yu
- Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Di-Jia Wu
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Sheng-Xiang Rao
- Shanghai Institute of Medical Imaging, 180 Fenglin Road, Shanghai, China.,Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China
| | - Chun Yang
- Shanghai Institute of Medical Imaging, 180 Fenglin Road, Shanghai, China. .,Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
| | - Meng-Su Zeng
- Shanghai Institute of Medical Imaging, 180 Fenglin Road, Shanghai, China. .,Department of Radiology, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China. .,Department of Medical Imaging, Shanghai Medical College, Fudan University, Shanghai, China.
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Peng Z, Wang Y, Wang Y, Jiang S, Fan R, Zhang H, Jiang W. Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci 2021; 17:475-486. [PMID: 33613106 PMCID: PMC7893590 DOI: 10.7150/ijbs.55716] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 12/10/2020] [Indexed: 02/07/2023] Open
Abstract
With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.
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Affiliation(s)
| | | | | | | | | | | | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha 410078, Hunan, China
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Shen JX, Zhou Q, Chen ZH, Chen QF, Chen SL, Feng ST, Li X, Wu TF, Peng S, Kuang M. Longitudinal radiomics algorithm of posttreatment computed tomography images for early detecting recurrence of hepatocellular carcinoma after resection or ablation. Transl Oncol 2020; 14:100866. [PMID: 33074127 PMCID: PMC7569222 DOI: 10.1016/j.tranon.2020.100866] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/09/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES To develop a radiomics algorithm, improving the performance of detecting recurrence, based on posttreatment CT images within one month and at suspicious time during follow-up. MATERIALS AND METHODS A total of 114 patients with 228 images were randomly split (7:3) into training and validation cohort. Radiomics algorithm was trained using machine learning, based on difference-in-difference (DD) features extracted from tumor and liver regions of interest on posttreatment CTs within one month after resection or ablation and when suspected recurrent lesion was observed but cannot be confirmed as HCC during follow-up. The performance was evaluated by area under the receiver operating characteristic curve (AUC) and was compared among radiomics algorithm, change of alpha-fetoprotein (AFP) and combined model of both. Five-folded cross validation (CV) was used to present the training error. RESULTS A radiomics algorithm was established by 34 DD features selected by random forest and multivariable logistic models and showed a better AUC than that of change of AFP (0.89 [95% CI: 0.78, 1.00] vs 0.63 [95% CI: 0.42, 0.84], P = .04) and similar with the combined model in detecting recurrence in the validation set. Five-folded CV error in the validation cohort was 21% for the algorithm and 26% for the changes of AFP. CONCLUSIONS The algorithm integrated radiomic features of posttreatment CT showed superior performance to that of conventional AFP and may act as a potential marker in the early detecting recurrence of HCC.
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Affiliation(s)
- Jing-Xian Shen
- State Key Laboratory of Oncology in Southern China, Department of Medical Imaging, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qian Zhou
- Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhi-Hang Chen
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qiao-Feng Chen
- Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shu-Ling Chen
- Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Sui Peng
- Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Ming Kuang
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review. World J Gastroenterol 2020; 26:5617-5628. [PMID: 33088156 PMCID: PMC7545389 DOI: 10.3748/wjg.v26.i37.5617] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/01/2020] [Accepted: 09/17/2020] [Indexed: 02/06/2023] Open
Abstract
Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.
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Affiliation(s)
- Miguel Jiménez Pérez
- UGC de Aparato Digestivo, Unidad de Hepatología-Trasplante Hepático, Hospital Regional Universitario de Málaga, Málaga 29010, Spain
| | - Rocío González Grande
- UGC de Aparato Digestivo, Unidad de Hepatología-Trasplante Hepático, Hospital Regional Universitario de Málaga, Málaga 29010, Spain
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119
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Finotti M, Vitale A, Volk M, Cillo U. A 2020 update on liver transplant for hepatocellular carcinoma. Expert Rev Gastroenterol Hepatol 2020; 14:885-900. [PMID: 32662680 DOI: 10.1080/17474124.2020.1791704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Hepatocellular carcinoma is the most frequent liver tumor and is associated with chronic liver disease in 90% of cases. In selected cases, liver transplantation represents an effective therapy with excellent overall survival. AREA COVERED Since the introduction of Milan criteria in 1996, numerous alternative selection systems to LT for HCC patients have been proposed. Debate remains about how best to select HCC patients for transplant and how to prioritize them on the waiting list. EXPERT OPINION The selection of the best scoring system to propose in the context of LT for HCC is far to be identified. In this review, we analyze and categorize the various selection systems, assessing their roles in the different decisional phases.
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Affiliation(s)
- Michele Finotti
- Department of Surgery, Oncology and Gastroenterology, Hepatobiliary Surgery and Liver Transplantation Unit, Padova University Hospital , Padova, Italy
| | - Alessandro Vitale
- Department of Surgery, Oncology and Gastroenterology, Hepatobiliary Surgery and Liver Transplantation Unit, Padova University Hospital , Padova, Italy
| | - Michael Volk
- Division of Gastroenterology and Hepatology, Loma Linda University Health , Loma Linda, California, USA
| | - Umberto Cillo
- Department of Surgery, Oncology and Gastroenterology, Hepatobiliary Surgery and Liver Transplantation Unit, Padova University Hospital , Padova, Italy
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120
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Huang J, Tian W, Zhang L, Huang Q, Lin S, Ding Y, Liang W, Zheng S. Preoperative Prediction Power of Imaging Methods for Microvascular Invasion in Hepatocellular Carcinoma: A Systemic Review and Meta-Analysis. Front Oncol 2020; 10:887. [PMID: 32676450 PMCID: PMC7333535 DOI: 10.3389/fonc.2020.00887] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 05/05/2020] [Indexed: 12/12/2022] Open
Abstract
Background: To compare the predictive power between radiomics and non-radiomics (conventional imaging and functional imaging methods) for preoperative evaluation of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods: Comprehensive publications were screened in PubMed, Embase, and Cochrane Library. Studies focusing on the discrimination values of imaging methods, including radiomics and non-radiomics methods, for MVI evaluation were included in our meta-analysis. Results: Thirty-three imaging studies with 5,462 cases, focusing on preoperative evaluation of MVI status in HCC, were included. The sensitivity and specificity of MVI prediction in HCC were 0.78 [95% confidence interval (CI): 0.75–0.80; I2 = 70.7%] and 0.78 (95% CI: 0.76–0.81; I2 = 0.0%) for radiomics, respectively, and were 0.73 (95% CI: 0.71–0.75; I2 = 83.7%) and 0.82 (95% CI: 0.80–0.83; I2 = 86.5%) for non-radiomics, respectively. The areas under the receiver operation curves for radiomics and non-radiomics to predict MVI status in HCC were 0.8550 and 0.8601, respectively, showing no significant difference. Conclusion: The imaging method is feasible to predict the MVI state of HCC. Radiomics method based on medical image data is a promising application in clinical practice and can provide quantifiable image features. With the help of these features, highly consistent prediction performance will be achieved in anticipation.
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Affiliation(s)
- Jiacheng Huang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Key Lab of Combined Multi-Organ Transplantation, Ministry of Public Health, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Wuwei Tian
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, China
| | - Lele Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Key Lab of Combined Multi-Organ Transplantation, Ministry of Public Health, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Qiang Huang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Shengzhang Lin
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yong Ding
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, China
| | - Wenjie Liang
- Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Shusen Zheng
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.,Key Lab of Combined Multi-Organ Transplantation, Ministry of Public Health, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Li S, Luo T, Ding C, Huang Q, Guan Z, Zhang H. Detailed identification of epidermal growth factor receptor mutations in lung adenocarcinoma: Combining radiomics with machine learning. Med Phys 2020; 47:3458-3466. [PMID: 32416013 DOI: 10.1002/mp.14238] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 04/15/2020] [Accepted: 05/07/2020] [Indexed: 12/24/2022] Open
Affiliation(s)
- Shu Li
- School of Medical Informatics China Medical University Shenyang Liaoning 110122 China
| | - Ting Luo
- Department of Radiology Liaoning Cancer Hospital & Institute Shenyang Liaoning 110042 China
| | - Changwei Ding
- Department of Radiology Shengjing Hospital of China Medical University Shenyang Liaoning 110004 China
| | - Qinlai Huang
- School of Medical Informatics China Medical University Shenyang Liaoning 110122 China
| | - Zhihao Guan
- Institute of Medical Information & Library Chinese Academy of Medical Sciences Beijing100005 China
| | - Hao Zhang
- School of Medical Informatics China Medical University Shenyang Liaoning 110122 China
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