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Su HZ, Hong LC, Su YM, Chen XS, Zhang ZB, Zhang XD. A Nomogram Based on Conventional Ultrasound Radiomics for Differentiating Between Radial Scar and Invasive Ductal Carcinoma of the Breast. Ultrasound Q 2024; 40:00013644-990000000-00077. [PMID: 38889436 DOI: 10.1097/ruq.0000000000000685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
ABSTRACT We aimed to develop and validate a nomogram based on conventional ultrasound (CUS) radiomics model to differentiate radial scar (RS) from invasive ductal carcinoma (IDC) of the breast. In total, 208 patients with histopathologically diagnosed RS or IDC of the breast were enrolled. They were randomly divided in a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 63). Overall, 1316 radiomics features were extracted from CUS images. Then a radiomics score was constructed by filtering unstable features and using the maximum relevance minimum redundancy algorithm and the least absolute shrinkage and selection operator logistic regression algorithm. Two models were developed using data from the training cohort: one using clinical and CUS characteristics (Clin + CUS model) and one using clinical information, CUS characteristics, and the radiomics score (radiomics model). The usefulness of nomogram was assessed based on their differentiating ability and clinical utility. Nine features from CUS images were used to build the radiomics score. The radiomics nomogram showed a favorable predictive value for differentiating RS from IDC, with areas under the curve of 0.953 and 0.922 for the training and validation cohorts, respectively. Decision curve analysis indicated that this model outperformed the Clin + CUS model and the radiomics score in terms of clinical usefulness. The results of this study may provide a novel method for noninvasively distinguish RS from IDC.
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Affiliation(s)
- Huan-Zhong Su
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Long-Cheng Hong
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | | | - Xiao-Shuang Chen
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Zuo-Bing Zhang
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiao-Dong Zhang
- Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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Li L, Liang X, Yu Y, Mao R, Han J, Peng C, Zhou J. Radiomics-Based Machine Learning Classification Strategy for Characterization of Hepatocellular Carcinoma on Contrast-Enhanced Ultrasound in High-Risk Patients with LI-RADS Category M Nodules. Indian J Radiol Imaging 2024; 34:405-415. [PMID: 38912232 PMCID: PMC11188750 DOI: 10.1055/s-0043-1777993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
Objective Accurate differentiation within the LI-RADS category M (LR-M) between hepatocellular carcinoma (HCC) and non-HCC malignancies (mainly intrahepatic cholangiocarcinoma [CCA] and combined hepatocellular and cholangiocarcinoma [cHCC-CCA]) is an area of active investigation. We aimed to use radiomics-based machine learning classification strategy for differentiating HCC from CCA and cHCC-CCA on contrast-enhanced ultrasound (CEUS) images in high-risk patients with LR-M nodules. Methods A total of 159 high-risk patients with LR-M nodules (69 HCC and 90 CCA/cHCC-CCA) who underwent CEUS within 1 month before pathologic confirmation from January 2006 to December 2019 were retrospectively included (111 patients for training set and 48 for test set). The training set was used to build models, while the test set was used to compare models. For each observation, six CEUS images captured at predetermined time points (T1, peak enhancement after contrast injection; T2, 30 seconds; T3, 45 seconds; T4, 60 seconds; T5, 1-2 minutes; and T6, 2-3 minutes) were collected for tumor segmentation and selection of radiomics features, which included seven types of features: first-order statistics, shape (2D), gray-level co-occurrence matrix, gray-level size zone matrix, gray-level run length matrix, neighboring gray tone difference matrix, and gray-level dependence matrix. Clinical data and key radiomics features were employed to develop the clinical model, radiomics signature (RS), and combined RS-clinical (RS-C) model. The RS and RS-C model were built using the machine learning framework. The diagnostic performance of these three models was calculated and compared. Results Alpha-fetoprotein (AFP), CA19-9, enhancement pattern, and time of washout were included as independent factors for clinical model (all p < 0.05). Both the RS and RS-C model performed better than the clinical model in the test set (area under the curve [AUC] of 0.698 [0.571-0.812] for clinical model, 0.903 [0.830-0.970] for RS, and 0.912 [0.838-0.977] for the RS-C model; both p < 0.05). Conclusions Radiomics-based machine learning classifiers may be competent for differentiating HCC from CCA and cHCC-CCA in high-risk patients with LR-M nodules.
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Affiliation(s)
- Lingling Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Xiaoxin Liang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Yiwen Yu
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Rushuang Mao
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Jing Han
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Chuan Peng
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
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Cappuccio M, Bianco P, Rotondo M, Spiezia S, D'Ambrosio M, Menegon Tasselli F, Guerra G, Avella P. Current use of artificial intelligence in the diagnosis and management of acute appendicitis. Minerva Surg 2024; 79:326-338. [PMID: 38477067 DOI: 10.23736/s2724-5691.23.10156-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
INTRODUCTION Acute appendicitis is a common and time-sensitive surgical emergency, requiring rapid and accurate diagnosis and management to prevent complications. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant potential to improve the diagnosis and management of acute appendicitis. This review provides an overview of the evolving role of AI in the diagnosis and management of acute appendicitis, highlighting its benefits, challenges, and future perspectives. EVIDENCE ACQUISITION We performed a literature search on articles published from 2018 to September 2023. We included only original articles. EVIDENCE SYNTHESIS Overall, 121 studies were examined. We included 32 studies: 23 studies addressed the diagnosis, five the differentiation between complicated and uncomplicated appendicitis, and 4 studies the management of acute appendicitis. CONCLUSIONS AI is poised to revolutionize the diagnosis and management of acute appendicitis by improving accuracy, speed and consistency. It could potentially reduce healthcare costs. As AI technologies continue to evolve, further research and collaboration are needed to fully realize their potential in the diagnosis and management of acute appendicitis.
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Affiliation(s)
- Micaela Cappuccio
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Paolo Bianco
- Hepatobiliary and Pancreatic Surgery Unit, Pineta Grande Hospital, Castel Volturno, Caserta, Italy
| | - Marco Rotondo
- V. Tiberio Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Salvatore Spiezia
- V. Tiberio Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Marco D'Ambrosio
- V. Tiberio Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | | | - Germano Guerra
- V. Tiberio Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy
| | - Pasquale Avella
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy -
- Hepatobiliary and Pancreatic Surgery Unit, Pineta Grande Hospital, Castel Volturno, Caserta, Italy
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Deng X, Liao Z. A machine-learning model based on dynamic contrast-enhanced MRI for preoperative differentiation between hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma. Clin Radiol 2024; 79:e817-e825. [PMID: 38413354 DOI: 10.1016/j.crad.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/31/2024] [Accepted: 02/04/2024] [Indexed: 02/29/2024]
Abstract
AIM To establish a machine-learning model based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from hepatocellular carcinoma (HCC) before surgery. MATERIALS AND METHODS Clinical and MRI data of 194 patients with histopathologically diagnosed cHCC-CC (n=52) or HCC (n=142) were analysed retrospectively. ITK-SNAP software was used to delineate three-dimensional (3D) lesions and extract high-throughput features. Feature selection was carried out based on Pearson's correlation coefficient and least absolute shrinkage and selection operator (LASSO) regression analysis. A radiomics model (radiomics features), a clinical model (i.e., clinical-image features), and a fusion model (i.e., radiomics features + clinical-image features) were established using six machine-learning classifiers. The performance of each model in distinguishing between cHCC-CC and HCC was evaluated with the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), sensitivity, and specificity. RESULTS Significant differences in liver cirrhosis, tumour number, shape, edge, peritumoural enhancement in the arterial phase, and lipid were identified between cHCC-CC and HCC patients (p<0.05). The AUC of the fusion model based on logistic regression was 0.878 (95% CI: 0.766-0.949) in the arterial phase in the test set, and the sensitivity/specificity was 0.844/0.714; however, the AUC of the clinical and radiomics models was 0.759 (95% CI: 0.663-0.861) and 0.838 (95% CI: 0.719-0.921) in the test set, respectively. CONCLUSION The fusion model based on DCE-MRI in the arterial phase can significantly improve the diagnostic rate of cHCC-CC and HCC as compared with conventional approaches.
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Affiliation(s)
- X Deng
- Medical Imaging Center, Ganzhou People's Hospital, 16th Meiguan Avenue, Ganzhou 341000, China; Ganzhou Institute of Medical Imaging, Ganzhou 341000, China; Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Ganzhou 341000, China
| | - Z Liao
- Medical Imaging Center, Ganzhou People's Hospital, 16th Meiguan Avenue, Ganzhou 341000, China; Ganzhou Institute of Medical Imaging, Ganzhou 341000, China; Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Ganzhou 341000, China.
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Wang Y, Lin W, Zhuang X, Wang X, He Y, Li L, Lyu G. Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review). Oncol Rep 2024; 51:46. [PMID: 38240090 PMCID: PMC10828921 DOI: 10.3892/or.2024.8705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.
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Affiliation(s)
- Yanli Wang
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Weihong Lin
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiaoling Zhuang
- Department of Pathology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Xiali Wang
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
| | - Yifang He
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Luhong Li
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
| | - Guorong Lyu
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian 362000, P.R. China
- Department of Clinical Medicine, Quanzhou Medical College, Quanzhou, Fujian 362000, P.R. China
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Mou M, Gao R, Wu Y, Lin P, Yin H, Chen F, Huang F, Wen R, Yang H, He Y. Endoscopic Rectal Ultrasound-Based Radiomics Analysis for the Prediction of Synchronous Liver Metastasis in Patients With Primary Rectal Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:361-373. [PMID: 37950599 DOI: 10.1002/jum.16369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 09/26/2023] [Accepted: 10/22/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVES To develop and validate an ultrasound-based radiomics model to predict synchronous liver metastases (SLM) in rectal cancer (RC) patients preoperatively. METHODS Two hundred and thirty-nine RC patients were included in this study and randomly divided into training and validation cohorts. A total of 5936 radiomics features were calculated on the basis of ultrasound images to build a radiomic model and obtain a radiomics score (Rad-score) using logistic regression. Meanwhile, clinical characteristics were collected to construct a clinical model. The radiomics-clinical model was developed and validated by integrating the radiomics features with the selected clinical characteristics. The performances of three models were evaluated and compared through their discrimination, calibration, and clinical usefulness. RESULTS The radiomics model was developed based on 13 radiomic features. The radiomics-clinical model, which incorporated Rad-score, CEA, and CA199, exhibited favorable discrimination and calibration with areas under the receiver operating characteristic curve (AUC) of 0.920 (95% CI: 0.874-0.965) in the training cohorts and 0.855 (95% CI: 0.759-0.951) in the validation cohorts. And the AUC of the radiomics-clinical model was 0.849 (95% CI: 0.771-0.927) for the training cohorts and 0.780 (95% CI: 0.655-0.905) for the validation cohorts, the clinical model was 0.811 (95% CI: 0.718-0.905) for the training cohorts and 0.805 (95% CI: 0.645-0.965) for the validation cohorts. Moreover, decision curve analysis (DCA) further confirmed the clinical utility of the radiomics-clinical model. CONCLUSIONS The radiomics-clinical model performed satisfactory predictive performance, which can help improve clinical diagnosis performance and outcome prediction for SLM in RC patients.
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Affiliation(s)
- Meiyan Mou
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Medical Ultrasound, Yulin No. 1 People's Hospital of Guangxi Zhuang Autonomous Region, Yulin, China
| | - Ruizhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yuquan Wu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hongxia Yin
- Department of Medical Ultrasound, Yulin No. 1 People's Hospital of Guangxi Zhuang Autonomous Region, Yulin, China
| | - Fenghuan Chen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Fen Huang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Huang J, Bai X, Qiu Y, He X. Application of AI on cholangiocarcinoma. Front Oncol 2024; 14:1324222. [PMID: 38347839 PMCID: PMC10859478 DOI: 10.3389/fonc.2024.1324222] [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: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.
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Affiliation(s)
| | | | | | - Xiaodong He
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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Di Pilla A, Nero C, Specchia ML, Ciccarone F, Boldrini L, Lenkowicz J, Alberghetti B, Fagotti A, Testa AC, Valentini V, Sala E, Scambia G. A cost-effectiveness analysis of an integrated clinical-radiogenomic screening program for the identification of BRCA 1/2 carriers (e-PROBE study). Sci Rep 2024; 14:928. [PMID: 38195911 PMCID: PMC10776619 DOI: 10.1038/s41598-023-51031-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 12/29/2023] [Indexed: 01/11/2024] Open
Abstract
Current approach to identify BRCA 1/2 carriers in the general population is ineffective as most of the carriers remain undiagnosed. Radiomics is an emerging tool for large scale quantitative analysis of features from standard diagnostic imaging and has been applied also to identify gene mutational status. The objective of this study was to evaluate the clinical and economic impact of integrating a radiogenomics model with clinical and family history data in identifying BRCA mutation carriers in the general population. This cost-effective analysis compares three different approaches to women selection for BRCA testing: established clinical criteria/family history (model 1); established clinical criteria/family history and the currently available radiogenomic model (49% sensitivity and 87% specificity) based on ultrasound images (model 2); same approach used in model 2 but simulating an improvement of the performances of the radiogenomic model (80% sensitivity and 95% specificity) (model 3). All models were trained with literature data. Direct costs were calculated according to the rates currently used in Italy. The analysis was performed simulating different scenarios on the generation of 18-year-old girls in Italy (274,000 people). The main outcome was to identify the most effective model comparing the number of years of BRCA-cancer healthy life expectancy (HLYs). An incremental cost-effectiveness ratio (ICER) was also derived to determine the cost in order to increase BRCA carriers-healthy life span by 1 year. Compared to model 1, model 2 increases the detection rate of BRCA carriers by 41.8%, reduces the rate of BRCA-related cancers by 23.7%, generating over a 62-year observation period a cost increase by 2.51 €/Year/Person. Moreover, model 3 further increases BRCA carriers detection (+ 68.3%) and decrease in BRCA-related cancers (- 38.4%) is observed compared to model 1. Model 3 increases costs by 0.7 €/Year/Person. After one generation, the estimated ICER in the general population amounts to about 3800€ and 653€ in model 2 and model 3 respectively. Model 2 has a massive effect after only one generation in detecting carriers in the general population with only a small cost increment. The clinical impact is limited mainly due to the current low acceptance rate of risk-reducing surgeries. Further multicentric studies are required before implementing the integrated clinical-radiogenomic model in clinical practice.
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Affiliation(s)
- A Di Pilla
- Dipartimento di Scienze della Vita e Sanità Pubblica - Sezione di Igiene, Università Cattolica del Sacro Cuore, Rome, Italy
| | - C Nero
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - M L Specchia
- Dipartimento di Scienze della Vita e Sanità Pubblica - Sezione di Igiene, Università Cattolica del Sacro Cuore, Rome, Italy.
- Università Cattolica del Sacro Cuore, Rome, Italy.
| | - F Ciccarone
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - L Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Radiomics Research Core Facility, Gemelli Science and Technology Park, Rome, Italy
| | - J Lenkowicz
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Radiomics Research Core Facility, Gemelli Science and Technology Park, Rome, Italy
| | - B Alberghetti
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - A Fagotti
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - A C Testa
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - V Valentini
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Radiomics Research Core Facility, Gemelli Science and Technology Park, Rome, Italy
| | - E Sala
- Università Cattolica del Sacro Cuore, Rome, Italy
- UOC Radiologia, Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - G Scambia
- UOC Ginecologia Oncologica, Dipartimento per le Scienze della salute della donna, del Bambino e di sanità pubblica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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Chen J, Zhang W, Bao J, Wang K, Zhao Q, Zhu Y, Chen Y. Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Abdom Radiol (NY) 2024; 49:93-102. [PMID: 37999743 DOI: 10.1007/s00261-023-04089-4] [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: 06/22/2023] [Revised: 10/08/2023] [Accepted: 10/09/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVES The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (cHCC-ICC). METHODS The B-mode ultrasound images of 465 patients with primary liver cancer were enrolled in model construction, comprising 264 HCCs, 105 ICCs, and 96 cHCC-ICCs, of which 50 cases were randomly selected to form an independent test cohort, and the rest of study population was assigned to a training and validation cohorts at the ratio of 4:1. Four deep learning models (Resnet18, MobileNet, DenseNet121, and Inception V3) were constructed, and the fivefold cross-validation was adopted to train and validate the performance of these models. The following indexes were calculated to determine the differential diagnosis performance of the models, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F-1 score, and area under the receiver operating characteristic curve (AUC) based on images in the independent test cohort. RESULTS Based on the fivefold cross-validation, the Resnet18 outperformed other models in terms of accuracy and robustness, with the overall training and validation accuracy as 99.73% (± 0.07%) and 99.35% (± 0.53%), respectively. Furthers validation based on the independent test cohort suggested that Resnet 18 yielded the best diagnostic performance in identifying HCC, ICC, and cHCC-ICC, with the sensitivity, specificity, accuracy, PPV, NPV, F1-score, and AUC of 84.59%, 92.65%, 86.00%, 85.82%, 92.99%, 92.37%, 85.07%, and 0.9237 (95% CI 0.8633, 0.9840). CONCLUSION Ultrasound-based deep learning algorithm appeared a promising diagnostic method for identifying cHCC-ICC, HCC, and ICC, which might play a role in clinical decision making and evaluation of prognosis.
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Affiliation(s)
- Jianan Chen
- The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Weibin Zhang
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingwen Bao
- School of Medical Science, Hexi University, Zhangye, China
| | - Kun Wang
- Department of Ultrasound, The Affiliated Hospital of Binzhou Medical University, Binzhou, China
| | - Qiannan Zhao
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuli Zhu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China.
| | - Yanling Chen
- Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China.
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Liu N, Wu Y, Tao Y, Zheng J, Huang X, Yang L, Zhang X. Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics. Cancers (Basel) 2023; 15:5373. [PMID: 38001633 PMCID: PMC10670473 DOI: 10.3390/cancers15225373] [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: 08/23/2023] [Revised: 10/25/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
The purpose of this study was to investigate the efficacy of magnetic resonance imaging (MRI) radiomics in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). The clinical and MRI data of 129 pathologically confirmed HCC patients and 48 ICC patients treated at the Affiliated Hospital of North Sichuan Medical College between April 2016 and December 2021 were retrospectively analyzed. The patients were randomly divided at a ratio of 7:3 into a training group of 124 patients (90 with HCC and 34 with ICC) and a validation group of 53 patients (39 with HCC and 14 with ICC). Radiomic features were extracted from axial fat suppression T2-weighted imaging (FS-T2WI) and axial arterial-phase (AP) and portal-venous-phase (PVP) dynamic-contrast-enhanced MRI (DCE-MRI) sequences, and the corresponding datasets were generated. The least absolute shrinkage and selection operator (LASSO) method was used to select the best radiomic features. Logistic regression was used to establish radiomic models for each sequence (FS-T2WI, AP and PVP models), a clinical model for optimal clinical variables (C model) and a joint radiomics model (JR model) integrating the radiomics features of all the sequences as well as a radiomics-clinical model combining optimal radiomic features and clinical risk factors (RC model). The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). The AUCs of the FS-T2WI, AP, PVP, JR, C and RC models for distinguishing HCC from ICC were 0.693, 0.863, 0.818, 0.914, 0.936 and 0.977 in the training group and 0.690, 0.784, 0.727, 0.802, 0.860 and 0.877 in the validation group, respectively. The results of this study suggest that MRI-based radiomics may help noninvasively differentiate HCC from ICC. The model integrating the radiomics features and clinical risk factors showed a further improvement in performance.
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Affiliation(s)
- Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
- Hospital of Chengdu Office of People’s Government of Tibetan Autonomous Region (Hospital. C.T.), Chengdu 610041, China
| | - Yaokun Wu
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Yunyun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Xiaohua Huang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
| | - Xiaoming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China; (N.L.); (Y.W.); (Y.T.); (J.Z.); (X.H.); (X.Z.)
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Wang Z, Qin H, Liu S, Sheng J, Zhang X. Precision diagnosis of hepatocellular carcinoma. Chin Med J (Engl) 2023; 136:1155-1165. [PMID: 36939276 PMCID: PMC10278703 DOI: 10.1097/cm9.0000000000002641] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Indexed: 03/21/2023] Open
Abstract
ABSTRACT Hepatocellular carcinoma (HCC) is the most common type of primary hepatocellular carcinoma (PHC). Early diagnosis of HCC remains the key to improve the prognosis. In recent years, with the promotion of the concept of precision medicine and more in-depth analysis of the biological mechanism underlying HCC, new diagnostic methods, including emerging serum markers, liquid biopsies, molecular diagnosis, and advances in imaging (novel contrast agents and radiomics), have emerged one after another. Herein, we reviewed and analyzed scientific advances in the early diagnosis of HCC and discussed their application and shortcomings. This review aimed to provide a reference for scientific research and clinical practice of HCC.
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Affiliation(s)
- Zhenxiao Wang
- Department of Hepatobiliary and Pancreatic Surgery, Second Hospital of Jilin University, Changchun, Jilin 130041, China
| | - Hanjiao Qin
- Department of Radiotherapy, Second Hospital of Jilin University, Changchun, Jilin 130041, China
| | - Shui Liu
- Department of Hepatobiliary and Pancreatic Surgery, Second Hospital of Jilin University, Changchun, Jilin 130041, China
| | - Jiyao Sheng
- Department of Hepatobiliary and Pancreatic Surgery, Second Hospital of Jilin University, Changchun, Jilin 130041, China
| | - Xuewen Zhang
- Department of Hepatobiliary and Pancreatic Surgery, Second Hospital of Jilin University, Changchun, Jilin 130041, China
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Brunese MC, Fantozzi MR, Fusco R, De Muzio F, Gabelloni M, Danti G, Borgheresi A, Palumbo P, Bruno F, Gandolfo N, Giovagnoni A, Miele V, Barile A, Granata V. Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma. Diagnostics (Basel) 2023; 13:diagnostics13081488. [PMID: 37189589 DOI: 10.3390/diagnostics13081488] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. METHODS The PubMed database was searched for papers published in the English language no earlier than October 2022. RESULTS We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. CONCLUSIONS It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
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Affiliation(s)
- Maria Chiara Brunese
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100 Campobasso, Italy
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100 Campobasso, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Alessandra Borgheresi
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria delle Marche", 60121 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L'Aquila, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L'Aquila, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Andrea Giovagnoni
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria delle Marche", 60121 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, 67100 L'Aquila, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131 Naples, Italy
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13
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Shen YT, Yue WW, Xu HX. Non-invasive imaging in the diagnosis of combined hepatocellular carcinoma and cholangiocarcinoma. Abdom Radiol (NY) 2023; 48:2019-2037. [PMID: 36961531 DOI: 10.1007/s00261-023-03879-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 03/25/2023]
Abstract
Combined hepatocellular-cholangiocarcinoma (cHCC-CC) is a rare type of primary liver cancer. It is a complex "biphenotypic" tumor type consisting of bipotential hepatic progenitor cells that can differentiate into cholangiocytes subtype and hepatocytes subtype. The prognosis of patients with cHCC-CC is quite poor with its specific and more aggressive nature. Furthermore, there are no definite demographic or clinical features of cHCC-CC, thus a clear preoperative identification and accurate non-invasive imaging diagnostic analysis of cHCC-CC are of great value. In this review, we first summarized the epidemiological features, pathological findings, molecular biological information and serological indicators of cHCC-CC disease. Then we reviewed the important applications of non-invasive imaging modalities-particularly ultrasound (US)-in cHCC-CC, covering both diagnostic and prognostic assessment of patients with cHCC-CC. Finally, we presented the shortcomings and potential outlooks for imaging studies in cHCC-CC.
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Affiliation(s)
- Yu-Ting Shen
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, China
| | - Wen-Wen Yue
- Department of Medical Ultrasound, Center of Minimally Invasive Treatment for Tumor, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Clinical Research Center for Interventional Medicine, School of Medicine, Tongji University, Shanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, 200072, China.
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, China.
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14
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Perioperative and oncologic outcomes of laparoscopic versus open liver resection for combined hepatocellular-cholangiocarcinoma: a propensity score matching analysis. Surg Endosc 2023; 37:967-976. [PMID: 36076103 DOI: 10.1007/s00464-022-09579-y] [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: 01/25/2022] [Accepted: 08/12/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Laparoscopic liver resection (LLR) has now been established as a safe and minimally invasive technique that is deemed feasible for treating hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). However, the role of LLR in treating combined hepatocellular-cholangiocarcinoma (cHCC-CC) patients has been rarely reported. This study aimed to assess the efficacy of LLR when compared with open liver resection (OLR) procedure for patients with cHCC-CC. METHODS A total of 229 cHCC-CC patients who underwent hepatic resection (34 LLR and 195 OLR patients) from January 2014 to December 2018 in Zhongshan Hospital, Fudan University were enrolled and underwent a 1:2 propensity score matching (PSM) analysis between the LLR and OLR groups to compare perioperative and oncologic outcomes. Overall survival (OS) and recurrence-free survival (RFS) parameters were assessed by the log-rank test and the sensitivity analysis. RESULTS A total of 34 LLR and 68 OLR patients were included after PSM analysis. The LLR group displayed a shorter postoperative hospital stay (6.61 vs. 8.26 days; p value < 0.001) when compared with the OLR group. No significant differences were observed in the postoperative complications' incidence or a negative surgical margin rate between the two groups (p value = 0.409 and p value = 1.000, respectively). The aspartate aminotransferase (AST), alanine aminotransferase (ALT), and inflammatory indicators in the LLR group were significantly lower than those in the OLR group on the first and third postoperative days. Additionally, OS and RFS were comparable in both the LLR and OLR groups (p value = 0.700 and p value = 0.780, respectively), and similar results were obtained by conducting a sensitivity analysis. CONCLUSION LLR can impart less liver function damage, better inflammatory response attenuation contributing to a faster recovery, and parallel oncologic outcomes when compared with OLR. Therefore, LLR can be recommended as a safe and effective therapeutic modality for treating selected cHCC-CC patients, especially for those with small tumors in favorable location.
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Wang L, Wen D, Yin Y, Zhang P, Wen W, Gao J, Jiang Z. Musculoskeletal Ultrasound Image-Based Radiomics for the Diagnosis of Achilles Tendinopathy in Skiers. JOURNAL OF ULTRASOUND IN MEDICINE 2023; 42:363-371. [PMID: 35841273 PMCID: PMC10084008 DOI: 10.1002/jum.16059] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 05/10/2022] [Accepted: 06/23/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES Our study aimed to develop and validate an efficient ultrasound image-based radiomic model for determining the Achilles tendinopathy in skiers. METHODS A total of 88 feet of skiers clinically diagnosed with unilateral chronic Achilles tendinopathy and 51 healthy feet were included in our study. According to the time order of enrollment, the data were divided into a training set (n = 89) and a test set (n = 50). The regions of interest (ROIs) were segmented manually, and 833 radiomic features were extracted from red, green, blue color channels and grayscale of ROIs using Pyradiomics, respectively. Three feature selection and three machine learning modeling algorithms were implemented respectively, for determining the optimal radiomics pipeline. Finally, the area under the receiver operating characteristic curve (AUC), consistency analysis, and decision analysis were used to evaluate the diagnostic performance. RESULTS By comparing nine radiomics analysis strategies of three color channels and grayscale, the radiomic model under the green channel obtained the best diagnostic performance, using the Random Forest selection and Support Vector Machine modeling, which was selected as the final machine learning model. All the selected radiomic features were significantly associated with the Achilles tendinopathy (P < .05). The radiomic model had a training AUC of 0.98, a test AUC of 0.99, a sensitivity of 0.90, and a specificity of 1, which could bring sufficient clinical net benefits. CONCLUSIONS Ultrasound image-based radiomics achieved high diagnostic performance, which could be used as an intelligent auxiliary tool for the diagnosis of Achilles tendinopathy.
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Affiliation(s)
- Likun Wang
- Department of Ultrasound, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Dehui Wen
- Department of Ultrasound, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Yanlin Yin
- Department of Orthopedics, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Peinan Zhang
- Department of Orthopedics, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Wen Wen
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Jun Gao
- College of Computer Science, Sichuan University, Chengdu, 610000, China
| | - Zekun Jiang
- College of Computer Science, Sichuan University, Chengdu, 610000, China.,West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610000, China
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Characterization of spatially mapped volumetric molecular ultrasound signals for predicting response to anti-vascular therapy. Sci Rep 2023; 13:1686. [PMID: 36717575 PMCID: PMC9886917 DOI: 10.1038/s41598-022-26273-0] [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: 02/03/2022] [Accepted: 12/13/2022] [Indexed: 01/31/2023] Open
Abstract
Quantitative three-dimensional molecular ultrasound is a promising technology for longitudinal imaging applications such as therapy monitoring; the risk profile is favorable compared to positron emission tomography and computed tomography. However, clinical translation of quantitative methods for this technology are limited in that they assume that tumor tissues are homogeneous, and often depend on contrast-destruction events that can produce unintended bioeffects. Here, we develop quantitative features (henceforth image features) that capture tumor spatial information, and that are extracted without contrast destruction. We compare these techniques with the contrast-destruction derived differential targeted enhancement parameter (dTE) in predicting response to therapy. We found thirty-three reproducible image features that predict response to antiangiogenic therapy, without the need for a contrast agent disruption pulse. Multiparametric analysis shows that several of these image features can differentiate treated versus control animals with comparable performance to post-destruction measurements, suggesting that these can potentially replace parameters such as the dTE. The highest performing pre-destruction image features showed strong linear correlations with conventional dTE parameters with less overall variance. Thus, our study suggests that image features obtained during the wash in of the molecular agent, pre-destruction, may replace conventional post-destruction image features or the dTE parameter.
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Wei J, Jiang H, Zhou Y, Tian J, Furtado FS, Catalano OA. Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma. Dig Liver Dis 2023:S1590-8658(22)00863-5. [PMID: 36641292 DOI: 10.1016/j.dld.2022.12.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/16/2023]
Abstract
The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.
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Affiliation(s)
- Jingwei Wei
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China.
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, PR. China
| | - Yu Zhou
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; School of Life Science and Technology, Xidian University, Xi'an, PR. China
| | - Jie Tian
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR. China; Beijing Key Laboratory of Molecular Imaging, Beijing 100190, PR. China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, PR. China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, PR. China.
| | - Felipe S Furtado
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States
| | - Onofrio A Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States.
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18
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Xu ML, Zeng SE, Li F, Cui XW, Liu GF. Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound. Front Oncol 2022; 12:1071677. [PMID: 36568215 PMCID: PMC9770991 DOI: 10.3389/fonc.2022.1071677] [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: 10/16/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose The aim of this study was to develop a radiomics nomogram based on grayscale ultrasound (US) for preoperatively predicting Lymphovascular invasion (LVI) in patients with pathologically confirmed T1 (pT1) breast invasive ductal carcinoma (IDC). Methods One hundred and ninety-two patients with pT1 IDC between September 2020 and August 2022 were analyzed retrospectively. Study population was randomly divided in a 7: 3 ratio into a training dataset of 134 patients (37 patients with LVI-positive) and a validation dataset of 58 patients (19 patients with LVI-positive). Clinical information and conventional US (CUS) features (called clinic_CUS features) were recorded and evaluated to predict LVI. In the training dataset, independent predictors of clinic_CUS features were obtained by univariate and multivariate logistic regression analyses and incorporated into a clinic_CUS prediction model. In addition, radiomics features were extracted from the grayscale US images, and the radiomics score (Radscore) was constructed after radiomics feature selection. Subsequent multivariate logistic regression analysis was also performed for Radscore and the independent predictors of clinic_CUS features, and a radiomics nomogram was developed. The performance of the nomogram model was evaluated via its discrimination, calibration, and clinical usefulness. Results The US reported axillary lymph node metastasis (LNM) (US_LNM) status and tumor margin were determined as independent risk factors, which were combined for the construction of clinic_CUS prediction model for LVI in pT1 IDC. Moreover, tumor margin, US_LNM status and Radscore were independent predictors, incorporated as the radiomics nomogram model, which achieved a superior discrimination to the clinic_CUS model in the training dataset (AUC: 0.849 vs. 0.747; P < 0.001) and validation dataset (AUC: 0.854 vs. 0.713; P = 0.001). Calibration curve for the radiomic nomogram showed good concordance between predicted and actual probability. Furthermore, decision curve analysis (DCA) confirmed that the radiomics nomogram had higher clinical net benefit than the clinic_CUS model. Conclusion The US-based radiomics nomogram, incorporating tumor margin, US_LNM status and Radscore, showed a satisfactory preoperative prediction of LVI in pT1 IDC patients.
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Affiliation(s)
- Mao-Lin Xu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shu-E Zeng
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang Li
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Fang Li, ; Xin-Wu Cui, ; Gui-Feng Liu,
| | - Xin-Wu Cui
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Fang Li, ; Xin-Wu Cui, ; Gui-Feng Liu,
| | - Gui-Feng Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, China,*Correspondence: Fang Li, ; Xin-Wu Cui, ; Gui-Feng Liu,
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Soleymani Y, Jahanshahi AR, Pourfarshid A, Khezerloo D. Reproducibility assessment of radiomics features in various ultrasound scan settings and different scanner vendors. J Med Imaging Radiat Sci 2022; 53:664-671. [PMID: 36266173 DOI: 10.1016/j.jmir.2022.09.018] [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: 03/19/2022] [Revised: 09/09/2022] [Accepted: 09/22/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Radiomics in Ultrasound (US) imaging has been investigated for the prediction and prognosis of cancers. However, inter-scanner and intra-scanner variations may affect the reproducibility of radiomics results. This study aims to evaluate the reproducibility of US textural radiomics features across various scan settings and scanner vendors. MATERIALS AND METHODS US images in quality control (QC) phantom were obtained by three scanners (Philips, Samsung, and Siemens) with different scan settings and parameters. Circular regions of interest (ROIs) inside isoechoic, hypoechoic, and hyperechoic objects were manually delineated. Forty textural radiomics features were extracted from each ROI, and then the robust features that could distinguish different echogenic objects were obtained by the Mann-Whitney U test. Reproducibility of the robust radiomics features was assessed by the intraclass correlation coefficient (ICC) and coefficient of variation (CV); ICC>0.90 and %CV<20 were considered reproducible. RESULTS According to the Mann-Whitney U test results, ten robust features could differentiate the hypoechoic, and 15 robust features could differentiate the hyperechoic objects from the isoechoic objects (P<0.001). The total ICC of the robust features for each echogenic object was >0.95 in different scanners and scan settings. Four and seven features were individually reproducible (%CV < 20, ICC>0.90) in hypoechoic and hyperechoic objects, respectively. Also, four features seem reproducible by changing the ROI location across the horizontal and vertical lines for both convex and linear array transducers. CONCLUSIONS Most of the US textural radiomics features in this study were not reproducible. However, several features showed high reproducibility at different scan settings and scanners. These features may also be reproducible when ROI size and location change slightly.
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Affiliation(s)
- Yunus Soleymani
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Reza Jahanshahi
- Tabriz University of Medical Sciences, Faculty of Medicine, Imam Reza Hospital, Department of Radiology
| | - Amin Pourfarshid
- Department of Medical Physics, Faculty of Medicine, Urmia University of Medical Sciences, Urmia, Iran
| | - Davood Khezerloo
- Department of Radiology, Faculty of Alliance Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran & Medical Radiation Sciences Research Group, Tabriz University of Medical Sciences, Tabriz, Iran.
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Zhuo M, Guo J, Tang Y, Tang X, Qian Q, Chen Z. Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors. Front Oncol 2022; 12:905036. [PMID: 36091148 PMCID: PMC9459166 DOI: 10.3389/fonc.2022.905036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 08/09/2022] [Indexed: 11/24/2022] Open
Abstract
This study aimed to develop and evaluate a nomogram based on an ultrasound radiomics model to predict the risk grade of gastrointestinal stromal tumors (GISTs). 216 GIST patients pathologically diagnosed between December 2016 and December 2021 were reviewed and divided into a training cohort (n = 163) and a validation cohort (n = 53) in a ratio of 3:1. The tumor region of interest was depicted on each patient’s ultrasound image using ITK-SNAP, and the radiomics features were extracted. By filtering unstable features and using Spearman’s correlation analysis, and the least absolute shrinkage and selection operator algorithm, a radiomics score was derived to predict the malignant potential of GISTs. a radiomics nomogram that combines the radiomics score and clinical ultrasound predictors was constructed and assessed in terms of calibration, discrimination, and clinical usefulness. The radiomics score from ultrasound images was significantly associated with the malignant potential of GISTs. The radiomics nomogram was superior to the clinical ultrasound nomogram and the radiomics score, and it achieved an AUC of 0.90 in the validation cohort. Based on the decision curve analysis, the radiomics nomogram was found to be more clinically significant and useful. A nomogram consisting of radiomics score and the maximum tumor diameter demonstrated the highest accuracy in the prediction of risk grade in GISTs. The outcomes of our study provide vital insights for important preoperative clinical decisions.
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Tang ZP, Ma Z, He Y, Liu RC, Jin BB, Wen DY, Wen R, Yin HH, Qiu CC, Gao RZ, Ma Y, Yang H. Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery. BMC Med Imaging 2022; 22:147. [PMID: 35996097 PMCID: PMC9396799 DOI: 10.1186/s12880-022-00879-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/16/2022] [Indexed: 12/21/2022] Open
Abstract
Objective To evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer. Methods A total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions. The data for the 206 lesions were randomly divided into a training set (53 type I + 71 type II) and a test set (36 type I + 46 type II) by random sampling. ITK-SNAP software was used to manually outline the boundary of the tumor, that is, the region of interest, and 4976 features were extracted. The quantitative expression values of the radiomics features were normalized by the Z-score method, and the 7 features with the most differences were screened by using the Lasso regression tenfold cross-validation method. The radiomics model was established by logistic regression. The training set was used to construct the model, and the test set was used to evaluate the predictive efficiency of the model. On the basis of multifactor logistic regression analysis, combined with the radiomics score of each patient, a comprehensive prediction model was established, the nomogram was drawn, and the prediction effect was evaluated by analyzing the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. Results The AUCs of the training set and test set in the radiomics model and comprehensive model were 0.817 and 0.731 and 0.982 and 0.886, respectively. The calibration curve showed that the two models were in good agreement. The clinical decision curve showed that both methods had good clinical practicability. Conclusion The radiomics model based on ultrasound images has a good predictive effect for the preoperative differential diagnosis of type I and type II epithelial ovarian cancer. The comprehensive model has higher prediction efficiency.
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Affiliation(s)
- Zhi-Ping Tang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Zhen Ma
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.,Department of Medical Ultrasound, Guangxi International Zhuang Medical Hospital, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Ruo-Chuan Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Bin-Bin Jin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Dong-Yue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Hai-Hui Yin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Cheng-Cheng Qiu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yan Ma
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
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Bao J, Feng X, Ma Y, Wang Y, Qi J, Qin C, Tan X, Tian Y. The latest application progress of radiomics in prediction and diagnosis of liver diseases. Expert Rev Gastroenterol Hepatol 2022; 16:707-719. [PMID: 35880549 DOI: 10.1080/17474124.2022.2104711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Early detection and individualized treatment of patients with liver disease is the key to survival. Radiomics can extract high-throughput quantitative features by multimode imaging, which has good application prospects for the diagnosis, staging and prognosis of benign and malignant liver diseases. Therefore, this paper summarizes the current research status in the field of liver disease, in order to help these patients achieve personalized and precision medical care. AREAS COVERED This paper uses several keywords on the PubMed database to search the references, and reviews the workflow of traditional radiomics, as well as the characteristics and influencing factors of different imaging modes. At the same time, the references on the application of imaging in different benign and malignant liver diseases were also summarized. EXPERT OPINION For patients with liver disease, the traditional imaging evaluation can only provide limited information. Radiomics exploits the characteristics of high-throughput and high-dimensional extraction, enabling liver imaging capabilities far beyond the scope of traditional visual image analysis. Recent studies have demonstrated the prospect of this technology in personalized diagnosis and treatment decision in various fields of the liver. However, further clinical validation is needed in its application and practice.
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Affiliation(s)
- Jiaying Bao
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xiao Feng
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Yan Ma
- Department of Ultrasound, Zibo Central Hospital, Zibo, P.R. China
| | - Yanyan Wang
- Departments of Emergency Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Jianni Qi
- Central Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Chengyong Qin
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, P.R. China
| | - Xu Tan
- Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
| | - Yongmei Tian
- Department of Geriatrics, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China
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Chen Y, Lu Q, Zhu Y, Huang B, Dong Y, Wang W. Prediction of Microvascular Invasion in Combined Hepatocellular-Cholangiocarcinoma Based on Pre-operative Clinical Data and Contrast-Enhanced Ultrasound Characteristics. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:1190-1201. [PMID: 35397928 DOI: 10.1016/j.ultrasmedbio.2022.02.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 01/27/2022] [Accepted: 02/11/2022] [Indexed: 06/14/2023]
Abstract
The goal of the study described here was to define the predictive value of pre-operative clinical information and contrast-enhanced ultrasound (CEUS) imaging characteristics in combined hepatocellular-cholangiocarcinoma (CHC) patients with microvascular invasion (MVI). Seventy-six patients with pathologically confirmed CHC were enrolled in this study, comprising 18 patients with MVI-positive status and 58 with MVI-negative CHC nodules. The pre-operative clinical data and CEUS imaging features were retrospectively analyzed. Univariate and multivariate analyses were performed to identify the potential predictors of MVI in CHC. Recurrence-free survival (RFS) after hepatectomy was compared between patients with different MVI status using the log-rank test and Kaplan-Meier survival curves. Univariate analysis indicated that the following parameters of patients with CHC significantly differed between the MVI-positive and MVI-negative groups (p<0.05): tumor size, α-fetoprotein ≥400 ng/mL, enhancement patterns in arterial phase and marked washout during the portal venous phase on CEUS. On multivariate logistic regression analysis, only the CEUS characteristics of heterogeneous enhancement (odds ratio = 6.807; 95% confidence interval [CI]: 1.099, 42.147; p = 0.039) and marked washout (odds ratio = 4.380; 95% CI: 1.050,18.270; p = 0.043) were identified as independent predictors of MVI in CHC. The combination of the two risk factors in predicting MVI achieved a better diagnostic performance than each parameter alone, with an area under the receiver operating characteristic curve of 0.736 (0.622, 0.830). After hepatectomy, CHC patients with MVI exhibited earlier recurrence compared with those without MVI (hazard ratio = 1.859; 95% CI: 0.8699-3.9722, p = 0.046). The CEUS imaging features of heterogeneous enhancement in the arterial phase and marked washout during the portal venous phase were the potential predictors of MVI in CHC. Aside from that, CHC patients with MVI had an earlier recurrence rate than those without MVI after surgery.
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Affiliation(s)
- Yanling Chen
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qing Lu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuli Zhu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Beijian Huang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China; Institute of Ultrasound Medicine and Engineering, Fudan University, Shanghai, China
| | - Yi Dong
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China
| | - Wenping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China; Institute of Ultrasound Medicine and Engineering, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China.
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Radiomics analysis of ultrasonic image predicts sensitive effects of microwave ablation in treatment of patient with benign breast tumors. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhang WB, Hou SZ, Chen YL, Mao F, Dong Y, Chen JG, Wang WP. Deep Learning for Approaching Hepatocellular Carcinoma Ultrasound Screening Dilemma: Identification of α-Fetoprotein-Negative Hepatocellular Carcinoma From Focal Liver Lesion Found in High-Risk Patients. Front Oncol 2022; 12:862297. [PMID: 35720017 PMCID: PMC9204304 DOI: 10.3389/fonc.2022.862297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/14/2022] [Indexed: 12/02/2022] Open
Abstract
Background First-line surveillance on hepatitis B virus (HBV)-infected populations with B-mode ultrasound is relatively limited to identifying hepatocellular carcinoma (HCC) without elevated α-fetoprotein (AFP). To improve the present HCC surveillance strategy, the state of the art of artificial intelligence (AI), a deep learning (DL) approach, is proposed to assist in the diagnosis of a focal liver lesion (FLL) in HBV-infected liver background. Methods Our proposed deep learning model was based on B-mode ultrasound images of surgery that proved 209 HCC and 198 focal nodular hyperplasia (FNH) cases with 413 lesions. The model cohort and test cohort were set at a ratio of 3:1, in which the test cohort was composed of AFP-negative HBV-infected cases. Four additional deep learning models (MobileNet, Resnet50, DenseNet121, and InceptionV3) were also constructed as comparative baselines. To evaluate the models in terms of diagnostic power, sensitivity, specificity, accuracy, confusion matrix, F1-score, and area under the receiver operating characteristic curve (AUC) were calculated in the test cohort. Results The AUC of our model, Xception, achieved 93.68% in the test cohort, superior to other baselines (89.06%, 85.67%, 83.94%, and 78.13% respectively for MobileNet, Resnet50, DenseNet121, and InceptionV3). In terms of diagnostic power, our model showed sensitivity, specificity, accuracy, and F1-score of 96.08%, 76.92%, 86.41%, and 87.50%, respectively, and PPV, NPV, FPR, and FNR calculated from the confusion matrix were respectively 80.33%, 95.24%, 23.08%, and 3.92% in identifying AFP-negative HCC from HBV-infected FLL cases. Satisfactory robustness of our proposed model was shown based on 5-fold cross-validation performed among the models above. Conclusions Our DL approach has great potential to assist B-mode ultrasound in identifying AFP-negative HCC from FLL found in surveillance of HBV-infected patients.
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Affiliation(s)
- Wei-Bin Zhang
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China.,Department of Ultrasound, Zhongshan hospital of Fudan University (Xiamen Branch), Xiamen, China
| | - Si-Ze Hou
- Department of Mathematical Sciences, School of Physical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Yan-Ling Chen
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Feng Mao
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Yi Dong
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China
| | - Wen-Ping Wang
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
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Katsuta Y, Kadoya N, Sugai Y, Katagiri Y, Yamamoto T, Takeda K, Tanaka S, Jingu K. Feasibility of Differential Dose-Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence. Diagnostics (Basel) 2022; 12:diagnostics12061354. [PMID: 35741164 PMCID: PMC9221601 DOI: 10.3390/diagnostics12061354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/16/2022] [Accepted: 05/20/2022] [Indexed: 11/18/2022] Open
Abstract
The purpose of this study is to introduce differential dose−volume histogram (dDVH) features into machine learning for radiation pneumonitis (RP) prediction and to demonstrate the predictive performance of the developed model based on integrated cumulative dose−volume histogram (cDVH) and dDVH features. Materials and methods: cDVH and dDVH features were calculated for 153 patients treated for non-small-cell lung cancer with 60−66 Gy and dose bins ranging from 2 to 8 Gy in 2 Gy increments. RP prediction models were developed with the least absolute shrinkage and selection operator (LASSO) through fivefold cross-validation. Results: Among the 152 patients in the patient cohort, 41 presented ≥grade 2 RP. The interdependencies between cDVH features evaluated by Spearman’s correlation were significantly resolved by the inclusion of dDVH features. The average area under curve for the RP prediction model using cDVH and dDVH model was 0.73, which was higher than the average area under curve using cDVH model for 0.62 with statistically significance (p < 0.01). An analysis using the entire set of regression coefficients determined by LASSO demonstrated that dDVH features represented four of the top five frequently selected features in the model fitting, regardless of dose bin. Conclusions: We successfully developed an RP prediction model that integrated cDVH and dDVH features. The best RP prediction model was achieved using dDVH (dose bin = 4 Gy) features in the machine learning process.
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Affiliation(s)
- Yoshiyuki Katsuta
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
- Correspondence: ; Tel.: +81-22-717-7312; Fax: +81-22-717-7316
| | - Noriyuki Kadoya
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Yuto Sugai
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Yu Katagiri
- Department of Radiation Oncology, Japan Red Cross Ishinomaki Hospital, Ishinomaki 986-8522, Japan;
| | - Takaya Yamamoto
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Kazuya Takeda
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Shohei Tanaka
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
| | - Keiichi Jingu
- Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-machi, Aoba-ku, Sendai 980-8574, Japan; (N.K.); (Y.S.); (T.Y.); (K.T.); (S.T.); (K.J.)
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An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer. BMC Med Imaging 2022; 22:84. [PMID: 35538520 PMCID: PMC9087958 DOI: 10.1186/s12880-022-00813-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/25/2022] [Indexed: 11/24/2022] Open
Abstract
Objective To investigate whether radiomics based on ultrasound images can predict lymphovascular invasion (LVI) of rectal cancer (RC) before surgery. Methods A total of 203 patients with RC were enrolled retrospectively, and they were divided into a training set (143 patients) and a validation set (60 patients). We extracted the radiomic features from the largest gray ultrasound image of the RC lesion. The intraclass correlation coefficient (ICC) was applied to test the repeatability of the radiomic features. The least absolute shrinkage and selection operator (LASSO) was used to reduce the data dimension and select significant features. Logistic regression (LR) analysis was applied to establish the radiomics model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the comprehensive performance of the model. Results Among the 203 patients, 33 (16.7%) were LVI positive and 170 (83.7%) were LVI negative. A total of 5350 (90.1%) radiomic features with ICC values of ≥ 0.75 were reported, which were subsequently subjected to hypothesis testing and LASSO regression dimension reduction analysis. Finally, 15 selected features were used to construct the radiomics model. The area under the curve (AUC) of the training set was 0.849, and the AUC of the validation set was 0.781. The calibration curve indicated that the radiomics model had good calibration, and DCA demonstrated that the model had clinical benefits. Conclusion The proposed endorectal ultrasound-based radiomics model has the potential to predict LVI preoperatively in RC. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00813-6.
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Fiz F, Jayakody Arachchige VS, Gionso M, Pecorella I, Selvam A, Wheeler DR, Sollini M, Viganò L. Radiomics of Biliary Tumors: A Systematic Review of Current Evidence. Diagnostics (Basel) 2022; 12:diagnostics12040826. [PMID: 35453878 PMCID: PMC9024804 DOI: 10.3390/diagnostics12040826] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/19/2022] [Accepted: 03/25/2022] [Indexed: 02/06/2023] Open
Abstract
Biliary tumors are rare diseases with major clinical unmet needs. Standard imaging modalities provide neither a conclusive diagnosis nor robust biomarkers to drive treatment planning. In several neoplasms, texture analyses non-invasively unveiled tumor characteristics and aggressiveness. The present manuscript aims to summarize the available evidence about the role of radiomics in the management of biliary tumors. A systematic review was carried out through the most relevant databases. Original, English-language articles published before May 2021 were considered. Three main outcome measures were evaluated: prediction of pathology data; prediction of survival; and differential diagnosis. Twenty-seven studies, including a total of 3605 subjects, were identified. Mass-forming intrahepatic cholangiocarcinoma (ICC) was the subject of most studies (n = 21). Radiomics reliably predicted lymph node metastases (range, AUC = 0.729−0.900, accuracy = 0.69−0.83), tumor grading (AUC = 0.680−0.890, accuracy = 0.70−0.82), and survival (C-index = 0.673−0.889). Textural features allowed for the accurate differentiation of ICC from HCC, mixed HCC-ICC, and inflammatory masses (AUC > 0.800). For all endpoints (pathology/survival/diagnosis), the predictive/prognostic models combining radiomic and clinical data outperformed the standard clinical models. Some limitations must be acknowledged: all studies are retrospective; the analyzed imaging modalities and phases are heterogeneous; the adoption of signatures/scores limits the interpretability and applicability of results. In conclusion, radiomics may play a relevant role in the management of biliary tumors, from diagnosis to treatment planning. It provides new non-invasive biomarkers, which are complementary to the standard clinical biomarkers; however, further studies are needed for their implementation in clinical practice.
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Affiliation(s)
- Francesco Fiz
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy; (F.F.); (M.S.)
| | - Visala S Jayakody Arachchige
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Matteo Gionso
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Apoorva Selvam
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Dakota Russell Wheeler
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy; (F.F.); (M.S.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy; (V.S.J.A.); (M.G.); (I.P.); (A.S.); (D.R.W.)
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy
- Correspondence: ; Tel.: +39-02-8224-7361
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Chen Y, Lu Q, Zhang W, Cao J, Dong Y, Wang W. Preoperative Differentiation of Combined Hepatocellular-Cholangiocarcinoma From Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Nomogram Based on Ultrasonographic Features and Clinical Indicators. Front Oncol 2022; 12:757774. [PMID: 35242699 PMCID: PMC8885729 DOI: 10.3389/fonc.2022.757774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To establish a predictive nomogram to distinguish combined hepatocellular-cholangiocarcinoma (CHC) from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) based on preoperative clinical and ultrasound findings. METHODS A total of 261 patients with pathologically confirmed primary liver cancers (PLCs) were enrolled in this retrospective study, comprising 87 CHCs, 87 HCCs, and 87 ICCs matched by propensity score matching. Patients were randomly assigned to a training cohort and a validation one at the ratio of 7:3. A nomogram integrating ultrasound imaging characteristics and clinical features was established based on the independent risk factors selected by least absolute shrinkage and selection operator (LASSO) regression. The performance of the nomogram was evaluated in the training and validation cohorts in terms of discrimination, calibration, and clinical usefulness. RESULTS The nomogram, consisting of ultrasound imaging features (shape and margin on B-mode ultrasound, enhanced pattern on contrast-enhanced ultrasound) and clinical information [elevated alpha fetoprotein (AFP) level and serum protein electrophoresis (SPE) α1 level], showed promising performance in differentiating CHC from HCC and ICC, with the concordance index (C-index) of 0.8275 and 0.8530 in the training cohort and the validation cohort, respectively. Hosmer-Lemeshow test and the calibration curves suggested good consistency between predictions and observations. High clinical practicability was confirmed by the decision curve analysis. CONCLUSIONS The nomogram based on clinical and ultrasound imaging characteristics showed good performance in the discrimination of CHC from other subtypes of PLC and would be valuable in clinical decision-making.
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Affiliation(s)
- Yanling Chen
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qing Lu
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weibin Zhang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jiaying Cao
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yi Dong
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wenping Wang
- Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China.,Shanghai Institute of Medical Imaging, Shanghai, China
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Li CQ, Zheng X, Guo HL, Cheng MQ, Huang Y, Xie XY, Lu MD, Kuang M, Wang W, Chen LD. Differentiation between combined hepatocellular carcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017. BMC Med Imaging 2022; 22:36. [PMID: 35241004 PMCID: PMC8896152 DOI: 10.1186/s12880-022-00765-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 02/24/2022] [Indexed: 01/10/2023] Open
Abstract
Background The imaging findings of combined hepatocellular cholangiocarcinoma (CHC) may be similar to those of hepatocellular carcinoma (HCC). CEUS LI-RADS may not perform well in distinguishing CHC from HCC. Studies have shown that radiomics has an excellent imaging analysis ability. This study aimed to establish and confirm an ultrasomics model for differentiating CHC from HCC. Methods Between 2004 and 2016, we retrospectively identified 53 eligible CHC patients and randomly included 106 eligible HCC patients with a ratio of HCC:CHC = 2:1, all of whom were categorized according to Contrast-Enhanced (CE) ultrasonography (US) Liver Imaging Reporting and Data System (LI-RADS) version 2017. The model based on ultrasomics features of CE US was developed in 74 HCC and 37 CHC and confirmed in 32 HCC and 16 CHC. The diagnostic performance of the LI-RADS or ultrasomics model was assessed by the area under the curve (AUC), accuracy, sensitivity and specificity. Results In the entire and validation cohorts, 67.0% and 81.3% of HCC cases were correctly assigned to LR-5 or LR-TIV contiguous with LR-5, and 73.6% and 87.5% of CHC cases were assigned to LR-M correctly. Up to 33.0% of HCC and 26.4% of CHC were misclassified by CE US LI-RADS. A total of 90.6% of HCC as well as 87.5% of CHC correctly diagnosed by the ultrasomics model in the validation cohort. The AUC, accuracy, sensitivity of the ultrasomics model were higher though without significant difference than those of CE US LI-RADS in the validation cohort. Conclusion The proposed ultrasomics model showed higher ability though the difference was not significantly different for differentiating CHC from HCC, which may be helpful in clinical diagnosis. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00765-x.
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Affiliation(s)
- Chao-Qun Li
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China
| | - Xin Zheng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China
| | - Huan-Ling Guo
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China
| | - Mei-Qing Cheng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China
| | - Yang Huang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China
| | - Xiao-Yan Xie
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China
| | - Ming-de Lu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.,Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming Kuang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.,Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China
| | - Li-da Chen
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China.
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Haghbin H, Aziz M. Artificial intelligence and cholangiocarcinoma: Updates and prospects. World J Clin Oncol 2022; 13:125-134. [PMID: 35316928 PMCID: PMC8894273 DOI: 10.5306/wjco.v13.i2.125] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/09/2022] [Accepted: 01/25/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is the timeliest field of computer science and attempts to mimic cognitive function of humans to solve problems. In the era of “Big data”, there is an ever-increasing need for AI in all aspects of medicine. Cholangiocarcinoma (CCA) is the second most common primary malignancy of liver that has shown an increase in incidence in the last years. CCA has high mortality as it is diagnosed in later stages that decreases effect of surgery, chemotherapy, and other modalities. With technological advancement there is an immense amount of clinicopathologic, genetic, serologic, histologic, and radiologic data that can be assimilated together by modern AI tools for diagnosis, treatment, and prognosis of CCA. The literature shows that in almost all cases AI models have the capacity to increase accuracy in diagnosis, treatment, and prognosis of CCA. Most studies however are retrospective, and one study failed to show AI benefit in practice. There is immense potential for AI in diagnosis, treatment, and prognosis of CCA however limitations such as relative lack of studies in use by human operators in improvement of survival remains to be seen.
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Affiliation(s)
- Hossein Haghbin
- Department of Gastroenterology, Ascension Providence Southfield, Southfield, MI 48075, United States
| | - Muhammad Aziz
- Department of Gastroenterology, University of Toledo Medical Center, Toledo, OH 43614, United States
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32
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Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022; 12:773840. [PMID: 35251962 PMCID: PMC8891653 DOI: 10.3389/fonc.2022.773840] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022] Open
Abstract
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
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Affiliation(s)
- Xingping Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Yanchun Zhang
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, China
| | - Guijuan Zhang
- Department of Respiratory Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China
| | - Xiaoxia Yin
- Institute of Advanced Cyberspace Technology, Guangzhou University, Guangzhou, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
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33
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Gao RZ, Wen R, Wen DY, Huang J, Qin H, Li X, Wang XR, He Y, Yang H. Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2021; 40:2685-2697. [PMID: 33615528 DOI: 10.1002/jum.15659] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 01/21/2021] [Accepted: 01/31/2021] [Indexed: 05/28/2023]
Abstract
OBJECTIVES To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients. METHODS We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z-score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort. RESULTS A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort. CONCLUSIONS The ultrasound-based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA.
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Affiliation(s)
- Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dong-Yue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jing Huang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hui Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Ren S, Li Q, Liu S, Qi Q, Duan S, Mao B, Li X, Wu Y, Zhang L. Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study. Front Oncol 2021; 11:749137. [PMID: 34804935 PMCID: PMC8604281 DOI: 10.3389/fonc.2021.749137] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/19/2021] [Indexed: 12/12/2022] Open
Abstract
Objective This study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Methods The clinical data and ultrasonic images of 226 patients from three hospitals were retrospectively collected and divided into training set (n = 149), test set (n = 38), and independent validation set (n = 39). Manual segmentation of tumor lesion was performed with ITK-SNAP, the ultrasomics features were extracted by the pyradiomics, and ultrasomics signatures were generated using variance filtering and lasso regression. The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine (SVM). The performance of the three models was evaluated by the area under curve (AUC), sensitivity, specificity, and accuracy. Results The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC (p < 0.05). The combined model had a better performance than either the clinical model or the ultrasomics model. In addition to stability, the combined model also had a stronger generalization ability (p < 0.05). The AUC (along with 95% CI), sensitivity, specificity, and accuracy of the combined model on the test set and the independent validation set were 0.936 (0.806-0.989), 0.900, 0.857, 0.868, and 0.874 (0.733-0.961), 0.889, 0.867, and 0.872, respectively. Conclusion The ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC. The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.
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Affiliation(s)
- Shanshan Ren
- Henan University People's Hospital, Zhengzhou, China.,Henan Provincial People's Hospital, Zhengzhou, China
| | - Qian Li
- Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Shunhua Liu
- Henan Provincial People's Hospital, Zhengzhou, China
| | - Qinghua Qi
- First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaobo Duan
- Henan Provincial People's Hospital, Zhengzhou, China
| | - Bing Mao
- Henan Provincial People's Hospital, Zhengzhou, China
| | - Xin Li
- Henan Provincial People's Hospital, Zhengzhou, China
| | - Yuejin Wu
- Henan Provincial People's Hospital, Zhengzhou, China
| | - Lianzhong Zhang
- Henan University People's Hospital, Zhengzhou, China.,Henan Provincial People's Hospital, Zhengzhou, China
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35
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Bartolotta TV, Taibbi A, Randazzo A, Gagliardo C. New frontiers in liver ultrasound: From mono to multi parametricity. World J Gastrointest Oncol 2021; 13:1302-1316. [PMID: 34721768 PMCID: PMC8529919 DOI: 10.4251/wjgo.v13.i10.1302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 07/17/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023] Open
Abstract
Modern liver ultrasonography (US) has become a “one-stop shop” able to provide not only anatomic and morphologic but also functional information about vascularity, stiffness and other various liver tissue properties. Modern US techniques allow a quantitative assessment of various liver diseases. US scanning is no more limited to the visualized plane, but three-dimensional, volumetric acquisition and consequent post-processing are also possible. Further, US scan can be consistently merged and visualized in real time with Computed Tomography and Magnetic Resonance Imaging examinations. Effective and safe microbubble-based contrast agents allow a real time, dynamic study of contrast kinetic for the detection and characterization of focal liver lesions. Ultrasound can be used to guide loco-regional treatment of liver malignancies and to assess tumoral response either to interventional procedures or medical therapies. Microbubbles may also carry and deliver drugs under ultrasound exposure. US plays a crucial role in diagnosing, treating and monitoring focal and diffuse liver disease. On the basis of personal experience and literature data, this paper is aimed to review the main topics involving recent advances in the field of liver ultrasound.
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Affiliation(s)
- Tommaso Vincenzo Bartolotta
- Department of Radiology, University Hospital "Paolo Giaccone", Palermo 90127, Italy
- Radiology Department, Fondazione Istituto G. Giglio Hospital, Cefalù 90015, Italy
| | - Adele Taibbi
- Department of Radiology, University Hospital "Paolo Giaccone", Palermo 90127, Italy
| | - Angelo Randazzo
- Department of Radiology, University Hospital "Paolo Giaccone", Palermo 90127, Italy
| | - Cesare Gagliardo
- Department of Biopathology and Medical and Forensic Biotechnologies, University of Palermo, Palermo 90127, Italy
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36
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Wang S, Liu X, Zhao J, Liu Y, Liu S, Liu Y, Zhao J. Computer auxiliary diagnosis technique of detecting cholangiocarcinoma based on medical imaging: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106265. [PMID: 34311415 DOI: 10.1016/j.cmpb.2021.106265] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Cholangiocarcinoma (CCA) is one of the most aggressive human malignant tumors and is becoming one of the main factors of death and disability globally. Specifically, 60% to 70% of CCA patients were diagnosed with local invasion or distant metastasis and lost the chance of radical operation. The overall median survival time was less than 12 months. As a non-invasive diagnostic technology, medical imaging consisting of computed tomography (CT) imaging, magnetic resonance imaging (MRI), and ultrasound (US) imaging, is the most effectively and commonly used method to detect CCA. The computer auxiliary diagnosis (CAD) system based on medical imaging is helpful for rapid diagnosis and provides credible "second opinion" for specialists. The purpose of this review is to categorize and review the CAD technique of detecting CCA based on medical imaging. METHODS This work applies a four-level screening process to choose suitable publications. 125 research papers published in different academic research databases were selected and analyzed according to specific criteria. From the five steps of medical image acquisition, processing, analysis, understanding and verification of CAD combined with artificial intelligence algorithms, we obtain the most advanced insights related to CCA detection. RESULTS This work provides a comprehensive analysis and comparison analysis of the current CAD systems of detecting CCA. After careful investigation, we find that the main detection methods are traditional machine learning method and deep learning method. For the detection, the most commonly used method is semi-automatic segmentation algorithm combined with support vector machine classifier method, combination of which has good detection performance. The end-to-end training mode makes deep learning method more and more popular in CAD systems. However, due to the limited medical training data, the accuracy of deep learning method is unsatisfactory. CONCLUSIONS Based on analysis of artificial intelligence methods applied in CCA, this work is expected to be truly applied in clinical practice in the future to improve the level of clinical diagnosis and treatment of it. This work concludes by providing a prediction of future trends, which will be of great significance for researchers in the medical imaging of CCA and artificial intelligence.
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Affiliation(s)
- Shiyu Wang
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Xiang Liu
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Jingwen Zhao
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Yiwen Liu
- School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Shuhong Liu
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
| | - Yisi Liu
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China
| | - Jingmin Zhao
- Department of Pathology and Hepatology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing 100039, China.
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37
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Wu L, Zhao Y, Lin P, Qin H, Liu Y, Wan D, Li X, He Y, Yang H. Preoperative ultrasound radiomics analysis for expression of multiple molecular biomarkers in mass type of breast ductal carcinoma in situ. BMC Med Imaging 2021; 21:84. [PMID: 34001017 PMCID: PMC8130392 DOI: 10.1186/s12880-021-00610-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 04/21/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The molecular biomarkers of breast ductal carcinoma in situ (DCIS) have important guiding significance for individualized precision treatment. This study was intended to explore the significance of radiomics based on ultrasound images to predict the expression of molecular biomarkers of mass type of DCIS. METHODS 116 patients with mass type of DCIS were included in this retrospective study. The radiomics features were extracted based on ultrasound images. According to the ratio of 7:3, the data sets of molecular biomarkers were split into training set and test set. The radiomics models were developed to predict the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki67, p16, and p53 by using combination of multiple feature selection and classifiers. The predictive performance of the models were evaluated using the area under the curve (AUC) of the receiver operating curve. RESULTS The investigators extracted 5234 radiomics features from ultrasound images. 12, 23, 41, 51, 31 and 23 features were important for constructing the models. The radiomics scores were significantly (P < 0.05) in each molecular marker expression of mass type of DCIS. The radiomics models showed predictive performance with AUC greater than 0.7 in the training set and test set: ER (0.94 and 0.84), PR (0.90 and 0.78), HER2 (0.94 and 0.74), Ki67 (0.95 and 0.86), p16 (0.96 and 0.78), and p53 (0.95 and 0.74), respectively. CONCLUSION Ultrasonic-based radiomics analysis provided a noninvasive preoperative method for predicting the expression of molecular markers of mass type of DCIS with good accuracy.
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Affiliation(s)
- Linyong Wu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Yujia Zhao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Hui Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Yichen Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Da Wan
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Xin Li
- GE Healthcare, Shanghai, People's Republic of China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
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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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