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Salehi MA, Harandi H, Mohammadi S, Shahrabi Farahani M, Shojaei S, Saleh RR. Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1297-1311. [PMID: 38438694 PMCID: PMC11300422 DOI: 10.1007/s10278-024-01058-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 03/06/2024]
Abstract
Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians' performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.
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Affiliation(s)
| | - Hamid Harandi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheil Mohammadi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | | | - Shayan Shojaei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ramy R Saleh
- Department of Oncology, McGill University, Montreal, QC, H3A 0G4, Canada
- Division of Medical Oncology, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
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Zhou H, Ding J, Zhou Y, Wang Y, Zhao L, Shih CC, Xu J, Wang J, Tong L, Chen Z, Lin Q, Jing X. Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning. BMC Med Imaging 2024; 24:68. [PMID: 38515044 PMCID: PMC10956289 DOI: 10.1186/s12880-024-01247-y] [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: 03/10/2023] [Accepted: 03/14/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND Contrast-enhanced ultrasound (CEUS) is considered as an efficient tool for focal liver lesion characterization, given it allows real-time scanning and provides dynamic tissue perfusion information. An accurate diagnosis of liver lesions with CEUS requires a precise interpretation of CEUS images. However,it is a highly experience dependent task which requires amount of training and practice. To help improve the constrains, this study aims to develop an end-to-end method based on deep learning to make malignancy diagnosis of liver lesions using CEUS. METHODS A total of 420 focal liver lesions with 136 benign cases and 284 malignant cases were included. A deep learning model based on a two-dimensional convolution neural network, a long short-term memory (LSTM), and a linear classifier (with sigmoid) was developed to analyze the CEUS loops from different contrast imaging phases. For comparison, a 3D-CNN based method and a machine-learning (ML)-based time-intensity curve (TIC) method were also implemented for performance evaluation. RESULTS Results of the 4-fold validation demonstrate that the mean AUC is 0.91, 0.88, and 0.78 for the proposed method, the 3D-CNN based method, and the ML-based TIC method, respectively. CONCLUSIONS The proposed CNN-LSTM method is promising in making malignancy diagnosis of liver lesions in CEUS without any additional manual features selection.
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Affiliation(s)
- Hongyu Zhou
- The Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Jianmin Ding
- The Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Yan Zhou
- The Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Yandong Wang
- The Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | - Lei Zhao
- The Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China
- Artificial Cell Engineering Technology Research Center, Tianjin, China
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China
| | | | - Jingping Xu
- Philips Ultrasound R&D Research, Shanghai, China
| | - Jianan Wang
- Philips Ultrasound R&D Research, Shanghai, China
| | - Ling Tong
- Philips Ultrasound R&D Research, Shanghai, China
| | - Zhouye Chen
- Philips Ultrasound R&D Research, Shanghai, China
| | - Qizhong Lin
- Philips Ultrasound R&D Research, Shanghai, China
| | - Xiang Jing
- The Third Central Hospital of Tianjin, 83 Jintang Road, Hedong District, Tianjin, 300170, China.
- Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China.
- Artificial Cell Engineering Technology Research Center, Tianjin, China.
- Tianjin Institute of Hepatobiliary Disease, Tianjin, China.
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Urhuț MC, Săndulescu LD, Streba CT, Mămuleanu M, Ciocâlteu A, Cazacu SM, Dănoiu S. Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians. Diagnostics (Basel) 2023; 13:3387. [PMID: 37958282 PMCID: PMC10650544 DOI: 10.3390/diagnostics13213387] [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: 09/21/2023] [Revised: 10/29/2023] [Accepted: 11/02/2023] [Indexed: 11/15/2023] Open
Abstract
Contrast-enhanced ultrasound (CEUS) is widely used in the characterization of liver tumors; however, the evaluation of perfusion patterns using CEUS has a subjective character. This study aims to evaluate the accuracy of an automated method based on CEUS for classifying liver lesions and to compare its performance with that of two experienced clinicians. The system used for automatic classification is based on artificial intelligence (AI) algorithms. For an interpretation close to the clinical setting, both clinicians knew which patients were at high risk for hepatocellular carcinoma (HCC), but only one was aware of all the clinical data. In total, 49 patients with 59 liver tumors were included. For the benign and malignant classification, the AI model outperformed both clinicians in terms of specificity (100% vs. 93.33%); still, the sensitivity was lower (74% vs. 93.18% vs. 90.91%). In the second stage of multiclass diagnosis, the automatic model achieved a diagnostic accuracy of 69.93% for HCC and 89.15% for liver metastases. Readers demonstrated greater diagnostic accuracy for HCC (83.05% and 79.66%) and liver metastases (94.92% and 96.61%) compared to the AI system; however, both were experienced sonographers. The AI model could potentially assist and guide less-experienced clinicians to discriminate malignant from benign liver tumors with high accuracy and specificity.
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Affiliation(s)
- Marinela-Cristiana Urhuț
- Department of Gastroenterology, Emergency County Hospital of Craiova, Doctoral School, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
| | - Larisa Daniela Săndulescu
- Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (C.T.S.); (A.C.); (S.M.C.)
| | - Costin Teodor Streba
- Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (C.T.S.); (A.C.); (S.M.C.)
- Department of Pulmonology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
- Oncometrics S.R.L., 200677 Craiova, Romania;
| | - Mădălin Mămuleanu
- Oncometrics S.R.L., 200677 Craiova, Romania;
- Department of Automatic Control and Electronics, University of Craiova, 200585 Craiova, Romania
| | - Adriana Ciocâlteu
- Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (C.T.S.); (A.C.); (S.M.C.)
| | - Sergiu Marian Cazacu
- Department of Gastroenterology, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania; (C.T.S.); (A.C.); (S.M.C.)
| | - Suzana Dănoiu
- Department of Pathophysiology, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania;
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Vetter M, Waldner MJ, Zundler S, Klett D, Bocklitz T, Neurath MF, Adler W, Jesper D. Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review. ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2023; 44:395-407. [PMID: 37001563 DOI: 10.1055/a-2066-9372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.
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Affiliation(s)
- Marcel Vetter
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Maximilian J Waldner
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Sebastian Zundler
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Daniel Klett
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Thomas Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-Universitat Jena, Jena, Germany
- Leibniz-Institute of Photonic Technology, Friedrich Schiller University Jena, Jena, Germany
| | - Markus F Neurath
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
| | - Werner Adler
- Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Daniel Jesper
- Department of Internal Medicine 1, Erlangen University Hospital Department of Medicine 1 Gastroenterology Endocrinology and Pneumology, Erlangen, Germany
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Han X, Gong B, Guo L, Wang J, Ying S, Li S, Shi J. B-mode ultrasound based CAD for liver cancers via multi-view privileged information learning. Neural Netw 2023; 164:369-381. [PMID: 37167750 DOI: 10.1016/j.neunet.2023.03.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 01/21/2023] [Accepted: 03/21/2023] [Indexed: 03/29/2023]
Abstract
B-mode ultrasound-based computer-aided diagnosis model can help sonologists improve the diagnostic performance for liver cancers, but it generally suffers from the bottleneck due to the limited structure and internal echogenicity information in B-mode ultrasound images. Contrast-enhanced ultrasound images provide additional diagnostic information on dynamic blood perfusion of liver lesions for B-mode ultrasound images with improved diagnostic accuracy. Since transfer learning has indicated its effectiveness in promoting the performance of target computer-aided diagnosis model by transferring knowledge from related imaging modalities, a multi-view privileged information learning framework is proposed to improve the diagnostic accuracy of the single-modal B-mode ultrasound-based diagnosis for liver cancers. This framework can make full use of the shared label information between the paired B-mode ultrasound images and contrast-enhanced ultrasound images to guide knowledge transfer It consists of a novel supervised dual-view deep Boltzmann machine and a new deep multi-view SVM algorithm. The former is developed to implement knowledge transfer from the multi-phase contrast-enhanced ultrasound images to the B-mode ultrasound-based diagnosis model via a feature-level learning using privileged information paradigm, which is totally different from the existing learning using privileged information paradigm that performs knowledge transfer in the classifier. The latter further fuses and enhances feature representation learned from three pre-trained supervised dual-view deep Boltzmann machine networks for the classification task. An experiment is conducted on a bimodal ultrasound liver cancer dataset. The experimental results show that the proposed framework outperforms all the compared algorithms with the best classification accuracy of 88.91 ± 1.52%, sensitivity of 88.31 ± 2.02%, and specificity of 89.50 ± 3.12%. It suggests the effectiveness of our proposed MPIL framework for the BUS-based CAD of liver cancers.
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Martinino A, Aloulou M, Chatterjee S, Scarano Pereira JP, Singhal S, Patel T, Kirchgesner TPE, Agnes S, Annunziata S, Treglia G, Giovinazzo F. Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review. J Clin Med 2022; 11:6368. [PMID: 36362596 PMCID: PMC9655417 DOI: 10.3390/jcm11216368] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 09/21/2023] Open
Abstract
Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.
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Affiliation(s)
| | | | - Surobhi Chatterjee
- Department of Internal Medicine, King George’s Medical University, Lucknow 226003, Uttar Pradesh, India
| | | | - Saurabh Singhal
- Department of HPB Surgery and Liver Transplantation, BLK-MAX Superspeciality Hospital, New Delhi 110005, Delhi, India
| | - Tapan Patel
- Department of Surgery, Baroda Medical College and SSG Hospital, Vadodara 390001, Gujarat, India
| | - Thomas Paul-Emile Kirchgesner
- Département of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1348 Brussels, Belgium
| | - Salvatore Agnes
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Salvatore Annunziata
- Unit of Nuclear Medicine, Department of Radiology, Radiotherapy and Hematology, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Giorgio Treglia
- Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6900 Lugano, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, 1015 Lausanne, Switzerland
| | - Francesco Giovinazzo
- General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
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Peng Y, He P, Gao X, Liu G, Cheng H. A Superstable Homogeneous Lipiodol-Nanoformulation to Overcome the Dilemma of Interventional Embolization Chemotherapy. Front Bioeng Biotechnol 2022; 10:952194. [PMID: 35800328 PMCID: PMC9253561 DOI: 10.3389/fbioe.2022.952194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 06/03/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
| | | | | | - Gang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China
| | - Hongwei Cheng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China
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Tiyarattanachai T, Apiparakoon T, Marukatat S, Sukcharoen S, Yimsawad S, Chaichuen O, Bhumiwat S, Tanpowpong N, Pinjaroen N, Rerknimitr R, Chaiteerakij R. The feasibility to use artificial intelligence to aid detecting focal liver lesions in real-time ultrasound: a preliminary study based on videos. Sci Rep 2022; 12:7749. [PMID: 35545628 PMCID: PMC9095624 DOI: 10.1038/s41598-022-11506-z] [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: 09/09/2021] [Accepted: 04/11/2022] [Indexed: 11/09/2022] Open
Abstract
Despite the wide availability of ultrasound machines for hepatocellular carcinoma surveillance, an inadequate number of expert radiologists performing ultrasounds in remote areas remains a primary barrier for surveillance. We demonstrated feasibility of artificial intelligence (AI) to aid in the detection of focal liver lesions (FLLs) during ultrasound. An AI system for FLL detection in ultrasound videos was developed. Data in this study were prospectively collected at a university hospital. We applied a two-step training strategy for developing the AI system by using a large collection of ultrasound snapshot images and frames from full-length ultrasound videos. Detection performance of the AI system was evaluated and then compared to detection performance by 25 physicians including 16 non-radiologist physicians and 9 radiologists. Our dataset contained 446 videos (273 videos with 387 FLLs and 173 videos without FLLs) from 334 patients. The videos yielded 172,035 frames with FLLs and 1,427,595 frames without FLLs for training on the AI system. The AI system achieved an overall detection rate of 89.8% (95%CI: 84.5-95.0) which was significantly higher than that achieved by non-radiologist physicians (29.1%, 95%CI: 21.2-37.0, p < 0.001) and radiologists (70.9%, 95%CI: 63.0-78.8, p < 0.001). Median false positive detection rate by the AI system was 0.7% (IQR: 1.3%). AI system operation speed reached 30-34 frames per second, showing real-time feasibility. A further study to demonstrate whether the AI system can assist operators during ultrasound examinations is warranted.
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Affiliation(s)
| | - Terapap Apiparakoon
- Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sanparith Marukatat
- Image Processing and Understanding Team, Artificial Intelligence Research Group, National Electronics and Computer Technology Center, Pathum Thani, Thailand
| | - Sasima Sukcharoen
- Division of Gastroenterology, Department of Medicine, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand
| | - Sirinda Yimsawad
- Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Oracha Chaichuen
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Siwat Bhumiwat
- Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Natthaporn Tanpowpong
- Department of Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Nutcha Pinjaroen
- Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Rungsun Rerknimitr
- Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Roongruedee Chaiteerakij
- Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
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Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14:765-793. [PMID: 35582107 PMCID: PMC9048537 DOI: 10.4251/wjgo.v14.i4.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/24/2021] [Accepted: 03/27/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.
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Affiliation(s)
- Chrysanthos D Christou
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
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Sheng R, Huang J, Zhang W, Jin K, Yang L, Chong H, Fan J, Zhou J, Wu D, Zeng M. A Semi-Automatic Step-by-Step Expert-Guided LI-RADS Grading System Based on Gadoxetic Acid-Enhanced MRI. J Hepatocell Carcinoma 2021; 8:671-683. [PMID: 34235105 PMCID: PMC8255313 DOI: 10.2147/jhc.s316385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 06/11/2021] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Liver imaging reporting and data system (LI-RADS) classification, especially the identification of LR-3 to 5 lesions with hepatocellular carcinoma (HCC) probability, is of great significance to treatment strategy determination. We aimed to develop a semi-automatic LI-RADS grading system on multiphase gadoxetic acid-enhanced MRI using deep convolutional neural networks (CNN). PATIENTS AND METHODS An internal data set of 439 patients and external data set of 71 patients with suspected HCC were included and underwent gadoxetic acid-enhanced MRI. The expert-guided LI-RADS grading system consisted of four deep 3D CNN models including a tumor segmentation model for automatic diameter estimation and three classification models of LI-RADS major features including arterial phase hyper-enhancement (APHE), washout and enhancing capsule. An end-to-end learning system comprising single deep CNN model that directly classified the LI-RADS grade was developed for comparison. RESULTS On internal testing set, the segmentation model reached a mean dice of 0.84, with the accuracy of mapped diameter intervals as 82.7% (95% CI: 74.4%, 91.7%). The area under the curves (AUCs) were 0.941 (95% CI: 0.914, 0.961), 0.859 (95% CI: 0.823, 0.890) and 0.712 (95% CI: 0.668, 0.754) for APHE, washout and capsule, respectively. The expert-guided system significantly outperformed the end-to-end system with a LI-RADS grading accuracy of 68.3% (95% CI: 60.8%, 76.5%) vs 55.6% (95% CI: 48.8%, 63.0%) (P<0.0001). On external testing set, the accuracy of mapped diameter intervals was 91.5% (95% CI: 81.9%, 100.0%). The AUCs were 0.792 (95% CI: 0.745, 0.833), 0.654 (95% CI: 0.602, 0.703) and 0.658 (95% CI: 0.606, 0.707) for APHE, washout and capsule, respectively. The expert-guided system achieved an overall grading accuracy of 66.2% (95% CI: 58.0%, 75.2%), significantly higher than the end-to-end system of 50.1% (95% CI: 43.1%, 58.1%) (P<0.0001). CONCLUSION We developed a semi-automatic step-by-step expert-guided LI-RADS grading system (LR-3 to 5), superior to the conventional end-to-end learning system. This deep learning-based system may improve workflow efficiency for HCC diagnosis in clinical practice.
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Affiliation(s)
- Ruofan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China
| | - Jing Huang
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, People’s Republic of China
| | - Weiguo Zhang
- Dushuhu District, No. 1 Affiliated Hospital, Suzhou University, Suzhou, Jiangsu, People’s Republic of China
| | - Kaipu Jin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China
| | - Li Yang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China
| | - Huanhuan Chong
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China
| | - Jia Fan
- Department of Liver Surgery, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
- Cancer Center, Shanghai Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
| | - Jian Zhou
- Department of Liver Surgery, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
- Cancer Center, Shanghai Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
| | - Dijia Wu
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, People’s Republic of China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
- Shanghai Institute of Medical Imaging, Shanghai, People’s Republic of China
- Cancer Center, Shanghai Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China
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11
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Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol 2021; 31:4576-4586. [PMID: 33447862 DOI: 10.1007/s00330-020-07562-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To investigate the application of machine learning-based ultrasound radiomics in preoperative classification of primary and metastatic liver cancer. METHODS Data of 114 consecutive histopathologically confirmed patients with liver cancer from January 2018 to November 2019 were retrospectively analyzed. All patients underwent liver ultrasonography within 1 week before hepatectomy or fine-needle biopsy. The liver lesions were manually segmented by two experts using ITK-SNAP software. Seven categories of radiomics features, including first-order, two-dimensional shape, gray-level co-occurrence matrices, gray-level run-length matrix, gray-level size-zone matrix, neighboring gray tone difference matrix, and gray-level dependence matrix, were extracted on the Pyradiomics platform. Fourteen filters were applied to the original images, and derived images were obtained. Then, the dimensions of radiomics features were reduced by least absolute shrinkage and selection operator (Lasso) method. Finally, k-nearest neighbor (KNN), logistic regression (LR), multilayer perceptron (MLP), random forest (RF), and support vector machine (SVM) were employed to distinguish primary liver cancer from metastatic liver cancer by a fivefold cross-validation strategy. The performance of the established model was mainly evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy. RESULTS One thousand four hundred nine radiomics features were extracted from the original images and/or derived images for each patient. The mentioned five machine learning classifiers were able to differentiate primary liver cancer from metastatic liver cancer. LR outperformed other classifiers, with the accuracy of 0.843 ± 0.078 (AUC, 0.816 ± 0.088; sensitivity, 0.768 ± 0.232; specificity, 0.880 ± 0.117). CONCLUSIONS Machine learning-based ultrasound radiomics features are able to non-invasively distinguish primary liver tumors from metastatic liver tumors. KEY POINTS • Ultrasound-based radiomics was initially used for preoperative classification of primary versus metastatic liver cancer. • Multiple machine learning-based algorithms with cross-validation strategy were applied to extract machine learning-based ultrasound radiomics features. • Distinction between primary and metastatic tumors was obtained with a sensitivity of 0.768 and a specificity of 0.880.
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12
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Zheng Q, Yang L, Zeng B, Li J, Guo K, Liang Y, Liao G. Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis. EClinicalMedicine 2021; 31:100669. [PMID: 33392486 PMCID: PMC7773591 DOI: 10.1016/j.eclinm.2020.100669] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 11/14/2020] [Accepted: 11/17/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging. METHODS We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis. FINDINGS We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79-84%), specificity of 84% (82-87%) and AUC of 0·90 (0·87-0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83-90%) for machine learning and 86% (82-89%) for deep learning, and a pooled specificity of 89% (82-93%) for machine learning, and 87% (82-91%) for deep learning. INTERPRETATION AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field. FUNDING College students' innovative entrepreneurial training plan program .
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Affiliation(s)
- Qiuhan Zheng
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Le Yang
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Bin Zeng
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Jiahao Li
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Kaixin Guo
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Yujie Liang
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Guiqing Liao
- Department of Oral and Maxillofacial Surgery, Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
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13
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Lee JY, Minami Y, Choi BI, Lee WJ, Chou YH, Jeong WK, Park MS, Kudo N, Lee MW, Kamata K, Iijima H, Kim SY, Numata K, Sugimoto K, Maruyama H, Sumino Y, Ogawa C, Kitano M, Joo I, Arita J, Liang JD, Lin HM, Nolsoe C, Gilja OH, Kudo M. The AFSUMB Consensus Statements and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound using Sonazoid. Ultrasonography 2020; 39:191-220. [PMID: 32447876 PMCID: PMC7315291 DOI: 10.14366/usg.20057] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 04/27/2020] [Indexed: 12/11/2022] Open
Abstract
The first edition of the guidelines for the use of ultrasound contrast agents was published in 2004, dealing with liver applications. The second edition of the guidelines in 2008 reflected changes in the available contrast agents and updated the guidelines for the liver, as well as implementing some nonliver applications. The third edition of the contrast-enhanced ultrasound (CEUS) guidelines was the joint World Federation for Ultrasound in Medicine and Biology-European Federation of Societies for Ultrasound in Medicine and Biology (WFUMB-EFSUMB) venture in conjunction with other regional US societies such as Asian Federation of Societies for Ultrasound in Medicine and Biology, resulting in a simultaneous duplicate on liver CEUS in the official journals of both WFUMB and EFSUMB in 2013. However, no guidelines were described mainly for Sonazoid due to limited clinical experience only in Japan and Korea. The new proposed consensus statements and recommendations provide general advice on the use of Sonazoid and are intended to create standard protocols for the use and administration of Sonazoid in hepatic and pancreatobiliary applications in Asian patients and to improve patient management.
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Affiliation(s)
- Jae Young Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Yasunori Minami
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, Higashi-Osaka, Japan
| | - Byung Ihn Choi
- Department of Radiology, Chung Ang University Hospital, Seoul, Korea
| | - Won Jae Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yi-Hong Chou
- Department of Medical Imaging and Radiological Technology, Yuanpei University of Medical Technology, Hsinchu, Taiwan.,Department of Radiology, National Yang Ming University, Taipei, Taiwan
| | - Woo Kyoung Jeong
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Mi-Suk Park
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Nobuki Kudo
- Laboratory of Biomedical Engineering, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Min Woo Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ken Kamata
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, Higashi-Osaka, Japan
| | - Hiroko Iijima
- Department of Ultrasound, Hepatobiliary and Pancreatic Disease, Hyogo College of Medicine, Nishinomiya, Japan
| | - So Yeon Kim
- Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Kazushi Numata
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Katsutoshi Sugimoto
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Hitoshi Maruyama
- Department of Gastroenterology, Juntendo University, Tokyo, Japan
| | - Yasukiyo Sumino
- Department of Gastroenterology and Hepatology, Toho University Medical Center, Tokyo, Japan
| | - Chikara Ogawa
- Department of Gastroenterology and Hepatology, Takamatsu Red Cross Hospital, Takamatsu, Japan
| | - Masayuki Kitano
- Department of Gastroenterology and Hepatology, Wakayama Medical University Hospital, Wakayama, Japan
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Junichi Arita
- Hepato-Biliary-Pancreatic Surgery Division and Artificial Organ and Transplantation Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ja-Der Liang
- Department of Gastroenterology and Hepatology, National Taiwan University, Taipei, Taiwan
| | - Hsi-Ming Lin
- Department of Gastroenterology and Hepatology, Chang Gung University, Taipei, Taiwan
| | - Christian Nolsoe
- Ultrasound Section, Division of Surgery, Department of Gastroenterology, Herlev Hospital, Copenhagen Academy for Medical Education and Simulation, University of Copenhagen, Copenhagen, Denmark
| | - Odd Helge Gilja
- National Centre for Ultrasound in Gastroenterology, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, Higashi-Osaka, Japan
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14
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Lee JY, Minami Y, Choi BI, Lee WJ, Chou YH, Jeong WK, Park MS, Kudo N, Lee MW, Kamata K, Iijima H, Kim SY, Numata K, Sugimoto K, Maruyama H, Sumino Y, Ogawa C, Kitano M, Joo I, Arita J, Liang JD, Lin HM, Nolsoe C, Gilja OH, Kudo M. The AFSUMB Consensus Statements and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound using Sonazoid. J Med Ultrasound 2020; 28:59-82. [PMID: 32874864 PMCID: PMC7446696 DOI: 10.4103/jmu.jmu_124_19] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 02/09/2020] [Accepted: 02/17/2020] [Indexed: 12/13/2022] Open
Abstract
The first edition of the guidelines for the use of ultrasound contrast agents was published in 2004, dealing with liver applications. The second edition of the guidelines in 2008 reflected changes in the available contrast agents and updated the guidelines for the liver, as well as implementing some nonliver applications. The third edition of the contrast-enhanced ultrasound (CEUS) guidelines was the joint World Federation for Ultrasound in Medicine and Biology-European Federation of Societies for Ultrasound in Medicine and Biology (WFUMB-EFSUMB) venture in conjunction with other regional US societies such as Asian Federation of Societies for Ultrasound in Medicine and Biology, resulting in a simultaneous duplicate on liver CEUS in the official journals of both WFUMB and EFSUMB in 2013. However, no guidelines were described mainly for Sonazoid due to limited clinical experience only in Japan and Korea. The new proposed consensus statements and recommendations provide general advice on the use of Sonazoid and are intended to create standard protocols for the use and administration of Sonazoid in hepatic and pancreatobiliary applications in Asian patients and to improve patient management.
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Affiliation(s)
- Jae Young Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Yasunori Minami
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, Higashi-Osaka, Japan
| | - Byung Ihn Choi
- Department of Radiology, Chung Ang University Hospital, Seoul, Korea
| | - Won Jae Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yi-Hong Chou
- Department of Medical Imaging and Radiological Technology, Yuanpei University of Medical Technology, Hsinchu, Taiwan
- Department of Radiology, National Yang Ming University, Taipei, Taiwan
| | - Woo Kyoung Jeong
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Mi-Suk Park
- Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Nobuki Kudo
- Laboratory of Biomedical Engineering, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Min Woo Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ken Kamata
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, Higashi-Osaka, Japan
| | - Hiroko Iijima
- Department of Ultrasound, Hepatobiliary and Pancreatic Disease, Hyogo College of Medicine, Nishinomiya, Japan
| | - So Yeon Kim
- Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Kazushi Numata
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Katsutoshi Sugimoto
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Hitoshi Maruyama
- Department of Gastroenterology, Juntendo University, Tokyo, Japan
| | - Yasukiyo Sumino
- Department of Gastroenterology and Hepatology, Toho University Medical Center, Tokyo, Japan
| | - Chikara Ogawa
- Department of Gastroenterology and Hepatology, Takamatsu Red Cross Hospital, Takamatsu, Japan
| | - Masayuki Kitano
- Department of Gastroenterology and Hepatology, Wakayama Medical University Hospital, Wakayama, Japan
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Junichi Arita
- Hepato-Biliary-Pancreatic Surgery Division and Artificial Organ and Transplantation Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ja-Der Liang
- Department of Gastroenterology and Hepatology, National Taiwan University, Taipei, Taiwan
| | - Hsi-Ming Lin
- Department of Gastroenterology and Hepatology, Chang Gung University, Taipei, Taiwan
| | - Christian Nolsoe
- Ultrasound Section, Division of Surgery, Department of Gastroenterology, Herlev Hospital, Copenhagen Academy for Medical Education and Simulation, University of Copenhagen, Copenhagen, Denmark
| | - Odd Helge Gilja
- National Centre for Ultrasound in Gastroenterology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, Higashi-Osaka, Japan
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15
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Huang Q, Pan F, Li W, Yuan F, Hu H, Huang J, Yu J, Wang W. Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic Semantics. IEEE J Biomed Health Inform 2020; 24:2860-2869. [PMID: 32149699 DOI: 10.1109/jbhi.2020.2977937] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases.
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16
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Turco S, Frinking P, Wildeboer R, Arditi M, Wijkstra H, Lindner JR, Mischi M. Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:518-543. [PMID: 31924424 DOI: 10.1016/j.ultrasmedbio.2019.11.008] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 11/13/2019] [Accepted: 11/14/2019] [Indexed: 05/14/2023]
Abstract
Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.
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Affiliation(s)
- Simona Turco
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | | | - Rogier Wildeboer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marcel Arditi
- École polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Hessel Wijkstra
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jonathan R Lindner
- Knight Cardiovascular Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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17
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Chou YH, Liang JD, Wang SY, Hsu SJ, Hu JT, Yang SS, Wang HK, Lee TY, Tiu CM. Safety of Perfluorobutane (Sonazoid) in Characterizing Focal Liver Lesions. J Med Ultrasound 2019; 27:81-85. [PMID: 31316217 PMCID: PMC6607878 DOI: 10.4103/jmu.jmu_44_19] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 06/03/2019] [Indexed: 12/21/2022] Open
Abstract
Background: The purpose of this study was to report the safety of perfluorobutane (Sonazoid) as a vascular-phase imaging agent in characterizing focal liver lesions (FLLs). Materials and Methods: From May 2014 to April 2015, a total of 54 individuals who received Sonazoid contrast-enhanced ultrasound (CEUS) were enrolled at 5 hospitals of 4 medical centers. All individuals were included in safety evaluation. A prospective study to evaluate the adverse effect (AE) incidences after intravenous administration of Sonazoid. Results: Sonazoid was well tolerated. Treatment-emergent adverse events (TEAEs) representing AE were recorded for 13 (24.1%) patients. The most common AE was abdominal pain (9.3%), followed by heart rate irregularity (5.6%). The majority of these patients (69.2%) experienced TEAEs that were mild in intensity. Sonazoid causes no significant AEs after intravenous injection. The only noteworthy AEs are related to tolerable myalgia (3.7%), abdominal pain (1.9%), and headache (1.9%). None of the 54 patients showed serious adverse effects. Conclusion: Sonazoid shows good safety and tolerance of intravenous use during CEUS of the liver for evaluation of FLLs.
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Affiliation(s)
- Yi-Hong Chou
- Department of Medical Imaging and Radiological Technology, Yuanpei University of Medical Technology, Hsinchu, Taiwan.,Department of Radiology, Taipei Veterans General Hospital and School of Medicine, National Yang Ming University, Taipei, Taiwan.,Department of Radiology, Yee Zen General Hospital, Taoyuan, Taiwan
| | - Ja-Der Liang
- Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Shen-Yung Wang
- Department of Medicine, Division of Gastroenterology, MacKay Memorial Hospital, Tamshui Branch, Taipei, Taiwan
| | - Shih-Jer Hsu
- Department of Internal Medicine, National Taiwan University Hospital Yun-Lin Branch, Yun-Lin, Taiwan
| | - Jui-Ting Hu
- Liver Center, Cathay General Hospital and School of Medicine, Fu-Jen Catholic University College of Medicine, Taipei, Taiwan
| | - Sien-Sing Yang
- Liver Center, Cathay General Hospital and School of Medicine, Fu-Jen Catholic University College of Medicine, Taipei, Taiwan
| | - Hsin-Kai Wang
- Department of Radiology, Taipei Veterans General Hospital and School of Medicine, National Yang Ming University, Taipei, Taiwan
| | - Tien-Ying Lee
- Department of Radiology, Yee Zen General Hospital, Taoyuan, Taiwan
| | - Chui-Mei Tiu
- Department of Radiology, Taipei Veterans General Hospital and School of Medicine, National Yang Ming University, Taipei, Taiwan.,Department of Radiology, Yee Zen General Hospital, Taoyuan, Taiwan
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18
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Bakas S, Doulgerakis-Kontoudis M, Hunter GJA, Sidhu PS, Makris D, Chatzimichail K. Evaluation of Indirect Methods for Motion Compensation in 2-D Focal Liver Lesion Contrast-Enhanced Ultrasound (CEUS) Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2019; 45:1380-1396. [PMID: 30952468 DOI: 10.1016/j.ultrasmedbio.2019.01.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2018] [Revised: 01/05/2019] [Accepted: 01/27/2019] [Indexed: 05/14/2023]
Abstract
This study investigates the application and evaluation of existing indirect methods, namely point-based registration techniques, for the estimation and compensation of observed motion included in the 2-D image plane of contrast-enhanced ultrasound (CEUS) cine-loops recorded for the characterization and diagnosis of focal liver lesions (FLLs). The value of applying motion compensation in the challenging modality of CEUS is to assist in the quantification of the perfusion dynamics of an FLL in relation to its parenchyma, allowing for a potentially accurate diagnostic suggestion. Towards this end, this study also proposes a novel quantitative multi-level framework for evaluating the quantification of FLLs, which to the best of our knowledge remains undefined, notwithstanding many relevant studies. Following quantitative evaluation of 19 indirect algorithms and configurations, while also considering the requirement for computational efficiency, our results suggest that the "compact and real-time descriptor" (CARD) is the optimal indirect motion compensation method in CEUS.
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Affiliation(s)
- Spyridon Bakas
- Digital Information Research Centre (DIRC), School of Computer Science & Mathematics, Faculty of Science, Engineering and Computing (SEC), Kingston University, London, United Kingdom; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Hamilton Walk, Philadelphia, Pennsylvania, USA.
| | - Matthaios Doulgerakis-Kontoudis
- Digital Information Research Centre (DIRC), School of Computer Science & Mathematics, Faculty of Science, Engineering and Computing (SEC), Kingston University, London, United Kingdom; Medical Imaging and Image Interpretation Group, School of Computer Science, University of Birmingham, Edgbaston, United Kingdom
| | - Gordon J A Hunter
- Digital Information Research Centre (DIRC), School of Computer Science & Mathematics, Faculty of Science, Engineering and Computing (SEC), Kingston University, London, United Kingdom
| | - Paul S Sidhu
- Department of Radiology, King's College Hospital, London, United Kingdom
| | - Dimitrios Makris
- Digital Information Research Centre (DIRC), School of Computer Science & Mathematics, Faculty of Science, Engineering and Computing (SEC), Kingston University, London, United Kingdom
| | - Katerina Chatzimichail
- Radiology & Imaging Research Centre, Evgenidion Hospital, National and Kapodistrian University, Ilisia, Athens, Greece
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19
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Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol 2019; 29:3338-3347. [PMID: 31016442 DOI: 10.1007/s00330-019-06205-9] [Citation(s) in RCA: 166] [Impact Index Per Article: 33.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/06/2019] [Accepted: 03/26/2019] [Indexed: 02/07/2023]
Abstract
OBJECTIVES To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI. METHODS A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set. RESULTS The DLS demonstrated a 92% accuracy, a 92% sensitivity (Sn), and a 98% specificity (Sp). Test set performance in a single run of random unseen cases showed an average 90% Sn and 98% Sp. The average Sn/Sp on these same cases for radiologists was 82.5%/96.5%. Results showed a 90% Sn for classifying hepatocellular carcinoma (HCC) compared to 60%/70% for radiologists. For HCC classification, the true positive and false positive rates were 93.5% and 1.6%, respectively, with a receiver operating characteristic area under the curve of 0.992. Computation time per lesion was 5.6 ms. CONCLUSION This preliminary deep learning study demonstrated feasibility for classifying lesions with typical imaging features from six common hepatic lesion types, motivating future studies with larger multi-institutional datasets and more complex imaging appearances. KEY POINTS • Deep learning demonstrates high performance in the classification of liver lesions on volumetric multi-phasic MRI, showing potential as an eventual decision-support tool for radiologists. • Demonstrating a classification runtime of a few milliseconds per lesion, a deep learning system could be incorporated into the clinical workflow in a time-efficient manner.
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CEUS-based classification of liver tumors with deep canonical correlation analysis and multi-kernel learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:1748-1751. [PMID: 29060225 DOI: 10.1109/embc.2017.8037181] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The contrast-enhanced ultrasound (CEUS) has been a widely accepted imaging modality for diagnosis of liver cancers. In clinical practice, several typical images selected from enhancement patterns of the arterial, portal venous and late phases can provide reliable information basis for diagnosis. In this work, we propose to develop a CEUS-based computer-aided diagnosis (CAD) for liver cancers with only three typical CEUS images selected from three phases, which simulates the clinical diagnosis mode of radiologists. In the proposed CAD, the deep canonical correlation analysis (DCCA) is first performed on three CEUS pairs between arterial and portal venous phases, arterial and late phases, respectively, due to the effectiveness of multi-view fusion of DCCA. The generated six-view features are then fed to a multiple kernel learning (MKL) classifier to further promote the predictive diagnosis result. The experimental results indicate that the proposed DCCA-MKL algorithm achieves best performance for discriminating benign liver tumors from malignant liver cancers.
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Guo LH, Wang D, Qian YY, Zheng X, Zhao CK, Li XL, Bo XW, Yue WW, Zhang Q, Shi J, Xu HX. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin Hemorheol Microcirc 2018; 69:343-354. [PMID: 29630528 DOI: 10.3233/ch-170275] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE With the fast development of artificial intelligence techniques, we proposed a novel two-stage multi-view learning framework for the contrast-enhanced ultrasound (CEUS) based computer-aided diagnosis for liver tumors, which adopted only three typical CEUS images selected from the arterial phase, portal venous phase and late phase. MATERIALS AND METHODS In the first stage, the deep canonical correlation analysis (DCCA) was performed on three image pairs between the arterial and portal venous phases, arterial and delayed phases, and portal venous and delayed phases respectively, which then generated total six-view features. While in the second stage, these multi-view features were then fed to a multiple kernel learning (MKL) based classifier to further promote the diagnosis result. Two MKL classification algorithms were evaluated in this MKL-based classification framework. We evaluated proposed DCCA-MKL framework on 93 lesions (47 malignant cancers vs. 46 benign tumors). RESULTS The proposed DCCA-MKL framework achieved the mean classification accuracy, sensitivity, specificity, Youden index, false positive rate, and false negative rate of 90.41 ± 5.80%, 93.56 ± 5.90%, 86.89 ± 9.38%, 79.44 ± 11.83%, 13.11 ± 9.38% and 6.44 ± 5.90%, respectively, by soft margin MKL classifier. CONCLUSION The experimental results indicate that the proposed DCCA-MKL framework achieves best performance for discriminating benign liver tumors from malignant liver cancers. Moreover, it is also proved that the three-phase CEUS image based CAD is feasible for liver tumors with the proposed DCCA-MKL framework.
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Affiliation(s)
- Le-Hang Guo
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Dan Wang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Yi-Yi Qian
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xiao Zheng
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Chong-Ke Zhao
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Xiao-Long Li
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Xiao-Wan Bo
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Wen-Wen Yue
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
| | - Qi Zhang
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Jun Shi
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Hui-Xiong Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai, China
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Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Comput Biol Med 2018; 94:11-18. [PMID: 29353161 DOI: 10.1016/j.compbiomed.2017.12.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 12/29/2017] [Accepted: 12/29/2017] [Indexed: 12/12/2022]
Abstract
Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.
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Contrast-enhanced US for characterization of focal liver lesions: a comprehensive meta-analysis. Eur Radiol 2017; 28:2077-2088. [PMID: 29189932 DOI: 10.1007/s00330-017-5152-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Revised: 10/17/2017] [Accepted: 10/19/2017] [Indexed: 02/06/2023]
Abstract
OBJECTIVES This meta-analysis was performed to evaluate the accuracy of contrast-enhanced ultrasound (CEUS) in differentiating malignant from benign focal liver lesions (FLLs). METHODS Cochrane Library, PubMed and Web of Science databases were systematically searched and checked for studies using CEUS in characterization of FLLs. Data necessary to construct 2×2 contingency tables were extracted from included studies. The QUADAS tool was utilized to assess the methodologic quality of the studies. Meta-analysis included data pooling, subgroup analyses, meta-regression and investigation of publication bias was comprehensively performed. RESULTS Fifty-seven studies were included in this meta-analysis and the overall diagnostic accuracy in characterization of FLLs was as follows: pooled sensitivity, 0.92 (95%CI: 0.91-0.93); pooled specificity, 0.87 (95%CI: 0.86-0.88); diagnostic odds ratio, 104.20 (95%CI: 70.42-154.16). Subgroup analysis indicated higher diagnostic accuracy of the second-generation contrast agents (CAs) than the first-generation CA (Levovist; DOR: 118.27 vs. 62.78). Furthermore, Sonazoid demonstrated the highest diagnostic accuracy among three major CAs (SonoVue, Levovist and Sonazoid; DOR: 118.82 vs. 62.78 vs. 227.39). No potential publication bias was observed of the included studies. CONCLUSION CEUS is an accurate tool to stratify the risk of malignancy in FLLs. The second-generation CAs, especially Sonazoid may greatly improve diagnostic performance. KEY POINTS • CEUS shows excellent diagnostic accuracy in differentiating malignant from benign FLLs. • The second-generation CAs have higher diagnostic accuracy than first-generation CAs. • Sonazoid demonstrates the highest diagnostic accuracy among three major CAs.
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Ta CN, Kono Y, Eghtedari M, Oh YT, Robbin ML, Barr RG, Kummel AC, Mattrey RF. Focal Liver Lesions: Computer-aided Diagnosis by Using Contrast-enhanced US Cine Recordings. Radiology 2017; 286:1062-1071. [PMID: 29072980 DOI: 10.1148/radiol.2017170365] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Purpose To assess the performance of computer-aided diagnosis (CAD) systems and to determine the dominant ultrasonographic (US) features when classifying benign versus malignant focal liver lesions (FLLs) by using contrast material-enhanced US cine clips. Materials and Methods One hundred six US data sets in all subjects enrolled by three centers from a multicenter trial that included 54 malignant, 51 benign, and one indeterminate FLL were retrospectively analyzed. The 105 benign or malignant lesions were confirmed at histologic examination, contrast-enhanced computed tomography (CT), dynamic contrast-enhanced magnetic resonance (MR) imaging, and/or 6 or more months of clinical follow-up. Data sets included 3-minute cine clips that were automatically corrected for in-plane motion and automatically filtered out frames acquired off plane. B-mode and contrast-specific features were automatically extracted on a pixel-by-pixel basis and analyzed by using an artificial neural network (ANN) and a support vector machine (SVM). Areas under the receiver operating characteristic curve (AUCs) for CAD were compared with those for one experienced and one inexperienced blinded reader. A third observer graded cine quality to assess its effects on CAD performance. Results CAD, the inexperienced observer, and the experienced observer were able to analyze 95, 100, and 102 cine clips, respectively. The AUCs for the SVM, ANN, and experienced and inexperienced observers were 0.883 (95% confidence interval [CI]: 0.793, 0.940), 0.829 (95% CI: 0.724, 0.901), 0.843 (95% CI: 0.756, 0.903), and 0.702 (95% CI: 0.586, 0.782), respectively; only the difference between SVM and the inexperienced observer was statistically significant. Accuracy improved from 71.3% (67 of 94; 95% CI: 60.6%, 79.8%) to 87.7% (57 of 65; 95% CI: 78.5%, 93.8%) and from 80.9% (76 of 94; 95% CI: 72.3%, 88.3%) to 90.3% (65 of 72; 95% CI: 80.6%, 95.8%) when CAD was in agreement with the inexperienced reader and when it was in agreement with the experienced reader, respectively. B-mode heterogeneity and contrast material washout were the most discriminating features selected by CAD for all iterations. CAD selected time-based time-intensity curve (TIC) features 99.0% (207 of 209) of the time to classify FLLs, versus 1.0% (two of 209) of the time for intensity-based features. None of the 15 video-quality criteria had a statistically significant effect on CAD accuracy-all P values were greater than the Holm-Sidak α-level correction for multiple comparisons. Conclusion CAD systems classified benign and malignant FLLs with an accuracy similar to that of an expert reader. CAD improved the accuracy of both readers. Time-based features of TIC were more discriminating than intensity-based features. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Casey N Ta
- From the Department of Electrical and Computer Engineering (C.N.T.), Departments of Medicine and Radiology (Y.K.), Department of Radiology (M.E.), and Department of Chemistry and Biochemistry (A.C.K.), University of California, San Diego, La Jolla, Calif; Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.T.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Southwoods Imaging, Youngstown, Ohio and Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); and Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room D1.204, Dallas, TX 75390-8514 (R.F.M.)
| | - Yuko Kono
- From the Department of Electrical and Computer Engineering (C.N.T.), Departments of Medicine and Radiology (Y.K.), Department of Radiology (M.E.), and Department of Chemistry and Biochemistry (A.C.K.), University of California, San Diego, La Jolla, Calif; Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.T.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Southwoods Imaging, Youngstown, Ohio and Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); and Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room D1.204, Dallas, TX 75390-8514 (R.F.M.)
| | - Mohammad Eghtedari
- From the Department of Electrical and Computer Engineering (C.N.T.), Departments of Medicine and Radiology (Y.K.), Department of Radiology (M.E.), and Department of Chemistry and Biochemistry (A.C.K.), University of California, San Diego, La Jolla, Calif; Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.T.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Southwoods Imaging, Youngstown, Ohio and Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); and Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room D1.204, Dallas, TX 75390-8514 (R.F.M.)
| | - Young Taik Oh
- From the Department of Electrical and Computer Engineering (C.N.T.), Departments of Medicine and Radiology (Y.K.), Department of Radiology (M.E.), and Department of Chemistry and Biochemistry (A.C.K.), University of California, San Diego, La Jolla, Calif; Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.T.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Southwoods Imaging, Youngstown, Ohio and Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); and Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room D1.204, Dallas, TX 75390-8514 (R.F.M.)
| | - Michelle L Robbin
- From the Department of Electrical and Computer Engineering (C.N.T.), Departments of Medicine and Radiology (Y.K.), Department of Radiology (M.E.), and Department of Chemistry and Biochemistry (A.C.K.), University of California, San Diego, La Jolla, Calif; Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.T.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Southwoods Imaging, Youngstown, Ohio and Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); and Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room D1.204, Dallas, TX 75390-8514 (R.F.M.)
| | - Richard G Barr
- From the Department of Electrical and Computer Engineering (C.N.T.), Departments of Medicine and Radiology (Y.K.), Department of Radiology (M.E.), and Department of Chemistry and Biochemistry (A.C.K.), University of California, San Diego, La Jolla, Calif; Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.T.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Southwoods Imaging, Youngstown, Ohio and Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); and Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room D1.204, Dallas, TX 75390-8514 (R.F.M.)
| | - Andrew C Kummel
- From the Department of Electrical and Computer Engineering (C.N.T.), Departments of Medicine and Radiology (Y.K.), Department of Radiology (M.E.), and Department of Chemistry and Biochemistry (A.C.K.), University of California, San Diego, La Jolla, Calif; Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.T.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Southwoods Imaging, Youngstown, Ohio and Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); and Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room D1.204, Dallas, TX 75390-8514 (R.F.M.)
| | - Robert F Mattrey
- From the Department of Electrical and Computer Engineering (C.N.T.), Departments of Medicine and Radiology (Y.K.), Department of Radiology (M.E.), and Department of Chemistry and Biochemistry (A.C.K.), University of California, San Diego, La Jolla, Calif; Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.T.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Southwoods Imaging, Youngstown, Ohio and Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); and Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Room D1.204, Dallas, TX 75390-8514 (R.F.M.)
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Zhang J, Yu Y, Li Y, Wei L. Diagnostic value of contrast-enhanced ultrasound in hepatocellular carcinoma: a meta-analysis with evidence from 1998 to 2016. Oncotarget 2017; 8:75418-75426. [PMID: 29088877 PMCID: PMC5650432 DOI: 10.18632/oncotarget.20049] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Accepted: 07/26/2017] [Indexed: 11/28/2022] Open
Abstract
Background This meta-analysis is aimed at determining the diagnostic value of hepatocellular carcinoma (HCC) with contrast-enhanced ultrasound (CEUS). Materials and Methods A comprehensive literature search of Pubmed, Web of Science, and the Cochrane Library was performed to identify published studies. The methodological quality of the included studies was evaluated. Data from eligible studies were used to estimate the pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratio (LR) and summary receiver operating characteristic (SROC) curve. Meta-Disc and STATA softwares were utilized for all statistical analyses. Results Fifty-three eligible studies (publication years ranged from 1998 to 2016) were selected according to inclusion criteria. The meta-analysis showed that the pooled sensitivity and specificity of CEUS to detect HCC were 0.85 (95% CI: 0.84–0.86) and 0.91 (95% CI: 0.90–0.92), respectively. The pooled positive and negative LRs were 6.28 (95% CI: 4.49–8.77) and 0.16 (95% CI: 0.12–0.22), respectively. The pooled DOR was 55.01 (95% CI: 35.25–83.47). The area under the SCOR curve was 0.9432. Meta-regression and funnel plot indicated that sample size, type of contrast agents and publication bias might be the major sources of heterogeneity. Conclusions CEUS is a valuable diagnostic tool for identifying HCC in clinic with highly sensitive and specific.
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Affiliation(s)
- Juanjuan Zhang
- Department of Ultrasound, Huaihe Hospital of Henan University, Kaifeng, China
| | - Yanyan Yu
- Department of Ultrasound, Huaihe Hospital of Henan University, Kaifeng, China
| | - Ying Li
- Department of Ultrasound, Huaihe Hospital of Henan University, Kaifeng, China
| | - Lunshou Wei
- Department of Gastroenterology, Huaihe Hospital of Henan University, Kaifeng, China
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Qian Y, Shi J, Zheng X, Zhang Q, Guo L, Wang D, Xu H. Multimodal Ultrasound imaging based diagnosis of liver cancers with a two-stage multi-view learning framework. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3232-3235. [PMID: 29060586 DOI: 10.1109/embc.2017.8037545] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Computer-aided diagnosis (CAD) of liver cancers on contrast-enhanced ultrasound (CEUS) has attracted considerable attention in recent years. The enhancement patterns on CEUS for liver lesions consist of the arterial, portal venous and late phases. Several typical images selected from these three phases can provide reliable information basis for diagnosis of liver lesions. Therefore, we propose to develop a CAD framework for liver cancers with only one B-mode image and three typical CEUS images selected from three enhancement patterns, which simulates the clinical diagnosis mode of radiologists. Moreover, a framework of two-stage multi-view learning (TS-MVL) is proposed to perform both feature-level and classifier-level MVL for the diagnosis of liver cancers with multimodal ultrasound images. We propose to apply the nonlinear kernel matrix (NKM) algorithm to effectively fuse the features of multimodal ultrasound images, and then perform the multiple kernel boosting (MKB) algorithm to promote the predictive performance of multiple classifiers according to multi-view features. The experimental results indicate that the proposed algorithm outperforms the commonly used multi-view learning algorithms.
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Kondo S, Takagi K, Nishida M, Iwai T, Kudo Y, Ogawa K, Kamiyama T, Shibuya H, Kahata K, Shimizu C. Computer-Aided Diagnosis of Focal Liver Lesions Using Contrast-Enhanced Ultrasonography With Perflubutane Microbubbles. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1427-1437. [PMID: 28141517 DOI: 10.1109/tmi.2017.2659734] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
This paper proposes an automatic classification method based on machine learning in contrast-enhanced ultrasonography (CEUS) of focal liver lesions using the contrast agent Sonazoid. This method yields spatial and temporal features in the arterial phase, portal phase, and post-vascular phase, as well as max-hold images. The lesions are classified as benign or malignant and again as benign, hepatocellular carcinoma (HCC), or metastatic liver tumor using support vector machines (SVM) with a combination of selected optimal features. Experimental results using 98 subjects indicated that the benign and malignant classification has 94.0% sensitivity, 87.1% specificity, and 91.8% accuracy, and the accuracy of the benign, HCC, and metastatic liver tumor classifications are 84.4%, 87.7%, and 85.7%, respectively. The selected features in the SVM indicate that combining features from the three phases are important for classifying FLLs, especially, for the benign and malignant classifications. The experimental results are consistent with CEUS guidelines for diagnosing FLLs. This research can be considered to be a validation study, that confirms the importance of using features from these phases of the examination in a quantitative manner. In addition, the experimental results indicate that for the benign and malignant classifications, the specificity without the post-vascular phase features is significantly lower than the specificity with the post-vascular phase features. We also conducted an experiment on the operator dependency of setting regions of interest and observed that the intra-operator and inter-operator kappa coefficients were 0.45 and 0.77, respectively.
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Sugimoto K, Shiraishi J, Tanaka H, Tsuchiya K, Aso K, Kobayashi Y, Iijima H, Moriyasu F. Computer-aided diagnosis for estimating the malignancy grade of hepatocellular carcinoma using contrast-enhanced ultrasound: an ROC observer study. Liver Int 2016; 36:1026-32. [PMID: 26681659 DOI: 10.1111/liv.13043] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2015] [Accepted: 12/07/2015] [Indexed: 02/13/2023]
Abstract
BACKGROUND & AIMS We are developing a computer-aided diagnosis (CAD) system for estimating the malignancy grade of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS). In this study, observers estimated the malignancy grade of HCC with and without the cues provided by CAD. MATERIALS AND METHODS Institutional review board approval was obtained and informed consent was waived. A total of 232 histologically confirmed HCCs were studied: 76 well-differentiated HCC (w-HCC), 133 moderately differentiated HCC (m-HCC), and 23 poorly differentiated HCC (p-HCC). In this observer study, CEUS vascular images acquired using the maximum intensity projection technique were displayed together with static B-mode and Kupffer-phase (defined as 10 min after injection) images. Five hepatologists independently assigned confidence ratings for the malignancy grade of each HCC. Each hepatologist first read each case without CAD and then immediately afterwards with CAD. The observers' rating data were evaluated by multireader multicase receiver operating characteristic (ROC) analysis. RESULTS The overall sensitivity of our CAD system for discrimination between three histological differentiation grades of HCC was 87.5% (203/232). For discrimination between w-HCC and m/p-HCC, the mean area under the ROC curve (AUC) for the five observers was significantly improved from 0.779 ± 0.074 to 0.872 ± 0.090 with CAD (P = 0.0069). For discrimination between m-HCC and p-HCC, the mean AUC was also significantly improved from 0.713 ± 0.107 to 0.863 ± 0.101 with CAD (P = 0.0321). CONCLUSION The use of our CAD system can significantly improve the diagnostic performance of hepatologists in discriminating between three histological differentiation grades of HCC using CEUS.
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Affiliation(s)
- Katsutoshi Sugimoto
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Junji Shiraishi
- School of Health Sciences, Kumamoto University, Kumamoto, Japan
| | - Hironori Tanaka
- Ultrasound Imaging Center, Hyogo College of Medicine, Hyogo, Japan.,Department of Gastroenterology and Hepatology, Takarazuka City Hospital, Osaka, Japan
| | - Kaoru Tsuchiya
- Department of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Tokyo, Japan
| | - Kazunobu Aso
- Division of Metabolism and Biosystemic Science, Department of Medicine, Asahikawa Medical University, Asahikawa, Japan
| | - Yoshiyuki Kobayashi
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
| | - Hiroko Iijima
- Ultrasound Imaging Center, Hyogo College of Medicine, Hyogo, Japan
| | - Fuminori Moriyasu
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, Japan
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Yang C, Lee DH, Mangraviti A, Su L, Zhang K, Zhang Y, Zhang B, Li W, Tyler B, Wong J, Wang KKH, Velarde E, Zhou J, Ding K. Quantitative correlational study of microbubble-enhanced ultrasound imaging and magnetic resonance imaging of glioma and early response to radiotherapy in a rat model. Med Phys 2016; 42:4762-72. [PMID: 26233204 DOI: 10.1118/1.4926550] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Radiotherapy remains a major treatment method for malignant tumors. Magnetic resonance imaging (MRI) is the standard modality for assessing glioma treatment response in the clinic. Compared to MRI, ultrasound imaging is low-cost and portable and can be used during intraoperative procedures. The purpose of this study was to quantitatively compare contrast-enhanced ultrasound (CEUS) imaging and MRI of irradiated gliomas in rats and to determine which quantitative ultrasound imaging parameters can be used for the assessment of early response to radiation in glioma. METHODS Thirteen nude rats with U87 glioma were used. A small thinned skull window preparation was performed to facilitate ultrasound imaging and mimic intraoperative procedures. Both CEUS and MRI with structural, functional, and molecular imaging parameters were performed at preradiation and at 1 day and 4 days postradiation. Statistical analysis was performed to determine the correlations between MRI and CEUS parameters and the changes between pre- and postradiation imaging. RESULTS Area under the curve (AUC) in CEUS showed significant difference between preradiation and 4 days postradiation, along with four MRI parameters, T2, apparent diffusion coefficient, cerebral blood flow, and amide proton transfer-weighted (APTw) (all p < 0.05). The APTw signal was correlated with three CEUS parameters, rise time (r = - 0.527, p < 0.05), time to peak (r = - 0.501, p < 0.05), and perfusion index (r = 458, p < 0.05). Cerebral blood flow was correlated with rise time (r = - 0.589, p < 0.01) and time to peak (r = - 0.543, p < 0.05). CONCLUSIONS MRI can be used for the assessment of radiotherapy treatment response and CEUS with AUC as a new technique and can also be one of the assessment methods for early response to radiation in glioma.
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Affiliation(s)
- Chen Yang
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, China
| | - Dong-Hoon Lee
- Division of MR Research, Department of Radiology, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21287
| | - Antonella Mangraviti
- Department of Neurosurgery, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21287
| | - Lin Su
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21231
| | - Kai Zhang
- Division of MR Research, Department of Radiology, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21287
| | - Yin Zhang
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21231
| | - Bin Zhang
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21231
| | - Wenxiao Li
- Division of MR Research, Department of Radiology, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21287
| | - Betty Tyler
- Department of Neurosurgery, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21287
| | - John Wong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21231
| | - Ken Kang-Hsin Wang
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21231
| | - Esteban Velarde
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21231
| | - Jinyuan Zhou
- Division of MR Research, Department of Radiology, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21287
| | - Kai Ding
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland 21231
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Liang X, Lin L, Cao Q, Huang R, Wang Y. Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:713-27. [PMID: 26513779 DOI: 10.1109/tmi.2015.2492618] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
This work investigates how to automatically classify Focal Liver Lesions (FLLs) into three specific benign or malignant types in Contrast-Enhanced Ultrasound (CEUS) videos, and aims at providing a computational framework to assist clinicians in FLL diagnosis. The main challenge for this task is that FLLs in CEUS videos often show diverse enhancement patterns at different temporal phases. To handle these diverse patterns, we propose a novel structured model, which detects a number of discriminative Regions of Interest (ROIs) for the FLL and recognize the FLL based on these ROIs. Our model incorporates an ensemble of local classifiers in the attempt to identify different enhancement patterns of ROIs, and in particular, we make the model reconfigurable by introducing switch variables to adaptively select appropriate classifiers during inference. We formulate the model learning as a non-convex optimization problem, and present a principled optimization method to solve it in a dynamic manner: the latent structures (e.g. the selections of local classifiers, and the sizes and locations of ROIs) are iteratively determined along with the parameter learning. Given the updated model parameters in each step, the data-driven inference is also proposed to efficiently determine the latent structures by using the sequential pruning and dynamic programming method. In the experiments, we demonstrate superior performances over the state-of-the-art approaches. We also release hundreds of CEUS FLLs videos used to quantitatively evaluate this work, which to the best of our knowledge forms the largest dataset in the literature. Please find more information at "http://vision.sysu.edu.cn/projects/fllrecog/".
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Gatos I, Tsantis S, Spiliopoulos S, Skouroliakou A, Theotokas I, Zoumpoulis P, Hazle JD, Kagadis GC. A new automated quantification algorithm for the detection and evaluation of focal liver lesions with contrast-enhanced ultrasound. Med Phys 2015; 42:3948-59. [PMID: 26133595 DOI: 10.1118/1.4921753] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Detect and classify focal liver lesions (FLLs) from contrast-enhanced ultrasound (CEUS) imaging by means of an automated quantification algorithm. METHODS The proposed algorithm employs a sophisticated segmentation method to detect and contour focal lesions from 52 CEUS video sequences (30 benign and 22 malignant). Lesion detection involves wavelet transform zero crossings utilization as an initialization step to the Markov random field model toward the lesion contour extraction. After FLL detection across frames, time intensity curve (TIC) is computed which provides the contrast agents' behavior at all vascular phases with respect to adjacent parenchyma for each patient. From each TIC, eight features were automatically calculated and employed into the support vector machines (SVMs) classification algorithm in the design of the image analysis model. RESULTS With regard to FLLs detection accuracy, all lesions detected had an average overlap value of 0.89 ± 0.16 with manual segmentations for all CEUS frame-subsets included in the study. Highest classification accuracy from the SVM model was 90.3%, misdiagnosing three benign and two malignant FLLs with sensitivity and specificity values of 93.1% and 86.9%, respectively. CONCLUSIONS The proposed quantification system that employs FLLs detection and classification algorithms may be of value to physicians as a second opinion tool for avoiding unnecessary invasive procedures.
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Affiliation(s)
- Ilias Gatos
- Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece
| | - Stavros Tsantis
- Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece
| | - Stavros Spiliopoulos
- Department of Radiology, School of Medicine, University of Patras, Rion GR 26504, Greece
| | - Aikaterini Skouroliakou
- Department of Energy Technology Engineering, Technological Education Institute of Athens, Athens 12210, Greece
| | - Ioannis Theotokas
- Diagnostic Echotomography SA, 317C Kifissias Avenue, Kifissia GR 14561, Greece
| | - Pavlos Zoumpoulis
- Diagnostic Echotomography SA, 317C Kifissias Avenue, Kifissia GR 14561, Greece
| | - John D Hazle
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
| | - George C Kagadis
- Department of Medical Physics, School of Medicine, University of Patras, Rion GR 26504, Greece and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
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Chi Y, Zhou J, Venkatesh SK, Tian Q, Liu J. Content-based image retrieval of multiphase CT images for focal liver lesion characterization. Med Phys 2014; 40:103502. [PMID: 24089935 DOI: 10.1118/1.4820539] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
PURPOSE Characterization of focal liver lesions with various imaging modalities can be very challenging in the clinical practice and is experience-dependent. The authors' aim is to develop an automatic method to facilitate the characterization of focal liver lesions (FLLs) using multiphase computed tomography (CT) images by radiologists. METHODS A multiphase-image retrieval system is proposed to retrieve a preconstructed database of FLLs with confirmed diagnoses, which can assist radiologists' decision-making in FLL characterization. It first localizes the FLL on multiphase CT scans using a hybrid generative-discriminative FLL detection method and a nonrigid B-spline registration method. Then, it extracts the multiphase density and texture features to numerically represent the FLL. Next, it compares the query FLL with the model FLLs in the database in terms of the feature and measures their similarities using the L1-norm based similarity scores. The model FLLs are ranked by similarities and the top results are finally provided to the users for their evidence studies. RESULTS The system was tested on a database of 69 four-phase contrast-enhanced CT scans, consisting of six classes of liver lesions, and evaluated in terms of the precision-recall curve and the Bull's Eye Percentage Score (BEP). It obtained a BEP score of 78%. Compared with any single-phase based representation, the multiphase-based representation increased the BEP scores of the system, from 63%-65% to 78%. In a pilot study, two radiologists performed characterization of FLLs without and with the knowledge of the top five retrieved results. The results were evaluated in terms of the diagnostic accuracy, the receiver operating characteristic (ROC) curve and the mean diagnostic confidence. One radiologist's accuracy improved from 75% to 92%, the area under ROC curves (AUC) from 0.85 to 0.95 (p = 0.081), and the mean diagnostic confidence from 4.6 to 7.3 (p = 0.039). The second radiologist's accuracy did not change, at 75%, with AUC increasing from 0.72 to 0.75 (p = 0.709), and the mean confidence from 4.5 to 4.9 (p = 0.607). CONCLUSIONS Multiphase CT images can be used in content-based image retrieval for FLL's categorization and result in good performance in comparison with single-phase CT images. The proposed method has the potential to improve the radiologists' diagnostic accuracy and confidence by providing visually similar lesions with confirmed diagnoses for their interpretation of clinical studies.
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Affiliation(s)
- Yanling Chi
- Singapore Bioimaging Consortium, Agency for Science, Technology and Research, 30 Biopolis Street, #07-01, Matrix, Singapore 138671
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Montejo LD, Jia J, Kim HK, Netz UJ, Blaschke S, Müller GA, Hielscher AH. Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction. JOURNAL OF BIOMEDICAL OPTICS 2013; 18:076001. [PMID: 23856915 PMCID: PMC3710917 DOI: 10.1117/1.jbo.18.7.076001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
This is the first part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT). An approach for extracting heuristic features from DOT images and a method for using these features to diagnose rheumatoid arthritis (RA) are presented. Feature extraction is the focus of Part 1, while the utility of five classification algorithms is evaluated in Part 2. The framework is validated on a set of 219 DOT images of proximal interphalangeal (PIP) joints. Overall, 594 features are extracted from the absorption and scattering images of each joint. Three major findings are deduced. First, DOT images of subjects with RA are statistically different (p<0.05) from images of subjects without RA for over 90% of the features investigated. Second, DOT images of subjects with RA that do not have detectable effusion, erosion, or synovitis (as determined by MRI and ultrasound) are statistically indistinguishable from DOT images of subjects with RA that do exhibit effusion, erosion, or synovitis. Thus, this subset of subjects may be diagnosed with RA from DOT images while they would go undetected by reviews of MRI or ultrasound images. Third, scattering coefficient images yield better one-dimensional classifiers. A total of three features yield a Youden index greater than 0.8. These findings suggest that DOT may be capable of distinguishing between PIP joints that are healthy and those affected by RA with or without effusion, erosion, or synovitis.
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Affiliation(s)
- Ludguier D. Montejo
- Columbia University, Department of Biomedical Engineering, New York, New York 10027
- Address all correspondence to: Ludguier D. Montejo and Andreas H. Hielscher, Columbia University, Department of Biomedical Engineering, 500 West 120th Street, ET 351 Mudd Bldg, MC8904, New York, New York 10027. Ludguier D. Montejo, Tel: 212-854-2320; Fax: 212-854-8725; E-mail: ; Andreas H. Hielscher, Tel: 212-854-5020; Fax: 212-854-8725; E-mail:
| | - Jingfei Jia
- Columbia University, Department of Biomedical Engineering, New York, New York 10027
| | - Hyun K. Kim
- Columbia University Medical Center, Department of Radiology, New York, New York 10032
| | - Uwe J. Netz
- Laser-und Medizin-Technologie GmbH Berlin, Berlin-Dahlem, 14195, Germany
- Charité-Universitätsmedizin Berlin, Department of Medical Physics and Laser Medicine, Berlin 10117, Germany
| | - Sabine Blaschke
- University Medical Center Göttingen, Department of Nephrology and Rheumatology, Göttingen 37075, Germany
| | - Gerhard A. Müller
- University Medical Center Göttingen, Department of Nephrology and Rheumatology, Göttingen 37075, Germany
| | - Andreas H. Hielscher
- Columbia University, Department of Biomedical Engineering, New York, New York 10027
- Columbia University Medical Center, Department of Radiology, New York, New York 10032
- Columbia University, Department of Electrical Engineering, New York, New York 10025
- Address all correspondence to: Ludguier D. Montejo and Andreas H. Hielscher, Columbia University, Department of Biomedical Engineering, 500 West 120th Street, ET 351 Mudd Bldg, MC8904, New York, New York 10027. Ludguier D. Montejo, Tel: 212-854-2320; Fax: 212-854-8725; E-mail: ; Andreas H. Hielscher, Tel: 212-854-5020; Fax: 212-854-8725; E-mail:
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Martin KH, Dayton PA. Current status and prospects for microbubbles in ultrasound theranostics. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2013; 5:329-345. [PMID: 23504911 DOI: 10.1002/wnan.219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Encapsulated microbubbles have been developed over the past two decades to provide improvements both in imaging as well as new therapeutic applications. Microbubble contrast agents are used currently for clinical imaging where increased sensitivity to blood flow is required, such as echocardiography. These compressible spheres oscillate in an acoustic field, producing nonlinear responses which can be uniquely distinguished from surrounding tissue, resulting in substantial enhancements in imaging signal-to-noise ratio. Furthermore, with sufficient acoustic energy the oscillation of microbubbles can mediate localized biological effects in tissue including the enhancement of membrane permeability or increased thermal energy deposition. Structurally, microbubbles are comprised of two principal components--an encapsulating shell and an inner gas core. This configuration enables microbubbles to be loaded with drugs or genes for additional therapeutic effect. Application of sufficient ultrasound energy can release this payload, resulting in site-specific delivery. Extensive preclinical studies illustrate that combining microbubbles and ultrasound can result in enhanced drug delivery or gene expression at spatially selective sites. Thus, microbbubles can be used for imaging, for therapy, or for both simultaneously. In this sense, microbubbles combined with acoustics may be one of the most universal theranostic tools.
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Affiliation(s)
- K Heath Martin
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
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Martin KH, Dayton PA. Current status and prospects for microbubbles in ultrasound theranostics. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2013; 5:329-45. [PMID: 23504911 DOI: 10.1002/wnan.1219] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Encapsulated microbubbles have been developed over the past two decades to provide improvements both in imaging as well as new therapeutic applications. Microbubble contrast agents are used currently for clinical imaging where increased sensitivity to blood flow is required, such as echocardiography. These compressible spheres oscillate in an acoustic field, producing nonlinear responses which can be uniquely distinguished from surrounding tissue, resulting in substantial enhancements in imaging signal-to-noise ratio. Furthermore, with sufficient acoustic energy the oscillation of microbubbles can mediate localized biological effects in tissue including the enhancement of membrane permeability or increased thermal energy deposition. Structurally, microbubbles are comprised of two principal components--an encapsulating shell and an inner gas core. This configuration enables microbubbles to be loaded with drugs or genes for additional therapeutic effect. Application of sufficient ultrasound energy can release this payload, resulting in site-specific delivery. Extensive preclinical studies illustrate that combining microbubbles and ultrasound can result in enhanced drug delivery or gene expression at spatially selective sites. Thus, microbbubles can be used for imaging, for therapy, or for both simultaneously. In this sense, microbubbles combined with acoustics may be one of the most universal theranostic tools.
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Affiliation(s)
- K Heath Martin
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
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Zhang J, Ding M, Meng F, Zhang X. Quantitative Evaluation of Two-Factor Analysis Applied to Hepatic Perfusion Study Using Contrast-enhanced Ultrasound. IEEE Trans Biomed Eng 2013; 60:259-67. [DOI: 10.1109/tbme.2011.2171038] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Focal Liver Lesion Tracking in CEUS for Characterisation Based on Dynamic Behaviour. ADVANCES IN VISUAL COMPUTING 2012. [DOI: 10.1007/978-3-642-33179-4_4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Indeterminate focal liver lesions incidentally discovered at gray-scale US: role of contrast-enhanced sonography. Invest Radiol 2011; 46:106-15. [PMID: 20938345 DOI: 10.1097/rli.0b013e3181f44184] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVES to assess the role of contrast-enhanced ultrasound (CEUS) in the characterization of focal liver lesions (FLLs) incidentally discovered but indeterminate at gray-scale ultrasound (US). MATERIALS AND METHODS one hundred forty-two consecutive patients with 174 FLLs (169 benign and 5 malignant) incidentally discovered but indeterminate at gray-scale US, underwent CEUS after the administration of SonoVue. Two readers independently reviewed CEUS scans and: (1) classified each lesion as malignant or benign on a 5-point scale of confidence by means of definite diagnostic criteria; (2) provided if possible a specific diagnosis; (3) were requested if further imaging was needed for lesion characterization. Sensitivity, specificity, and areas under the receiver-operating characteristic curve (Az) as well as interobserver agreement were calculated. RESULTS at CEUS, both readers correctly differentiated benign from malignant lesions in 168 of 174 (96.5%) cases (P < 0.0001). A specific correct diagnosis was provided in 123 of 174 (70.7%) and 127 of 174 (72.9%) cases for reader 1 and 2, respectively (P < 0.0001). A further imaging study to characterize the lesion after CEUS was requested in 67 cases (38.5%) for reader 1 (P < 0.001) and 46 cases (26.4%) for reader 2 (P < 0.001). Receiver-operating characteristic analysis after CEUS revealed Az value of 1 for both readers and sensitivity and specificity values of 100% and 97.04% for reader 1 and 100% and 96.45 for reader 2 respectively (P < 0.0001). Inter-reader agreement at CEUS was good (weighted k = 0.779). CONCLUSION CEUS improves the diagnostic performance of radiologists in the characterization of indeterminate FLLs incidentally discovered at US and reduces the need for further radiologic work-up.
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Gessner RC, Kothadia R, Feingold S, Dayton PA. 3-D microvessel-mimicking ultrasound phantoms produced with a scanning motion system. ULTRASOUND IN MEDICINE & BIOLOGY 2011; 37:827-33. [PMID: 21439718 PMCID: PMC3119338 DOI: 10.1016/j.ultrasmedbio.2010.12.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2010] [Revised: 12/17/2010] [Accepted: 12/21/2010] [Indexed: 05/13/2023]
Abstract
Ultrasound techniques are currently being developed that can assess the vascularization of tissue as a marker for therapeutic response. Some of these ultrasound imaging techniques seek to extract quantitative features about vessel networks, whereas high-frequency imaging also allows individual vessels to be resolved. The development of these new techniques, and subsequent imaging analysis strategies, necessitates an understanding of their sensitivities to vessel and vessel network structural abnormalities. Constructing in-vitro flow phantoms for this purpose can be prohibitively challenging, because simulating precise flow environments with nontrivial structures is often impossible using conventional methods of construction for flow phantoms. Presented in this manuscript is a method to create predefined structures with <10 μm precision using a three-axis motion system. The application of this technique is demonstrated for the creation of individual vessel and vessel networks, which can easily be made to simulate the development of structural abnormalities typical of diseased vasculature in vivo. In addition, beyond facilitating the creation of phantoms that would otherwise be very challenging to construct, the method presented herein enables one to precisely simulate very slow blood flow and respiration artifacts, and to measure imaging resolution.
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Affiliation(s)
- Ryan C Gessner
- Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill and North Carolina State University at Raleigh, NC
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The real capabilities of contrast-enhanced ultrasound in the characterization of solid focal liver lesions. Eur Radiol 2010; 21:457-62. [DOI: 10.1007/s00330-010-2007-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2010] [Revised: 10/06/2010] [Accepted: 10/07/2010] [Indexed: 01/24/2023]
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Rognin NG, Arditi M, Mercier L, Frinking PJA, Schneider M, Perrenoud G, Anaye A, Meuwly JY, Tranquart F. Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2010; 57:2503-11. [PMID: 21041137 DOI: 10.1109/tuffc.2010.1716] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
The differentiation between benign and malignant focal liver lesions plays an important role in diagnosis of liver disease and therapeutic planning of local or general disease. This differentiation, based on characterization, relies on the observation of the dynamic vascular patterns (DVP) of lesions with respect to adjacent parenchyma, and may be assessed during contrast-enhanced ultrasound imaging after a bolus injection. For instance, hemangiomas (i.e., benign lesions) exhibit hyper-enhanced signatures over time, whereas metastases (i.e., malignant lesions) frequently present hyperenhanced foci during the arterial phase and always become hypo-enhanced afterwards. The objective of this work was to develop a new parametric imaging technique, aimed at mapping the DVP signatures into a single image called a DVP parametric image, conceived as a diagnostic aid tool for characterizing lesion types. The methodology consisted in processing a time sequence of images (DICOM video data) using four consecutive steps: (1) pre-processing combining image motion correction and linearization to derive an echo-power signal, in each pixel, proportional to local contrast agent concentration over time; (2) signal modeling, by means of a curve-fitting optimization, to compute a difference signal in each pixel, as the subtraction of adjacent parenchyma kinetic from the echopower signal; (3) classification of difference signals; and (4) parametric image rendering to represent classified pixels as a support for diagnosis. DVP parametric imaging was the object of a clinical assessment on a total of 146 lesions, imaged using different medical ultrasound systems. The resulting sensitivity and specificity were 97% and 91%, respectively, which compare favorably with scores of 81 to 95% and 80 to 95% reported in medical literature for sensitivity and specificity, respectively.
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Sugimoto K, Shiraishi J, Moriyasu F, Doi K. Computer-aided diagnosis for contrast-enhanced ultrasound in the liver. World J Radiol 2010; 2:215-23. [PMID: 21160633 PMCID: PMC2998841 DOI: 10.4329/wjr.v2.i6.215] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2010] [Revised: 05/06/2010] [Accepted: 05/13/2010] [Indexed: 02/06/2023] Open
Abstract
Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide computer output as a second opinion to assist radiologists’ image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time. To date, research on CAD in ultrasound (US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology, with most studies being conducted using B-mode US images. Two CAD schemes with contrast-enhanced US (CEUS) that are used in classifying focal liver lesions (FLLs) as liver metastasis, hemangioma, or three histologically differentiated types of hepatocellular carcinoma (HCC) are introduced in this article: one is based on physicians’ subjective pattern classifications (subjective analysis) and the other is a computerized scheme for classification of FLLs (quantitative analysis). Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis, 93.3% and 93.8% for hemangioma, and 98.6% and 86.9% for all HCCs, respectively. In addition, the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs, 41.7% and 50.0% for moderately differentiated HCCs, and 80.0% and 77.8% for poorly differentiated HCCs, respectively. There are a number of issues concerning the clinical application of CAD for CEUS, however, it is likely that CAD for CEUS of the liver will make great progress in the future.
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Sugimoto K, Shiraishi J, Moriyasu F, Ichimura S, Metoki R, Doi K. Analysis of intrahepatic vascular morphological changes of chronic liver disease for assessment of liver fibrosis stages by micro-flow imaging with contrast-enhanced ultrasound: preliminary experience. Eur Radiol 2010; 20:2749-57. [PMID: 20571803 DOI: 10.1007/s00330-010-1852-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2010] [Revised: 05/10/2010] [Accepted: 05/24/2010] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To assess morphological vascular changes due to an increase in liver fibrosis by using micro-flow imaging (MFI) of contrast-enhanced ultrasound. METHODS MFI was performed in 47 patients who underwent liver biopsy, and in 10 normal cases. For 27/57 cases, we performed MFI twice in order to assess the reproducibility of the examination, thus yielding a total of 84 examinations. Seven physicians interpreted each case individually by assigning confidence levels for the presence or absence of three imaging features that were related to alteration of portal vein morphology: angle widening, tapering/interruption and tortuosity. RESULTS Pearson's correlation coefficient between the average rating scores based on tortuosity and the histological fibrosis stage was 0.806 (p < 0.001). The diagnostic accuracy of the average area under the ROC curve, which was estimated by use of the confidence levels of tapering/interruption, tortuosity and angle widening, was 0.964 for F1 vs. F2-4, 0.968 for F1-2 vs. F3-4 and 0.910 for F1-3 vs. F4. The average correlation coefficient between the ratings on different images from the same patients was 0.838. CONCLUSION Assessment of morphological intrahepatic vascular changes on MFI may be useful for grading liver fibrosis.
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Affiliation(s)
- Katsutoshi Sugimoto
- Department of Gastroenterology and Hepatology, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan.
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Numata K, Luo W, Morimoto M, Kondo M, Kunishi Y, Sasaki T, Nozaki A, Tanaka K. Contrast enhanced ultrasound of hepatocellular carcinoma. World J Radiol 2010; 2:68-82. [PMID: 21160920 PMCID: PMC2998925 DOI: 10.4329/wjr.v2.i2.68] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2010] [Revised: 02/21/2010] [Accepted: 02/26/2010] [Indexed: 02/06/2023] Open
Abstract
Sonazoid (Daiichi Sankyo, Tokyo, Japan), a second-generation of a lipid-stabilized suspension of a perfluorobutane gas microbubble contrast agent, has been used clinically in patients with liver tumors and for harmonic gray-scale ultrasonography (US) in Japan since January 2007. Sonazoid-enhanced US has two phases of contrast enhancement: vascular and late. In the late phase of Sonazoid-enhanced US, we scanned the whole liver using this modality at a low mechanical index (MI) without destroying the microbubbles, and this method allows detection of small viable hepatocellular carcinoma (HCC) lesions which cannot be detected by conventional US as perfusion defects in the late phase. Re-injection of Sonazoid into an HCC lesion which previously showed a perfusion defect in the late phase is useful for confirming blood flow into the defects. High MI intermittent imaging at 2 frames per second in the late phase is also helpful in differentiation between necrosis and viable hypervascular HCC lesions. Sonazoid-enhanced US by the coded harmonic angio mode at a high MI not only allows clear observation of tumor vessels and tumor enhancement, but also permits automatic scanning with Sonazoid-enhanced three dimensional (3D) US. Fusion images combining US with contrast-enhanced CT or contrast-enhanced MRI have made it easy to detect typical or atypical HCC lesions. By these methods, Sonazoid-enhanced US can characterize liver tumors, grade HCC lesions histologically, recognize HCC dedifferentiation, evaluate the efficacy of ablation therapy or transcatheter arterial embolization, and guide ablation therapy for unresectable HCC. This article reviews the current developments and applications of Sonazoid-enhanced US and Sonazoid-enhanced 3D US for diagnosing and treating hepatic lesions, especially HCC.
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Sugimoto K, Shiraishi J, Moriyasu F, Doi K. Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography. Acad Radiol 2009; 16:401-11. [PMID: 19268851 DOI: 10.1016/j.acra.2008.09.018] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2008] [Revised: 09/23/2008] [Accepted: 09/24/2008] [Indexed: 12/11/2022]
Abstract
RATIONAL AND OBJECTIVES To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US). MATERIALS AND METHODS A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model. RESULTS The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC. CONCLUSIONS This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.
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Affiliation(s)
- Katsutoshi Sugimoto
- Kurt Rossmann Laboratories for Radiologic Imaging Research, Department of Radiology, The University of Chicago, 5841 S. Maryland Ave., MC 2026, Chicago, IL 60637, USA.
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Computer-aided Diagnosis Using Neural Networks and Support Vector Machines for Breast Ultrasonography. J Med Ultrasound 2009. [DOI: 10.1016/s0929-6441(09)60011-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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