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Luo QQ, Li QN, Cai D, Jiang S, Liu SS, Liu MS, Lv C, Wang JK, Zhang KH, Wang T. The Index sAGP is Valuable for Distinguishing Atypical Hepatocellular Carcinoma from Atypical Benign Focal Hepatic Lesions. J Hepatocell Carcinoma 2024; 11:317-325. [PMID: 38348099 PMCID: PMC10860805 DOI: 10.2147/jhc.s443273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/23/2024] [Indexed: 02/15/2024] Open
Abstract
Purpose The differential diagnosis of atypical hepatocellular carcinoma (aHCC) and atypical benign focal hepatic lesions (aBFHL) usually depends on pathology. This study aimed to develop non-invasive approaches based on conventional blood indicators for the differential diagnosis of aHCC and aBFHL. Patients and Methods Hospitalized patients with pathologically confirmed focal hepatic lesions and their clinical data were retrospectively collected, in which patients with HCC with serum alpha-fetoprotein (AFP) levels of ≤200 ng/mL and atypical imaging features were designated as the aHCC group (n = 224), and patients with benign focal hepatic lesions without typical imaging features were designated as the aBFHL group (n = 178). The performance of indexes (both previously reported and newly constructed) derived from conventional blood indicators by four mathematical operations in distinguishing aHCC and aBFHL was evaluated using the receiver operating characteristic (ROC) curve and diagnostic validity metrics. Results Among ten previously reported derived indexes related to HCC, the index GPR, the ratio of γ-glutamyltransferase (GGT) to platelet (PLT), showed the best performance in distinguishing aHCC from aBFHL with the area under ROC curve (AUROC) of 0.853 (95% CI 0.814-0.892), but the other indexes were of little value (AUROCs from 0.531 to 0.700). A new derived index, sAGP [(standardized AFP + standardized GGT)/standardized PLT], was developed and exhibited AUROCs of 0.905, 0.894, 0.891, 0.925, and 0.862 in differentiating overall, BCLC stage 0/A, TNM stage I, small, and AFP-negative aHCC from aBFHL, respectively. Conclusion The sAGP index is an efficient, simple, and practical metric for the non-invasive differentiation of aHCC from aBFHL.
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Affiliation(s)
- Qing-Qing Luo
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Qiao-Nan Li
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Dan Cai
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Song Jiang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Shao-Song Liu
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Mao-Sheng Liu
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Cong Lv
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Jin-Ke Wang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Kun-He Zhang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
| | - Ting Wang
- Department of Gastroenterology, the First Affiliated Hospital of Nanchang University, Jiangxi Institute of Gastroenterology & Hepatology, Nanchang, People’s Republic of China
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Pan Y, Liu D, Liang F, Kong Z, Zhang X, Ai Q. Perfluorobutane application value in microwave ablation of Small Hepatocellular Carcinoma (<3 cm). Clin Hemorheol Microcirc 2024:CH232055. [PMID: 38277286 DOI: 10.3233/ch-232055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
BACKGROUND No studies have been retrieved comparing perfluorobutane with sulfur hexafluoride for microwave ablation (MWA) in small hepatocellular carcinoma(sHCC). OBJECTIVE To retrospective investigate the value of perfluorobutane ultrasonography contrast agent in ultrasonography (US)-guided MWA of sHCC. METHODS We conducted a retrospective clinical controlled study about US-guided percutaneous MWA in patients with sHCC, and in patients undergoing intra-operative treatment with perfluorobutane or sulfur hexafluoride. In both groups, a contrast agent was injected to clear the tumor and then a needle was inserted. A 5-point needle prick difficulty score was developed to compare needle prick difficulty in the two groups of cases. RESULTS A total of 67 patients were included: 25 patients in group perfluorobutane, aged 41-82 (60.64±9.46), tumor size 1.1-2.8 (1.78±0.45) cm. 42 patients in group sulfur hexafluoride, aged 38-78 (62.26±9.27), with tumor size of 1.1-3.0 (1.89±0.49) cm. There was no significant difference in age or tumor size in both groups (P > 0.05). Puncture difficulty score (5-point): 2.0-2.7 (2.28±0.29) in group perfluorobutane, and 2.0-4.7 (2.95±0.85) in group sulfur hexafluoride, and the difference between the two groups was statistically significant (P < 0.05). Enhanced imaging results within 3 months after surgery: complete ablation rate was 100% (25/25) in the group perfluorobutane, 95.2% (40/42 in the group sulfur hexafluoride), with no significant difference between the two groups (P > 0.05). CONCLUSION Perfluorobutane kupffer phase can make the operator accurately deploy the ablation needle and reduce the difficulty of operation.
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Affiliation(s)
- Yanghong Pan
- Department of Emergency, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
| | - Delin Liu
- Department of Ultrasonography, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
| | - Fei Liang
- Department of Ultrasonography, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
| | - Zixiang Kong
- Department of Ultrasonography, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
| | - Xu Zhang
- Department of Ultrasonography, Hangzhou Red Cross Hospital, Hangzhou, Zhejiang Province, China
| | - Qinqin Ai
- Department of Hepatology, Hangzhou Xixi Hospital, Hangzhou, Zhejiang Province, China
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Kamiyama N, Sugimoto K, Nakahara R, Kakegawa T, Itoi T. Deep learning approach for discrimination of liver lesions using nine time-phase images of contrast-enhanced ultrasound. J Med Ultrason (2001) 2024; 51:83-93. [PMID: 38051461 DOI: 10.1007/s10396-023-01390-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/07/2023] [Indexed: 12/07/2023]
Abstract
PURPOSE Contrast-enhanced ultrasound (CEUS) shows different enhancement patterns depending on the time after administration of the contrast agent. The aim of this study was to evaluate the diagnostic performance of liver nodule characterization using our proposed deep learning model with input of nine CEUS images. METHODS A total of 181 liver lesions (48 benign, 78 hepatocellular carcinoma (HCC), and 55 non-HCC malignant) were included in this prospective study. CEUS were performed using the contrast agent Sonazoid, and in addition to B-mode images before injection, image clips were stored every minute up to 10 min. A deep learning model was developed by arranging three ResNet50 transfer learning models in parallel. This proposed model allowed inputting up to nine datasets of different phases of CEUS and performing image augmentation of nine images synchronously. Using the results, the correct prediction rate, sensitivity, and specificity between "benign" and "malignant" cases were analyzed for each combination of the time phase. These accuracy values were also compared with the washout score judged by a human. RESULTS The proposed model showed performance superior to the referential standard model when the dataset from B-mode to the 10-min images were used (sensitivity: 93.2%, specificity: 65.3%, average correct answer rate: 60.1%). It also maintained 90.2% sensitivity and 61.2% specificity even when the dataset was limited to 2 min after injection, and this accuracy was equivalent to or better than human scoring by experts. CONCLUSION Our proposed model has the potential to identify tumor types earlier than the Kupffer phase, but at the same time, machine learning confirmed that Kupffer-phase Sonazoid images contain essential information for the classification of liver nodules.
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Affiliation(s)
- Naohisa Kamiyama
- Ultrasound General Imaging, GE HealthCare Japan, 127 Asahigaoka-4, Hino, Tokyo, 191-0065, Japan.
| | - Katsutoshi Sugimoto
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, 160-0023, Japan
| | - Ryuichi Nakahara
- Department of Orthopedic Surgery, Dentistry and Pharmaceutical Sciences, Okayama University Graduate School of Medicine, Okayama, 700-8558, Japan
| | - Tatsuya Kakegawa
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, 160-0023, Japan
| | - Takao Itoi
- Department of Gastroenterology and Hepatology, Tokyo Medical University, Tokyo, 160-0023, Japan
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Feng S, Wang J, Wang L, Qiu Q, Chen D, Su H, Li X, Xiao Y, Lin C. Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma. J Clin Transl Hepatol 2023; 11:1184-1191. [PMID: 37577233 PMCID: PMC10412715 DOI: 10.14218/jcth.2022.00077s] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/11/2022] [Accepted: 02/21/2023] [Indexed: 07/03/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.
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Affiliation(s)
- Sijia Feng
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Jianhua Wang
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Liheng Wang
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Qixuan Qiu
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Dongdong Chen
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Huo Su
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Xiaoli Li
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Yao Xiao
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
| | - Chiayen Lin
- General Surgery, Central South University Xiangya Hospital, Changsha, Hunan, China
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Campello CA, Castanha EB, Vilardo M, Staziaki PV, Francisco MZ, Mohajer B, Watte G, Moraes FY, Hochhegger B, Altmayer S. Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysis. Abdom Radiol (NY) 2023; 48:3114-3126. [PMID: 37365266 DOI: 10.1007/s00261-023-03984-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/10/2023] [Accepted: 06/12/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVES To perform a meta-analysis of the diagnostic performance of learning (ML) algorithms (conventional and deep learning algorithms) for the classification of malignant versus benign focal liver lesions (FLLs) on US and CEUS. METHODS Available databases were searched for relevant published studies through September 2022. Studies met eligibility criteria if they evaluate the diagnostic performance of ML for the classification of malignant and benign focal liver lesions on US and CEUS. The pooled per-lesion sensitivities and specificities for each modality with 95% confidence intervals were calculated. RESULTS A total of 8 studies on US, 11 on CEUS, and 1 study evaluating both methods met the inclusion criteria with a total of 34,245 FLLs evaluated. The pooled sensitivity and specificity of ML for the malignancy classification of FLLs were 81.7% (95% CI, 77.2-85.4%) and 84.8% (95% CI, 76.0-90.8%) for US, compared to 87.1% (95% CI, 81.8-91.0%) and 87.0% (95% CI, 83.1-90.1%) for CEUS. In the subgroup analysis of studies that evaluated deep learning algorithms, the sensitivity and specificity of CEUS (n = 4) increased to 92.4% (95% CI, 88.5-95.0%) and 88.2% (95% CI, 81.1-92.9%). CONCLUSIONS The diagnostic performance of ML algorithms for the malignant classification of FLLs was high for both US and CEUS with overall similar sensitivity and specificity. The similar performance of US may be related to the higher prevalence of DL models in that group.
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Affiliation(s)
- Carlos Alberto Campello
- School of Medicine, Universidade Federal do Mato Grosso, 2367 Quarenta e Nove St, Cuiabá, Brazil
| | - Everton Bruno Castanha
- School of Medicine, Universidade Federal de Pelotas, 538 Prof. Dr. Araújo St. Pelotas, Pelotas, Brazil
| | - Marina Vilardo
- School of Medicine, Universidade Catolica de Brasilia, QS 07, Brasília, Brazil
| | - Pedro V Staziaki
- Department of Radiology, University of Vermont Medical Center, 111 Colchester Ave, Burlington, USA
| | - Martina Zaguini Francisco
- Department of Radiology, Universidade Federal de Ciencias da Saude de Porto Alegre, 245 Sarmento Leite St, Porto Alegre, Brazil
| | - Bahram Mohajer
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, USA
| | - Guilherme Watte
- Department of Radiology, Universidade Federal de Ciencias da Saude de Porto Alegre, 245 Sarmento Leite St, Porto Alegre, Brazil
| | - Fabio Ynoe Moraes
- Department of Oncology, Queen's University, 76 Stuart St, Kingston, Canada
| | - Bruno Hochhegger
- Department of Radiology, University of Florida, 1600 SW Archer Rd, Gainesville, USA
| | - Stephan Altmayer
- Department of Radiology, Stanford University, 300 Pasteur Drive, Suite H1330, Stanford, USA.
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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 Med 2023; 44:395-407. [PMID: 37001563 DOI: 10.1055/a-2066-9372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Nishida N, Kudo M. Artificial intelligence models for the diagnosis and management of liver diseases. Ultrasonography 2023; 42:10-19. [PMID: 36443931 PMCID: PMC9816706 DOI: 10.14366/usg.22110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023] Open
Abstract
With the development of more advanced methods for the diagnosis and treatment of diseases, the data required for medical care are becoming complex, and misinterpretation of information due to human error may result in serious consequences. Human error can be avoided with the support of artificial intelligence (AI). AI models trained with various medical data for diagnosis and management of liver diseases have been applied to hepatitis, fatty liver disease, liver cirrhosis, and liver cancer. Some of these models have been reported to outperform human experts in terms of performance, indicating their potential for supporting clinical practice given their high-speed output. This paper summarizes the recent advances in AI for liver disease and introduces the AI-aided diagnosis of liver tumors using B-mode ultrasonography.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan,Correspondence to: Naoshi Nishida, MD, PhD, Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, 377-2 Ohno-higashi, Osaka-sayama, Osaka 589-8511, Japan Tel. +81-72-366-0221 Fax. +81-72-367-8220 E-mail:
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
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Xie D, Ying M, Lian J, Li X, Liu F, Yu X, Ni C. Serological indices and ultrasound variables in predicting the staging of hepatitis B liver fibrosis: A comparative study based on random forest algorithm and traditional methods. J Cancer Res Ther 2022; 18:2049-2057. [PMID: 36647969 DOI: 10.4103/jcrt.jcrt_1394_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Objective To compare the diagnostic efficacy of serological indices and ultrasound (US) variables in hepatitis B virus (HBV) liver fibrosis staging using random forest algorithm (RFA) and traditional methods. Methods The demographic and serological indices and US variables of patients with HBV liver fibrosis were retrospectively collected and divided into serology group, US group, and serology + US group according to the research content. RFA was used for training and validation. The diagnostic efficacy was compared to logistic regression analysis (LRA) and APRI and FIB-4 indices. Results For the serology group, the diagnostic performance of RFA was significantly higher than that of APRI and FIB-4 indices. The diagnostic accuracy of RFA in the four classifications (S0S1/S2/S3/S4) of the hepatic fibrosis stage was 79.17%. The diagnostic accuracy for significant fibrosis (≥S2), advanced fibrosis (≥S3), and cirrhosis (S4) was 87.99%, 90.69%, and 92.40%, respectively. The area under the curve (AUC) values were 0.945, 0.959, and 0.951, respectively. For the US group, there was no significant difference in diagnostic performance between RFA and LRA. The diagnostic performance of RFA in the serology + US group was significantly better than that of LRA. The diagnostic accuracy of the four classifications (S0S1/S2/S3/S4) of the hepatic fibrosis stage was 77.21%. The diagnostic accuracy for significant fibrosis (≥S2), advanced fibrosis (≥S3), and cirrhosis (S4) was 87.50%, 90.93%, and 93.38%, respectively. The AUC values were 0.948, 0.959, and 0.962, respectively. Conclusion RFA can significantly improve the diagnostic performance of HBV liver fibrosis staging. RFA based on serological indices has a good ability to predict liver fibrosis staging. RFA can help clinicians accurately judge liver fibrosis staging and reduce unnecessary biopsies.
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Affiliation(s)
- Daolin Xie
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou; Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Minghua Ying
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jingru Lian
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xin Li
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Fangyi Liu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiaoling Yu
- Department of Interventional Ultrasound, Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Caifang Ni
- Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Zhu D, Li J, Li Y, Wu J, Zhu L, Li J, Wang Z, Xu J, Dong F, Cheng J. Multimodal ultrasound fusion network for differentiating between benign and malignant solid renal tumors. Front Mol Biosci 2022; 9:982703. [PMID: 36148014 PMCID: PMC9488515 DOI: 10.3389/fmolb.2022.982703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: We aim to establish a deep learning model called multimodal ultrasound fusion network (MUF-Net) based on gray-scale and contrast-enhanced ultrasound (CEUS) images for classifying benign and malignant solid renal tumors automatically and to compare the model’s performance with the assessments by radiologists with different levels of experience.Methods: A retrospective study included the CEUS videos of 181 patients with solid renal tumors (81 benign and 100 malignant tumors) from June 2012 to June 2021. A total of 9794 B-mode and CEUS-mode images were cropped from the CEUS videos. The MUF-Net was proposed to combine gray-scale and CEUS images to differentiate benign and malignant solid renal tumors. In this network, two independent branches were designed to extract features from each of the two modalities, and the features were fused using adaptive weights. Finally, the network output a classification score based on the fused features. The model’s performance was evaluated using five-fold cross-validation and compared with the assessments of the two groups of radiologists with different levels of experience.Results: For the discrimination between benign and malignant solid renal tumors, the junior radiologist group, senior radiologist group, and MUF-Net achieved accuracy of 70.6%, 75.7%, and 80.0%, sensitivity of 89.3%, 95.9%, and 80.4%, specificity of 58.7%, 62.9%, and 79.1%, and area under the receiver operating characteristic curve of 0.740 (95% confidence internal (CI): 0.70–0.75), 0.794 (95% CI: 0.72–0.83), and 0.877 (95% CI: 0.83–0.93), respectively.Conclusion: The MUF-Net model can accurately classify benign and malignant solid renal tumors and achieve better performance than senior radiologists.Key points: The CEUS video data contain the entire tumor microcirculation perfusion characteristics. The proposed MUF-Net based on B-mode and CEUS-mode images can accurately distinguish between benign and malignant solid renal tumors with an area under the receiver operating characteristic curve of 0.877, which surpasses senior radiologists’ assessments by a large margin.
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Affiliation(s)
- Dongmei Zhu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
- Department of Ultrasound, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Junyu Li
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Yan Li
- Department of Ultrasound, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Ji Wu
- Department of Urology Surgery, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Lin Zhu
- Department of Ultrasound, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, China
| | - Jian Li
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
| | - Zimo Wang
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
- *Correspondence: Jinfeng Xu, ; Fajin Dong, ; Jun Cheng,
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen, China
- *Correspondence: Jinfeng Xu, ; Fajin Dong, ; Jun Cheng,
| | - Jun Cheng
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
- *Correspondence: Jinfeng Xu, ; Fajin Dong, ; Jun Cheng,
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Zhang WB, Hou SZ, Chen YL, Mao F, Dong Y, Chen JG, Wang WP. Deep Learning for Approaching Hepatocellular Carcinoma Ultrasound Screening Dilemma: Identification of α-Fetoprotein-Negative Hepatocellular Carcinoma From Focal Liver Lesion Found in High-Risk Patients. Front Oncol 2022; 12:862297. [PMID: 35720017 PMCID: PMC9204304 DOI: 10.3389/fonc.2022.862297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/14/2022] [Indexed: 12/02/2022] Open
Abstract
Background First-line surveillance on hepatitis B virus (HBV)-infected populations with B-mode ultrasound is relatively limited to identifying hepatocellular carcinoma (HCC) without elevated α-fetoprotein (AFP). To improve the present HCC surveillance strategy, the state of the art of artificial intelligence (AI), a deep learning (DL) approach, is proposed to assist in the diagnosis of a focal liver lesion (FLL) in HBV-infected liver background. Methods Our proposed deep learning model was based on B-mode ultrasound images of surgery that proved 209 HCC and 198 focal nodular hyperplasia (FNH) cases with 413 lesions. The model cohort and test cohort were set at a ratio of 3:1, in which the test cohort was composed of AFP-negative HBV-infected cases. Four additional deep learning models (MobileNet, Resnet50, DenseNet121, and InceptionV3) were also constructed as comparative baselines. To evaluate the models in terms of diagnostic power, sensitivity, specificity, accuracy, confusion matrix, F1-score, and area under the receiver operating characteristic curve (AUC) were calculated in the test cohort. Results The AUC of our model, Xception, achieved 93.68% in the test cohort, superior to other baselines (89.06%, 85.67%, 83.94%, and 78.13% respectively for MobileNet, Resnet50, DenseNet121, and InceptionV3). In terms of diagnostic power, our model showed sensitivity, specificity, accuracy, and F1-score of 96.08%, 76.92%, 86.41%, and 87.50%, respectively, and PPV, NPV, FPR, and FNR calculated from the confusion matrix were respectively 80.33%, 95.24%, 23.08%, and 3.92% in identifying AFP-negative HCC from HBV-infected FLL cases. Satisfactory robustness of our proposed model was shown based on 5-fold cross-validation performed among the models above. Conclusions Our DL approach has great potential to assist B-mode ultrasound in identifying AFP-negative HCC from FLL found in surveillance of HBV-infected patients.
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Affiliation(s)
- Wei-Bin Zhang
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China.,Department of Ultrasound, Zhongshan hospital of Fudan University (Xiamen Branch), Xiamen, China
| | - Si-Ze Hou
- Department of Mathematical Sciences, School of Physical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Yan-Ling Chen
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Feng Mao
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Yi Dong
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, China
| | - Wen-Ping Wang
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
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Abstract
Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.
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Gao R, Qin H, Lin P, Ma C, Li C, Wen R, Huang J, Wan D, Wen D, Liang Y, Huang J, Li X, Wang X, Chen G, He Y, Yang H. Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma. Front Oncol 2021; 11:613668. [PMID: 34295804 PMCID: PMC8290524 DOI: 10.3389/fonc.2021.613668] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 06/01/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC). Methods A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis. Results A total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation. Conclusions The nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.
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Affiliation(s)
- Ruizhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hui Qin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chenjun Ma
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chengyang Li
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jing Huang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Da Wan
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dongyue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yiqiong Liang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jiang Huang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- GE Healthcare Global Research, GE, Shanghai, China
| | - Xinrong Wang
- GE Healthcare Global Research, GE, Shanghai, China
| | - Gang Chen
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Liao M, Wang C, Zhang B, Jiang Q, Liu J, Liao J. Distinguishing Hepatocellular Carcinoma From Hepatic Inflammatory Pseudotumor Using a Nomogram Based on Contrast-Enhanced Ultrasound. Front Oncol 2021; 11:737099. [PMID: 34692513 PMCID: PMC8529164 DOI: 10.3389/fonc.2021.737099] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/14/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) and hepatic iflammatory pseudotumor (IPT) share similar symptoms and imaging features, which makes it challenging to distinguish from each other in clinical practice. This study aims to develop a predictive model based on contrast-enhanced ultrasound (CEUS) and clinical features to discriminate HCC from IPT. METHODS Sixty-two IPT and 146 HCC patients were enrolled in this study, where pathological diagnosis served as the reference standard for diagnosis. Clinical and ultrasound imaging data including CEUS features: enhancement degree during arterial phase, portal phase and delayed phase, enhancement pattern, early washout within 60 s, feeding artery, peritumoral vessels, peritumoral enhancement, and margin of nonenhanced area were retrospectively collected. Imaging data were reviewed by two experienced ultrasound doctors. Patients were randomly assigned to training and validation sets. Chi-squared test followed by LASSO regression was performed on ultrasonographic features in the training set to identify the most valuable features that distinguish HCC from IPT, based on which the sonographic score formula was generated. With the significant clinical and ultrasonographic indicators, a nomogram was developed. The performance of the nomogram was verified by ROC curve and decision curve analysis (DCA) with the comparison with sonographic score and the ultrasound doctor's diagnosis. RESULTS The most valuable ultrasonographic features that distinguish between HCC and IPT were enhancement degree during arterial phase, early washout, peritumoral vessels, peritumoral enhancement, and liver background. The sonographic score based on these features was verified to be an independent factor that predicts the diagnosis (p = 0.003). Among the clinical indicators, AFP (p = 0.009) and viral hepatitis infection (p = 0.004) were significant. Sonographic score, AFP, and viral hepatitis were used to construct a predictive nomogram. The AUC of the nomogram was 0.989 and 0.984 in training and validation sets, respectively, which were higher than those of sonographic score alone (0.938 and 0.958) or the ultrasound doctor's diagnosis (0.794 and 0.832). DCA showed the nomogram provided the greatest clinical usefulness. CONCLUSION A predictive nomogram based on a sonographic signature improved the diagnostic performance in distinguishing HCC and IPT, which may help with individualized diagnosis and treatment in clinical practice.
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Affiliation(s)
- Mengting Liao
- Department of Ultrasonography, Xiangya Hospital, Central South University, Changsha, China
- Health Management Center, Xiangya Hospital, Central South University, Changsha, China
| | - Chenshan Wang
- Department of Ultrasonography, Xiangya Hospital, Central South University, Changsha, China
- Department of Medical Ultrasound, Wuhan First Hospital, Wuhan, China
| | - Bo Zhang
- Department of Ultrasonography, Xiangya Hospital, Central South University, Changsha, China
| | - Qin Jiang
- Department of Ultrasonography, Xiangya Hospital, Central South University, Changsha, China
| | - Juan Liu
- Department of Ultrasonography, Xiangya Hospital, Central South University, Changsha, China
| | - Jintang Liao
- Department of Ultrasonography, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Jintang Liao,
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