Artificial intelligence for the classification of focal liver lesions in ultrasound – a systematic review

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.

Zusammenfassung

Raumforderungen der Leber finden sich in ca. 15 % abdominaler Ultraschalluntersuchungen. Die Diagnose der meisten benignen Läsionen kann bei charakteristischem Befund, z.B. bei Zysten, Hämangiomen und typischen fokalen Fettverteilungsvarianten, oft bereits in der B-Bild-Sonographie zuverlässig gestellt werden. Bei unklaren Befunden erhöht der Einsatz der Kontrastmittelsonographie (CEUS) die diagnostische Treffsicherheit bezüglich der Unterscheidung von benignen und malignen Leberläsionen. Künstliche Intelligenz beschreibt Anwendungen, die versuchen, in Bereichen wie der Klassifikation von Bildern humane Intelligenz nachzubilden. Da die Sonographie als besonders untersucherabhängig gilt, könnte die Anwendung künstlicher Intelligenz ein interessanter Ansatz für eine objektive und treffsichere Diagnose sein. Im Rahmen dieser systematischen Übersichtsarbeit haben wir analysiert, wie gut künstliche Intelligenz die Dignität und Entität von Leberläsionen auf Basis von B-Bild- oder CEUS-Daten bestimmen kann. Basierend auf einer strukturierten Recherche in Scopus, Web of Science, PubMed und IEEE fanden wir 52 Studien, welche die Einschlusskriterien erfüllten. Es zeigte sich eine gute diagnostische Genauigkeit sowohl für die Differenzierung der Dignität als auch der verschiedener Tumorentitäten. Die Ergebnisse ließen sich durch die zusätzliche Berücksichtigung klinischer Parameter verbessern und waren bezüglich der diagnostischen Genauigkeit mit der erfahrener Untersucher vergleichbar. Aufgrund des begrenzten Spektrums untersuchter Läsionen und häufig fehlender unabhängiger Validierungskohorten sind die Ergebnisse jedoch nur begrenzt auf die klinische Anwendung übertragbar.

Keywords

focal liver lesions - artificial intelligence - systematic review - METHODS & TECHNIQUES - ultrasound - deep learning

Publication History

Received: 30 November 2022

Accepted after revision: 31 March 2023

Accepted Manuscript online:
31 March 2023

Article published online:
12 May 2023

• References

• 1 Kaltenbach TEM, Engler P, Kratzer W. et al. Prevalence of benign focal liver lesions: ultrasound investigation of 45,319 hospital patients. Abdom Radiol (NY) 2016; 41: 25-32 DOI: 10.1007/s00261-015-0605-7.
• 2 Strobel D, Seitz K, Blank W. et al. Contrast-enhanced ultrasound for the characterization of focal liver lesions--diagnostic accuracy in clinical practice (DEGUM multicenter trial). Ultraschall in Med 2008; 29: 499-505 DOI: 10.1055/s-2008-1027806.
• 3 Wu M, Li L, Wang J. et al. Contrast-enhanced US for characterization of focal liver lesions: a comprehensive meta-analysis. Eur Radiol 2018; 28: 2077-2088 DOI: 10.1007/s00330-017-5152-x.
• 4 Friedrich-Rust M, Klopffleisch T, Nierhoff J. et al. Contrast-Enhanced Ultrasound for the differentiation of benign and malignant focal liver lesions: a meta-analysis. Liver Int 2013; 33: 739-755 DOI: 10.1111/liv.12115.
• 5 Vetter M, Kremer AE, Agaimy A. et al. The amount of liver tissue is essential for accurate histological staging in patients with autoimmune hepatitis. J Physiol Pharmacol 2021; 72 DOI: 10.26402/jpp.2021.1.13.
• 6 Nishida N, Yamakawa M, Shiina T. et al. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatol Int 2019; 13: 416-421 DOI: 10.1007/s12072-019-09937-4.
• 7 Survarachakan S, Prasad PJR, Naseem R. et al. Deep learning for image-based liver analysis – A comprehensive review focusing on malignant lesions. Artif Intell Med 2022; 130: 102331 DOI: 10.1016/j.artmed.2022.102331.
• 8 Whiting PF, Rutjes AW, Westwood ME. et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of internal medicine 2011; 155 DOI: 10.7326/0003-4819-155-8-201110180-00009.
• 9 Acharya UR, Koh JEW, Hagiwara Y. et al. Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Computers in Biology and Medicine 2018; 94: 11-18 DOI: 10.1016/j.compbiomed.2017.12.024.
• 10 Hassan T, Alzoubi A, Du H. et al. Towards optimal cropping: breast and liver tumor classification using ultrasound images. In: Agaian SS. Multimodal Image Exploitation and Learning 2021: 12–16 April 2021, online only, United States. volume 11734. Proceedings of SPIE. Bellingham, Washington, USA: SPIE; 2021: 15 DOI: 10.1117/12.2589038
• 11 Ryu H, Shin SY, Lee JY. et al. Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning. Eur Radiol 2021; 31: 8733-8742 DOI: 10.1007/s00330-021-07850-9.
• 12 Sato M, Kobayashi T, Soroida Y. et al. Development of novel deep multimodal representation learning-based model for the differentiation of liver tumors on B-mode ultrasound images. J Gastroenterol Hepatol 2022; 37: 678-684 DOI: 10.1111/jgh.15763.
• 13 Tiyarattanachai T, Chaiteerakij R, Marukatat S. et al. 685 – Computer-Assisted Ultrasonographic Image Analysis for Differentiation Between Hepatocellular Carcinoma (HCC) and Benign Focal Liver Lesions. Gastroenterology 2019; 156: S-1211 DOI: 10.1016/S0016-5085(19)40011-5.
• 14 Xi IL, Wu J, Guan J. et al. Deep learning for differentiation of benign and malignant solid liver lesions on ultrasonography. Abdom Radiol (NY) 2021; 46: 534-543 DOI: 10.1007/s00261-020-02564-w.
• 15 Yang Q, Wei J, Hao X. et al. Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study. EBioMedicine 2020; 56: 102777 DOI: 10.1016/j.ebiom.2020.102777.
• 16 Yamakawa M, Shiina T, Nishida N. et al. Computer aided diagnosis system developed for ultrasound diagnosis of liver lesions using deep learning. In: 2019 IEEE International Ultrasonics Symposium (IUS). Piscataway, NJ: IEEE; 2019: 2330-2333 DOI: 10.1109/ULTSYM.2019.8925698
• 17 Yamakawa M, Shiina T, Tsugawa K. et al. Deep-learning framework based on a large ultrasound image database to realize computer-aided diagnosis for liver and breast tumors. In: IEEE IUS 2021: International Ultrasonics Symposium : virtual symposium, September 11–16, 2021 (September 11–12: short courses/September 12–16: technical program) : 2021 symposium proceedings. Piscataway, NJ, USA: IEEE; 2021: 1-4 DOI: 10.1109/IUS52206.2021.9593518
• 18 Yoshida H, Casalino DD, Keserci B. et al. Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images. Phys Med Biol 2003; 48: 3735-3753 DOI: 10.1088/0031-9155/48/22/008.
• 19 Balasubramanian D, Srinivasan P, Gurupatham R. Automatic classification of focal lesions in ultrasound liver images using principal component analysis and neural networks. Annu Int Conf IEEE Eng Med Biol Soc 2007; 2007: 2134-2137 DOI: 10.1109/IEMBS.2007.4352744.
• 20 Hassan TM, Elmogy M, Sallam E. A classification framework for diagnosis of focal liver diseases. In: Abdelaal WGA. ICCES: 2015 Tenth International Conference on Computer Engineering & Systems (ICCES): Ain Shams University Guest House, Cairo, Egypt, December 23rd-24th, 2015: proceedings. Piscataway, NJ: IEEE; 2015: 395-401 DOI: 10.1109/ICCES.2015.7393083
• 21 Hassan TM, Elmogy M, Sallam ES. Diagnosis of Focal Liver Diseases Based on Deep Learning Technique for Ultrasound Images. Arab J Sci Eng 2017; 42: 3127-3140 DOI: 10.1007/s13369-016-2387-9.
• 22 Hwang YN, Lee JH, Kim GY. et al. Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network. Biomed Mater Eng 2015; 26: S1599-611 DOI: 10.3233/BME-151459.
• 23 Lee S, Jo IA, Kim KW. et al. Enhanced classification of focal hepatic lesions in ultrasound images using novel texture features. In: 2011 18th IEEE International Conference on Image Processing (ICIP 2011): Brussels, Belgium, 11 – 14 September 2011. Piscataway, NJ: IEEE; 2011: 2025-2028 DOI: 10.1109/ICIP.2011.6115876
• 24 Mao B, Ma J, Duan S. et al. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol 2021; 31: 4576-4586 DOI: 10.1007/s00330-020-07562-6.
• 25 Mitrea D, Nedevschi S, Mitrea P. et al. HCC Recognition Within Ultrasound Images Employing Advanced Textural Features with Deep Learning Techniques. In: Li Q, Wang L. Proceedings 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics: CISP-BMEI2019 : 19–21 October 2019, Huaqiao, China. Piscataway, NJ: IEEE; 2019: 1-6 DOI: 10.1109/CISP-BMEI48845.2019.8965874
• 26 Mittal D, Kumar V, Saxena SC. et al. Neural network based focal liver lesion diagnosis using ultrasound images. Comput Med Imaging Graph 2011; 35: 315-323 DOI: 10.1016/j.compmedimag.2011.01.007.
• 27 Peng JB, Peng YT, Lin P. et al. Differentiating infected focal liver lesions from malignant mimickers: value of ultrasound-based radiomics. Clin Radiol 2022; 77: 104-113 DOI: 10.1016/j.crad.2021.10.009.
• 28 Qiu W, Wang R, Xiao F. et al. Research on Fuzzy Enhancement in the Diagnosis of Liver Tumor from B-mode Ultrasound Images. In: Staff I. 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation. Place of publication not identified: IEEE; 2011: 74-80 DOI: 10.1109/ICBMI.2011.17
• 29 Ren S, Li Q, Liu S. et al. Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study. Front Oncol 2021; 11: 749137 DOI: 10.3389/fonc.2021.749137.
• 30 Sritunyarat Y, Chaiteerakij R, Tiyarattanachai T. et al. 456 PERFORMANCE OF ARTIFICIAL INTELLIGENCE IN DIAGNOSING FOCAL LIVER LESIONS DETECTED BY VARIOUS TRANS-ABDOMINAL ULTRASONOGRAPHIC MACHINES: A VALIDATION STUDY. Gastroenterology 2020; 158: S-1278 DOI: 10.1016/S0016-5085(20)33857-9.
• 31 Schmauch B, Herent P, Jehanno P. et al. Diagnosis of focal liver lesions from ultrasound using deep learning. Diagn Interv Imaging 2019; 100: 227-233 DOI: 10.1016/j.diii.2019.02.009.
• 32 Tiyarattanachai T, Apiparakoon T, Marukatat S. et al. Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. PLoS One 2021; 16: e0252882 DOI: 10.1371/journal.pone.0252882.
• 33 Virmani J, Kumar V, Kalra N. et al. Characterization of primary and secondary malignant liver lesions from B-mode ultrasound. J Digit Imaging 2013; 26: 1058-1070 DOI: 10.1007/s10278-013-9578-7.
• 34 Virmani J, Kumar V, Kalra N. et al. A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound. J Med Eng Technol 2013; 37: 292-306 DOI: 10.3109/03091902.2013.794869.
• 35 Virmani J, Kumar V, Kalra N. et al. Neural network ensemble-based CAD system for focal liver lesions from B-mode ultrasound. J Digit Imaging 2014; 27: 520-537 DOI: 10.1007/s10278-014-9685-0.
• 36 Virmani J, Vinod V, Kalra N. et al. PCA-SVM based CAD System for Focal Liver Lesions using B-Mode Ultrasound Images. DSJ 2013; 63: 478-486 DOI: 10.14429/dsj.63.3951.
• 37 Xu SSD, Chang CC, Su CT. et al. Classification of Hepatocellular Carcinoma and Liver Abscess by Applying Neural Network to Ultrasound Images. Sensors and Materials 2020; 32: 2659 DOI: 10.18494/SAM.2020.2801.
• 38 Zhang XY, Diao XF, Wang TF. et al. Study on Feature Extraction for Ultrasonic Differentiation of Liver Space-Occupying Lesions. In: 2010 4th International Conference on Bioinformatics and Biomedical Engineering: ICBBE 2010; Chengdu, China, 18 – 20 June 2010. Piscataway, NJ: IEEE; 2010: 1-4 DOI: 10.1109/ICBBE.2010.5517018
• 39 Zhou H, Jiang T, Li Q. et al. US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients. Front Oncol 2021; 11: 672055 DOI: 10.3389/fonc.2021.672055.
• 40 Lehang G, Dan W, Huixiong X. et al. CEUS-based classification of liver tumors with deep canonical correlation analysis and multi-kernel learning. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017: 1748-1751 DOI: 10.1109/EMBC.2017.8037181.
• 41 Guo L-H, Wang D, Qian YY. et al. 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 DOI: 10.3233/CH-170275.
• 42 Hu HT, Wang W, Chen LD. et al. Artificial intelligence assists identifying malignant versus benign liver lesions using contrast-enhanced ultrasound. J Gastroenterol Hepatol 2021; 36: 2875-2883 DOI: 10.1111/jgh.15522.
• 43 Kondo S, Takagi K, Nishida M. et al. Computer-Aided Diagnosis of Focal Liver Lesions Using Contrast-Enhanced Ultrasonography With Perflubutane Microbubbles. IEEE Trans Med Imaging 2017; 36: 1427-1437 DOI: 10.1109/TMI.2017.2659734.
• 44 Yiyi Q, Jun S, Xiao Z. et al. Multimodal Ultrasound imaging-based diagnosis of liver cancers with a two-stage multi-view learning framework. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017: 3232-3235 DOI: 10.1109/EMBC.2017.8037545.
• 45 Ta CN, Kono Y, Eghtedari M. et al. Focal Liver Lesions: Computer-aided Diagnosis by Using Contrast-enhanced US Cine Recordings. Radiology 2018; 286: 1062-1071 DOI: 10.1148/radiol.2017170365.
• 46 Wu K, Chen X, Ding M. Deep learning-based classification of focal liver lesions with contrast-enhanced ultrasound. Optik 2014; 125: 4057-4063 DOI: 10.1016/j.ijleo.2014.01.114.
• 47 Zhang H, Guo L, Wang D. et al. Multi-Source Transfer Learning Via Multi-Kernel Support Vector Machine Plus for B-Mode Ultrasound-Based Computer-Aided Diagnosis of Liver Cancers. IEEE J Biomed Health Inform 2021; 25: 3874-3885 DOI: 10.1109/JBHI.2021.3073812.
• 48 Caleanu CD, Simion G, David C. et al. A study over the importance of arterial phase temporal parameters in focal liver lesions CEUS based diagnosis. In: 2014 11th International Symposium on Electronics and Telecommunications (ISETC 2014): Timişoara, România, 14 – 15 November 2014 ; [conference proceedings. Piscataway, NJ: IEEE; 2014: 1-4 DOI: 10.1109/ISETC.2014.7010799
• 49 Căleanu CD, Sîrbu CL, Simion G. Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis. Sensors (Basel) 2021; 21 DOI: 10.3390/s21124126.
• 50 Denis de Senneville B, Frulio N, Laumonier H. et al. Liver contrast-enhanced sonography: computer-assisted differentiation between focal nodular hyperplasia and inflammatory hepatocellular adenoma by reference to microbubble transport patterns. Eur Radiol 2020; 30: 2995-3003 DOI: 10.1007/s00330-019-06566-1.
• 51 Hu HT, Kuang M, Lu MD. et al. IDDF2019-ABS-0148 Focal liver lesion classification using a convolutional neural network-based transfer-learning algorithm on tri-phase images of contrast-enhanced ultrasound. In: Clinical Hepatology. BMJ Publishing Group Ltd and British Society of Gastroenterology 062019: A140.1-A140 DOI: 10.1136/gutjnl-2019-IDDFAbstracts.274.
• 52 Huang Q, Pan F, Li W. et al. Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic Semantics. IEEE J. Biomed. Health Inform 2020; 24: 2860-2869 DOI: 10.1109/JBHI.2020.2977937.
• 53 Li W, Lv XZ, Zheng X. et al. Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma. Front Oncol 2021; 11: 544979 DOI: 10.3389/fonc.2021.544979.
• 54 Liang X, Lin L, Cao Q. et al. Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models. IEEE Trans Med Imaging 2016; 35: 713-727 DOI: 10.1109/TMI.2015.2492618.
• 55 Shiraishi J, Sugimoto K, Moriyasu F. et al. Computer-aided diagnosis for the classification of focal liver lesions by use of contrast-enhanced ultrasonography. Med Phys 2008; 35: 1734-1746 DOI: 10.1118/1.2900109.
• 56 Sirbu CL, Simion G, Caleanu CD. Deep CNN for Contrast-Enhanced Ultrasound Focal Liver Lesions Diagnosis. In: 2020 14th International Symposium on Electronics and Telecommunications (ISETC): November 05–06, 2020, Timişoara, România : conference proceedings. Piscataway, NJ: IEEE; 2020: 1-4 DOI: 10.1109/ISETC50328.2020.9301116
• 57 Streba CT, Ionescu M, Gheonea DI. et al. Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors. World J Gastroenterol 2012; 18: 4427-4434 DOI: 10.3748/wjg.v18.i32.4427.
• 58 Sugimoto K, Shiraishi J, Moriyasu F. et al. 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: 401-411 DOI: 10.1016/j.acra.2008.09.018.
• 59 Sugimoto K, Shiraishi J, Moriyasu F. et al. Computer-aided diagnosis for contrast-enhanced ultrasound in the liver. World J Radiol 2010; 2: 215-223 DOI: 10.4329/wjr.v2.i6.215.
• 60 Zhou J, Pan F, Li W. et al. Feature Fusion for Diagnosis of Atypical Hepatocellular Carcinoma in Contrast- Enhanced Ultrasound. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2022; 69: 114-123 DOI: 10.1109/TUFFC.2021.3110590.
• 61 Šimundić AM. Measures of Diagnostic Accuracy: Basic Definitions. EJIFCC 2009; 19: 203-211
• 62 Wojciech Samek, Grégoire Montavon, Andrea Vedaldi. et al. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer; 2019
• 63 Tiyarattanachai T, Apiparakoon T, Marukatat S. et al. 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 DOI: 10.1038/s41598-022-11506-z.
• 64 Nam D, Chapiro J, Paradis V. et al. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep 2022; 4: 100443 DOI: 10.1016/j.jhepr.2022.100443.