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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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Focal Liver Lesion Detection in Ultrasound Image Using Deep Feature Fusions and Super Resolution. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2020. [DOI: 10.3390/make2030010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This research presents a machine vision approach to detect lesions in liver ultrasound as well as resolving some issues in ultrasound such as artifacts, speckle noise, and blurring effect. The anisotropic diffusion is modified using the edge preservation conditions which found better than traditional ones in quantitative evolution. To dig for more potential information, a learnable super-resolution (SR) is embedded into the deep CNN. The feature is fused using Gabor Wavelet Transform (GWT) and Local Binary Pattern (LBP) with a pre-trained deep CNN model. Moreover, we propose a Bayes rule-based informative patch selection approach to reduce the processing time with the selective image patches and design an algorithm to mark the lesion region from identified ultrasound image patches. To train this model, standard data ensures promising resolution. The testing phase considers generalized data with a varying resolution and test the performance of the model. Exploring cross-validation, it finds that a 5-fold strategy can successfully eradicate the overfitting problem. Experiment data are collected using 298 consecutive ultrasounds comprising 15,296 image patches. This proposed feature fusion technique confirms satisfactory performance compared to the current relevant works with an accuracy of 98.40%.
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Kriti, Virmani J, Agarwal R. Effect of despeckle filtering on classification of breast tumors using ultrasound images. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.02.004] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018. [DOI: 10.1007/s13369-018-3454-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Dohare AK, Kumar V, Kumar R. Detection of myocardial infarction in 12 lead ECG using support vector machine. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.12.001] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Kriti, Virmani J, Agarwal R. Evaluating the Efficacy of Gabor Features in the Discrimination of Breast Density Patterns Using Various Classifiers. LECTURE NOTES IN COMPUTATIONAL VISION AND BIOMECHANICS 2018. [DOI: 10.1007/978-3-319-65981-7_5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Chen Y, Yue X, Fujita H, Fu S. Three-way decision support for diagnosis on focal liver lesions. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.04.008] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Sharma K, Virmani J. A Decision Support System for Classification of Normal and Medical Renal Disease Using Ultrasound Images. ACTA ACUST UNITED AC 2017. [DOI: 10.4018/ijaci.2017040104] [Citation(s) in RCA: 79] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GLCM range feature vector are computed for inter-pixel distance d varying from 1 to 10. These texture feature vectors are combined in various ways yielding GLCM ratio feature vector, GLCM additive feature vector and GLCM concatenated feature vector. The present work explores the potential of five texture feature vectors computed using GLCM statistics exhaustively for differential diagnosis between normal and MRD images using SVM classifier. The result of the study indicates that GLCM range feature vector computed with d = 1 yields the highest overall classification accuracy of 85.7% with individual classification accuracy values of 93.3% and 77.9% for normal and MRD classes respectively.
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Affiliation(s)
- Komal Sharma
- Electrical and Instrumentation Engineering Department, Thapar University, Patiala, India
| | - Jitendra Virmani
- Electrical and Instrumentation Engineering Department, Thapar University, Patiala, India
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Parekh V, Jacobs MA. Radiomics: a new application from established techniques. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016; 1:207-226. [PMID: 28042608 PMCID: PMC5193485 DOI: 10.1080/23808993.2016.1164013] [Citation(s) in RCA: 217] [Impact Index Per Article: 27.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of "big data". Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used to develop computational models using advanced machine learning algorithms that may serve as a tool for personalized diagnosis and treatment guidance.
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Affiliation(s)
- Vishwa Parekh
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Department of Computer Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
| | - Michael A. Jacobs
- The Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
- Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD 21205
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Application of Statistical Texture Features for Breast Tissue Density Classification. IMAGE FEATURE DETECTORS AND DESCRIPTORS 2016. [DOI: 10.1007/978-3-319-28854-3_16] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification. INTELLIGENT SYSTEMS REFERENCE LIBRARY 2016. [DOI: 10.1007/978-3-319-21212-8_7] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Kriti, Virmani J. Comparison of CAD Systems for Three Class Breast Tissue Density Classification Using Mammographic Images. MEDICAL IMAGING IN CLINICAL APPLICATIONS 2016. [DOI: 10.1007/978-3-319-33793-7_5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Breast Tissue Density Classification Using Wavelet-Based Texture Descriptors. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2016. [DOI: 10.1007/978-81-322-2526-3_56] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Poddar MG, Kumar V, Sharma YP. Automated diagnosis of coronary artery diseased patients by heart rate variability analysis using linear and non-linear methods. J Med Eng Technol 2015; 39:331-41. [DOI: 10.3109/03091902.2015.1063721] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS One 2014; 9:e110300. [PMID: 25330171 PMCID: PMC4203782 DOI: 10.1371/journal.pone.0110300] [Citation(s) in RCA: 110] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 09/15/2014] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice. METHODOLOGY The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared. PRINCIPAL FINDINGS Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description. CONCLUSIONS In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.
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Affiliation(s)
- Lejla Alic
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Intelligent Imaging, Netherlands Organization for Applied Scientific Research (TNO), The Hague, The Netherlands
| | - Wiro J. Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Jifke F. Veenland
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
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Virmani J, Kumar V, Kalra N, Khandelwal N. Neural network ensemble based CAD system for focal liver lesions from B-mode ultrasound. J Digit Imaging 2014; 27:520-37. [PMID: 24687642 PMCID: PMC4090414 DOI: 10.1007/s10278-014-9685-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
A neural network ensemble (NNE) based computer-aided diagnostic (CAD) system to assist radiologists in differential diagnosis between focal liver lesions (FLLs), including (1) typical and atypical cases of Cyst, hemangioma (HEM) and metastatic carcinoma (MET) lesions, (2) small and large hepatocellular carcinoma (HCC) lesions, along with (3) normal (NOR) liver tissue is proposed in the present work. Expert radiologists, visualize the textural characteristics of regions inside and outside the lesions to differentiate between different FLLs, accordingly texture features computed from inside lesion regions of interest (IROIs) and texture ratio features computed from IROIs and surrounding lesion regions of interests (SROIs) are taken as input. Principal component analysis (PCA) is used for reducing the dimensionality of the feature space before classifier design. The first step of classification module consists of a five class PCA-NN based primary classifier which yields probability outputs for five liver image classes. The second step of classification module consists of ten binary PCA-NN based secondary classifiers for NOR/Cyst, NOR/HEM, NOR/HCC, NOR/MET, Cyst/HEM, Cyst/HCC, Cyst/MET, HEM/HCC, HEM/MET and HCC/MET classes. The probability outputs of five class PCA-NN based primary classifier is used to determine the first two most probable classes for a test instance, based on which it is directed to the corresponding binary PCA-NN based secondary classifier for crisp classification between two classes. By including the second step of the classification module, classification accuracy increases from 88.7 % to 95 %. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in differential diagnosis of FLLs.
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Affiliation(s)
- Jitendra Virmani
- Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, Solan, 173234 Himachal Pradesh, India,
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Abstract
We previously developed a network phenotyping strategy (NPS), a graph theory-based transformation of clinical practice data, for recognition of two primary subgroups of hepatocellular cancer (HCC), called S and L, which differed significantly in their tumor masses. In the current study, we have independently validated this result on 641 HCC patients from another continent. We identified the same HCC subgroups with mean tumor masses 9 cm x n (S) and 22 cm x n (L), P<10(-14). The means of survival distribution (not available previously) for this new cohort were also significantly different (S was 12 months, L was 7 months, P<10(-5)). We characterized nine unique reference patterns of interactions between tumor and clinical environment factors, identifying four subtypes for S and five subtypes for L phenotypes, respectively. In L phenotype, all reference patterns were portal vein thrombosis (PVT)-positive, all platelet/alpha fetoprotein (AFP) levels were high, and all were chronic alcohol consumers. L had phenotype landmarks with worst survival. S phenotype interaction patterns were PVT-negative, with low platelet/AFP levels. We demonstrated that tumor-clinical environment interaction patterns explained how a given parameter level can have a different significance within a different overall context. Thus, baseline bilirubin is low in S1 and S4, but high in S2 and S3, yet all are S subtype patterns, with better prognosis than in L. Gender and age, representing macro-environmental factors, and bilirubin, prothrombin time, and AST levels representing micro-environmental factors, had a major impact on subtype characterization. Clinically important HCC phenotypes are therefore represented by complete parameter relationship patterns and cannot be replaced by individual parameter levels.
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Affiliation(s)
- Petr Pancoska
- Department of Medicine and Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA
| | - Brian I Carr
- Department of Liver Tumor Biology IRCCS de Bellis, National Institute for Digestive Diseases, Castellana Grotte , BA, Italy.
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