Artificial intelligence in liver ultrasound

Cao, Liu-Liu; Mei, Peng; Xie, Xiang; Chen, Gong-Quan; Huang, Shuyan; Wang, Jiayu; Jiang, Fan; Cui, Xin-Wu; Dietrich, Christoph F.

doi:10.3748/wjg.v28.i27.3398

Cited by 12 publications

(5 citation statements)

References 57 publications

(66 reference statements)

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“…Studies have shown that radiomics based on image features can extract objective characteristics and provide valuable insights in predicting clinical outcomes(28).Indeed, both domestic and international researchers have been exploring the clinical utility of radiomics in diagnosing hepatic steatosis. For example, Chou and colleagues classi ed the severity of hepatic steatosis using patient ultrasound images (29)(30), while Sim and others used radiomics derived from MR-PDFF to diagnose the degree of hepatic fat deposition in NAFLD patients (31).Ultrasound grayscale images indeed contain a wealth of raw image information, including re ections and scattering of small structures within the liver parenchyma (32). In this study, ultrasound radiomics was used to extract image features from these grayscale ultrasound images.After dimension reduction using LASSO, a total of 25 features were retained, and a radiomics model was developed using the K-nearest neighbors (KNN) algorithm.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Model for Non-Alcoholic Steatohepatitis Diagnosis Based on Ultrasound Radiomics

xia,

wei,

wang

et al. 2024

Preprint

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Background Non-Alcoholic Steatohepatitis(NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predicting the diagnosis of Non-Alcoholic Steatohepatitis. Method An SD rat model of hepatic steatosis was established through a high-fat diet and subcutaneous injection of CCl4. Liver ultrasound images and elastography were acquired, along with serum data and histopathological results of rat livers.The Pyradiomics software was used to extract radiomic features from 2D ultrasound images of rat livers. The rats were then randomly divided into a training set and a validation set, and feature selection was performed through dimensionality reduction. Various machine learning (ML) algorithms were employed to build clinical diagnostic models, radiomic models, and combined diagnostic models. The efficiency of each diagnostic model for diagnosing NASH was evaluated using Receiver Operating Characteristic (ROC) curves, Clinical Decision Curve Analysis (DCA), and calibration curves. Results In the machine learning radiomic model for predicting the diagnosis of NASH, the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve for the clinical radiomic model in the training set and validation set were 0.989 and 0.885, respectively. The Decision Curve Analysis revealed that the clinical radiomic model had the highest net benefit within the probability threshold range of > 65%. The calibration curve in the validation set demonstrated that the clinical combined radiomic model is the optimal method for diagnosing Non-Alcoholic Steatohepatitis. Conclusion The combined diagnostic model constructed using machine learning algorithms based on ultrasound image radiomics has a high clinical predictive performance in diagnosing Non-Alcoholic Steatohepatitis.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Model for Non-Alcoholic Steatohepatitis Diagnosis Based on Ultrasound Radiomics

xia,

wei,

wang

et al. 2024

Preprint

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“…Furthermore, we found that the Rad-score was also an independent predictor of recurrence in PTC. Recently, the US radiomics signatures have been widely used to predict many aspects of tumor behaviors in various organs [ 42 – 45 ]. And Park et al found that radiomics features extracted from ultrasound images might be potential imaging biomarkers for risk stratification in patients with PTC [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning

Zhou,

Liu,

Yang

et al. 2024

BMC Cancer

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Background and aims The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatures to predict the recurrence in PTC. Methods A total of 554 patients with PTC who underwent ultrasound imaging before total thyroidectomy were included. Among them, 79 experienced at least one recurrence. Then 388 were divided into the training cohort and 166 into the validation cohort. The radiomics features were extracted from the region of interest (ROI) we manually drew on the tumor image. The feature selection was conducted using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. And multivariate Cox regression analysis was used to build the combined nomogram using radiomics signatures and significant clinicopathological characteristics. The efficiency of the nomogram was evaluated by receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Kaplan-Meier analysis was used to analyze the recurrence-free survival (RFS) in different radiomics scores (Rad-scores) and risk scores. Results The combined nomogram demonstrated the best performance and achieved an area under the curve (AUC) of 0.851 (95% CI: 0.788 to 0.913) in comparison to that of the radiomics signature and the clinical model in the training cohort at 3 years. In the validation cohort, the combined nomogram (AUC = 0.885, 95% CI: 0.805 to 0.930) also performed better. The calibration curves and DCA verified the clinical usefulness of combined nomogram. And the Kaplan-Meier analysis showed that in the training cohort, the cumulative RFS in patients with higher Rad-score was significantly lower than that in patients with lower Rad-score (92.0% vs. 71.9%, log rank P < 0.001), and the cumulative RFS in patients with higher risk score was significantly lower than that in patients with lower risk score (97.5% vs. 73.5%, log rank P < 0.001). In the validation cohort, patients with a higher Rad-score and a higher risk score also had a significantly lower RFS. Conclusion We proposed a nomogram combining clinicopathological variables and ultrasound radiomics signatures with excellent performance for recurrence prediction in PTC patients.

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“…Ultrasomics is a branch of radiomics, which can extract a large number of quantitative features from ultrasonic images, including texture, shape, intensity, trend, and wavelet features, etc., which represent different pathological types of diseases, and provide clinicians with comprehensive quantitative phenotypes to help clinical decision-making ( 26 ). The application of ultrasomics has been gradually broadened by the updating of ultrasonic technology and the swift development of computerized algorithms, with studies now being conducted in the areas of thyroid ( 5 ), breast ( 31 ), liver ( 32 ), prostate ( 33 ), and head and neck tumors ( 34 ).…”

Section: Discussionmentioning

confidence: 99%

Research on ultrasound-based radiomics: a bibliometric analysis

Yu,

Che,

et al. 2024

Quant Imaging Med Surg

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Background A large number of studies related to ultrasound-based radiomics have been published in recent years; however, a systematic bibliometric analysis of this topic has not yet been conducted. In this study, we attempted to identify the hotspots and frontiers in ultrasound-based radiomics through bibliometrics and to systematically characterize the overall framework and characteristics of studies through mapping and visualization. Methods A literature search was carried out in Web of Science Core Collection (WoSCC) database from January 2016 to December 2023 according to a predetermined search formula. Bibliometric analysis and visualization of the results were performed using CiteSpace, VOSviewer, R, and other platforms. Results Ultimately, 466 eligible papers were included in the study. Publication trend analysis showed that the annual publication trend of journals in ultrasound-based radiomics could be divided into three phases: there were no more than five documents published in this field in any year before 2018, a small yearly increase in the number of annual publications occurred between 2018 and 2022, and a high, stable number of publications appeared after 2022. In the analysis of publication sources, China was found to be the main contributor, with a much higher number of publications than other countries, and was followed by the United States and Italy. Frontiers in Oncology was the journal with the highest number of papers in this field, publishing 60 articles. Among the academic institutions, Fudan University, Sun Yat-sen University, and the Chinese Academy of Sciences ranked as the top three in terms of the number of documents. In the analysis of authors and cocited authors, the author with the most publications was Yuanyuan Wang, who has published 19 articles in 8 years, while Philippe Lambin was the most cited author, with 233 citations. Visualization of the results from the cocitation analysis of the literature revealed a strong centrality of the subject terms papillary thyroid cancer, biological behavior, potential biomarkers, and comparative assessment, which may be the main focal points of research in this subject. Based on the findings of the keyword analysis and cluster analysis, the keywords can be categorized into two major groups: (I) technological innovations that enable the construction of radiomics models such as machine learning and deep learning and (II) applications of predictive models to support clinical decision-making in certain diseases, such as papillary thyroid cancer, hepatocellular carcinoma (HCC), and breast cancer. Conclusions Ultrasound-based radiomics has received widespread attention in the medical field and has been gradually been applied in clinical research. Radiomics, a relatively late development in medical technology, has made substantial contributions to the diagnosis, prediction, and prognostic evaluation of diseases. Addition...

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Artificial intelligence in liver ultrasound

Cited by 12 publications

References 57 publications

Machine Learning Model for Non-Alcoholic Steatohepatitis Diagnosis Based on Ultrasound Radiomics

Machine Learning Model for Non-Alcoholic Steatohepatitis Diagnosis Based on Ultrasound Radiomics

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning

Research on ultrasound-based radiomics: a bibliometric analysis

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