Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging

Stollmayer, Róbert; Budai, Bettina Katalin; Tóth, Ambrus; Kalina, Ildikó; Hartmann, Erika; Szoldán, Péter; Bérczi, Viktor; Maurovich-Horvat, Pál; Kaposi, Pál Novák

doi:10.3748/wjg.v27.i35.5978

Cited by 17 publications

(9 citation statements)

References 21 publications

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“…Stollmayer et al [34] 2021 216 images FNH, HCC, liver metastases The deep learning method was used in order to build a 2D and a 3D model as well. The 2D model achieved an AUC of 0.99, 0.96, and 0.995 for the correct identification of the evaluated tumors.…”

Section: Artificial Intelligence Based On Magnetic Resonance Imagingmentioning

confidence: 99%

Section: Artificial Intelligence Based On Magnetic Resonance Imagingmentioning

confidence: 99%

“…Stollmayer et al [34] used deep learning in order to create two networks, a 2D and a 3D one, respectively, in order to diagnose FNH, HCC, and liver metastases on hepatocytespecific contrast-enhanced MRI. Thus, 69 patients were included in the study, and a total of 216 images were used in order to train and test the algorithms [34]. Overall, the 2D model performed better than the 3D one, achieving an AUC of 0.99, 0.9664, and 0.96 for the three investigated pathologies (in comparison with 0.97, 0.905, and 0.955 for the 3D model) [34].…”

Section: Artificial Intelligence Based On Magnetic Resonance Imagingmentioning

confidence: 99%

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Applications of Artificial Intelligence in the Automatic Diagnosis of Focal Liver Lesions: A Systematic Review

Popa

Grad

Chiarioni

et al. 2023

JGLD

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Background and Aims: Focal liver lesions (FLLs) are defined as abnormal solid or liquid masses differentiated from normal liver, frequently being clinically asymptomatic. The aim of this systematic review is to provide a comprehensive overview of current artificial intelligence (AI) applications, deep learning systems and convolutional neural networks, capable of performing a completely automated diagnosis of FLLs. Methods: We searched PubMed, Cochrane Library, EMBASE, and WILEY databases using predefined keywords. Articles were screened for relevant publications about AI applications capable of automated diagnosis of FLLs. The search terms included: (focal liver lesions OR FLLs OR hepatic focal lesions OR liver focal lesions OR liver tumor OR hepatic tumor) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR ultrasound OR US OR computer scan OR CT OR magnetic resonance imaging OR MRI OR computer-aided diagnosis OR automated computer tomography OR automated magnetic imaging). Results: Our search identified a total of 32 articles analyzing complete automated imagistic diagnosis of FLLs, out of which 14 studies analyzing liver ultrasound images, 8 studies analyzing computer tomography images and 10 studies analyzing images obtained from magnetic resonance imaging. Conclusions: We found significant evidence demonstrating that implementing a complete automated system for FLLs diagnosis using AI-based applications is currently feasible. Various automated AI-based applications have been analyzed. However, there is no clear evidence about the superiority of any of the systems.

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Section: Artificial Intelligence Based On Magnetic Resonance Imagingmentioning

confidence: 99%

Section: Artificial Intelligence Based On Magnetic Resonance Imagingmentioning

confidence: 99%

Section: Artificial Intelligence Based On Magnetic Resonance Imagingmentioning

confidence: 99%

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Applications of Artificial Intelligence in the Automatic Diagnosis of Focal Liver Lesions: A Systematic Review

Popa

Grad

Chiarioni

et al. 2023

JGLD

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“…US is a highly accessible, cost-effective, non-ionizing imaging modality used to investigate suspected liver diseases and follow up patients with malignancies. Over 50% of incidentally detected FLLs do not allow a conclusive diagnosis; inconclusive US scans often lead to further imaging studies such as dynamic contrast-enhanced computed tomography (CT) and hepatocyte-specific contrast-enhanced magnetic resonance imaging (MRI) [ 1 ] or invasive procedures, such as liver biopsy [ 2 , 3 ]. Therefore, there is a growing demand for novel multi-parametric imaging techniques facilitating the diagnosis of benign and malignant tumors.…”

Section: Introductionmentioning

confidence: 99%

Microvascular flow imaging to differentiate focal hepatic lesions: the spoke-wheel pattern as a specific sign of focal nodular hyperplasia

et al. 2023

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Microvascular flow imaging (MVFI) is an advanced Doppler ultrasound technique designed to detect slow-velocity blood flow in small-caliber microvessels. This technique is capable of realtime, highly detailed visualization of tumor vessels without using a contrast agent. MVFI has been recently applied for the characterization of focal liver lesions and has revealed typical vascularity distributions in multiple types thereof. Focal nodular hyperplasia (FNH) constitutes an important differential diagnosis of malignant liver tumors. In this essay, we provide iconographic documentation of the MVFI appearance of FNH and other common solid liver lesions. Identifying the typical patterns of vascularity, including the spoke-wheel pattern with MVFI, can expedite the diagnosis, spare patients from unnecessary procedures, and save costs.

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“…The clinical management and prognosis differ between benign and malignant solid focal liver lesions (FLLs), and among different pathological types of malignant FLLs[ 1 - 3 ]. Accurate diagnosis of solid FLLs, not only as benign or malignant FLLs, but also the possible types of malignant FLLs, is important[ 4 - 6 ].…”

Section: Introductionmentioning

confidence: 99%

Differential diagnosis of different types of solid focal liver lesions using two-dimensional shear wave elastography

Guo¹,

Dong²,

Qian³

et al. 2022

WJG

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BACKGROUND The clinical management and prognosis differ between benign and malignant solid focal liver lesions (FLLs), as well as among different pathological types of malignant FLLs. Accurate diagnosis of the possible types of solid FLLs is important. Our previous study confirmed the value of shear wave elastography (SWE) using maximal elasticity (Emax) as the parameter in the differential diagnosis between benign and malignant FLLs. However, the value of SWE in the differential diagnosis among different pathological types of malignant FLLs has not been proved. AIM To explore the value of two-dimensional SWE (2D-SWE) using Emax in the differential diagnosis of FLLs, especially among different pathological types of malignant FLLs. METHODS All the patients enrolled in this study were diagnosed as benign, malignant or undetermined FLLs by conventional ultrasound. Emax of FLLs and the periphery of FLLs was measured using 2D-SWE and compared between benign and malignant FLLs or among different pathological types of malignant FLLs. RESULTS The study included 32 benign FLLs in 31 patients and 100 malignant FLLs in 96 patients, including 16 cholangiocellular carcinomas (CCCs), 72 hepatocellular carcinomas (HCCs) and 12 liver metastases. Thirty-five FLLs were diagnosed as undetermined by conventional ultrasound. There were significant differences between Emax of malignant (2.21 ± 0.57 m/s) and benign (1.59 ± 0.37 m/s) FLLs ( P = 0.000), and between Emax of the periphery of malignant (1.52 ± 0.39 m/s) and benign (1.36 ± 0.44 m/s) FLLs ( P = 0.040). Emax of liver metastases (2.73 ± 0.99 m/s) was significantly higher than that of CCCs (2.14 ± 0.34 m/s) and HCCs (2.14 ± 0.46 m/s) ( P = 0.002). The sensitivity, specificity and accuracy were 71.00%, 84.38% and 74.24% respectively, using Emax > 1.905 m/s (AUC 0.843) to diagnose as malignant and 23 of 35 (65.74%) FLLs with undetermined diagnosis by conventional ultrasound were diagnosed correctly. CONCLUSION Malignant FLLs were stiffer than benign ones and liver metastases were stiffer than primary liver carcinomas. 2D-SWE with Emax was a useful complement to conventional ultrasound for the differential diagnosis of FLLs.

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Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging

Cited by 17 publications

References 21 publications

Applications of Artificial Intelligence in the Automatic Diagnosis of Focal Liver Lesions: A Systematic Review

Applications of Artificial Intelligence in the Automatic Diagnosis of Focal Liver Lesions: A Systematic Review

Microvascular flow imaging to differentiate focal hepatic lesions: the spoke-wheel pattern as a specific sign of focal nodular hyperplasia

Differential diagnosis of different types of solid focal liver lesions using two-dimensional shear wave elastography

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