Detection Of Focal Liver Lesions Research Articles

Relevance of lesion size in navigator-triggered and free-breathing diffusion-weighted liver MRI.

The purpose of this study was to investigate the relevance of focal liver lesions (FLL) size for lesion detection comparing navigator triggering (TRIG) to free breathing (FB) liver Diffusion-weighted magnetic resonance imaging (DWI). Patients with known or suspected FLL were prospectively (registry number 276_19 B) included from October to December 2019 in this study, out of which 32 had liver lesions. Echo planar spin-echo DWI data both with TRIG and FB were with approximately constant acquisition times acquired at 1.5 T. Lesions were segmented in the b = 800 s/mm² images in both the TRIG and FB images. The lesion size, location (liver segment), liver lesion visibility, as well as contrast-to-noise ratio (CNR) were recorded. The CNR was assessed with the Wilcoxon-Mann-Whitney test and the number of visible lesions with the Fisher test. Data from 43 patients (22 female) were analyzed. The mean patient age was 58 ± 14 years. A total of 885 FLL (Ntotal) were segmented. Among these, 811 lesions (Nboth) were detected with TRIG and FB, 65 lesions exclusively with TRIG (NTRIG_Only), and nine exclusively in FB (NFB_Only). The largest additional lesion in TRIG/FB had a diameter of 10.4 mm/7.6 mm. The number of additional lesions detected with TRIG decreased with size. Among all lesions ≤ 4.7 mm, the relative number of additional lesions was 15.6%. Additional lesions were found in all liver segments with TRIG. In the left liver lobe, the relative proportion was 9.2%, and in the right liver lobe 5.4%. CNR and visibility were significantly higher in TRIG than in FB (p < 0.001). In relation to size, the difference is significant in terms of visibility and CNR for lesion diameters ≤ 8 mm. Respiration triggering can improve the detection of small liver lesions with diameters up to approx. 1 cm in the whole liver. Question Can respiration triggering (TRIG) improve the detection of small FLL compared to FB diffusion-weighted imaging? Findings Among 885 segmented FLL, TRIG was superior to FB for lesions smaller than 8 mm and had improved CNR and visibility. Clinical relevance Diffusion-weighted magnetic resonance imaging is used for the detection of focal liver lesions and image quality is influenced by breathing motion. Navigator triggering becomes more important for smaller lesions, and seems recommendable for the detection of small focal liver lesions.

Comparison of adaptive imaging receiver coil and traditional coil for multiplexed sensitivity encoding diffusion-weighted imaging of the liver.

To compare the image quality and efficacy of the adaptive imaging receiver (AIR) coil (GE Healthcare) and the traditional coil for multiplexed sensitivity encoding diffusion-weighted imaging (MUSE-DWI) in the detection of focal liver lesions (FLLs). Two groups of MUSE-DWI were obtained. Image quality was qualitatively evaluated by 3 independent blinded radiologists on a 5-point scale, and quantitative parameters were calculated by measurements of the region of interest in the liver and FLLs. McNemar's test were used to compare the characteristics and detectability. Less image noise, sharper contours, milder susceptibility artefacts, and better liver lesion conspicuity were found by all radiologists in 60 livers with 140 FLLs with the AIR coil than with the traditional coil (reader average mean, 4.3-4.4 vs. 3.7-4.0, P < .001). The signal-to-noise ratio (SNR) of the liver was significantly higher with the AIR coil than with the traditional coil (right lobe: mean, 8.89 vs.7.76, P < .05; left lobe: mean, 7.14 vs.6.19, P < .001), and the SNR of FLLs (mean, 24.62 vs. 21.01, P < .001) and lesion-to-liver CNR (mean, 16.61 vs. 14.02, P < .001) exhibited significant differences between the AIR coil and the traditional coil. Besides, superior detection of FLLs was observed with the AIR coil compared to the traditional coil (95.7% [134/140] vs. 85.7% [120/140], P < .001). The AIR coil yields less noise, fewer distortions, better lesion detectability, higher SNR of the liver and FLLs, and improved lesion-to-liver CNR during liver MUSE-DWI. Thus, it is a feasible and effective scanning scheme in liver MRI. The AIR coil improves SNR and the quality of liver MR imaging compared with the traditional coil.

Comments and illustrations of the WFUMB CEUS liver guidelines: Rare benign focal liver lesion, part I.

Improved detection and characterization of common focal liver lesions (FLL) are the main topics of the World Federation for Ultrasound in Medicine and Biology (WFUMB) guidelines on the use of contrast-enhanced ultrasound (CEUS). On stateof-the-art CEUS imaging, to create a library of rare FLL, especially concerning their atypical imaging characteristics, might be helpful for improving clinical diagnostic efficiency. In this review, we aim to summarize the ultrasound and CEUS features of rare benign FLL. Currently there are limited reports and images published.

A multicenter clinical AI system study for detection and diagnosis of focal liver lesions

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists’ F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

Compressed sensing with deep learning reconstruction: Improving capability of gadolinium-EOB-enhanced 3D T1WI

PurposeThe purpose of this study was to determine the utility of compressed sensing (CS) with deep learning reconstruction (DLR) for improving spatial resolution, image quality and focal liver lesion detection on high-resolution contrast-enhanced T1-weighted imaging (HR-CE-T1WI) obtained by CS with DLR as compared with conventional CE-T1WI with parallel imaging (PI). MethodsSeventy-seven participants with focal liver lesions underwent conventional CE-T1WI with PI and HR-CE-T1WI, surgical resection, transarterial chemoembolization, and radiofrequency ablation, followed by histopathological or >2-year follow-up examinations in our hospital. Signal-to-noise ratios (SNRs) of liver, spleen and kidney were calculated for each patient, after which each SNR was compared by means of paired t-test. To compare focal lesion detection capabilities of the two methods, a 5-point visual scoring system was adopted for a per lesion basis analysis. Jackknife free-response receiver operating characteristic (JAFROC) analysis was then performed, while sensitivity and false positive rates (/data set) for consensus assessment of the two methods were also compared by using McNemar's test or the signed rank test. ResultsEach SNR of HR-CE-T1WI was significantly higher than that of conventional CE-T1WI with PI (p < 0.05). Sensitivities for consensus assessment showed that HR-CE-MRI had significantly higher sensitivity than conventional CE-T1WI with PI (p = 0.004). Moreover, there were significantly fewer FP/cases for HR-CE-T1WI than for conventional CE-T1WI with PI (p = 0.04). ConclusionCS with DLR are useful for improving spatial resolution, image quality and focal liver lesion detection capability of Gd-EOB-DTPA enhanced 3D T1WI without any need for longer breath-holding time.

Comments and illustrations of the WFUMB CEUS liver guidelines: Rare focal liver lesion - infectious parasitic, fungus.

In this series of papers on comments and illustrations of the World Federation for Medicine and Biology (WFUMB) guidelines on contrast enhanced ultrasound (CEUS) the topics of parasitic and fungus infections are discussed. Improved detection and characterization of common focal liver lesions (FLL) are the main topics of these guidelines but detailed and illustrating information is missing. The focus in this paper on infectious (parasitic and fungus) focal liver lesions is on their appearance on B-mode and Doppler ultrasound and CEUS features. Knowledge of these data should help to raise awareness of these rarer findings, to think of these clinical pictures in the corresponding clinical situation, to interpret the ultrasound images correctly and thus to initiate the appropriate diagnostic and therapeutic steps in time.

Application of liver-specific contrast agents for evaluation of focal liver lesions - Expert recommendations from the Gastrointestinal and Abdominal Imaging Workgroup of the German Roentgen Society.

Contrast-enhanced MRI is the imaging modality of choice for the detection and differential diagnosis of focal liver lesions. Liver-specific contrast agents (CAs) are now well established in addition to extracellular contrast agents. However, there is a lack of explicit recommendations reflecting the pros and cons of each specific contrast agent in the daily routine. Development of recommendations for the clinical application of liver-specific CAs by members of the Gastrointestinal and Abdominal Imaging Workgroup within the Germany Radiological Society, using methodology comparable to that of an S1 guideline with informal consensus. The diagnostic criteria for the evaluation of liver lesions are intentionally outside the scope of this article, as there are already plenty of excellent publications available. The application of liver-specific CAs in the daily routine is associated with advantages and disadvantages due to the specific pharmacokinetic and pharmacodynamic properties and necessitates adjustment of the imaging technique as well consideration during image interpretation. Recommendations for the application of liver-specific CAs are presented based on different clinical scenarios, taking into account current evidence and guidelines. · Both liver-specific and extracellular contrast agents are established. · Liver-specific contrast agents make it possible to draw conclusions about the hepatocellular function of a lesion. · Recommendations for the use of liver-specific contrast agents in the daily routine are presented. · Ringe KI, Fischbach F, Grenacher L et al. Einsatz leberspezifischer Kontrastmittel in der MRT zur Beurteilung von Leberläsionen - Expertenempfehlungen der AG Gastrointestinal- und Abdominaldiagnostik der Deutschen Röntgengesellschaft. Fortschr Röntgenstr 2023; DOI: 10.1055/a-2192-9921.

Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma.

To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI) in patients at a high risk of hepatocellular carcinoma. In this retrospective analysis, 83 high-risk patients with hepatocellular carcinoma underwent gadoxetic acid-enhanced liver MRI using a 3-T scanner. Triple arterial phase, high-resolution portal venous phase, and high-resolution hepatobiliary phase images were reconstructed using conventional reconstruction techniques and DLRA (AIRTM Recon DL; GE Healthcare) for subsequent comparison. Image quality and solid focal lesion detection were assessed by three abdominal radiologists and compared between conventional and DL methods. Focal liver lesion detection was evaluated using figures of merit (FOMs) from a jackknife alternative free-response receiver operating characteristic analysis on a per-lesion basis. DLRA-reconstructed images exhibited significantly improved overall image quality, image contrast, lesion conspicuity, vessel conspicuity, and liver edge sharpness and reduced subjective image noise, ringing artifacts, and motion artifacts compared to conventionally reconstructed images (all P < 0.05). Although there was no significant difference in the FOMs of non-cystic focal liver lesions between the conventional and DL methods, DLRA-reconstructed images showed notably higher pooled sensitivity than conventionally reconstructed images (P < 0.05) in all phases and higher detection rates for viable post-treatment HCCs in the arterial and hepatobiliary phases (all P < 0.05). Implementing DLRA can enhance the image quality in 3D T1-weighted gradient-echo sequences of gadoxetic acid-enhanced liver MRI examinations, leading to improved detection of viable post-treatment HCCs.

Development and evaluation of an integrated liver nodule diagnostic method by combining the liver segment division and lesion localization/classification models for enhanced focal liver lesion detection.

The purpose of the study was to develop a liver nodule diagnostic method that accurately localizes and classifies focal liver lesions and identifies the specific liver segments in which they reside by integrating a liver segment division algorithm using a four-dimensional (4D) fully convolutional residual network (FC-ResNet) with a localization and classification model. We retrospectively collected data and divided 106 gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance examinations into Case-sets 1, 2, and 3. A liver segment division algorithm was developed using a 4D FC-ResNet and trained with semi-automatically created silver-standard annotations; performance was evaluated using manually created gold-standard annotations by calculating the Dice scores for each liver segment. The performance of the liver nodule diagnostic method was assessed by comparing the results with those of the original radiology reports. The mean Dice score between the output of the liver segment division model and the gold standard was 0.643 for Case-set 2 (normal liver contours) and 0.534 for Case-set 1 (deformed liver contours). Among the 64 lesions in Case-set 3, the diagnostic method localized 37 lesions, classified 33 lesions, and identified the liver segments for 30 lesions. A total of 28 lesions were true positives, matching the original radiology reports. The liver nodule diagnostic method, which integrates a liver segment division algorithm with a lesion localization and classification model, exhibits great potential for localizing and classifying focal liver lesions and identifying the liver segments in which they reside. Further improvements and validation using larger sample sizes will enhance its performance and clinical applicability.

Comments and illustrations of the WFUMB CEUS liver guidelines: Rare focal liver lesions - infectious (bacterial).

In this series of papers on comments and illustrations of the World Federation for Medicine and Biology (WFUMB) guidelines on contrast enhanced ultrasound (CEUS) the topics of bacterial infections are discussed. Improved detection and characterization of common focal liver lesions (FLL) are the main topics of these guidelines but detailed and illustrating information is missing. The focus in this paper on infectious (bacterial) focal liver lesions is on their appearance on B-mode and Doppler ultrasound and CEUS features. Knowledge of these data should help to raise awareness of these rarer findings, to think of these clinical pictures in the corresponding clinical situation, to interpret the ultrasound images correctly and thus to initiate the appropriate diagnostic and therapeutic steps in time.

Diagnosis of hepatocellular carcinoma using Sonazoid: a comprehensive review.

Sonazoid contrast-enhanced ultrasonography (CEUS) is a promising technique for the detection and diagnosis of focal liver lesions, particularly hepatocellular carcinoma (HCC). Recently, a collaborative effort between the Korean Society of Radiology and Korean Society of Abdominal Radiology resulted in the publication of guidelines for diagnosing HCC using Sonazoid CEUS. These guidelines propose specific criteria for identifying HCC based on the imaging characteristics observed during Sonazoid CEUS. The suggested diagnostic criteria include nonrim arterial phase hyperenhancement, and the presence of late and mild washout, or Kupffer phase washout under the premise that the early or marked washout should not occur during the portal venous phase. These criteria aim to improve the accuracy of HCC diagnosis using Sonazoid CEUS. This review offers a comprehensive overview of Sonazoid CEUS in the context of HCC diagnosis. It covers the fundamental principles of Sonazoid CEUS and its clinical applications, and introduces the recently published guidelines. By providing a summary of this emerging technique, this review contributes to a better understanding of the potential role of Sonazoid CEUS for diagnosing HCC.

Artificial intelligence assists operators in real-time detection of focal liver lesions during ultrasound: A randomized controlled study

PurposeDetection of hepatocellular carcinoma (HCC) is crucial during surveillance by ultrasound. We previously developed an artificial intelligence (AI) system based on convolutional neural network for detection of focal liver lesions (FLLs) in ultrasound. The primary aim of this study was to evaluate whether the AI system can assist non-expert operators to detect FLLs in real-time, during ultrasound examinations. MethodThis single-center prospective randomized controlled study evaluated the AI system in assisting non-expert and expert operators. Patients with and without FLLs were enrolled and had ultrasound performed twice, with and without AI assistance. McNemar’s test was used to compare paired FLL detection rates and false positives between groups with and without AI assistance. Results260 patients with 271 FLLs and 244 patients with 240 FLLs were enrolled into the groups of non-expert and expert operators, respectively. In non-experts, FLL detection rate in the AI assistance group was significantly higher than the no AI assistance group (36.9 % vs 21.4 %, p < 0.001). In experts, FLL detection rates were not significantly different between the groups with and without AI assistance (66.7 % vs 63.3 %, p = 0.32). False positive detection rates in the groups with and without AI assistance were not significantly different in both non-experts (14.2 % vs 9.2 %, p = 0.08) and experts (8.6 % vs 9.0 %, p = 0.85). ConclusionsThe AI system resulted in significant increase in detection of FLLs during ultrasound examinations by non-experts. Our findings may support future use of the AI system in resource-limited settings where ultrasound examinations are performed by non-experts.The study protocol was registered under the Thai Clinical Trial Registry (TCTR20201230003), which is part of the WHO ICTRP Registry Network. The registry can be accessed via the following URL: https://trialsearch.who.int/Trial2.aspx?TrialID=TCTR20201230003.

Gadoxetic Acid-Enhanced Liver MRI: Everything You Need to Know.

Since its introduction in the worldwide medical market, gadoxetic acid has attracted considerable interest. The year 2023 marks the 15th anniversary of the introduction of gadoxetic acid in Japan. Gadoxetic acid-enhanced magnetic resonance imaging (GA-MRI) is the predominantly performed contrast MRI examination for the liver. Its most essential characteristic, namely, the hepatobiliary phase, revolutionized the clinical management of liver disease. Gadoxetic acid-enhanced MRI is currently the most efficient method for focal liver lesion detection and analysis. Meta-analyses demonstrated its excellent effectiveness for the diagnosis of hepatocellular carcinoma and liver metastases. Owing to the extensive usage of gadoxetic acid, a hepatobiliary phase hypointense nodule without arterial phase hyperenhancement is well documented. The existence of such nodules may be a sign of hypervascular hepatocellular carcinoma in nodules and other areas in the liver. Apart from its role in tumor identification and characterization, GA-MRI can help assess response to therapy and liver fibrosis. Therefore, it is proposed to use gadoxetic acid as the first option for MRI of the liver in the majority of patients. The efficacy of gadoxetic acid surpasses its disadvantages, rendering this contrast agent the preferred choice for routine MRI of the liver. The clinical use of GA-MRI is discussed in this review article.

Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study.

To qualitatively and quantitatively compare a single breath-hold fast half-Fourier single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE) with T2-weighted BLADE sequence for liver MRI at 3T. From December 2020 to January 2021, patients with liver MRI were prospectively included. For qualitative analysis, sequence quality, presence of artifacts, conspicuity, and presumed nature of the smallest lesion were assessed using the chi-squared and McNemar tests. For quantitative analysis, number of liver lesions, size of the smallest lesion, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in both sequences were assessed using the paired Wilcoxon signed-rank test. Intraclass correlation coefficients (ICCs) and kappa coefficients were used to assess agreement between the two readers. One hundred and twelve patients were evaluated. Overall image quality (p = .006), artifacts (p < .001), and conspicuity of the smallest lesion (p = .001) were significantly better for the DL HASTE sequence than for the T2-weighted BLADE sequence. Significantly more liver lesions were detected with the DL HASTE sequence (356 lesions) than with the T2-weighted BLADE sequence (320 lesions; p < .001). CNR was significantly higher for the DL HASTE sequence (p < .001). SNR was higher for the T2-weighted BLADE sequence (p < .001). Interreader agreement was moderate to excellent depending on the sequence. Of the 41 supernumerary lesions visible only on the DL HASTE sequence, 38 (93%) were true-positives. The DL HASTE sequence can be used to improve image quality and contrast and reduces artifacts, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. The DL HASTE sequence is superior to the T2-weighted BLADE sequence for the detection of focal liver lesions and can be used in daily practice as a standard sequence. • The half-Fourier acquisition single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE sequence) has better overall image quality, reduced artifacts (particularly motion artifacts), and improved contrast, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. • The acquisition time of the DL HASTE sequence is at least eight times faster (21s) than that of the T2-weighted BLADE sequence (3-5min). • The DL HASTE sequence could replace the conventional T2-weighted BLADE sequence to meet the growing indication for hepatic MRI in clinical practice, given its diagnostic and time-saving performance.

Current and Advanced Applications of Gadoxetic Acid–enhanced MRI in Hepatobiliary Disorders

Gadoxetic acid is an MRI contrast agent that has specific applications in the study of hepatobiliary disease. After being distributed in the vascular and extravascular spaces during the dynamic phase, gadoxetic acid is progressively taken up by hepatocytes and excreted to the bile ducts during the hepatobiliary phase. The information derived from the enhancement characteristics during dynamic and hepatobiliary phases is particularly relevant in the detection and characterization of focal liver lesions and in the evaluation of the structure and function of the liver and biliary system. The use of new MRI sequences and advanced imaging techniques (eg, relaxometry, multiparametric imaging, and analysis of heterogeneity), the introduction of artificial intelligence, and the development of biomarkers and radiomic and radiogenomic tools based on gadoxetic acid-enhanced MRI findings will play an important role in the future in assessing liver function, chronic liver disease, and focal liver lesions; in studying biliary pathologic conditions; and in predicting treatment responses and prognosis. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Screening and Early Diagnosis of Hepatocellular Cancer and Optimization of Diagnostic Imaging Techniques: A Review and Conclusion of the Expert Panel

Аim: to describe modern approaches for screening and early diagnosis of hepatocellular carcinoma (HCC).Key points. Screening for HCC in high-risk groups (cirrhosis of any etiology, patients with chronic viral hepatitis B and patients with F3 liver fibrosis) should be organized as regular (every 6 months) liver ultrasound in combination with determination of the serum alpha-fetoprotein (AFP) level. At an AFP level of ≥ 20 ng/ml, even in the absence of changes according to ultrasound data, it is advisable to perform MRI with a hepatospecific contrast agent (gadoxetic acid) which makes it possible to detect very small focal liver lesions. If focal liver lesions of 1–2 cm are detected on ultrasound, additional imaging of the liver using MRI with a hepatospecific contrast agent gadoxetic acid helps to identify HCC at an earlier stage or high degree dysplastic nodes. When planning surgical treatment and liver transplantation, it is preferable to use MRI with a hepatospecific contrast agent, since the presence of the hepatobiliary phase may allow the detection of additional smaller focal liver lesions and assess the nature of the focal liver lesion. When a patient is included in the waiting list for liver transplantation, the optimal frequency of liver MRI is 1 time in 3 months.Conclusion. MRI with hepatospecific contrast agent gadoxetic acid is effective in screening, early diagnosis and treatment planning for HCC.

Clinical Significance of Liver MR Imaging.

MRI is widely used in clinical practice for detecting liver diseases. Since the introduction of gadoxetic acid, MRI has become the most effective modality for the detection and characterization of focal liver lesions. According to previous meta-analyses, the area under the receiver operating characteristic curve (AUROC) was 0.97-0.99 for the diagnosis of small hepatocellular carcinoma (≥ 2 cm) by gadoxetic-acid-enhanced MRI. Moreover, the AUROC for the diagnosis of colorectal liver metastases was significantly high (0.98). Despite gadoxetic acid's drawbacks, its clinical utility outweighs them, making it the contrast agent of choice in routine liver MRIs. Moreover, clinically, liver MRI has become more prevalent for a quantitative assessment. Liver fibrosis can be evaluated using MR elastography; whereas, hepatic steatosis and iron overload can be evaluated using proton density fat fraction, with high accuracy and reproducibility. This article reviewed the usefulness of liver MRI, which can be a comprehensive imaging modality in clinical practice.

Fusion and CNN based classification of liver focal lesions using magnetic resonance imaging phases

The diagnosis and follow-up of focal liver lesions have an important place in radiology practice and in planning the treatment of patients. Lesions detected in the liver can be benign or malign. While benign lesions do not require any treatment, some treatments and surgical operations may be required for malign lesions. Magnetic resonance imaging provides some advantages over other imaging modalities in the detection and characterization of focal liver lesions with its superior soft tissue contrast. Additionally, different phases help make a clear diagnosis of different contrast agent retention properties in magnetic resonance imaging. This study aims to classify focal liver lesions based on convolutional neural networks by fusing magnetic resonance liver images obtained in pre-contrast, venous, arterial, and delayed phases. Magnetic resonance imaging data were obtained from Selcuk University, Faculty of Medicine, Department of Radiology in Turkey. The experiments were performed using 460 magnetic resonance images in four phases of 115 patients. Two experiments were conducted. Two-dimensional discrete wavelet transform was used to fuse the phases in both experiments. In the first experiment, the best model was determined using the original data, different number of convolution layers and different activation functions. In the second experiment, the best-found model was used. Additionally, the number of data was increased using data augmentation methods in this experiment. The results were compared with other state-of-the art methods and the superiority of the proposed method was proved. As a result of the classification, 96.66% accuracy, 86.67% sensitivity and 98.76% specificity rates were obtained. When the results are examined, CNN efficiency increases by fusing MR liver images taken in different phases.

Applicability of multidimensional convolutional neural networks on automated detection of diverse focal liver lesions in multiphase CT images.

To investigate the applicability of multidimensional convolutional neural networks (CNNs) together with multiphase contrast-enhanced CT images on automated detection of diverse focal liver lesions (FLLs). We trained detection models based on 2.5D and 3D CNN frameworks using 567 patients with 3892 FLLs and validated on a relatively large independent cohort of 1436 patients with 4723 lesions. The detection performance across different phases (arterial, portal venous [PV], and combined phases) was assessed for the 2.5D model. The lesions were divided into two groups with a cutoff size of 20mm, and further subdivided into four subgroups of<10, 10-20, 20-50, and ≥50mm, to verify the detection rates for lesions of different sizes for the 2.5D and 3D models. McNemar's test was used to compare the detection sensitivities among different methods. In addition, sensitivity with 95% confidence intervals and free-response receiver operating characteristics (FROC) curves were plotted for visualization of the detectability. In the 2.5D model, the detection rate of PV phase outperformed arterial phase, and a combination of the two phases further improved the performance over a single phase. The detection sensitivities in the arterial, PV, and combined phases were 0.737versus 0.802versus 0.832 for all lesions. The 3D model was superior to the 2.5D model for detecting benign lesions (0.896vs. 0.807, p<0.001), malignant lesions (0.940vs. 0.918, p=0.013), and all lesions (0.902vs. 0.832, p<0.001) regardless of size division. Particularly, the 3D model showed higher sensitivity than the 2.5D model in detecting lesions smaller than 20mm (0.868vs. 0.759, p<0.001). For lesions larger than 20mm, both the 3D and the 2.5D models achieved excellent detection performance. The proposed CNN detection model was demonstrated to adaptively learn the feature representations of diverse FLLs and generalize well to a large-scale validation dataset. The use of multiphase significantly improved the detectability of FLLs compared to single phase. 3D CNN framework showed an enhanced capability over the 2.5D in the detection of FLLs, particularly small lesions. The promising performance shows that the proposed CNN detection system could be a powerful clinical tool for the early detection of hepatic tumors.

Artificial intelligence-based ultrasound imaging technologies for hepatic diseases

Ultrasound (US) imaging is a non-invasive, real-time, economical, and convenient imaging modality that has been widely used in diagnosing and treating hepatic diseases. Artificial intelligence (AI) technology can predict or make decisions based on the experience of clinical experts and knowledge obtained from training data. This technology can help clinicians improve the detection efficiency and evaluate hepatic diseases, promote clinical treatment of the liver, and predict the response of the liver after treatment. This review summarizes the current rapid development of US technology and related AI methods in the diagnosis and treatment of hepatic diseases. Covered topics include steatosis grading, fibrosis staging, detection of focal liver lesions, US image segmentation, multimodal image registration, and other applications. At present, the field of AI in US imaging is still in its early stages. With future progress of AI technology, AI-based US imaging can further improve diagnosis, reduce medical costs, and optimize US-based clinical workflow. This technology has broad prospects for application to hepatic diseases.