Diagnosis Of Focal Liver Lesions Research Articles

Endoscopic Ultrasound-Guided Tissue Sampling for the Cytohistological Diagnosis of Focal Liver Lesions.

Focal liver lesions (FLL) often require cytohistological evaluation. Endoscopic Ultrasound (EUS)-guided tissue acquisition (EUS-TA) is highly accurate in diagnosing pancreatic and gastrointestinal malignancies. The aim of our study was to evaluate the role of EUS-TA in the characterization of FLL. A retrospective analysis of a prospective database of patients who underwent EUS-TA for the evaluation of FLL. Diagnostic yield, adverse events and factors associated with diagnostic yield were evaluated as endpoints. The effect of variables such as needle size, lesion size, rapid on-site evaluation (ROSE) and the use of cytological or histological needles were analyzed. A total of 114 cases were included (mean age 68.05 ± 11.35 years, 64 male). A correct diagnosis was made using EUS-TA in 100 of the 114 cases (diagnostic yield of 88%). The EUS-TA of additional extrahepatic lesions during the same EUS procedure increased the diagnostic yield to 94%. No adverse events were reported. Multivariate analysis did not identify any factor influencing the diagnostic yield. EUS-TA is a highly accurate and safe technique for the differential diagnosis of FLL and could be considered as the primary approach in this setting.

OA-GAN: organ-aware generative adversarial network for synthesizing contrast-enhanced medical images

Contrast-enhanced computed tomography (CE-CT) images are vital for clinical diagnosis of focal liver lesions (FLLs). However, the use of CE-CT images imposes a significant burden on patients due to the injection of contrast agents and extended shooting. Deep learning-based image synthesis models offer a promising solution that synthesizes CE-CT images from non-contrasted CT (NC-CT) images. Unlike natural images, medical image synthesis requires a specific focus on certain organs or localized regions to ensure accurate diagnosis. Determining how to effectively emphasize target organs poses a challenging issue in medical image synthesis. To solve this challenge, we present a novel CE-CT image synthesis model called, Organ-Aware Generative Adversarial Network (OA-GAN). The OA-GAN comprises an organ-aware (OA) network and a dual decoder-based generator. First, the OA network learns the most discriminative spatial features about the target organ (i.e. liver) by utilizing the ground truth organ mask as localization cues. Subsequently, NC-CT image and captured feature are fed into the dual decoder-based generator, which employs a local and global decoder network to simultaneously synthesize the organ and entire CECT image. Moreover, the semantic information extracted from the local decoder is transferred to the global decoder to facilitate better reconstruction of the organ in entire CE-CT image. The qualitative and quantitative evaluation on a CE-CT dataset demonstrates that the OA-GAN outperforms state-of-the-art approaches for synthesizing two types of CE-CT images such as arterial phase and portal venous phase. Additionally, subjective evaluations by expert radiologists and a deep learning-based FLLs classification also affirm that CE-CT images synthesized from the OA-GAN exhibit a remarkable resemblance to real CE-CT images.

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.

The added value of color parameter imaging for the evaluation of focal liver lesions with "homogenous hyperenhancement and no wash out" on contrast enhanced ultrasound.

The purpose of this study was to investigate the added value of color parameter imaging (CPI) in the differential diagnosis of focal liver lesions (FLLs) with "homogeneous hyperenhancement but not wash out" on contrast-enhanced ultrasound (CEUS). A total of 101 patients with 108 FLLs were enrolled in this study. All the FLLs received US and CEUS examinations. The stored CEUS clips of target lesions were postprocessed with CPI analysis by radiologists. The receiver operator characteristic (ROC) curve was used to evaluate the added value of CPI. The McNamara test was used to compare the diagnostic sensitivity, specificity, and accuracy between CEUS and CPI patterns. Univariate and multivariate logistic regression analyses were used to develop a CPI nomogram. The C index and calibration curve were used to evaluate the predictive ability of the nomogram. The intraclass correlation coefficient was used to test the reproducibility and reliability of CPI. Decision curve analysis (DCA) was used to evaluate the added value of applying CPI. The following CPI features were more frequently observed in malignant FLLs: eccentric perfusion (malignant: 70.0% vs. benign: 29.2%, p < 0.001), feeding artery (51.7% vs. 4.2%, p < 0.001), mosaic (63.3% vs. 6.3%, p < 0.001), red ingredients >1/3 (90.0% vs. 14.6%, p < 0.001). In addition, centripetal (43.8% vs. 18.3%, p = 0.004), peripheral nodular (54.2% vs. 1.7%, p < 0.001), subcapsular vessel (12.5% vs. 0.0%, p = 0.004), spoke-wheel vessels (25.0% vs. 5.0%, p = 0.003), branched vessels (22.9% vs. 5.0%, p = 0.006), blue and pink ingredients >2/3 (85.4% vs. 10.0%, p < 0.001) were more observed in benign FLLs. A nomogram incorporating peripheral nodular, spoke-wheel vessels, and red ingredients >1/3 was constructed. The model had satisfactory discrimination (AUC = 0.937), and the optimal diagnostic threshold value was 0.740 (0.983, 0.850). By the DCA, the model offered a net benefit over the treat-all-patients scheme or the treat-none scheme at a threshold probability 5%-93%. Using CPI can detect and render subtle information of the main features of FLLs on CEUS; it is conducive to the radiologist for imaging interpretation, and a combining read of the CEUS and CPI of the FLLs with features of "homogenous hyperenhancement and no washout" can improve significantly the diagnostic performance of CEUS for FLLs.

Use of artificial intelligence (AI) in diagnosis of focal liver lesions

Use of artificial intelligence (AI) in diagnosis of focal liver lesions

Advantages and Limitations of Point Shear Wave Elastography and 2-Dimensional Shear Wave Elastography against PercutaneousLiver Biopsy for Assessing Focal Liver Lesions

BACKGROUND: Ultrasound based noninvasive techniques for the evaluation of tissue elasticity are becoming popular in practice. An accurate diagnosis of focal liver lesions (FLLs) is very important and essential for the adequate treatment and management of different conditions. AIM: The study aims to evaluate the advantages and limitations of Point Shear Wave Elastography and 2-dimensional Shear Wave Elastography against percutaneous liver biopsy for assessing focal liver lesions. METHODS: This document reviews the advantages and limitations of point shear wave elastography (pSWE) and 2-dimensional shear wave elastography (2D-SWE) against percutaneous liver biopsy for assessing focal liver lesions. RESULTS: Ultrasound elastography has shown promising results and plays an important role in the assessment of focal liver lesions but the lack of histological confirmation is a limitation, especially in cases of suspected FLLs. The Tru cut percutaneous liver biopsy has been proven as a very helpful method for an accurate histological diagnosis. The main disadvantage of this procedure is that is invasive and the complications may be life threatening. CONCLUSION: Noninvasive methods to aid clinical decisions are preferred by patients rather than liver biopsy. US elastography, in the form of pSWE, as well as 2D SWE has proved reliable for the evaluation of liver parenchyma. The investigation of pSWE and 2D-SWE for assessing the elasticity of focal liver lesions and their differentiating is still a challenging goal. Percutaneous liver biopsy provides an option for accurate histological confirmation of liver pathologies, particularly FLLs. It continues to be an important tool in the diagnosis, prognosis, and treatment of those affected with liver disorders.

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.

Diagnosis of focal liver lesions from ultrasound images using a pretrained residual neural network.

This study aims to develop a ResNet50-based deep learning model for focal liver lesion (FLL) classification in ultrasound images, comparing its performance with other models and prior research. We retrospectively collected 581 ultrasound images from the Chulabhorn Hospital's HCC surveillance and screening project (2010-2018). The dataset comprised five classes: non-FLL, hepatic cyst (Cyst), hemangioma (HMG), focal fatty sparing (FFS), and hepatocellular carcinoma (HCC). We conducted 5-fold cross-validation after random dataset partitioning, enhancing training data with data augmentation. Our models used modified pre-trained ResNet50, GGN, ResNet18, and VGG16 architectures. Model performance, assessed via confusion matrices for sensitivity, specificity, and accuracy, was compared across models and with prior studies. ResNet50 outperformed other models, achieving a 5-fold cross-validation accuracy of 87±2.2%. While VGG16 showed similar performance, it exhibited higher uncertainty. In the testing phase, the pretrained ResNet50 excelled in classifying non-FLL, cysts, and FFS. To compare with other research, ResNet50 surpassed the prior methods like two-layered feed-forward neural networks (FFNN) and CNN+ReLU in FLL diagnosis. ResNet50 exhibited good performance in FLL diagnosis, especially for HCC classification, suggesting its potential for developing computer-aided FLL diagnosis. However, further refinement is required for HCC and HMG classification in future studies.

Comparative Analysis of Contrast-Enhanced Ultrasound and Computed Tomography in the Differential Diagnosis of Focal Liver Lesions in Patients with a History of Advanced Cancer

Цель. Проанализировать диагностическую эффективность мультипараметрического ультразвукового исследования с контрастным усилением в дифференциальной диагностике очаговых образований печени у онкопациентов и сравнить полученные данные с компьютерной томографией и гистологическим заключением.&#x0D; Материалы и методы. Проведён ретроспективный анализ результатов исследований 123 пациентов с очаговыми изменения печени, из них 96 женщин (средний возраст 56,1 ±13,6) и 27 мужчин (средний возраст 55,0±14,0). Всем пациентам проводилось мультипараметрическое ультразвуковое исследование и компьютерная томография с контрастированием. При подозрении на злокачественный процесс выполнялась трепан-биопсия печени под ультразвуковым наведением. В случае выявления признаков, характерных для доброкачественных образований, пациент находился на динамическом контроле каждые 3 месяца в первый год наблюдения, отсутствие динамики через 6-12 месяцев свидетельствовало в пользу доброкачественного процесса. При увеличении размеров образования также выполнялась морфологическая верификация.&#x0D; Результаты. Результаты для серошкального режима составили: чуствительность (Ч)-58,1%, специфичность (С)-68,4%, точность (Т)-60,2%, прогностическая ценность положительного результата (ПЦПР)- 87,8%, прогностическая ценность отрицательного результата (ПЦОР)-29,5%. Эффективность контрастно-усиленного ультразвукового исследования (КУУЗИ) значимо превышала исследование в традиционном серошкальном режиме и составила: Ч-90,5%, С-84,2%, Т-89,2%, ПЦПР-95,7%, ПЦОР-69,6%. Для компьютерной томографии с контрастным усилением (КТ с КУ) результаты были сопоставимы с КУУЗИ и составили: Ч-83,6%, С-78,9%, Т-82,6%, ПЦПР-93,8%, ПЦОР-55,6%.&#x0D; Полученные нами значения подтверждают высокую информативность КУУЗИ в оценке очагового поражения печени, в связи с чем в ряде случаев (отсутствие технической возможности проведения или наличие противопоказаний к КТ с КУ, а также полученные неоднозначные результаты по другим методика) может выступить в качестве метода выбора.&#x0D; Заключение. Мультимодальная диагностика превосходит единый метод визуализации по чувствительности скрининга и точности диагностики. КТ с КУ и КУУЗИ дополняют друг друга при дифференциальной диагностике очагов в печени неясного генеза. Использование КТ с КУ в сочетании с КУУЗИ показывает клиническую ценность для пациентов, что особенно важно у лиц с отягощённым онкоанамнезом.

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.

Differences between CEUS LI-RADS and CECT LI-RADS in the diagnosis of focal liver lesions in patients at risk for HCC

ObjectivesTo compare the inter-modality consistency and diagnostic performances of the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) and contrast-enhanced computed tomography (CECT) LI-RADS in patients at risk for hepatocellular carcinoma (HCC), so as to help clinicians to select a more appropriate modality to follow the focal liver lesions (FLLs).MethodsThis retrospective study included untreated 277 FLLs from 247 patients who underwent both CEUS and CECT within 1 month. The ultrasound contrast medium used was SonoVue. FLL categories were independently assigned by two ultrasound physicians and two radiologists using CEUS LI-RADS v2017 and CECT LI-RADS v2018, respectively. The diagnostic performances of CEUS and CECT LI-RADS were evaluated using sensitivity, specificity, positive predictive value (PPV), and negative predictive value. Cohen’s Kappa was employed to evaluate the concordance of the LI-RADS category.ResultsThe inter-modality consistency for CEUS and CECT LI-RADS was 0.31 (p < 0.001). HCC was more frequently observed in CECT LR-3 and LR-4 hepatic lesions than in CEUS (7.3% vs. 19.5%, p < 0.001). The specificity and PPV of CEUS and CECT LR-5 for the diagnosis of HCC were 89.5%, 95.0%, and 82.5%, 94.4%, respectively. The sensitivity of CEUS LR-5 + LR-M for the diagnosis of hepatic malignancies was higher than that of CECT (93.7% vs. 82.7%, p < 0.001). The specificity and PPV of CEUS LR-M for the diagnosis of non-HCC malignancies were lower than those of CECT (59.7% vs. 95.5%, p < 0.001; 23.4% vs. 70.3%, p < 0.001).ConclusionsThe inter-modality consistency between the CEUS and CECT LI-RADS categories is fair. CEUS LI-RADS was more sensitive than CECT LI-RADS in terms of identifying hepatic malignancies, but weaker in terms of separating HCC from non-HCC malignancies.

A unified end-to-end classification model for focal liver lesions

Accurate diagnosis of focal liver lesions (FLLs) plays a crucial role in patients’ management, surveillance, and prognosis. Contrast-enhanced ultrasound (CEUS) as a vital diagnostic tool for FLLs still faces the challenge of image feature overlap among several FLLs. In this study, we proposed a deep learning-based model, denoted as a unified end-to-end (UEE) model, to fully capture the lesion information to achieve the classification of FLLs by adopting CEUS. We first exploited ResNet50 as the backbone to extract multi-scale features from several CEUS frames. Secondly, the hybrid attention enhancement module (HAEM) was designed to enhance the significant features with various scales. The enhanced features were then concatenated and passed into the nested feature aggregation module (NFAM) to add nonlinearity to the features with various scales. Finally, all features from different frames were averaged and fed into a Sigmoid classifier for FLL classification. The experiments are developed on a multi-center dataset which ensured diversity. The extensive experimental results revealed that the UEE model achieved 88.64 % accuracy on benign (Be) and malignant (Ma) classification, and 91.27 % accuracy on hepatocellular carcinoma (HCC) and intrahepatic cholangiocellular carcinoma (ICC) classification.

Focal Liver Lesions in Budd-Chiari Syndrome: Spectrum of Imaging Findings.

Budd-Chiari syndrome (BCS) is a rare clinical entity characterized by hepatic venous outflow obstruction, resulting in liver congestion and subsequent chronic parenchymal damage. This condition often leads to the development of focal liver lesions, including benign focal nodular hyperplasia-like regenerative nodules, hepatocellular carcinoma, and perfusion-related pseudo-lesions. Computed tomography, ultrasound, and magnetic resonance are the commonly employed imaging modalities for the follow-up of BCS patients and for the detection and characterization of new-onset lesions. The accurate differentiation between benign and malignant nodules is crucial for optimal patient management and treatment planning. However, it can be challenging due to the variable and overlapping characteristics observed. This review aims to provide a comprehensive overview of the imaging features and differential diagnosis of focal liver lesions in BCS, emphasizing the key findings and discussing the challenges associated with their interpretation, with the purpose of facilitating the subsequent clinical decision-making.

Non-invasive evaluation of vascular architecture of focal liver lesions by micro vascular imaging.

To explore the value of vascular architecture detected by micro vascular imaging (MVI) in preoperative diagnosis of focal liver lesions (FLLs). In this retrospective study, patients with surgery and histopathologically proved or radiologically confirmed FLLs were included. Vascular architecture of FLLs were acquired by color Doppler flow imaging (CDFI) and MVI on LOGIQ™ E20 ultrasound machine (C1-6 convex array probes). Alder semiquantitative analysis (grade 0-3) and morphologic features of blood vessels (pattern a-f) were used to assess the blood flow within the FLLs. Interobserver agreement for evaluating blood flow of FLLs was analyzed. Using Adler's grading or morphologic patterns as diagnostic criteria for malignant FLLs, the diagnostic efficiency was analyzed and compared. From October 2021 and February 2022, 50 patients diagnosed with 40 malignant FLLs and 10 benign FLLs were finally included. The Kappa value within two observers for evaluating the blood flow of FLLs was 0.78 for MVI and 0.55 for CDFI. According to Alder semiquantitative analysis, more high-level blood flow signals (grade 2-3) were detected by MVI than CDFI (P < 0.05). Based on high-level blood flow signals (grade 2-3) and hypervascular supply patterns (pattern e and f), the diagnostic accuracy for malignant FLLs were 76% and 68% for MVI, 56% and 38% for CDFI, respectively. MVI is superior to CDFI in evaluating vascular architecture of FLLs. The high-level flow signals and hypervascular pattern detected by MVI have a useful and complementary value in preoperative non-invasive identification of malignant FLLs.

Contribution of semiquantitative analysis with dynamic contrast enhanced magnetic resonance imaging to the differential diagnosis of focal liver lesions

Introduction and aim. We aimed to evaluate the usefulness of dynamic contrast-enhanced (DCE) MRI semiquantitative analysis values in focal liver lesions (FLL) to provide additional qualities that can be used in daily practice in the differential diagnosis of lesions. Material and methods. This retrospective study included 91 patients with liver masses on DCE-MRI. The sensitivity and specificity of time intensity curves (TIC) and semiquantitative analysis values were evaluated to differentiate benign and malignant lesions. Results. The study included 91 patients (376 lesions), aged between 28-81 years. Of the lesions, 303 were malignant and 73 were benign. In TIC semiquantitative analysis, it was found that “Tpeak” and “wash-out” rate values showed differences, especially in the differentiation of HCC, metastasis, and hemangioma. Area under curve, maximum relative enhancement, and “wash-in” and “wash-out” values of metastases and hemangiomas were different. Brevity of enhancement values of HSK, hemangiomas, and metastases were found to be different. The risk of malignancy was found to be high when the “wash-out” ratio was above 0.08 (sensitivity: 64.3%, specificity: 70.4%). Conclusion. We think that the 0.08 threshold value we found for the washout ratio with DCE-MRI semiquantitative analysis data will be useful in daily practice in the differentiation of malignant and benign FLL.

The diagnostic value of Doppler Resistive Index in the differential diagnosis of focal liver lesions.

To investigate the diagnostic value of resistance index (RI) in differentiating focal liver lesions. In this retrospective study, a total of 576 patients with histologically confirmed focal liver lesions were included. Each patient underwent B-mode ultrasound examination and color Doppler ultrasound examination. The RI values of different focal liver lesions were recorded and compared. The mean RI value of benign lesions was significantly lower than that of malignant lesions (0.54 ± 0.10 vs. 0.71 ± 0.12) (p <0.05). In malignant lesions, the RI value of intrahepatic cholangiocarcinoma was significantly lower than that of hepatocellular carcinoma lesions. Furthermore, in hepatocellular carcinoma lesions, the RI of large lesions (group 4: >10 cm) was significantly lower than that of small lesions (group 1: ≤2 cm, group 2: 2-5 cm) (p <0.05). Taken RI of 0.615 as a cutoff value to differentiate malignant and benign lesions, the sensitivity, specificity, positive predictive value and negative predictive value were 82.80%, 81.00%, 81.34% and 82.48%, respectively. Color Doppler ultrasound examination is a valuable imaging method in detecting blood flow signal within liver lesions. The RI parameter should be helpful in differentiating malignant and benign liver tumors.

Robust End-to-End Focal Liver Lesion Detection Using Unregistered Multiphase Computed Tomography Images

The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multiphase images owing to its complete learning-based approach, which reduces the sensitivity of the model’s performance to the quality of registration and enables a standalone deployment of the model in clinical practice. Evaluation on a large-scale dataset with 280 patients confirmed that our method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images. The robustness of the proposed method can enhance the clinical adoption of the deep-learning-based computer-aided detection system.

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

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. 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). 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. 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.

Application of diffusion kurtosis imaging in differential diagnosis of focal liver lesions.

Diffusion kurtosis imaging (DKI) is an MRI method related to diffusion imaging (DWI) that is distinguished by a non-Gaussian calculation of water particles movements in tissues. The aim of the study was to assess DKI advantage over DWI in differentiating benign and malignant liver lesions. Analysis included prospectively acquired group of 83 patients referred consecutively for 3T-MRI liver tumor examination, with 95 liver lesions (31 benign, 59 malignant). MRI assessments were performed with standard protocol and DKI sequence with seven b-values (0-2,000 s/mm2). Quantitative data were acquired by placing ROIs in liver tumors on all b-value images, ROI data extracted, and calculation of DWI and DKI parameters. ADC was calculated for all b-values (ADC0-2000) and for three values of b = 0, 500, and 750 (s/mm2) (ADC0-500-750). DKI and ADC parameters for benign and malignant lesions were compared, and ROC curves were plotted. Significant differences were obtained for all DKI and ADC parameters. ROC analysis showed AUC of DK, K, ADC0-2000, and ADC0-500-750 was 0.74, 0.77, 0.77, and 0.75, respectively. The highest sensitivity (of 0.91) was obtained for ADC0-2000. The highest specificity (0.65) and accuracy (0.80) was obtained for K. DKI technique yields statistically comparable results with DWI technique.

Strategy for Accurate Diagnosis by Contrast-Enhanced Ultrasound of Focal Liver Lesions in Patients Not at High Risk for Hepatocellular Carcinoma: A Preliminary Study.

To develop an effective strategy for accurate diagnosis of focal liver lesions (FLLs) in patients with non-high risk for hepatocellular carcinoma (HCC). From January 2012 to December 2015, consecutive patients with non-high risk for HCC who underwent contrast-enhanced ultrasound (CEUS) were included in this retrospective double-reader study. All patients were stratified into 2 different risks (intermediate, low-risk) groups according to criteria based on clinical characteristics, known as clinical risk stratification criteria. For the intermediate-risk group, the CEUS criteria for identifying benign lesions and HCCs were constructed based on selected CEUS features. The diagnostic performance of the clinical risk stratification criteria, and CEUS criteria for identifying benign lesions and HCCs was evaluated. This study included 348 FLLs in 348 patients. The sensitivity and specificity of the clinical risk stratification criteria for malignancy was 97.8 and 69.8%. Patients were classified as intermediate risk if they were male, or older than 40 years of age, or HBcAb positive, or having positive tumor markers. Otherwise, patients were classified as low risk. Among the 348 patients, 327 were in the intermediate-risk group and 21 were in the low-risk group. In the intermediate-risk group, the CEUS criteria for identifying benign lesions were any of the following features: 1) hyper/isoenhancement in the arterial phase without washout, 2) nonenhancement in all phases, 3) peripheral discontinuous globular enhancement in the arterial phase, 4) centrifugal enhancement or peripheral enhancement followed by no central enhancement, or 5) enhanced septa. The accuracy, sensitivity, and specificity of the CEUS criteria for identifying benign lesions were 94.5, 83.0, and 99.6%, respectively. Arterial phase hyperenhancement followed by mild and late washout (>60 seconds) was more common in HCC patients than in non-HCC patients (P < .001). Using arterial phase hyperenhancement followed by mild and late washout as the CEUS criteria for identifying HCCs, the sensitivity and specificity were 52.6 and 95.3%, but unfortunately, the positive predictive value was only 82.0%. For the low-risk group, no further analysis was performed due to the small sample size. Initial clinical risk stratification followed by assessment of certain CEUS features appears to be a promising strategy for the accurate diagnosis of FLLs in patients not at high risk for HCC.