Dataset Information

Liver Imaging Reporting and Data System (LI-RADS) v2018: differential diagnostic value of ADC values for benign and malignant nodules with moderate probability (LR-3).

ABSTRACT:

Objective

To evaluate the usefulness of the apparent diffusion coefficient (ADC) in differentiating between benign and malignant LR-3 lesions classified by Liver Imaging Reporting and Data System 2018 (LI-RADS v2018).

Methods

Retrospectively analyzed 88 patients with liver nodules confirmed by pathology and classified as LR-3 by LI-RADS. All patients underwent preoperative contrast-enhanced MR examination, and the following patient-related imaging features were collected: tumor size,nonrim APHE, nonperipheral "washout", enhancing "capsule", mild-moderate T2 hyperintensity, fat in mass, restricted diffusion, and nodule-in-nodule architecture. We performed ROC analysis and calculated the sensitivity and specificity.

Results

A total of 122 lesions were found in 88 patients, with 68 benign and 54 malignant lesions. The mean ADC value for malignant and benign lesions were 1.01 ± 0.15 × 10³ mm²/s and 1.41 ± 0.31 × 10³ mm²/s, respectively. The ADC value of malignant lesions was significantly lower than that of benign lesions, p < 0.0001. Compared with other imaging features, ADC values had the highest AUC (AUC = 0.909), with a sensitivity of 92.6% and a specificity of 74.1% for the differentiation of benign and malignant lesions.

Conclusions

ADC values are useful for differentiating between benign and malignant liver nodules in LR-3 classification, it improves the sensitivity of LI-RADS in the diagnosis of HCC while maintaining high specificity, and we recommend including ADC values in the standard interpretation of LI-RADSv2018.

SUBMITTER: Chen X

PROVIDER: S-EPMC10478080 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Liver Imaging Reporting and Data System (LI-RADS) v2018: differential diagnostic value of ADC values for benign and malignant nodules with moderate probability (LR-3).

Chen Xue X Cai Quanyu Q Xia Jinju J Huang Huan H Li Zhaoxing Z Song Kairong K Jia Ningyang N Liu Wanmin W

Frontiers in oncology 20230822

<h4>Objective</h4>To evaluate the usefulness of the apparent diffusion coefficient (ADC) in differentiating between benign and malignant LR-3 lesions classified by Liver Imaging Reporting and Data System 2018 (LI-RADS v2018).<h4>Methods</h4>Retrospectively analyzed 88 patients with liver nodules confirmed by pathology and classified as LR-3 by LI-RADS. All patients underwent preoperative contrast-enhanced MR examination, and the following patient-related imaging features were collected: tumor si ...[more]

PMID: 37675222

Similar Datasets

Project description:Background & aimsAssessment of computed tomography (CT)/magnetic resonance imaging Liver Imaging Reporting and Data System (LI-RADS) v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. We assessed the performance and added-value of a machine learning (ML)-based algorithm in assessing CT LI-RADS major features and categorisation of liver observations compared with qualitative assessment performed by a panel of radiologists.MethodsHigh-risk patients as per LI-RADS v2018 with pathologically proven liver lesions who underwent multiphase contrast-enhanced CT at diagnosis between January 2015 and March 2019 in seven centres in five countries were retrospectively included and randomly divided into a training set (n = 84 lesions) and a test set (n = 345 lesions). An ML algorithm was trained to classify non-rim arterial phase hyperenhancement, washout, and enhancing capsule as present, absent, or of uncertain presence. LI-RADS major features and categories were compared with qualitative assessment of two independent readers. The performance of a sequential use of the ML algorithm and independent readers were also evaluated in a triage and an add-on scenario in LR-3/4 lesions. The combined evaluation of three other senior readers was used as reference standard.ResultsA total of 318 patients bearing 429 lesions were included. Sensitivity and specificity for LR-5 in the test set were 0.67 (95% CI, 0.62-0.72) and 0.91 (95% CI, 0.87-0.96) respectively, with 242 (70.1%) lesions accurately categorised. Using the ML algorithm in a triage scenario improved the overall performance for LR-5. (0.86 and 0.93 sensitivity, 0.82 and 0.76 specificity, 78% and 82.3% accuracy for the two independent readers).ConclusionsQuantitative assessment of CT LI-RADS v2018 major features is feasible and diagnoses LR-5 observations with high performance especially in combination with the radiologist's visual analysis in patients at high-risk for HCC.Impact and implicationsAssessment of CT/MRI LI-RADS v2018 major features leads to substantial inter-reader variability and potential decrease in hepatocellular carcinoma diagnostic accuracy. Rather than replacing radiologists, our results highlight the potential benefit from the radiologist-artificial intelligence interaction in improving focal liver lesions characterisation by using the developed algorithm as a triage tool to the radiologist's visual analysis. Such an AI-enriched diagnostic pathway may help standardise and improve the quality of analysis of liver lesions in patients at high risk for HCC, especially in non-expert centres in liver imaging. It may also impact the clinical decision-making and guide the clinician in identifying the lesions to be biopsied, for instance in patients with multiple liver focal lesions.

Project description:BackgroundTo develop a deep learning (DL) method based on multiphase, contrast-enhanced (CE) magnetic resonance imaging (MRI) to distinguish Liver Imaging Reporting and Data System (LI-RADS) grade 3 (LR-3) liver tumors from combined higher-grades 4 and 5 (LR-4/LR-5) tumors for hepatocellular carcinoma (HCC) diagnosis.MethodsA total of 89 untreated LI-RADS-graded liver tumors (35 LR-3, 14 LR-4, and 40 LR-5) were identified based on the radiology MRI interpretation reports. Multiphase 3D T1-weighted gradient echo imaging was acquired at six time points: pre-contrast, four phases immediately post-contrast, and one hepatobiliary phase after intravenous injection of gadoxetate disodium. Image co-registration was performed across all phases on the center tumor slice to correct motion. A rectangular tumor box centered on the tumor area was drawn to extract subset tumor images for each imaging phase, which were used as the inputs to a convolutional neural network (CNN). The pre-trained AlexNet CNN model underwent transfer learning using liver MRI data for LI-RADS tumor grade classification. The output probability number closer to 1 or 0 indicated a higher possibility of being combined LR-4/LR-5 tumor or LR-3 tumor, respectively. Five-fold cross validation was used for training (60% dataset), validation (20%) and testing processes (20%).ResultsThe DL CNN model for LI-RADS grading using inputs of multiphase liver MRI data acquired at three time points (pre-contrast, arterial, and washout phase) achieved a high accuracy of 0.90, sensitivity of 1.0, precision of 0.835, and AUC of 0.95 with reference to the expert human radiologist report. The CNN output of probability provided radiologists a confidence level of the model's grading for each liver lesion.ConclusionsAn AlexNet CNN model for LI-RADS grading of liver lesions provided diagnostic performance comparable to radiologists and offered valuable clinical guidance for differentiating intermediate LR-3 liver lesions from more-likely malignant LR-4/LR-5 lesions in HCC diagnosis.

Project description:ObjectiveTo develop a domain-specific large language model (LLM) for LI-RADS v2018 categorization of hepatic observations based on free-text descriptions extracted from MRI reports.Material and methodsThis retrospective study included 291 small liver observations, divided into training (n = 141), validation (n = 30), and test (n = 120) datasets. Of these, 120 were fictitious, and 171 were extracted from 175 MRI reports from a single institution. The algorithm's performance was compared to two independent radiologists and one hepatologist in a human replacement scenario, and considering two combined strategies (double reading with arbitration and triage). Agreement on LI-RADS category and dichotomic malignancy (LR-4, LR-5, and LR-M) were estimated using linear-weighted κ statistics and Cohen's κ, respectively. Sensitivity and specificity for LR-5 were calculated. The consensus agreement of three other radiologists served as the ground truth.ResultsThe model showed moderate agreement against the ground truth for both LI-RADS categorization (κ = 0.54 [95% CI: 0.42-0.65]) and the dichotomized approach (κ = 0.58 [95% CI: 0.42-0.73]). Sensitivity and specificity for LR-5 were 0.76 (95% CI: 0.69-0.86) and 0.96 (95% CI: 0.91-1.00), respectively. When the chatbot was used as a triage tool, performance improved for LI-RADS categorization (κ = 0.86/0.87 for the two independent radiologists and κ = 0.76 for the hepatologist), dichotomized malignancy (κ = 0.94/0.91 and κ = 0.87) and LR-5 identification (1.00/0.98 and 0.85 sensitivity, 0.96/0.92 and 0.92 specificity), with no statistical significance compared to the human readers' individual performance. Through this strategy, the workload decreased by 45%.ConclusionLI-RADS v2018 categorization from unlabelled MRI reports is feasible using our LLM, and it enhances the efficiency of data curation.Critical relevance statementOur proof-of-concept study provides novel insights into the potential applications of LLMs, offering a real-world example of how these tools could be integrated into a local workflow to optimize data curation for research purposes.Key pointsAutomatic LI-RADS categorization from free-text reports would be beneficial to workflow and data mining. LiverAI, a GPT-4-based model, supported various strategies improving data curation efficiency by up to 60%. LLMs can integrate into workflows, significantly reducing radiologists' workload.

Project description:Background The Liver Imaging Reporting and Data System (LI-RADS) for contrast-enhanced ultrasonography (CEUS) was invented to define suspected liver nodules based on their imaging characteristics. Among the categories of nodules of LI-RADS for CEUS, LR-5 is generally considered to be definitely malignant; however, the exact diagnostic performance of this liver nodule category has varied between different studies. Therefore, we performed this systematic review and meta-analysis to calculate the pooled diagnostic sensitivity, specificity based on important data extracted from some influential clinical studies. Methods A preliminary search of national and international databases, including PubMed/Ovid Medline, Embase, Cochrane Library, Web of Science, and Wan Fang Data, for relevant studies on CEUS LI-RADS LR-5 published between January 2017 and June 2021 was conducted. A literature screening and selection process was undertaken to evaluate the relevance of the articles, and studies deemed eligible for inclusion in the review were subsequently identified. The updated Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied as the main method to assess the risk of bias and applicability of the studies. A meta-analysis of the diagnostic sensitivity and specificity of CEUS LI-RADS LR-5 was performed using the free software, Meta-DiSc 1.4 (Ramóny Cajal Hospital, Madrid, Spain). The area under curve (AUC) was calculated to help determine the diagnostic efficiency. A meta-regression analysis was also performed to identify factors that could have contributed to heterogeneity between the studies. Results Twelve studies with 20 observations focused on investigating the relative diagnostic performance of the CEUS LI-RADS LR-5 category for hepatocellular carcinoma (HCC) detection were finally recruited into the systematic review and meta-analysis. The pooled diagnostic sensitivity was 0.71 [95% confidence interval (CI): 0.69–0.72], with heterogeneity (I2) of 88.4%, and the pooled specificity was 0.93 (95% CI: 0.92–0.95), with an I2 of 71.2%. Study heterogeneity was observed and statistically correlated with the number of centers and the reference standard. Conclusions The CEUS LI-RADS LR-5 category has satisfactory diagnostic efficacy for HCC, as evidenced by an acceptable diagnostic sensitivity of 0.71 and a good diagnostic specificity of 0.93.

Project description:Thyroid nodule (TN) has been becoming a great concern worldwide due to its high incidence. Although some studies have reported associations between trace elements exposure and the risk of TNs, the linkage was not inconclusive. The present study aimed to identify the association of selected serum trace elements (Ca, Mg, V, Fe, Co, Cu, Zn, Se, Mn and Mo) with TNs among general adults. A cross-sectional study was conducted in January 2021 in Chengdu, China. 1282 subjects completed the questionnaire and gave at least one human biological material after an overnight fast, venous blood, and urine, including 377 TN participants defined through ultrasound. Various trace elements in serum specimens were determined by inductively coupled plasma mass spectrometry. Thyroid functions were tested by chemiluminescent microparticle immunoassay (CMIA). The associations between trace elements levels and the risk of TNs were examined by restricted cubic splines (RCS) regression and bayesian kernel machine regression (BKMR) models. TNs were more common in females (P < 0.001) and in the elderly (P < 0.001) and that they were also frequently associated with fertility, marital status, annual household income, drinking, anxiety, vitamin supplement, tea consumption, hypertension and hyperlipidemia. After adjusting for confounders by a propensity score matching model, the association between trace elements concentrations and TNs risk was found to be statistically insignificant in the RCS (P for nonlinear > 0.05) and BKMR models. FT3 or T4 (total or free) increased significantly with increasing total trace elements mixture levels. In TI-RADS-4 TN subjects, TPO-Ab level increased significantly with increasing total trace elements mixture levels in the high-dose range. Ca, Zn, Mo at their 75th percentile showed positive individual effects on TPO-Ab, which was examined to be interactive. The detection of trace elements for TNs in general adults may be of no significance, but once individuals classified as TI-RADS-4 TNs are detected with abnormal TPO-Ab, Ca, Zn and Mo level are recommended to measure. The substantive association on it still needs to be continuously explored in the future.

Dataset Information

Liver Imaging Reporting and Data System (LI-RADS) v2018: differential diagnostic value of ADC values for benign and malignant nodules with moderate probability (LR-3).

Objective

Methods

Results

Conclusions

Publications

Liver Imaging Reporting and Data System (LI-RADS) v2018: differential diagnostic value of ADC values for benign and malignant nodules with moderate probability (LR-3).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets