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Background: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. Purpose: To assess the performance of the deep learning–based artificial intelligence (AI) software in identifying and measuring lesions on contrast‐enhanced magnetic resonance imaging (MRI) images in patients with FLLs. Study Type: Retrospective. Subjects: 395 patients with 1149 FLLs. Field Strength/Sequence: The 1.5 T and 3 T scanners, including T1‐, T2‐, diffusion‐weighted imaging, in/out‐phase imaging, and dynamic contrast‐enhanced imaging. Assessment: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut‐off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10–20, 20–40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. Statistical Tests: McNemar test, Bland–Altman analyses, Friedman test, Pearson's chi‐squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P‐value <0.05 was considered statistically significant. Results: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). Data Conclusion: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. Level of Evidence: 4. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Purpose: This study compares the diagnostic efficacy of non-contrast abbreviated MRI protocols with Gadoxetic acid-enhanced abbreviated MRI for detecting colorectal liver metastasis (CRLM), focusing on lesion characterization and surveillance. Methods: Ninety-four patients, including 55 with pathologically verified CRLM, were enrolled, totaling 422 lesions (287 metastatic, 135 benign). Two independent readers assessed three MRI protocols per patient: Protocol 1 included non-contrast sequences (T2-weighted turbo spin-echo, T1-weighted Dixon, diffusion-weighted imaging (DWI), and ADC mapping). Protocol 2 included gadoxetic acid enhancement with hepatobiliary phase imaging, T2 TSE, DWI, and ADC maps. Protocol 3 utilized the standard Gadoxetic Acid-enhanced MRI sequence, which included pre-contrast T1-weighted imaging, T1-weighted Dixon sequences, post-contrast T1-weighted imaging (including arterial, portal venous, transitional and hepatobiliary phases), and additional T2-weighted and DWI sequences. Diagnoses were scored on a 5-point scale (benign = 1; malignant = 5), with scores ≥ 3 indicating CRLM. ROC curves analyzed diagnostic accuracy, comparing area under the curve (AUC) values across protocols. Results: No significant difference in AUCs was observed between Protocol 1 (0.899–0.909) and Protocol 2 (0.906–0.931) versus Protocol 3 (0.935–0.939) (p = 0.091–0.195). For lesions ≤ 10 mm, Protocol 1 was slightly inferior to Protocol 3 (p = 0.002–0.032), while Protocol 2 remained comparably effective (p = 0.096–0.179). These findings held when using a threshold of ≥ 4 to define CRLM. Conclusion: The non-enhanced abbreviated MRI protocol is as effective as the gadoxetic acid-enhanced protocol in identifying CRLM. The proposed Ab-MRI approach may be a viable alternative for CRLM surveillance. Critical relevance statement: The non-enhanced abbreviated MRI (Ab-MRI) protocol is as effective as the gadoxetic acid-enhanced protocol in identifying colorectal liver metastasis (CRLM). The proposed Ab-MRI approach may be a viable alternative for CRLM surveillance. Key Points: Two abbreviated protocols are proposed for colorectal liver metastasis (CRLM) surveillance. The non-enhanced protocol showed equivalent efficacy and was more cost-effective. The non-enhanced protocol may be a viable alternative for CRLM surveillance. [ABSTRACT FROM AUTHOR]

The swift incorporation of artificial intelligence (AI) into higher education has significantly propelled the digital transformation of education. This advancement is crucial for educators aiming to augment teaching quality through AI technologies, such as ChatGPT. However, the acceptance of ChatGPT among college students remains underexplored. This paper aims to clarify the determinants influencing college students' acceptance of ChatGPT and to facilitate its widespread adoption in higher education. To achieve this, we integrate the Technology Readiness Index (TRI), Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB) to develop a novel research framework. Employing a mixed-method approach that includes PLS-SEM and fsQCA, we analyze data from 298 Chinese college students. Our findings indicate that discomfort and insecurity adversely affect Perceived Ease of Use (PEU) and Perceived Usefulness (PU) in the context of ChatGPT adoption. Additionally, both PEU and PU positively impact attitudes, which, in conjunction with Subjective Norms (SN) and Perceived Behavioral Control (PBC), bolster the intention to accept ChatGPT. Insights from fsQCA reveal that the acceptance of ChatGPT among students is not driven by a singular factor but by an amalgamation of these elements, underscoring the complex nature of technology adoption. The paper concludes with practical recommendations for educators and designers to refine curriculum design and teaching methodologies, boost student engagement and learning efficacy, and promote the broader adoption of educational technology. Plain language summary: Factors influencing the acceptance of ChatGPT in high education This study contributes to the existing literature by amalgamating the Technology Readiness Index (TRI), Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB) into a novel research model to identify significant factors that influence Chinese college students' acceptance of ChatGPT via a mixed approach that combines PLS-SEM and fsQCA within higher educational settings. [ABSTRACT FROM AUTHOR]

Purpose: To analyze and compare the differences in MRI features between combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) and intrahepatic cholangiocarcinoma (iCCA) with arterial phase peripheral enhancement, so as to provide valuable references for preoperative differential diagnosis. Methods: Seventy cHCC-CCA patients and 74 iCCA patients confirmed by pathology were included in this study. Their contrast-enhanced MRI showed rim arterial phase hyperenhancement (Rim APHE). The differences of clinicopathological data and MRI features between cHCC-CCA and iCCA were compared. Then, the sensitivity, specificity, and area under curve (AUC) were also analyzed and compared. Results: Seventy cHCC-CCA patients (mean age, 55.7 ± 10.6 years) and 74 iCCA patients (mean age, 61.1 ± 10.5 years) were evaluated. In this study, univariable and multivariable regression analysis showed that AFP > 20 ng/ml (OR = 5.824, p = 0.006), enhancing capsule (OR = 7.252, p = 0.001), and mosaic architecture (OR = 32.732, p < 0.001) were independent risk factors of cHCC-CCA with Rim APHE. However, only hepatic capsule retraction (OR = 0.091, p < 0.001) was an independent predictor of iCCA. In addition, combining AFP > 20 ng/ml with enhancing capsule (96.7% vs. 79.2%, p < 0.001) and/or mosaic architecture (96.4% vs. 94.7%, p < 0.001) can improve the sensitivity of differentiating cHCC-CCA (vs. iCCA) with Rim APHE. Conclusion: The combination of elevated AFP and MRI features, such as enhancing capsule and mosaic architecture, will help in preoperative differential diagnosis of cHCC-CCA and iCCA with Rim APHE. [ABSTRACT FROM AUTHOR]

Purpose: To study the MRI features (based on LI-RADS) and clinical characteristics of P53-mutated hepatocellular carcinoma (HCC) patients. Patients and Methods: This study enrolled 344 patients with histopathologically confirmed HCC (P53-mutated group [n = 196], non-P53-mutated group [n = 148]). We retrospectively evaluated the preoperative MRI features, clinical and pathologic features of the lesions and assigned each lesion according to the LI-RADS. MRI findings, clinical features, and pathologic findings were compared using the Student's t test, χ 2 test, and multivariable regression analysis. Results: Most HCC patients were categorized as LR-5. On multivariate analysis, the Edmondson–Steiner grade (odds ratio, 2.280; 95% CI: 1.268, 4.101; p = 0.006) and rim enhancement (odds ratio, 2.517; 95% CI: 1.095, 5.784; p = 0.030) were found to be independent variables associated with P53-mutated HCC. In the group of HCC lesions with the largest tumor diameter (LTD) greater than or equal to 10mm and less than or equal to 20mm, enhancing capsule was an independent predictor of P53-mutated HCC (odds ratio, 6.200; 95% CI: 1.116, 34.449; p = 0.037). Among the HCC lesions (20 mm ˂ LTD ≤ 50 mm), corona enhancement (odds ratio, 2.102; 95% CI: 1.022, 4.322; p = 0.043) and nodule-in-nodule architecture (odds ratio, 2.157; 95% CI: 1.033, 4.504; p = 0.041) were found to be independent risk factors for P53 mutation. Among the HCC lesions (50 mm ˂ LTD ≤ 100 mm), diameter (odds ratio, 1.035; 95% CI: 1.001, 1.069; p = 0.044) and AFP ≥ 400 (ng/mL) (odds ratio, 3.336; 95% CI: 1.052, 10.577; p = 0.041) were found to be independent variables associated with P53-mutated HCC. Conclusion: Poor differentiation and rim enhancement are potential predictive biomarkers for P53-mutated HCC, while HCCs of different diameters have different risk factors for predicting P53 mutations. [ABSTRACT FROM AUTHOR]

Purpose: To explore which preoperative clinical data and conventional magnetic resonance imaging (MRI) features may indicate the presence of hepatocellular carcinoma (HCC) in HCC patients coexisting with LR-3 and LR-4 lesions. Methods: HCC Patients coexisting with LR-3 and LR-4 lesions who participated in a prospective clinical trial (XX) were included in this study. Two radiologists independently assessed the preoperative MRI features and each lesion was assigned according to the liver imaging reporting and data system (LI-RADS). The preoperative clinical data were also evaluated. The relative values of these parameters were assessed as potential predictors of HCC for coexisting LR-3 and LR-4 lesions. Results: We enrolled 102 HCC patients (58.1 ± 11.5 years; 84.3% males) coexisting with 110 LR-3 and LR-4 lesions (HCCs group [n = 66]; non-HCCs group [n = 44]). The presence of restricted diffusion (OR: 18.590, p < 0.001), delayed enhancement (OR: 0.113, p < 0.001), and mild-moderate T2 hyperintensity (OR: 3.084, p = 0.048) were found to be independent predictors of HCC diagnosis. The sensitivity and specificity of the above independent variables for the diagnosis of HCC ranged from 66.7 to 80.3% and 56.8 to 88.6%, respectively. ROC analysis showed that, in discriminating HCC, the AUCs of the above factors were 0.777, 0.686, and 0.670, respectively. Combining these three findings for the prediction of HCC resulted in a specificity greater than 97%, and the AUC further increased to 0.874. Conclusion: The presence of restricted diffusion, delayed enhancement, and mild–moderate T2 hyperintensity can be useful features for risk stratification of coexisting LR-3 and LR-4 lesions in HCC patients. Trial registration a prospective clinical trial (ChiCTR2000036201). [ABSTRACT FROM AUTHOR]

Objectives: To establish an MRI-based radiomics model for predicting the microvascular invasion (MVI) status of cHCC-CCA and to investigate biological processes underlying the radiomics model. Methods: The study consisted of a retrospective dataset (82 in the training set, 36 in the validation set) and a prospective dataset (25 patients in the test set) from two hospitals. Based on the training set, logistic regression analyses were employed to develop the clinical-imaging model, while radiomic features were extracted to construct a radiomics model. The diagnosis performance was further validated in the validation and test sets. Prognostic aspects of the radiomics model were investigated using the Kaplan–Meier method and log-rank test. Differential gene expression analysis and gene ontology (GO) analysis were conducted to explore biological processes underlying the radiomics model based on RNA sequencing data. Results: One hundred forty-three patients (mean age, 56.4 ± 10.5; 114 men) were enrolled, in which 73 (51.0%) were confirmed as MVI-positive. The radiomics model exhibited good performance in predicting MVI status, with the area under the curve of 0.935, 0.873, and 0.779 in training, validation, and test sets, respectively. Overall survival (OS) was significantly different between the predicted MVI-negative and MVI-positive groups (median OS: 25 vs 18 months, p = 0.008). Radiogenomic analysis revealed associations between the radiomics model and biological processes involved in regulating the immune response. Conclusion: A robust MRI-based radiomics model was established for predicting MVI status in cHCC-CCA, in which potential prognostic value and underlying biological processes that regulate immune response were demonstrated. Critical relevance statement: MVI is a significant manifestation of tumor invasiveness, and the MR-based radiomics model established in our study will facilitate risk stratification. Furthermore, underlying biological processes demonstrated in the radiomics model will offer valuable insights for guiding immunotherapy strategies. Key Points: MVI is of prognostic significance in cHCC-CCA, but lacks reliable preoperative assessment. The MRI-based radiomics model predicts MVI status effectively in cHCC-CCA. The MRI-based radiomics model demonstrated prognostic value and underlying biological processes. The radiomics model could guide immunotherapy and risk stratification in cHCC-CCA. [ABSTRACT FROM AUTHOR]

Objective: To investigate the role of serum alpha-fetoprotein (AFP) in diagnosing subcentimeter hepatocellular carcinoma (HCC) on gadoxetic acid–enhanced MRI (EOB-MRI). Methods: This study retrospectively enrolled treatment-naïve patients with chronic hepatitis B who had a solitary subcentimeter observation on EOB-MRI from January 2017 to March 2023. Final diagnosis was confirmed by pathology for HCC and pathology or follow-up for benign controls. The AFP cutoff value for HCC was determined using Youden's index. Diagnostic criteria were developed according to significant findings in logistic regression analyses based on AFP and imaging features. The diagnostic performance of possible criteria was compared to the diagnostic hallmarks of HCC (arterial-phase hyperintensity and portal-phase hypointensity). Results: A total of 305 patients (mean age, 51.5 ± 10.7 years; 153 men) were divided into derivation and temporal validation cohorts. Four findings, namely AFP >13.7 ng/mL, arterial-phase hyperintensity, portal-phase hypointensity, and transitional-phase hypointensity, were predictors of HCC. A new criterion (at least three of the four findings) showed higher sensitivity than the diagnostic hallmarks (derivation cohort, 71.6% vs. 52.3%, p < 0.001; validation cohort, 75.0% vs. 47.5%, p = 0.003) without decreasing specificity (derivation cohort, 92.5% vs. 92.5%, p > 0.999; validation cohort, 92.0% vs. 92.0%, p > 0.999). Another criterion (all four findings) achieved a slightly higher specificity than the diagnostic hallmark (derivation cohort, 99.1% vs. 92.5%, p = 0.023; validation cohort, 100.0% vs. 92.0%, p = 0.134). Subgroup analysis for hepatobiliary hypointense observations yielded similar results. Conclusion: Including AFP in the diagnostic algorithm may improve the diagnostic performance for subcentimeter HCC. Clinical relevance statement: Combining imaging features on gadoxetic acid–enhanced MRI with alpha-fetoprotein may enhance the diagnostic performance for subcentimeter HCC in treatment-naïve patients with chronic hepatitis B. Key Points: • The traditional diagnostic hallmark of HCC (arterial-phase hyperintensity and portal-phase hypointensity) shows modest diagnostic performance for subcentimeter HCC on EOB-MRI. • Serum alpha-fetoprotein > 13.7 ng/mL, arterial-phase hyperintensity, portal-phase hypointensity, and transitional-phase hypointensity were independent predictors for subcentimeter HCC. • A criterion of at least three of the four above findings achieved a higher sensitivity without decreasing specificity. [ABSTRACT FROM AUTHOR]

Purpose: To construct an MRI-based habitat imaging model to help predict component percentage in combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) preoperatively, and investigate the biologic underpinnings of habitat imaging in cHCC-CCA. Methods: The study consisted of one retrospective model-building dataset and one prospective validation dataset from two hospitals. All voxels were assigned into different clusters according to the similarity of enhancement pattern by using K-means clustering method, and each habitat's volume fraction in each lesion was calculated. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select optimal predictors, and then to establish an MRI-based habitat imaging model. R-squared was calculated to evaluate performance of the prediction models. Model performance was also verified in the prospective dataset with RNA sequencing data, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was then applied to investigate the biologic underpinnings of habitat imaging. Results: A total of 129 patients were enrolled (mean age, 56.1 ± 10.4, 102 man), among which 104 patients were in the retrospective model-building set, while 25 patients in the prospective validation set. Three habitats, habitat1 (HCC-alike habitat), habitat2 (iCCA-alike habitat), and habitat3 (in-between habitat), were identified. Habitat 1's volume fraction, habitat 3's volume fraction, nonrim APHE, nonperipheral washout, and LI-RADS categorization were selected to develop an HCC percentage prediction model with R-squared of 0.611 in the model-building set and 0.541 in the validation set. Habitat 1's volume fraction was correlated with genes involved in regulation of actin cytoskeleton and Rap1 signaling pathway, which regulate cell migration and tumor metastasis. Conclusion: Preoperative prediction of HCC percentage in patients with cHCC-CCA was achieved using an MRI-based habitat imaging model, which may correlate with signaling pathways regulating cell migration and tumor metastasis. [ABSTRACT FROM AUTHOR]

We present an interface-type memristor utilizing native silicon oxide nanostructures achieved through controlled plasma oxidation engineering at room temperature. This process effectively reduces the concentration of oxygen vacancies on the surface of the native silicon oxide, facilitating oxygen-vacancy-mediated interfacial resistive switching with excellent COMS compatibility and low switching variability (1.03% for C2C and 3.96% for D2D variability). Moreover, this dynamic memristor exhibits favorable nonlinearity and short-term memory (STM) effects, which are crucial properties for the development of memristor-based reservoir computing (RC) systems. Compared with the conventional neural network, the RC system requires fewer trainable weights to achieve an accuracy of 96.7% for the Iris classification task. By the addition of virtual nodes to the reservoir with masks, the accuracy can be further increased to 100%. A simple digit recognition task is also employed, and noisy images that are not included in the original training set can be recognized correctly. These results highlight the great potential of our device for constructing reservoir computing hardware. [ABSTRACT FROM AUTHOR]

Background: The suitability of hepatectomy among patients with multinodular hepatocellular carcinoma (MHCC) beyond the Milan criteria remains controversial. There is a need for a reliable risk stratification tool among these patients for the selection of ideal candidates of curative resection. Purpose: To determine the clinicoradiological prognostic factors for patients with MHCC beyond the Milan criteria to further develop a stratification system. Study Type: Retrospective. Subjects: 176 patients with pathologically confirmed MHCC beyond the Milan criteria. Field Strength/Sequence: The 1.5 T scanner, including T1‐, T2‐, diffusion‐weighted imaging, in/out‐phase imaging, and dynamic contrast‐enhanced imaging. Assessment: Conventional MRI features and preoperative laboratory data including aspartate aminotransferase (AST) and α‐fetoprotein (AFP) were collected and analyzed. Two nomograms incorporating clinicoradiological variables were independently constructed to predict recurrence‐free survival (RFS) and overall survival (OS) with Cox regression analyses and verified with 5‐fold cross validation. Based on the nomograms, two prognostic stratification systems for RFS and OS were further developed. Statistical Tests: The Cohen's kappa/intraclass correlation coefficient, C‐index, calibration curve, Kaplan–Meier curve, log‐rank test. A P value <0.05 was considered statistically significant. Results: AST > 40 U/L, increased tumor burden score, radiological liver cirrhosis and nonsmooth tumor margin were independent predictors for poor RFS, while AST > 40 U/L, AFP > 400 ng/mL and radiological liver cirrhosis were independent predictors for poor OS. The two nomograms demonstrated good discrimination performance with C‐index of 0.653 (95% confidence interval [CI], 0.602–0.794) and 0.685 (95% CI, 0.623–0.747) for RFS and OS, respectively. The 5‐fold cross validation further validated the discrimination capability of the nomograms. Based on the nomogram models, MHCC patients beyond the Milan criteria were stratified into low−/medium−/high‐risk groups with significantly different RFS and OS. Data Conclusion: The proposed MRI‐based prognostic stratification system facilitates the refinement and further subclassification of patients with MHCC beyond the Milan criteria. Evidence Level: 4. Technical Efficacy: 2. [ABSTRACT FROM AUTHOR]

Purpose: To compare the imaging features, pathologic characteristics, and survival outcomes between subcentimeter and 1–2 cm hepatocellular carcinoma (HCC). Methods: This retrospective observational study evaluated the imaging features and medical records of patients with HCC smaller than 2 cm who underwent surgical resection with preoperative gadoxetic-acid-enhanced MRI (EOB-MRI) from January 2013 to December 2021. The incidence of EOB-MRI features and pathological characteristics between the subcentimeter and 1–2 cm HCC were compared. The recurrence-free survival (RFS), including early and overall tumor recurrence, and overall survival (OS) were assessed. Results: A total of 223 patients (82 with subcentimeter HCC and 141 with 1–2 cm HCC, 179 men) were enrolled. Compared with 1–2 cm HCC, subcentimeter HCC showed fewer restricted diffusion (87.8 vs. 95.7%, P = 0.027), portal-phase washout (58.5% vs. 73.8%, P = 0.013), typical enhancement pattern (50.0% vs. 66.7%, P =0.014), and microvascular invasion (4.9% vs. 14.9%, P = 0.022). Patients with subcentimeter HCC had higher RFS (P = 0.027) and better OS (P = 0.029). The estimated RFS rates at 5 years was 83.3% for subcentimeter HCC and 67.3% for 1–2 cm HCC, respectively. The estimated OS rates at 5 years was 97.3% for subcentimeter HCC and 89.5% for 1–2 cm HCC, respectively. Conclusion: Subcentimeter HCC showed less frequent EOB-MRI features seen typically in 1–2 cm HCC but better survival outcomes. Therefore, tailored early diagnostic criteria and immediate treatment for subcentimeter HCC may be warranted. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate whether the radiological similarity and clinicopathological factors determine the prognosis in bifocal hepatocellular carcinoma (bHCC) stratified by the Milan criteria. Methods: Consecutive patients with pathologically confirmed bHCC examined between January 2016 and December 2018 were retrospectively enrolled and grouped based on the Milan criteria. Two radiologists independently evaluated whether the imaging features of both tumors were consistent or not, which was defined as the radiological similarity. The clinicopathological data were also collected. The multivariable Cox regression was applied to separately identify the independent factors for recurrence-free survival (RFS) and overall survival (OS) in bHCC within and beyond the Milan criteria. Results: A total of 193 patients were evaluated and divided into the within the Milan criteria group (n = 72) and the beyond the Milan criteria group (n = 121). bHCC within the Milan criteria showed a significantly better prognosis than those beyond the criteria. In the within the Milan criteria group, HBV-DNA load >104 IU/mL, microvascular invasion (MVI), and different enhancement patterns were independently associated with poor RFS. MVI was an independent prognostic factor for poor OS. In the beyond the Milan criteria group, HBV infection, MVI, increased ratio of the larger to the smaller tumor diameter (RLSD) value, and low comprehensive similarity were associated with shorter RFS, whereas MVI and increased RLSD value were independent predictors for poor OS. Conclusions: Our study revealed that in addition to MVI- and HBV-related factors, similarity in imaging features between lesions of bHCC is associated with the long-term prognosis. Key Points: • The prognosis of bifocal HCC patients within the Milan criteria is significantly better than those beyond the criteria. • The similarity in imaging features between lesions of bHCC was an independent prognostic factor. • The more similar the bifocal lesions are in imaging features, the better the prognosis is. [ABSTRACT FROM AUTHOR]

Objectives: Accurate diagnosis of subcentimeter hepatocellular carcinoma (HCC) is a challenge also with gadoxetic acid–enhanced MRI (EOB-MRI). This study aimed to assess the diagnostic accuracy of the Liver Imaging Reporting and Data System (LI-RADS) for subcentimeter HCC and to determine whether new diagnostic criteria (washout either on portal venous phase (PVP) or transitional phase (TP)) would improve the diagnostic performance. Methods: We evaluated 240 subcentimeter observations in 225 consecutive treatment-naïve patients at risk of HCC. Final diagnoses were 132 HCCs (all by pathology) and 108 non-HCC (41 by pathology and 67 by follow-up). Two radiologists assessed MR imaging features and assigned LI-RADS categories. A variety of diagnostic criteria were developed by combining significant MRI features based on washout on PVP or TP. Diagnostic performance was compared. Results: Non-rim arterial phase hyperenhancement (non-rim APHE), washout on PVP or TP, and hepatobiliary-phase hypointensity were significant predictors for subcentimeter HCC diagnosis according to multivariable analysis. One criterion (non-rim APHE and washout on PVP or TP) yielded higher sensitivity (68.2% vs. 56.8%, p = 0.011) with comparable specificity (91.7% vs. 92.6%, p > 0.999) compared to the LR-4 category. This criterion had improved sensitivity (68.2% vs. 49.2%, p < 0.001) and slightly decreased specificity (91.7% vs. 94.4%, p = 0.250) compared to non-rim APHE with washout on PVP. Conclusions: LI-RADS exhibits modest diagnostic performance for subcentimeter HCC. Our new criterion (non-rim APHE and non-peripheral washout on PVP or TP) may increase the diagnostic sensitivity without compromised specificity compared to the LR-4 category. Key Points: • The LR-4 category shows modest diagnostic performance for the diagnosis of subcentimeter HCC on EOB-MRI with a sensitivity and specificity of 56.8% and 92.6%, respectively. • Non-rim APHE, non-peripheral washout on PVP or TP, and HBP hypointensity were independent predictors for the diagnosis of subcentimeter HCC. • The combination of non-rim APHE and non-peripheral washout on PVP or TP improves the sensitivity from 56.8 to 68.2% (p = 0.011) with comparable specificity (91.7 vs. 92.6%, p > 0.999). [ABSTRACT FROM AUTHOR]

Objectives: To determine the optimal cutoff value of HCC% for predicting the outcome of patients with combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) and to investigate how preoperative MR features based on the Liver Imaging Reporting and Data System (LI-RADS ver. 2018) are related to the HCC% in cHCC-CCA. Methods: The study enrolled 107 patients pathologically confirmed to have single cHCC-CCA according to the 2019 WHO classification. A receiver operating characteristic (ROC) curve was used to find the optimal cutoff value of HCC% based on overall survival (OS). The preoperative MR imaging features and clinicopathological findings were retrospectively evaluated and compared between the high HCC% and low HCC% groups. Results: In total, 107 patients (mean age, males vs. females: 56.6 ± 10.7 years vs. 54.2 ± 12.8 years) were evaluated. Analysis of the relationship between HCC% and OS by ROC curve suggested that the optimal cutoff value was 65%, by which 51 (47.7%) patients were assigned to the high HCC% group. LI-RADS categorization (OR = 3.657, p = 0.006 vs. OR = 4.075, p = 0.004) and serum carcinoembryonic antigen (CEA) >5 ng/mL (OR = 0.348, p = 0.089 vs. OR = 0.298, p = 0.040) were significant predictors for HCC% in cHCC-CCA in both univariate and multivariate analysis. Conclusions: cHCC-CCA patients with HCC components higher than 65% tend to exhibit better overall survival, and MRI-based LI-RADS categorization and serum CEA level are valuable for identifying HCC% in cHCC-CCA preoperatively. Key Points: • cHCC-CCA patients with HCC components higher than 65% tend to exhibit better overall survival. • MRI-based LI-RADS categorization and serum CEA level were significant predictors for HCC% in cHCC-CCA in both univariate and multivariate analyses and valuable for identifying HCC% in cHCC-CCA preoperatively. [ABSTRACT FROM AUTHOR]

We show that there is an intrinsic link between the use of Walsh aberration modes in adaptive optics (AO) and the mathematics of lattices. The discrete and binary nature of these modes means that there are infinite combinations of Walsh mode coefficients that can optimally correct the same aberration. Finding such a correction is hence a poorly conditioned optimisation problem that can be difficult to solve. This can be mitigated by confining the AO correction space defined in Walsh mode coefficients to the fundamental Voronoi cell of a lattice. By restricting the correction space in this way, one can ensure there is only one set of Walsh coefficients that corresponds to the optimum correction aberration. This property is used to enable the design of efficient estimation algorithms to solve the inverse problem of finding correction aberrations from a sequence of measurements in a wavefront sensorless AO system. The benefit of this approach is illustrated using a neural-network-based estimator. [ABSTRACT FROM AUTHOR]

Dynamic vision sensor (DVS) is a new type of image sensor, which has application prospects in the fields of automobiles and robots. Dynamic vision sensors are very different from traditional image sensors in terms of pixel principle and output data. Background activity (BA) in the data will affect image quality, but there is currently no unified indicator to evaluate the image quality of event streams. This paper proposes a method to eliminate background activity, and proposes a method and performance index for evaluating filter performance: noise in real (NIR) and real in noise (RIN). The lower the value, the better the filter. This evaluation method does not require fixed pattern generation equipment, and can also evaluate filter performance using natural images. Through comparative experiments of the three filters, the comprehensive performance of the method in this paper is optimal. This method reduces the bandwidth required for DVS data transmission, reduces the computational cost of target extraction, and provides the possibility for the application of DVS in more fields. [ABSTRACT FROM AUTHOR]