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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]

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]

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]

Background: Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment., Purpose: To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis., Study Type: Retrospective., Subjects: Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94)., Field Strength/sequence: 1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence., Assessment: Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews., Statistical Tests: Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance., Results: Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival., Data Conclusion: The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk., Evidence Level: 3 TECHNICAL EFFICACY: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

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., Technical Efficacy: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

Objective: To determine the preoperative clinicoradiological factors to predict microvascular invasion (MVI) in patients with resectable multinodular hepatocellular carcinoma (mHCC), and further to establish and validate a stratified risk scoring system., Methods: Two hundred and seventy-three patients with pathologically confirmed mHCC (≥2 lesions) without major vascular invasion and biliary tract tumor thrombosis, who underwent preoperative contrast-enhanced MRI and hepatectomy, were consecutively enrolled (training/validation cohort=193/80). Preoperative clinicoradiological variables were collected and analyzed. The multivariable logistic regression was performed to determine the independent predictors of MVI and create a risk score system. The C-index, calibration curve and decision curve were used to evaluate the performance of the risk score. A risk score-based prognostic stratification system was performed in mHCC patients. The risk score system was further verified in the validation cohort., Results: AFP > 400 ng/mL, presence of satellite nodule, mosaic architecture and increased total tumor diameter were independent predictors of MVI while fat in mass was an independent protective factor of MVI. The risk score yielded satisfactory C-index values (training/validation cohort: 0.777/0.758) and fitted well in calibration curves. Decision curve analysis further confirmed its clinical utility. Based on the risk score, mHCC patients were stratified into high-/low-MVI-risk subgroups with significantly different recurrence-free survival (both P < 0.001)., Conclusion: The presented risk score incorporating clinicoradiological parameters could stratify mHCC patients into high-risk and low-risk subgroups and predict prognosis in patients with resectable mHCC., Competing Interests: The authors report no conflicts of interest in this work., (© 2023 Wu et al.)

Objective: To compare the pathologic diagnosis and survival of patients with subcentimeter and 1-2 cm nodules that present with diagnostic hallmarks of hepatocellular carcinoma (HCC)., Methods: Diagnostic hallmarks of HCC were defined as hyperintensity on T2 weighted imaging, restricted diffusion, arterial phase hyperenhancement, washout on portal venous phase, and hypointensity on hepatobiliary phase. We retrospectively included 139 patients undergoing curative resection with single nodules ≤2 cm that present imaging features described above on gadoxetic acid-enhanced MRI. The final diagnosis was confirmed by histopathological assessment. Recurrence-free survival (RFS) was compared using Kaplan-Meier analysis with the Log-rank test. Factors associated with overall and early recurrence were identified using Cox regression analysis., Results: Among 139 nodules (49 nodules <1 cm), there was no significant difference in the percentage of HCC between subcentimeter and 1-2 cm nodules (94.0% vs 94.4%, P > 0.999). Microvascular invasion (MVI) was less common in subcentimeter HCC (4.3% vs 17.6%, P = 0.032). There were 27 recurrences during a median follow-up time of 46.7 months. Patients with subcentimeter HCC achieved less recurrence, with a 5-year RFS rate of 87.3%. The MVI-positive patients had more early and overall recurrence. A tumor size <1 cm was associated with lower overall recurrence (HR, 0.336; P = 0.047). No factors were independently associated with early recurrence., Conclusion: Subcentimeter nodules with diagnostic hallmarks of HCC are highly associated with HCC diagnosis and achieve less tumor recurrence after resection. Early diagnosis and treatment of subcentimeter HCC may be more appropriate., Competing Interests: The authors report no conflicts of interest in this work., (© 2023 Huang et al.)