INTRODUCTION:Accurate identification of the primary tumor origin and differentiation grade of neuroendocrine liver metastases (NELMs) is critical for guiding treatment. This study aimed to evaluate contrast-enhanced CT (CECT) features of NELMs to differentiate pancreatic from non-pancreatic origins and neuroendocrine carcinoma (NEC) from neuroendocrine tumor (NET). METHODS:This single-center retrospective study (July 2019 - December 2022) included consecutive patients with pathologically proven NELM who underwent CECT prior to surgery or biopsy. CT features, including tumor burden, basic characteristics, enhancement patterns, and malignancy-related features, were analyzed. The enhancement pattern was categorized as follows: (A) hypoenhancement (hypodensity in both the arterial phase and portal venous phase); (B) arterial phase hyperenhancement (APHE) and washout; (C) APHE and fade. Univariate and multivariate analyses were performed to identify CT features that differentiated pancreatic from non-pancreatic origins and NEC from NET. RESULTS:Among 83 patients (median age, 52 years; 34 females), 49 patients had pancreatic NELM and 25 patients had NEC. In univariate analyses, hypoenhancement was significantly more frequent in non-pancreatic NELM compared to pancreatic NELM (32.4% vs. 8.2%; p = 0.01). This association was independent of age, sex, tumor size, and differentiation grade (p = 0.03). In addition, NEC was associated with ill-defined margins and targetoid appearances compared to NET, independent of age, sex, tumor size, and tumor origin (p = 0.045 and 0.003, respectively). CONCLUSION:The hypoenhancement of NELM facilitates identification of non-pancreatic primaries, and the presence of an ill-defined margin and a targetoid appearance are indicative of NEC. These features can aid in optimizing management strategies in patients with NELM.

Background: The effectiveness of Liver Imaging Reporting and Data System treatment response algorithm version 2024 (LR-TRA v2024) in hepatocellular carcinoma (HCC) patients undergoing locoregional plus systemic combination therapy remains uncertain. We aimed to investigate the performance of LR-TRA v2024 on magnetic resonance imaging (MRI) in detecting residual HCC following transarterial chemoembolization (TACE) plus systemic therapy. Methods: This single-center retrospective study included consecutive adult patients who received TACE plus systemic therapy for HCC and subsequent surgical resection (July 2019 to November 2023). All contrast-enhanced preoperative MRIs were independently evaluated by three blinded radiologists for LR-TR, Liver Imaging Reporting and Data System treatment response (LR-TR) categories and two ancillary features. Postoperative pathology was used as the reference standard for residual tumors, which was further categorized as any (>0%) or major (>10%) residual tumors. When investigating the performances of LR-TR categories, the LR-TR Equivocal category was grouped into the LR-TR Viable category. The diagnostic performances were evaluated using positive predicting value (PPV) and negative predicting value (NPV). Results: Fifty-one patients (median age, 56 years; 45 males) with 63 HCCs were included. For the detection of any residual tumor, the per-lesion PPV and NPV of the LR-TR Viable category were 100.0% and 46.9%, respectively; the per-patient PPV and NPV were 100.0% and 45.5%, respectively. For the detection of major residual tumor, the per-lesion PPV and NPV of the LR-TR Viable category were 80.6% and 84.4%, respectively; the per-patient PPV and NPV were 82.8% and 86.4%, respectively. Conclusions: LR-TRA v2024 was effective in evaluating treatment response and detecting residuals of HCC to TACE plus systemic therapy.

Hepatocellular carcinoma (HCC) is characterized by distinct molecular and pathological subtypes, each with unique prognostic implications. This review aims to synthesize the imaging features associated with these HCC subtypes and discuss their potential to guide therapeutic decision-making. We searched PubMed and Embase for articles published from September 2004 to December 2024. The search strategy combined terms for imaging modalities (“CT,” “MRI”), the primary disease (“hepatocellular carcinoma”), and various molecular and pathological subtypes (e.g., “macrotrabecular-massive,” “steatohepatitic,” “CK19,” and “CTNNB1”). HCC is a biologically heterogeneous malignancy with varied prognosis and sensitivity to treatment. Assessment of its molecular and pathological subtypes relies on invasive histopathological examination, which is subject to sampling errors and often unavailable prior to treatment selection. A growing body of evidence suggests that radiologic features aid in the non-invasive classification of HCC subtypes, thereby informing individualized therapy. Given the substantial overlap between molecular, pathological, and imaging features, this review hypothesize that a comprehensive phenotyping system integrating all these information could significantly enhance personalized prognostication and treatment strategies. Radiologic imaging features not only provide valuable information for identifying molecular and pathological subtypes of HCC but also serve as practical tools to guide individualized therapeutic decision-making. Question Can CT and MRI reliably infer the molecular classification and pathological subtypes that drive prognosis in HCC? Findings Several imaging features have been found to reflect underlying molecular and pathological subtypes, but they do not demonstrate a one-to-one correlation. Clinical relevance An integrated classification system incorporating clinical, imaging, pathological, and molecular data may help mitigate the limitations of histologic and molecular analyses and facilitate individualized prognostication.

To develop an MRI-based score that enables individualized predictions of the survival benefit of wide over narrow resection margins. This single-center retrospective study (December 2011 to May 2022) included consecutive patients who underwent curative-intent resection for single Barcelona Clinic Liver Cancer (BCLC) 0/A HCC and preoperative contrast-enhanced MRI. In patients with narrow resection margins, preoperative demographic, laboratory, and MRI variables independently associated with early recurrence-free survival (RFS) were identified using Cox regression analyses, which were employed to develop a predictive score (named “MARGIN”). Survival outcomes were compared between wide and narrow resection margins in a propensity-score matched cohort for the score-stratified low- and high-risk groups, respectively. Four hundred nineteen patients (median age, 54 years; 361 men) were included, 282 (67.3

Abstract Background Late recurrence of hepatocellular carcinoma (HCC) after liver resection is regarded as a de novo tumor primarily related to the severity of underlying liver disease. We aimed to investigate risk factors, especially spleen volume, associated with late recurrence in patients with HCC and cirrhosis. Methods We retrospectively analyzed 301 patients with HCC and cirrhosis who received curative resection and preoperative MRI. Patients were followed for late recurrence for at least 2 years. Spleen volume was automatically measured on MRI with artificial intelligence techniques, and qualitative MRI imaging features reflecting tumor aggressiveness were evaluated. Uni- and multivariable Cox regression analyses were performed to identify independent predictors and a risk score was developed to predict late recurrence. Results Eighty-four (27.9%) patients developed late recurrence during follow-up. Preoperative spleen volume was independently associated with late recurrence, and patients with a volume > 370 cm3 had significantly higher recurrence risk (hazard ratio 2.02, 95%CI 1.31–3.12, p = 0.002). Meanwhile, no qualitative imaging features were associated with late recurrence. A risk score was developed based on the APRI score, spleen volume, and tumor number, which had time-dependent area under the curve ranging from 0.700 to 0.751. The risk score at a cutoff of 0.42 allowed for the identification of two risk categories with distinct risk of late recurrence. Conclusions Preoperative spleen volume on MRI was independently associated with late recurrence after curative-intent resection in patients with HCC and cirrhosis. A risk score was proposed for individualized risk prediction and tailoring of postoperative surveillance strategies. Critical relevance statement Spleen volume measured on MRI with the aid of AI techniques was independently predictive of late HCC recurrence after liver resection. A risk score based on spleen volume, APRI score, and tumor number was developed for accurate prediction of late recurrence. Key points • Preoperative spleen volume measured on MRI was independently associated with late recurrence after curative-intent resection in patients with HCC and cirrhosis. • Qualitative MRI features reflecting tumor aggressiveness were not associated with late recurrence. • A risk score based on spleen volume was developed for accurate prediction of late recurrence and risk stratification. Graphical Abstract

PURPOSE:To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC). METHODS:This single-center prospective study enrolled consecutive at-risk participants who underwent 3.0 T gadoxetate disodium-enhanced MRI. Conventional DWI was acquired using parallel imaging (PI) with SENSE (PI-DWI). In CS-DWI and DL-CS-DWI, CS but not PI with SENSE was used to accelerate the scan with 2.5 as the acceleration factor. Qualitative and quantitative image quality were independently assessed by two masked reviewers, and were compared using the Wilcoxon signed-rank test. The detection rates of clinically-relevant (LR-4/5/M based on the Liver Imaging Reporting and Data System v2018) liver lesions for each DWI sequence were independently evaluated by another two masked reviewers against their consensus assessments based on all available non-DWI sequences, and were compared by the McNemar test. RESULTS:67 participants (median age, 58.0 years; 56 males) with 197 clinically-relevant liver lesions were enrolled. Among the three DWI sequences, DL-CS-DWI showed the best qualitative and quantitative image qualities (p range, <0.001-0.039). For clinically-relevant liver lesions, the detection rates (91.4%-93.4%) of DL-CS-DWI showed no difference with CS-DWI (87.3%-89.8%, p = 0.230-0.231) but were superior to PI-DWI (82.7%-85.8%, p = 0.015-0.025). For lesions located in the hepatic dome, DL-CS-DWI demonstrated the highest detection rates (94.8%-97.4% vs 76.9%-79.5% vs 64.1%-69.2%, p = 0.002-0.045) among the three DWI sequences. CONCLUSION:In patients at high-risk for HCC, DL-CS-DWI improved image quality and detection for clinically-relevant liver lesions, especially for the hepatic dome.

Abstract Objectives To develop and externally validate a fully automated diagnostic convolutional neural network (CNN) model for cirrhosis based on liver MRI and serum biomarkers. Methods This multicenter retrospective study included consecutive patients receiving pathological evaluation of liver fibrosis stage and contrast-enhanced liver MRI between March 2010 and January 2024. On the training dataset, an MRI-based CNN model was constructed for cirrhosis against pathology, and then a combined model was developed integrating the CNN model and serum biomarkers. On the testing datasets, the area under the receiver operating characteristic curve (AUC) was computed to compare the diagnostic performance of the combined model with that of aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and radiologists. The influence of potential confounders on the diagnostic performance was evaluated by subgroup analyses. Results A total of 1315 patients (median age, 54 years; 1065 men; training, n = 840) were included, 855 (65%) with pathological cirrhosis. The CNN model was constructed on pre-contrast T1- and T2-weighted imaging, and the combined model was developed integrating the CNN model, age, and eight serum biomarkers. On the external testing dataset, the combined model achieved an AUC of 0.86, which outperformed FIB-4, APRI and two radiologists (AUC: 0.67 to 0.73, all p < 0.05). Subgroup analyses revealed comparable diagnostic performances of the combined model in patients with different sizes of focal liver lesions. Conclusion Based on pre-contrast T1- and T2-weighted imaging, age, and serum biomarkers, the combined model allowed diagnosis of cirrhosis with moderate accuracy, independent of the size of focal liver lesions. Critical relevance statement The fully automated convolutional neural network model utilizing pre-contrast MR imaging, age and serum biomarkers demonstrated moderate accuracy, outperforming FIB-4, APRI, and radiologists, independent of size of focal liver lesions, potentially facilitating noninvasive diagnosis of cirrhosis pending further validation. Key Points This fully automated convolutional neural network (CNN) model, using pre-contrast MRI, age, and serum biomarkers, diagnoses cirrhosis. The CNN model demonstrated an external testing dataset AUC of 0.86, independent of the size of focal liver lesions. The CNN model outperformed aminotransferase-to-platelet ratio index, fibrosis-4 index, and radiologists, potentially facilitating noninvasive diagnosis of cirrhosis. Graphical Abstract

A diagnosis of cirrhosis initiates a shift in the management of chronic liver disease and affects the diagnostic workflow and treatment decision of primary liver cancer. Liver biopsy remains the gold standard for cirrhosis diagnosis, but it is invasive and susceptible to sampling bias and observer variability. Various qualitative and quantitative imaging biomarkers based on ultrasound, CT and MRI have been proposed for noninvasive diagnosis of cirrhosis. Qualitative imaging features are easy to apply but have moderate diagnostic sensitivity. Elastography techniques allow quantitative assessment of liver stiffness and are highly accurate for cirrhosis diagnosis. Ultrasound elastography are widely used in clinical practice, while MR elastography has narrower availability. Although not applicable in clinical practice yet, other quantitative imaging features, including liver surface nodularity, linear and volumetric measurement, extracellular volume fraction, liver enhancement on hepatobiliary phase, and parameters derived from diffusion-weighted imaging, can provide additional information of liver morphology, perfusion, and function, thus may increase diagnosis performance. The introduction of radiomics and deep learning has further improved diagnostic accuracy while reducing subjectivity. Several imaging features may also help to assess liver function and outcomes in patients with cirrhosis. In this review, we summarize the qualitative and quantitative imaging biomarkers for noninvasive cirrhosis diagnosis, and the assessment of liver function and outcomes, and discuss the challenges and future directions in this field.

To evaluate the left ventricular (LV) myocardial tissue characteristics in early adult obesity and its association with regional adipose tissue and ectopic fat deposition. Forty-nine obese adults (mean body mass index: 29.9 ± 2.0 kg/m2) and 44 healthy controls were prospectively studied. LV native and post-contrast T1 values, extracellular volume fraction (ECV), regional adipose tissue (epicardial, visceral, and subcutaneous adipose tissue (EAT, VAT, and SAT)), and ectopic fat deposition (hepatic and pancreatic proton density fat fractions (H-PDFF and P-PDFF)) based on magnetic resonance imaging were compared. The association was assessed by multivariable linear regression. The obese participants showed reduced global ECV compared to the healthy controls (p < 0.05), but there was no significant difference in global native or post-contrast T1 values between the two groups. Additionally, the obese individuals exhibited higher EAT, VAT, SAT, H-PDFF, and P-PDFF than the controls (p < 0.05). ECV was associated with insulin resistance, dyslipidemia, and systolic blood pressure (SBP) (p < 0.05). Multiple linear regression demonstrated that H-PDFF and SAT were independently associated with ECV in entire population (β = − 0.123 and − 0.012; p < 0.05). Reduced myocardial ECV in patients with mild-to-moderate obesity and its relationship to SBP may indicate that cardiomyocyte hypertrophy, rather than extracellular matrix expansion, is primarily responsible for myocardial tissue remodeling in early adult obesity. Our findings further imply that H-PDFF and SAT are linked with LV myocardial tissue remodeling in this cohort beyond the growth difference and cardiovascular risk factors. Effect of lifestyle intervention on metabolism of obese patients based on smart phone software (ChiCTR1900026476). Myocardial fibrosis in severe obesity predicts poor prognosis. We showed that cardiomyocyte hypertrophy, not myocardial fibrosis, is the main myocardial tissue characteristic of early obesity. This finding raises the possibility that medical interventions, like weight loss, may prevent cardiac fibrosis. • Myocardial tissue characteristics in early adult obesity are unclear. • Myocardial extracellular volume fraction (ECV) can be quantitatively evaluated using T1 mapping based on cardiac magnetic resonance imaging (MRI). • Cardiac MRI-derived ECV may noninvasively evaluate myocardial tissue remodeling in early adult obesity.

Background Abdominal ectopic fat deposition and excess visceral fat depots in obesity may be related to cardiovascular disease (CVD) as both are involved in the metabolic syndrome (MetS). The awareness of the link between abdominal adiposity and subclinical cardiac remodeling would help improve treatment and outcome. Besides, liver fibrosis has also shown a potential relationship with cardiac dysfunction. Thus, we aimed to investigate the associations of magnetic resonance (MR)-based abdominal adiposity and hepatic shear stiffness with subclinical left ventricular (LV) remodeling while taking account of MetS-related confounders in adults free of overt CVD. Methods This was an exploratory, prospective study of 88 adults (46 subjects with obesity, 42 healthy controls) who underwent 3 T cardiac and body MR exams. Measures of abdominal MR included hepatic and pancreatic proton density fat fraction (H-PDFF and P-PDFF), hepatic shear stiffness by MR elastography, and subcutaneous and visceral adipose tissue (SAT and VAT). Cardiac measures included epicardial adipose tissue (EAT) and parameters of LV geometry and function. Associations were assessed using Pearson correlation and multivariable linear regression analyses, in which age, sex, and MetS-related confounders were adjusted for. Results The LV ejection fractions of all participants were within the normal range. Higher H-PDFF, P-PDFF, SAT and VAT were independently associated with lower LV global myocardial strain parameters (radial, circumferential and longitudinal peak strain [PS], longitudinal peak systolic strain rate and diastolic strain rate) (β = − 0.001 to − 0.41, p < 0.05), and P-PDFF, SAT and VAT were independently and positively associated with LV end-diastolic volume and stroke volume (β = 0.09 to 3.08, p ≤ 0.02) in the over-all cohort. In the obesity subgroup, higher P-PDFF and VAT were independently associated with lower circumferential and longitudinal PS, respectively (β = − 0.29 to − 0.05, p ≤ 0.01). No independent correlation between hepatic shear stiffness and EAT or LV remodeling was found (all p ≥ 0.05). Conclusions Ectopic fat depositions in the liver and pancreas, and excess abdominal adipose tissue pose a risk of subclinical LV remodeling beyond MetS-related CVD risk factors in adults without overt CVD. VAT may play a more considerable role as a risk factor for subclinical LV dysfunction than does SAT in individuals with obesity. The underlying mechanisms of these associations and their longitudinal clinical implications need further investigation.

Purpose: To develop a predictive model integrating clinical and MRI features for postoperative survival in patients with hepatocellular carcinoma (HCC) and portal vein tumor thrombus (PVTT).Method: Between January 2008 and May 2021, consecutive HCC patients with PVTT who underwent preoperative contrast-enhanced MRI and surgical resection at a tertiary hospital were retrospectively enrolled. The MR images were independently reviewed by two blinded radiologists. Univariate and multivariate Cox regression analyses were performed to construct a prognostic score for overall survival (OS).Results: Ninety-four patients were included (mean age, 50.1 years; 84 men). During a median follow-up period of 15.3 months, 72 (76.6%) patients died (median OS, 15.4 months; median disease-free survival [DFS], 4.6 months). The sum size of the two largest tumors (hazard ratio [HR], 3.050; p < 0.001) and tumor growth subtype (HR, 1.928; p = 0.006) on MRI, serum albumin (HR, 0.948; p = 0.02), and age (HR, 0.978; p = 0.04) were associated with OS and incorporated in the prognostic score. Accordingly, patients were stratified into a high-risk or low-risk group, and the OS in the high-risk group was shorter than that in the low-risk group for the entire cohort (11.7 vs. 25.0 months, p < 0.001) and for patients with Cheng's type I (12.1 vs. 25.9 months, p = 0.002) and type II PVTT (11.7 vs. 25.0 months, p = 0.004). The DFS in the high-risk group was shorter than that in the low-risk group for the entire cohort (4.5 vs. 6.1 months, p = 0.001).Conclusions: Based on the sum size of the two largest tumors, tumor growth subtype, albumin, and age, the prognostic score allowed accurate preoperative risk stratification in HCC patients with PVTT, independent of Cheng's PVTT classification.

To establish a risk score integrating preoperative gadoxetic acid–enhanced magnetic resonance imaging (EOB-MRI) and clinical parameters to predict recurrence after hepatectomy for patients with hepatocellular carcinoma (HCC) and to compare its performance with that of a postoperative score and four clinical staging systems. Consecutive patients with surgically confirmed HCC who underwent preoperative EOB-MRI between July 2015 and November 2020 were retrospectively included. Two recurrence risk scores, one incorporating only preoperative variables and the other incorporating all preoperative and postoperative variables, were constructed via Cox regression models. A total of 214 patients (derivation set, n = 150; test set, n = 64) were included. Six preoperative variables, namely tumor number, infiltrative appearance, corona enhancement, alpha-fetoprotein (AFP) level, aspartate aminotransferase (AST) level, and sex, were independently associated with recurrence. After adding postoperative features, microvascular invasion and tumor differentiation were additional significant variables in lieu of corona enhancement and AFP level. Using the above variables, the preoperative score achieved a C-index of 0.741 on the test set, which was comparable with that of the postoperative score (0.729; p = 0.235). The preoperative score yielded a larger time-dependent area under the receiver operating characteristic curve at 1 year (0.844) than three existing systems (0.734–0.742; p < 0.05 for all). Furthermore, the preoperative score stratified patients into two prognostically distinct risk strata with low and high risks of recurrence (p < 0.001). The preoperative score integrating EOB-MRI features, AFP and AST levels, and sex improves recurrence risk estimation in HCC. • The preoperative risk score incorporating three EOB-MRI findings, AFP and AST levels, and sex achieved comparable performance with that of the postoperative score for predicting recurrence after hepatectomy in patients with HCC. • Two risk strata with low and high risks of recurrence were obtained based on the preoperative score. • The preoperative score may help tailor pretreatment decision-making and facilitate candidate selection for adjuvant clinical trials.

Abstract Background Disease situations are more aggressive in patients with childhood-onset systemic lupus erythematosus (cSLE) than in those with adult-onset SLE (aSLE). However, information on pregnant women with cSLE and its association with pregnancy outcomes is limited. This study aimed to compare pregnancies in patients with cSLE vs. aSLE, and further analyse the characteristics of cSLE in pregnant women and explore its association with adverse pregnancy outcomes. Methods Altogether, data of 167 pregnancies from 150 women, including 22 pregnancies with cSLE and 145 pregnancies with aSLE, were retrospectively analysed. Characteristics and disease activity were compared between the cSLE and aSLE groups during pregnancy. Associations between cSLE and the risk of active SLE (SLEPDAI > 4), active lupus nephritis (LN), and adverse pregnancy outcomes were analysed using logistic regression. Results The cSLE group had a higher incidence of active SLE (12/22 vs. 30/145, P = 0.001) and active LN (11/22 vs. 26/145, P = 0.001) than the aSLE group. In the multivariable analysis, cSLE was a risk factor for active SLE and active LN during pregnancy, with ORs of 4.742 (95%CI 1.678–13.405, P = 0.003) and 4.652 (95%CI 1.630–13.279, P = 0.004), respectively. No significant association between cSLE and the risk of composite adverse gestational outcomes was identified after sequentially adjusting pre-pregnancy characteristics and pregnancy factors (P > 0.05). Conclusion Disease activity of women with cSLE in pregnancy was more aggressive than that of women with aSLE, which was similar to the characteristics of non-pregnant women with SLE. cSLE might have indirect effects on the risk of adverse pregnancy outcomes through LN and active disease. Therefore, closely monitoring patients with cSLE during pregnancy is crucial.

原发性肝癌是我国常见高发恶性肿瘤,严重威胁人民的生命健康.医学影像学在肝癌的诊疗过程中发挥着重要作用,近十年来中国肝癌影像研究发展取得了显著成效,以人工智能(artificial intelligence,AI)为代表的现代科技的迅猛发展以及超高磁场、多核MRI、光子CT等影像新技术的不断涌现为肝癌患者的早筛、早诊、早治和全程优化管理提供了可靠的技术保障.运用AI结合多维度组学(影像组学、基因组学、蛋白组学等)将会进一步揭示肝癌的"临床-影像-病理-分子"核心关联机制,为无创性评估肝癌的病理特点、基因特征、免疫表型、分子亚型、疗效及预后带来新的机遇,为肝癌患者更加精细化和个体化的临床管理提供技术支持.本文以影像技术为脉络主线,归纳总结了近年来MRI肝脏影像报告和数据系统(Liver Imaging Reporting and Data System,LI-RADS)的应用及肝胆特异性对比剂使用、扩散MRI技术、功能MRI技术、影像组学与AI、CT影像等在中国的临床研究与转化应用成果,展现了这些年我国肝癌影像领域的蓬勃发展和辉煌成就,同时也提出了当前肝癌影像研究的局限性,未来需要根据国内患者的发病特点及人群特征进行有针对性的研究设计,建立涵盖人群广、代表性强的多中心研究队列,建设同质化、高质量的全国肝癌影像数据库;同时需要注重原创性影像新技术的开发和应用,未来将运用AI结合影像组学、基因组学、蛋白组学深入研究肝癌的病理特征、基因表型和预后转归,促进医学影像深度参与肝癌患者的全流程临床管理,为精准医疗提供技术支持,助力实现国民大健康的战略目标.

Background: Artificial intelligence (AI) has breathed new life into the lung nodules detection and diagnosis. However, whether the output information from AI will translate into benefits for clinical workflow or patient outcomes in a real-world setting remains unknown. This study was to demonstrate the feasibility of an AI-based diagnostic system deployed as a second reader in imaging interpretation for patients screened for pulmonary abnormalities in a clinical setting. Methods: The study included patients from a lung cancer screening program conducted in Sichuan Province, China using a mobile computed tomography (CT) scanner which traveled to medium-size cities between July 10th, 2020 and September 10th, 2020. Cases that were suspected to have malignant nodules by junior radiologists, senior radiologists or AI were labeled a high risk (HR) tag as HR-junior, HR-senior and HR-AI, respectively, and included into final analysis. The diagnosis efficacy of the AI was evaluated by calculating negative predictive value and positive predictive value when referring to the senior readers' final results as the gold standard. Besides, characteristics of the lesions were compared among cases with different HR labels. Results: In total, 251/3,872 patients (6.48%, male/female: 91/160, median age, 66 years) with HR lung nodules were included. The AI algorithm achieved a negative predictive value of 88.2% [95% confidence interval (CI): 62.2-98.0%] and a positive predictive value of 55.6% (95% CI: 49.0-62.0%). The diagnostic duration was significantly reduced when AI was used as a second reader (223 +/- 145.6 vs. 270 +/- 143.17 s, P<0.001). The information yielded by AI affected the radiologist's decision-making in 35/145 cases. Lesions of HR cases had a higher volume [309.9 (214.9-732.5) vs. 141.3 (79.3-380.8) mm(3), P<0.001], lower average CT number [-511.0 (-576.5 to -100.5) vs. -191.5 (-487.3 to 22.5), P=0.010], and pure ground glass opacity rather than solid. Conclusions: The AI algorithm had high negative predictive value but low positive predictive value in diagnosing HR lung lesions in a clinical setting. Deploying AI as a second reader could help avoid missed diagnoses, reduce diagnostic duration, and strengthen diagnostic confidence for radiologists.

不同亚型和循环重组亚型(Circulating recombinant forms,CRFs)在同一人群中的传播往往导致独特重组型(Unique recombinant forms,URFs)的出现.在本研究中,我们发现了两条新的 HIV-1 CRF55_01B/CRF07_BC第二代重组体(Second-generation recombinant forms,SGRs),它们分别来自江门市的同性恋和异性恋群体.系统进化分析表明,JM.pj44和JM.pj64的近全长基因组(Near full-length genomes,NFLGs)均以CRF55_01B为骨架,插入了CRF07_BC的基因片段,但断点位置不同.将本研究发现的两种新的NFLGs与其他研究中发现的CRF55_01B/CRF07_BC SGRs进行比较,目前未发现有相同的CRF55_01B/CRF07_BC重组体.

为了了解境外输入的新型冠状病毒(SARS-CoV-2)变异株的分子特征,本研究对2021年6月深圳市一株从南非输入的SARS-CoV-2毒株进行了全基因组测序和序列分析.Illumina测序技术获得的SARS-CoV-2毒株基因组长度为29567nt.根据"Pango lineages"分型法,本研究测定的毒株属于C.1.2系,该谱系属世界卫生组织定义的监测变异株(Variants Under Monitoring,VUM)成员之一.与参考株 Wuhan-Hu-1(NC_045512.2)比较,本研究 C.1.2系毒株共出现了 58个核苷酸变异位点,其中56个变异位点位于编码区.氨基酸变异位点共有33个,氨基酸变异位点分布于6个开放阅读框,变异数由多到少依次为:S蛋白区12个,ORFlab蛋白区9个,ORF3a蛋白区2个,M区2个,ORF8区2个,E区1个.本研究测定的SARS-CoV-2毒株属我国大陆首例境外输入的C.1.2变异株.开展境外输入的SARS-CoV-2毒株基于基因组测序的分子监测,对防控由境外输入的SARS-CoV-2变异株引起本地新型冠状病毒肺炎(COVID-19)暴发与流行具有重要意义.

肝硬化结节多步癌变(multi-step hepatocarcinogenesis)是肝细胞癌(HCC)发生发展的重要病理特点.肿瘤新生血管生成是HCC最主要的组织病理学特征,成为影像学活体无创诊断HCC高特异性的基础.近年来,在形态学成像的基础上结合功能影像学技术可以实现对HCC多种微观病理改变的可视化和定量分析,使得对HCC的深层次精准影像评价出现了新的局面.结合肝硬化结节多步癌变的多层次病理特征,本文复习归纳了相关功能影像技术进展和HCC诊断新标准,旨在加深对影像成像的"术"和所反映的病理层面的"道"的认识和理解.

脂肪变性是常见的肝脏病理改变,与多种慢性弥漫性肝病相关.早期脂肪变性常可逆转,故肝脏脂肪变性早期检测和定量分析具有重要临床意义.目前,穿刺活检是诊断肝脏脂肪变性的金标准,但该检查手段有创且存在取样误差.近年来,多种磁共振成像(MRI)技术广泛应用于肝脏脂肪定量,其中质子密度脂肪分数(PDFF)技术准确性高、稳定性好、可重复性强,适用于脂肪肝治疗后的疗效评估等纵向研究,且有望取代肝穿活检成为无创量化肝脏脂肪的检查方法.本文就肝脏脂肪变性的磁共振定量分析新技术方法和临床应用作一述评.

Objectives The aim of this meta-analysis was to evaluate the diagnostic accuracy of hepatic magnetic resonance imaging-proton density fat fraction (MRI-PDFF) for the assessment of liver steatosis (LS) with histology as reference standard. Methods A systematic literature search was performed to identify pertinent studies. Quality analyses were conducted by Quality Assessment of Diagnostic Accuracy Studies-2. Diagnostic data were extracted and inconsistency index was calculated for LS≥G1, LS≥G2, and LS=G3, respectively. The area under summary receiver operating characteristic curve (AUC) served as the indicator of diagnostic accuracy. The pooled sensitivity and specificity were calculated if threshold effect was absent. Results Thirteen studies containing 1100 subjects were included. There was significant threshold effect for LS≥G1. The AUCs for LS≥G1, LS≥G2, and LS=G3 were 0.98 (95% confidence interval (CI) 0.76, 1.00), 0.91 (95% CI 0.89, 0.94), and 0.92 (95% CI 0.89, 0.94), respectively. The pooled sensitivities for LS≥G2 and LS=G3 were 0.83 (95% CI 0.75, 0.88) and 0.79 (95% CI 0.63, 0.90), respectively; the pooled specificities for LS≥G2 and LS=G3 were 0.89 (95% CI 0.84, 0.92) and 0.89 (95% CI 0.84, 0.92), respectively. Conclusions MRI-PDFF has high diagnostic accuracy at detecting and grading LS with histology as reference standard, suggesting that MRI-PDFF is able to provide an accurate quantification of LS in clinical trials and patient care. Key Point • MRI-PDFF is able to provide an accurate quantification of LS in clinical trials and patient care.