Visualizing the Habenula Using 3T High-Resolution MP2RAGE and QSM: A Preliminary Study.

BACKGROUND AND PURPOSE: The habenula is a key node in the regulation of emotion-related behavior. Accurate visualization of the habenula and its reliable quantitative analysis is vital for the assessment of psychiatric disorders. To obtain high-contrast habenula images and allow them to be compatible with clinical applications, this preliminary study compared 3T MP2RAGE and quantitative susceptibility mapping with MPRAGE by evaluating the habenula segmentation performance. MATERIALS AND METHODS: Ten healthy volunteers were scanned twice with 3T MPRAGE and MP2RAGE and once with quantitative susceptibility mapping. Image quality and visibility of habenula anatomic features were analyzed by 3 radiologists using a 5-point scale. Contrast assessments of the habenula and thalamus were also performed. The reproducibility of the habenula volume from MPRAGE and MP2RAGE was evaluated by manual segmentation and the Multiple Automatically Generated Template brain segmentation algorithm (MAGeTbrain). T1 values and susceptibility were measured in the whole habenula and habenula geometric subregion using MP2RAGE T1-mapping and quantitative susceptibility mapping. RESULTS: The 3T MP2RAGE and quantitative susceptibility mapping demonstrated clear boundaries and anatomic features of the habenula compared with MPRAGE, with a higher SNR and contrast-to-noise ratio (all P < .05). Additionally, 3T MP2RAGE provided reliable habenula manual and MAGeTbrain segmentation volume estimates with greater reproducibility. T1-mapping derived from MP2RAGE was highly reliable, and susceptibility contrast was highly nonuniform within the habenula. CONCLUSIONS: We identified an optimized sequence combination (3T MP2RAGE combined with quantitative susceptibility mapping) that may be useful for enhancing habenula visualization and yielding more reliable quantitative data.

Tumor microenvironment(TME) and single-source dual-energy CT(ssDECT) on assessment of inconformity between RECIST1.1 and pathological remission in neoadjuvant immunotherapy of NSCLC.

BACKGROUND: The inconformity (IC) between pathological and imaging remissions after neoadjuvant immunotherapy in patients with NSCLC can affect the evaluation of curative effect of neoadjuvant therapy and the decision regarding the chance of surgery. MATERIALS AND METHODS: Patients who achieved disease control(CR/PR/SD) after neoadjuvant chemoimmunotherapy from a clinical trial (NCT04326153) and after neoadjuvant chemotherapy during the same period were enrolled in this study. All patients underwent radical resection and systematic mediastinal lymphadenectomy after neoadjuvant treatments. The pathological remission, immunohistochemistry (CD4, CD8, CD20, CD56, FoxP3, CD68, CD163, CD11b tumor-infiltrating lymphocytes, or macrophages), and single-source dual-energy computed tomography (ssDECT) scans were assessed. The IC between imaging remission by CT and pathological remission was investigated. The underlying cause of IC, the correlation between IC and DFS, and prognostic biomarkers were explored. RESULTS: After neoadjuvant immunotherapy, enhanced immune killing and reduced immunosuppressive performance were observed. 70 % of neoadjuvant chemoimmunotherapy patients were in high/medium IC level. Massive necrosis and repair around and inside the cancer nest were the main pathological changes observed 30-45 days post-treatment with PD1/PD-L1 antibody and were the main causes of IC between the pathology and imaging responses after neoadjuvant immunotherapy. High IC and preoperative CD8 expression (H score ≥ 3) indicate a high pathological response rate and prolonged DFS. Iodine material density ssDECT images showed that the iodine content in the lesion causes hyperattenuation in post-neoadjuvant lesion in PCR patient. CONCLUSIONS: Compared to chemotherapy and targeted therapy, the efficacy of neoadjuvant immunotherapy was underestimated based on the RECIST criteria due to the unique antitumor therapeutic mechanism. Preoperative CD8+ expression and ssDECT predict this IC and evaluate the residual tumor cells. This is of great significance for screening immune beneficiaries and making more accurate judgments about the timing of surgery.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

Recent Advances in Habenula Imaging Technology: A Comprehensive Review.

The habenula (Hb) is involved in many natural human behaviors, and the relevance of its alterations in size and neural activity to several psychiatric disorders and addictive behaviors has been presumed and investigated in recent years using magnetic resonance imaging (MRI). Although the Hb is small, an increasing number of studies have overcome the difficulties in MRI. Conventional structural-based imaging also has great defects in observing the Hb contrast with adjacent structures. In addition, more and more attention should be paid to the Hb's functional, structural, and quantitative imaging studies. Several advanced MRI methods have recently been employed in clinical studies to explore the Hb and its involvement in psychiatric diseases. This review summarizes the anatomy and function of the human Hb; moreover, it focuses on exploring the human Hb with noninvasive MRI approaches, highlighting strategies to overcome the poor contrast with adjacent structures and the need for multiparametric MRI to develop imaging markers for diagnosis and treatment follow-up. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Preoperative detection of lymphovascular invasion in rectal cancer using intravoxel incoherent motion imaging based on radiomics.

BACKGROUND: Lymphovascular invasion (LVI) status plays an important role in treatment decision-making in rectal cancer (RC). Intravoxel incoherent motion (IVIM) imaging has been shown to detect LVI; however, making better use of IVIM data remains an important issue that needs to be discussed. PURPOSE: We proposed to explore the best way to use IVIM quantitative parameters and images to construct radiomics models for the noninvasive detection of LVI in RC. METHODS: A total of 83 patients (LVI negative (LVI-): LVI positive (LVI+) = 51:32) with postoperative pathology-confirmed LVI status in RC were divided into a training group (n = 58) and a validation group (n = 25). Images were acquired from a 3.0 Tesla machine, including oblique axial T2 weighted imaging (T2WI) and IVIM with 11 b values. The ADC, D, D* and f values were measured on IVIM maps. The ROIs of tumors were delineated on T2WI, DWI, ADCmap , and Dmap images, and three mapping methods were used: ROIs_mapping from DWI, ROIs_mapping from ADCmap , and ROIs_mapping from Dmap . Three-dimensional radiomics features were extracted from the delineated ROIs. Multivariate logistic regression was used for radiomics feature selection. Radiomics models based on different mapping methods were developed. Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were used to evaluate the performance of the models. RESULTS: Model B, which was constructed with radiomics features from ADCmap , Dmap and fmap by "ROIs_mapping from DWI" and T2WI (AUC 0.894), performed better than other models based on single sequence (AUC 0.600-0.806) and even better than Model A, which was based on "ROIs_mapping from ADC" and T2WI (AUC 0.838). Furthermore, an integrated model was constructed with Model B and the IVIM parameter (f value) with an AUC of 0.920 (95% CI: 0.820-1.000), which was higher than that of Model B, in the validation group. CONCLUSIONS: The integrated model incorporating the radiomics features and IVIM parameters accurately detected LVI of RC. The "ROIs_mapping from DWI" method provided the best results.

Automatic segmentation of the interscapular brown adipose tissue in rats based on deep learning using the dynamic magnetic resonance fat fraction images.

OBJECTIVE: The study aims to propose an accurate labelling method of interscapular BAT (iBAT) in rats using dynamic MR fat fraction (FF) images with noradrenaline (NE) stimulation and then develop an automatic iBAT segmentation method using a U-Net model. MATERIALS AND METHODS: Thirty-four rats fed different diets or housed at different temperatures underwent successive MR scans before and after NE injection. The iBAT were labelled automatically by identifying the regions with obvious FF change in response to the NE stimulation. Further, these FF images along with the recognized iBAT mask images were used to develop a deep learning network to accomplish the robust segmentation of iBAT in various rat models, even without NE stimulation. The trained model was then validated in rats fed with high-fat diet (HFD) in comparison with normal diet (ND). RESULT: A total of 6510 FF images were collected using a clinical 3.0 T MR scanner. The dice similarity coefficient (DSC) between the automatic and manual labelled results was 0.895 ± 0.022. For the network training, the DSC, precision rate, and recall rate were found to be 0.897 ± 0.061, 0.901 ± 0.068 and 0.899 ± 0.086, respectively. The volumes and FF values of iBAT in HFD rats were higher than ND rats, while the FF decrease was larger in ND rats after NE injection. CONCLUSION: An automatic iBAT segmentation method for rats was successfully developed using the dynamic labelled FF images of activated BAT and deep learning network.

From clinical variables to multiomics analysis: a margin morphology-based gross classification system for hepatocellular carcinoma stratification.

OBJECTIVE: Selecting interventions for patients with solitary hepatocellular carcinoma (HCC) remains a challenge. Despite gross classification being proposed as a potential prognostic predictor, its widespread use has been restricted due to inadequate studies with sufficient patient numbers and the lack of established mechanisms. We sought to investigate the prognostic impacts on patients with HCC of different gross subtypes and assess their corresponding molecular landscapes. DESIGN: A prospective cohort of 400 patients who underwent hepatic resection for solitary HCC was reviewed and analysed and gross classification was assessed. Multiomics analyses were performed on tumours and non-tumour tissues from 49 patients to investigate the mechanisms underlying gross classification. Inverse probability of treatment weight (IPTW) was used to control for confounding factors. RESULTS: Overall 3-year survival rates varied significantly among the four gross subtypes (type I: 91%, type II: 80%, type III: 74.6%, type IV: 38.8%). Type IV was found to be independently associated with poor prognosis in both the entire cohort and the IPTW cohort. The four gross subtypes exhibited three distinct transcriptional modules. Particularly, type IV tumours exhibited increased angiogenesis and immune score as well as decreased metabolic pathways, together with highest frequency of TP53 mutations. Patients with type IV HCC may benefit from adjuvant intra-arterial therapy other than the other three subtypes. Accordingly, a modified trichotomous margin morphological gross classification was established. CONCLUSION: Different gross types of HCC showed significantly different prognosis and molecular characteristics. Gross classification may aid in development of precise individualised diagnosis and treatment strategies for HCC.

Diagnosis of Acute Pancreatitis Using Postmortem Computed Tomography and Postmortem Magnetic Resonance Imaging: A Case Report and a Review of Literature.

ABSTRACT: Acute pancreatitis (AP) is inflammation of the pancreas, which may be due to a wide variety of etiologies that share a final common pathway of premature activation of pancreatic enzymes and resultant autodigestion of pancreatic parenchyma. Acute pancreatitis is easy to diagnose clinically, with the presence of at least 2 of the 3 criteria (upper abdominal pain, serum amylase or lipase level greater than 3 times the upper limit of normal, or characteristic findings on imaging studies) of the revised Atlanta classification. However, postmortem imaging examinations of pancreatitis are extremely rare, and very few successful cases have been reported. Here, we present a case report of a single patient who underwent autopsy and postmortem imaging. Postmortem computed tomography (PMCT) and postmortem magnetic resonance imaging (PMMRI) showed peripancreatic inflammation and acute peripancreatic fluid collection in the left anterior pararenal space, which is consistent with the examination by autopsy. The advantages of PMMRI in AP have also been demonstrated. Our study also confirmed the advantage of PMCT angiography in the diagnosis of AP. To the best of our knowledge, this is the first report of PMCT and PMMRI combined with postmortem pathology in the diagnosis of AP.

Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging.

Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.

The role of epicardial adipose tissue dysfunction in cardiovascular diseases: an overview of pathophysiology, evaluation, and management.

In recent decades, the epicardial adipose tissue (EAT) has been at the forefront of scientific research because of its diverse role in the pathogenesis of cardiovascular diseases (CVDs). EAT lies between the myocardium and the visceral pericardium. The same microcirculation exists both in the epicardial fat and the myocardium. Under physiological circumstances, EAT serves as cushion and protects coronary arteries and myocardium from violent distortion and impact. In addition, EAT acts as an energy lipid source, thermoregulator, and endocrine organ. Under pathological conditions, EAT dysfunction promotes various CVDs progression in several ways. It seems that various secretions of the epicardial fat are responsible for myocardial metabolic disturbances and, finally, leads to CVDs. Therefore, EAT might be an early predictor of CVDs. Furthermore, different non-invasive imaging techniques have been proposed to identify and assess EAT as an important parameter to stratify the CVD risk. We also present the potential therapeutic possibilities aiming at modifying the function of EAT. This paper aims to provide overview of the potential role of EAT in CVDs, discuss different imaging techniques to assess EAT, and provide potential therapeutic options for EAT. Hence, EAT may represent as a potential predictor and a novel therapeutic target for management of CVDs in the future.

Radiomics nomogram for the prediction of microvascular invasion of HCC and patients' benefit from postoperative adjuvant TACE: a multi-center study.

OBJECTIVES: To evaluate the performance of a radiomics nomogram developed based on gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) MRI for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization (PA-TACE). METHODS: A total of 260 eligible patients were retrospectively enrolled from three hospitals (140, 65, and 55 in training, standardized external, and non-standardized external validation cohort). Radiomics features and image characteristics were extracted from Gd-EOB-DTPA MRI image before hepatectomy for each lesion. In the training cohort, a radiomics nomogram which incorporated the radiomics signature and radiological predictors was developed. The performance of the radiomics nomogram was assessed with respect to discrimination calibration, and clinical usefulness with external validation. A score (m-score) was constructed to stratify the patients and explored whether it could accurately predict patient who benefit from PA-TACE. RESULTS: A radiomics nomogram integrated with the radiomics signature, max-D(iameter) > 5.1 cm, peritumoral low intensity (PTLI), incomplete capsule, and irregular morphology had favorable discrimination in the training cohort (AUC = 0.982), the standardized external validation cohort (AUC = 0.969), and the non-standardized external validation cohort (AUC = 0.981). Decision curve analysis confirmed the clinical usefulness of the novel radiomics nomogram. The log-rank test revealed that PA-TACE significantly decreased the early recurrence in the high-risk group (p = 0.006) with no significant effect in the low-risk group (p = 0.270). CONCLUSIONS: The novel radiomics nomogram combining the radiomics signature and clinical radiological features achieved preoperative non-invasive MVI risk prediction and patient benefit assessment after PA-TACE, which may help clinicians implement more appropriate interventions. CLINICAL RELEVANCE STATEMENT: Our radiomics nomogram could represent a novel biomarker to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization, which may help clinicians to implement more appropriate interventions and perform individualized precision therapies. KEY POINTS: • The novel radiomics nomogram developed based on Gd-EOB-DTPA MRI achieved preoperative non-invasive MVI risk prediction. • An m-score based on the radiomics nomogram could stratify HCC patients and further identify individuals who may benefit from the PA-TACE. • The radiomics nomogram could help clinicians to implement more appropriate interventions and perform individualized precision therapies.

Upconversion nanoparticle-based fluorescence resonance energy transfer sensing platform for the detection of cathepsin B activity in vitro and in vivo.

A simple fluorescence resonance energy transfer (FRET) sensing platform (termed as USP), comprised of upconversion nanoparticles (UCNPs) as the energy donor and Cy5 as the energy acceptor, has been synthesized for cathepsin B (CTSB) activity detection in vitro and in vivo. When Cy5-modified peptide substrate (peptide-Cy5) of CTSB is covalently linked on the surface of UCNPs, the FRET between the UCNPs (excitation: 980 nm; emission: 541 nm/655 nm) and Cy5 (excitation: 645 nm) leads to a reduction in the red upconversion luminescence (UCL) signal intensity of UCNPs. Cy5 can be liberated from UCNPs in the presence of CTSB through the cleavage of peptide-Cy5 by CTSB, leading to the recovery of the red UCL signal of UCNPs. Because the green UCL signal of UCNPs remains constant during the CTSB digestion, it can be considered as an internal reference. The findings demonstrate the ability of USP to detect CTSB with the linear detection ranges of 1 to 100 ng·mL-1 in buffer and 2 × 103 to 1 × 105 cells in 0.2 mL cell lysates. The limits of detection (LODs) are 0.30 ng·mL-1 in buffer and 887 cells in 0.2 mL of cell lysates (S/N = 3). The viability of USP to detect CTSB activity in tumor-bearing mice is has further been investigated using in vivo fluorescent imaging.

Automatic segmentation of human supraclavicular adipose tissue using high-resolution T2-weighted magnetic resonance imaging.

OBJECTIVE: To achieve efficient segmentation of human supraclavicular adipose tissue (sclavAT) using high-resolution T2-weighted magnetic resonance images. METHODS: High-resolution 1.0 mm isotropic 3D T2-weighted images covering human supraclavicular area were acquired in transverse or coronary plane from 29 volunteers using a 3.0 T MRI scanner. There were typically 144/288 slices for the transverse/coronary scans for each subject, which amounts to a total of 6816 images in 29 volunteers. A U-NET network was trained to segment the supraclavicular adipose tissue (sclavAT). The performance of the automatic segmentation method was evaluated by comparing the output results with the manual labels using the quantitative indices of dice similarity coefficient (DSC), precision rate (PR), and recall rate (RR). The auto-segmented images were used to calculate the sclavAT volumes and registered to the MR fat fraction (FF) images to quantify the fat component of the sclavAT area. The relationship between body mass index (BMI), the volume and FF of sclavAT area was evaluated for all subjects. RESULTS: The DSC, PR and RR of the automatic sclavAT segmentation method on the testing datasets were 0.920 ± 0.048, 0.915 ± 0.070 and 0.930 ± 0.058. The volume and the mean FF of sclavAT were both found to be strongly correlated to BMI, with the correlation coefficient of 0.703 and 0.625 (p < 0.05), respectively. The averaged computation time of the automatic segmentation method was approximately 0.06 s per slice, compared to more than 5 min for manual labeling. CONCLUSION: The present study demonstrates that the proposed automatic segmentation method using U-Net network is able to identify human sclavAT efficiently and accurately.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Application of postmortem MRI for identification of medulla oblongata contusion as a cause of death: a case report.

Whiplash injury is common in traffic accidents, and severe whiplash is characterized by cervical spinal cord injuries with cervical dislocation or fracture, that can be diagnosed by postmortem computed tomography (PMCT), postmortem magnetic resonance (PMMR), or conventional autopsy. However, for cervical spinal cord injury without fracture and dislocation, PMMR can be more informative because it provides higher resolution of soft tissues. We report the case of a 29-year-old male who died immediately following a traffic accident, in which the vehicle hit an obstacle at a high speed, causing deformation of the bumper and severe damage of the vehicle body. PMCT indicated no significant injuries or diseases related to death, but PMMR showed patchy abnormal signals in the medulla oblongata, and the lower edge of the cerebellar tonsil was herniated out of the foramen magnum. The subsequent pathological and histological results confirmed that death was caused by medulla oblongata contusion combined with cerebellar tonsillar herniation. Our description of this case of a rare but fatal whiplash injury in which there was no fracture or dislocation provides a better understanding of the potentially fatal consequences of cervical spinal cord whiplash injury without fracture or dislocation and of the underlying lethal mechanisms. Compared with PMCT, PMMR provides important diagnostic information in forensic practice for the identification of soft tissue injuries, and is therefore an important imaging modality for diagnosis of whiplash injury when there is no fracture or dislocation.

MRI-Based Artificial Intelligence in Rectal Cancer.

Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology.

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

Ulex Europaeus Agglutinin-I-Based Magnetic Isolation for the Efficient and Specific Capture of SW480 Circulating Colorectal Tumor Cells.

The efficient and specific capture of circulating tumor cells (CTCs) from patients' peripheral blood is of significant value in precise cancer diagnosis and cancer therapy. As fine targeting molecules, lectins can recognize cancer cells specifically due to the abnormal glycosylation of molecules on the cancer cell membrane and the specific binding of lectin with glycoconjugates. Herein, a Ulex europaeus agglutinin-I (UEA-I)-based magnetic isolation strategy was developed to efficiently and specifically capture α-1,2-fucose overexpression CTCs from colorectal cancer (CRC) patients' peripheral blood. Using UEA-I-modified Fe3O4 magnetic beads (termed MB-UEA-I), up to 94 and 89% of target cells (i.e., SW480 CRC cells) were captured from the cell spiking complete cell culture medium and whole blood, respectively. More than 90% of captured cells show good viability and proliferation ability without detaching from MB-UEA-I. In combination with three-color immunocytochemistry (ICC) identification, MB-UEA-I has been successfully used to capture CTCs from CRC patients' peripheral blood. The experimental results indicate a correlation between CTC characterization and tumor metastasis. Specifically, MB-UEA-I can be applied to screen early CRC by capturing CTCs when served as a liquid biopsy. The presented work offers a new insight into developing cost-effective lectin-functionalized methods for biomedical applications.

Non-invasive evaluation of NAFLD and the contribution of genes: an MRI-PDFF-based cross-sectional study.

OBJECTIVE: To investigate the clinical, laboratory and genetic features of NAFLD patients based on MRI-PDFF in China. DESIGN: Patients with high ALT and with a diagnosis of fatty liver were included in this cross-sectional study. Fasting blood was collected to test biomarkers and SNPs. A total of 266 patients underwent MRI-PDFF and FibroScan examinations, and 38 underwent liver biopsy. Diagnostic models (decision tree, LASSO, and elastic net) were developed based on the diagnosis from MRI-PDFF reports. RESULTS: Approximately, 1/3 of the patients were found to have NASH and fibrosis. After quantifying liver steatosis by MRI-PDFF (healthy: n = 47; mild NAFLD: n = 136; moderate/severe NAFLD: n = 83; liver fat content (LFC): 3.6% vs. 8.7% vs. 19.0%), most biomarkers showed significant differences among the three groups, and patients without obesity were found to have a similar LFC as those with obesity (11.1% vs. 12.3%). Models including biomarkers showed strong diagnostic ability (accuracy: 0.80-0.91). Variant alleles of PNPLA3, HSD17B13 and MBOAT7 were identified as genetic risk factors causing higher LFC (8.7% vs. 12.3%; 11.0% vs. 14.5%; 8.5% vs. 10.2%, p < 0.05); those with the UQCC1 rs878639 variant allele showed lower LFC (10.4% vs. 8.4%; OR = 0.58, p < 0.05). Patients with more risk alleles had higher LFCs (8.1% vs. 10.7% vs. 11.6% vs. 14.5%). CONCLUSIONS: Based on MRI-PDFF, a combination of several specific biomarkers can accurately predict disease status. When the effects of genes on liver steatosis were first quantified by MRI-PDFF, the UQCC1 rs878639 G allele was identified as a protective factor, and the MBOAT7 T allele was identified as a risk only among nonobese individuals.