OBJECTIVES:We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG PET/CT images to investigate the prognostic value of PET/CT-derived parameters for overall survival (OS) among lung cancer patients with malignant pleural effusion (MPE). METHODS:A total of 146 patients with MPEs were recruited. An integrated AI segmentation model combining 3D spatially weighted and 2D classical U-Net segmented pleural effusion for 18F-FDG PET/CT parameter extraction. Cox regression analyses revealed independent 12-month survival predictors. The area under the receiver operating characteristic curve (AUC) and DeLong's test were used to evaluate the discriminant power of the predictors and the LENT score. Bootstrap resampling was employed for internal validation. RESULTS:The patients comprised 81 males (55.5%) and had a mean age of 61.7 (SD = 11.5) years. The key survival predictors included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The combined PET/CT parameters demonstrated a statistically significant advantage over the LENT score for 12-month survival prediction (AUC: 0.849, 95% CI, 0.795-0.903 vs AUC: 0.732, 95% CI, 0.660-0.796). The internal bootstrap validation had an AUC of 0.840, (95% CI, 0.671-0.922) and demonstrated a well-fitting calibration curve. CONCLUSIONS:The baseline 18F-FDG-PET/CT parameters extracted using the deep learning model performed excellently in predicting MPE survival and may complement existing MPE survival models and guide clinical stratified treatment. ADVANCES IN KNOWLEDGE:AI-integrated 18F-FDG-PET/CT radiomics improved prognostic assessment of MPE, facilitating personalized interventions stratified by survival expectations.

Objective This study aimed to assess chest CT abnormalities and pulmonary function at 6-month and 1-year follow-ups in coronavirus disease 2019 (COVID-19) pneumonia patients of the China epidemic in the turn of 2022-2023. Methods A total of 156 hospitalized patients with COVID-19 pneumonia admitted between 29 November 2022 and 10 February 2023 were prospectively assessed at 6-month and 1-year follow-ups. Characteristics and CT scores of pulmonary abnormalities and pulmonary function were compared between different follow-up time points. The correlation of CT abnormalities and pulmonary function at 1-year were evaluated. Results Over 1 year, the proportion of pulmonary abnormalities gradually decreased (initial, 100%, 156/156; 6-month, 57.1%, 89/156; and 1-year, 37.8%, 59/156; P < 0.001), whereas fibrotic changes increased (initial, 6.4%, 10/156; 6-month, 14.1%, 22/156; and 1-year, 14.7%, 23/56; P < 0.001). Compared to participants of the subgroup with nonfibrotic changes, diffusion capacity of the lung for carbon monoxide (DLCO)(P = 0.01) and DLCO less than 80% predicted (P < 0.001) showed significantly decrease in participants of the subgroup with fibrotic changes. The extent of fibrotic changes was strongly correlated with lower DLCO (r = -0.734, P < 0.001). Conclusion Fibrotic changes might show a tendency to persist over time and correlate strongly with impairment of diffusion function, thus requiring more attention in future follow-ups.

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths. High-resolution computed tomography (HRCT) has improved the detection of ground glass nodules (GGNs), which are early indicators of lung cancer. Accurate assessment of GGN invasiveness is crucial for determining the appropriate surgical approach. Dual-layer spectral detector computed tomography (DLCT) offers advanced imaging capabilities, including electron density and iodine density, which enhance the evaluation of GGN invasiveness. This study aims to develop a machine learning (ML) model that integrates DLCT parameters and clinical features to predict the invasiveness of GGNs in LUAD, aiding in surgical decision-making and prognosis improvement. Methods: The retrospective study encompassed 272 patients who were diagnosed with LUAD, comprising 154 cases of invasive adenocarcinomas (IA) and 118 cases of pre-invasive minimally invasive adenocarcinoma (MIA) which were then randomly allocated into a training set and a test set. Six ML models were developed based on five DLCT parameters (conventional, iodine density, virtual noncontrast, electron density, and effective atomic number). Subsequently, a nomogram was constructed using multi-factor logistic regression, incorporating radiomic characteristics and clinicopathological risk factors. Results: The ML model based on conventional plus electron density performed better than the models with other DLCT parameters, with the area under the curves (AUCs) of 0.945 and 0.964 in the training and test sets, respectively. The clinical model and radiomics score (Rad-score) were combined in the logistic regression to construct a joint model, of which the AUCs were 0.974 in the training sets and 0.949 in the test sets. The ML model effectively differentiated between IA and pre-invasive MIA, and further classified patients into high and medium risk categories for invasion using waterfall plots. Conclusions: The ML model based on DLCT parameters helps predict the invasiveness of GGNs and classifies the GGNs into different risk grades.

To investigate the impact of deep learning reconstruction (DLR) on the image quality of diffusion-weighted imaging (DWI) for liver and its ability to differentiate benign from malignant focal liver lesions (FLLs). Consecutive patients with suspected liver disease who underwent liver MRI between January and May 2025 were included. All patients received conventional DWI (DWIC) and an accelerated reconstructed DWI (DWIDLR) in which acquisition time was prospectively halved by reducing signal averages. Image quality was compared qualitatively using Likert scores (e.g., lesion conspicuity, overall quality) and quantitatively by measuring signal-to-noise ratio of the liver (SNRLiver) and lesion (SNRLesion), contrast-to-noise ratio (CNR), and edge rise distance (ERD). Apparent diffusion coefficient (ADC) values and diagnostic performance for differentiating benign from malignant FLLs were assessed. A total of 193 patients (128 males, 65 females; age range, 23-81 years) were included. For quantitative assessment, DWIDLR demonstrated higher SNRLiver, SNRLesion, CNR, and a shorter ERD (all p < 0.05). For qualitative assessment, DWIDLR showed improved lesion conspicuity, liver edge sharpness, and overall image quality (all p < 0.01), with no significant difference in artifacts (p = 0.08). ADC values were lower with DWIDLR for both benign and malignant FLLs (p < 0.001). In differentiating benign from malignant lesions, DWIDLR achieved better diagnostic performance (AUC: 0.921 vs. 0.904, p < 0.05). Deep learning-enhanced DWI enables a 50

This work proposes a new retrospective motion correction method, termed DCGAN-MS, which employs disentangled CycleGAN based onmulti-mask k-space subsampling (DCGAN-MS) to address the image domain translation challenge. The multi-mask k-space subsampling operator is utilized to decrease the complexity of motion artifacts by randomly discarding motion-affected k-space lines. The network then disentangles the subsampled, motion-corrupted images into content and artifact features using specialized encoders, and generates motion-corrected images by decoding the content features. By utilizing multi-mask k-space subsampling, motion artifact features become more sparse compared to the original image domain, enhancing the efficiency of the DCGAN-MS network. This method effectively corrects motion artifacts in clinical gadoxetic acid-enhanced human liver MRI, human brain MRI from fastMRI, and preclinical rodent brain MRI. Quantitative improvements are demonstrated with SSIM values increasing from 0.75 to 0.86 for human liver MRI with simulated motion artifacts, and from 0.72 to 0.82 for rodent brain MRI with simulated motion artifacts. Correspondingly, PSNR values increased from 26.09 to 31.09 and from 25.10 to 31.77. The method’s performance was further validated on clinical and preclinical motion-corrupted MRI using the Kernel Inception Distance (KID) and Fréchet Inception Distance (FID) metrics. Additionally, ablation experiments were conducted to confirm the effectiveness of the multi-mask k-space subsampling approach.

Dual-energy CT has shown promising results in determining tumor characteristics and treatment effectiveness through spectral data by assessing normalized iodine concentration (nIC), normalized effective atomic number (nZeff), normalized electron density (nED), and extracellular volume (ECV). This study explores the value of quantitative parameters in contrast-enhanced dual-layer spectral detector CT (SDCT) as a potential tool for detecting lymph node activity in lymphoma patients. A retrospective analysis of 55 lymphoma patients with 289 lymph nodes, assessed through (18)FDG-PET/CT and the Deauville five-point scale, revealed significantly higher values of nIC, nZeff, nED, and ECV in active lymph nodes compared to inactive ones (p < 0.001). Generalized linear mixed models showed statistically significant fixed-effect parameters for nIC, nZeff, and ECV (p < 0.05). The area under the receiver operating characteristic curve (AUROC) values of nIC, nZeff, and ECV reached 0.822, 0.845, and 0.811 for diagnosing lymph node activity. In conclusion, the use of g nIC, nZeff, and ECV as alternative imaging biomarkers to PET/CT for identifying lymph node activity in lymphoma holds potential as a reliable diagnostic tool that can guide treatment decisions.

Abstract Background Deuterium metabolic imaging (DMI) has emerged as a promising non-invasive technique for studying metabolism in vivo. This review aims to summarize the current developments and discuss the futures in DMI technique in vivo. Methods A systematic literature review was conducted based on the PRISMA 2020 statement by two authors. Specific technical details and potential applications of DMI in vivo were summarized, including strategies of deuterated metabolites detection, deuterium-labeled tracers and corresponding metabolic pathways in vivo, potential clinical applications, routes of tracer administration, quantitative evaluations of metabolisms, and spatial resolution. Results Of the 2,248 articles initially retrieved, 34 were finally included, highlighting 2 strategies for detecting deuterated metabolites: direct and indirect DMI. Various deuterated tracers (e.g., [6,6′-2H2]glucose, [2,2,2′-2H3]acetate) were utilized in DMI to detect and quantify different metabolic pathways such as glycolysis, tricarboxylic acid cycle, and fatty acid oxidation. The quantifications (e.g., lactate level, lactate/glutamine and glutamate ratio) hold promise for diagnosing malignancies and assessing early anti-tumor treatment responses. Tracers can be administered orally, intravenously, or intraperitoneally, either through bolus administration or continuous infusion. For metabolic quantification, both serial time point methods (including kinetic analysis and calculation of area under the curves) and single time point quantifications are viable. However, insufficient spatial resolution remains a major challenge in DMI (e.g., 3.3-mL spatial resolution with 10-min acquisition at 3 T). Conclusions Enhancing spatial resolution can facilitate the clinical translation of DMI. Furthermore, optimizing tracer synthesis, administration protocols, and quantification methodologies will further enhance their clinical applicability. Relevance statement Deuterium metabolic imaging, a promising non-invasive technique, is systematically discussed in this review for its current progression, limitations, and future directions in studying in vivo energetic metabolism, displaying a relevant clinical potential. Key points • Deuterium metabolic imaging (DMI) shows promise for studying in vivo energetic metabolism. • This review explores DMI’s current state, limits, and future research directions comprehensively. • The clinical translation of DMI is mainly impeded by limitations in spatial resolution. Graphical Abstract

Purpose: To discriminate between benignities and hepatocellular carcinomas (HCCs) in patients at high risk of HCC using a novel enhancement flux analysis for gadoxetic-acid enhanced MRI.Method: This study retrospectively collected 181 liver nodules in 156 patients at high risk of HCC who underwent gadoxetic acid-enhanced MRI examinations with following surgical resection from 1st August 2017 to 31st December 2021 as the training set; another 42 liver nodules in 36 patients were prospectively collected from 1st January 2022 to 1st October 2022 as the test set. The time-intensity curves (TICs) of liver nodules were formed with consecutive time points: 0 s, 20 s, 1 min, 2 min, 5 min, 10 min, 15 min, and 20 min since contrast injection. A novel enhancement flux analysis was applied by using a biexponential function fitting to distinguish benig-nities and HCC. Besides, previously published models including maximum enhancement ratio (ERmax), per-centage signal ratio (PSR), and ERmax +PSR were compared. The areas under the receiver operating characteristic curves (AUCs) were compared among these methods.Results: The novel enhancement flux analysis showed the highest AUCs in the training set (0.897, 95%CI: 0.833-0.960) and the test set (0.859, 95%CI: 0.747-0.970) among all models. The AUCs of PSR, ERmax and ERmax +PSR were 0.801 (95%CI: 0.710-0.891), 0.620 (95%CI: 0.510-0.729), and 0.799 (95%CI: 0.709-0.889) in the training set, and were 0.701 (95%CI: 0.539-0.863), 0.529 (95%CI: 0.342-0.717), and 0.708 (95%CI: 0.549-0.867) in the test set.Conclusions: The biexponential flux analysis for gadoxetic-acid enhanced MRI presents a better potential in ac-curate diagnosis of small HCC nodules.

Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-wise accuracy, complete topology structure and robustness to various contrast variations are critical and challenging, and most existing methods focus only on achieving one or two of these aspects. In this paper, we present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach. Specifically, we compute a multiscale affinity field for each pixel, capturing its semantic relationships with neighboring pixels in the predicted mask image. This field represents the local geometry of vessel segments of different sizes, allowing us to learn spatial- and scale-aware adaptive weights to strengthen vessel features. We evaluate our AFN on four different types of vascular datasets: X-ray angiography coronary vessel dataset (XCAD), portal vein dataset (PV), digital subtraction angiography cerebrovascular vessel dataset (DSA) and retinal vessel dataset (DRIVE). Extensive experimental results demonstrate that our AFN outperforms the state-of-the-art methods in terms of both higher accuracy and topological metrics, while also being more robust to various contrast changes.

The current artificial intelligence (AI) models are still insufficient in multi-disease diagnosis for real-world data, which always present a long-tail distribution. To tackle this issue, a long-tail public dataset, "ChestX-ray14," which involved fourteen (14) disease labels, was randomly divided into the train, validation, and test sets with ratios of 0.7, 0.1, and 0.2. Two pretrained state-of-the-art networks, EfficientNet-b5 and CoAtNet-0-rw, were chosen as the backbones. After the fully-connected layer, a final layer of 14 sigmoid activation units was added to output each disease's diagnosis. To achieve better adaptive learning, a novel loss (Lours) was designed, which coalesced reweighting and tail sample focus. For comparison, a pretrained ResNet50 network with weighted binary cross-entropy loss (LWBCE) was used as a baseline, which showed the best performance in a previous study. The overall and individual areas under the receiver operating curve (AUROC) for each disease label were evaluated and compared among different models. Group-score-weighted class activation mapping (Group-CAM) is applied for visual interpretations. As a result, the pretrained CoAtNet-0-rw + Lours showed the best overall AUROC of 0.842, significantly higher than ResNet50 + LWBCE (AUROC: 0.811, p = 0.037). Group-CAM presented that the model could pay the proper attention to lesions for most disease labels (e.g., atelectasis, edema, effusion) but wrong attention for the other labels, such as pneumothorax; meanwhile, mislabeling of the dataset was found. Overall, this study presented an advanced AI diagnostic model achieving a significant improvement in the multi-disease diagnosis of chest X-rays, particularly in real-world data with challenging long-tail distributions.

To present a preclinical in vivo approach for standardization and training of lymphangiography and lymphatic interventions using a pictorial review. Different lipiodol- and gadolinium-based lymphangiography and lymphatic interventions were performed in twelve (12) landrace pigs with a mean bodyweight of 34 ± 2 kg using various imaging and guiding modalities, similar to the procedures used in humans. The techniques used were explicitly introduced and illustrated. The potential applications of each technique in preclinical training were also discussed. By applying visual, ultrasonography, fluoroscopy, CT, cone-beam CT, and/or MRI examination or guidance, a total of eleven techniques were successfully implemented in twelve pigs. The presented techniques include inguinal postoperative lymphatic leakage (PLL) establishment, interstitial dye test, five types of lymphangiography [incl. lipiodol-based translymphatic lymphangiography (TL), lipiodol-based percutaneous intranodal lymphangiography (INL), lipiodol-based laparotomic INL, lipiodol-based interstitial lymphangiography, and interstitial magnetic resonance lymphangiography (MRL)], and four types of percutaneous interventions in the treatment of PLL [incl. thoracic duct embolization (TDE), intranodal embolization (INE), afferent lymphatic vessel sclerotherapy (ALVS), and afferent lymphatic vessel embolization (ALVE)]. This study provides a valuable resource for inexperienced interventional radiologists to undergo the preclinical training in lymphangiography and lymphatic interventions using healthy pig models.

Background: In recent years, spectral computed tomography (CT) has shown excellent performance in the diagnosis of ground-glass nodules (GGNs) invasiveness; however, no research has combined spectral multimodal data and radiomics analysis for comprehensive analysis and exploration. Therefore, this study goes a step further on the basis of the previous research: to investigate the value of dual-layer spectral CT-based multimodal radiomics in accessing the invasiveness of lung adenocarcinoma manifesting as GGNs. Methods: In this study, 125 GGNs with pathologically confirmed preinvasive adenocarcinoma (PIA) and lung adenocarcinoma were divided into a training set (n=87) and a test set (n=38). Each lesion was automatically detected and segmented by the pre-trained neural networks, and 63 multimodal radiomic features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select target features, and a rad-score was constructed in the training set. Logistic regression analysis was conducted to establish a joint model which combined age, gender, and the rad-score. The diagnostic performance of the two models was compared by the receiver operating characteristic (ROC) curve and precision-recall curve. The difference between the two models was compared by the ROC analysis. The test set was used to evaluate the predictive performance and calibrate the model. Results: Five radiomic features were selected. In the training and test sets, the area under the curve (AUC) of the radiomics model was 0.896 (95% CI: 0.830, 0.962) and 0.881 (95% CI: 0.777, 0.985) respectively, and the AUC of the joint model was 0.932 (95% CI: 0.882-0.982) and 0.887 (95% CI: 0.786, 0.988) respectively. There was no significant difference in AUC between the radiomics model and joint model in the training and test sets (0.896 vs. 0.932, P=0.088; 0.881 vs. 0.887, P=0.480). Conclusions: Multimodal radiomics based on dual-layer spectral CT showed good predictive performance in differentiating the invasiveness of GGNs, which could assist in the decision of clinical treatment strategies.

Cancer metabolism has emerged as a pivotal area of research recently. The ability to visualize and comprehend the metabolic processes of cancer holds immense clinical value, particularly in the diagnosis of malignant tumors and the assessment of treatment responses. Deuterium Metabolic Imaging (DMI), as a robust, simple, and versatile MR spectroscopic imaging tool, demonstrates promise in tumor diagnosis and treatment efficacy assessment. This review explored the latest developments and applications of DMI in oncology across various tumor metabolic axes, with a specific emphasis on its potential for clinical translation. DMI offers invaluable insights into tumor biology, treatment responses, and prognostic outcomes. Notably, DMI can identify early responses to immunotherapy, a prominent area of current research interest. In conclusion, DMI harbors the potential to evolve into a convenient and efficient imaging technique in clinical practice, thereby advancing precision medicine and improving the diagnosis and evaluation of cancer treatments.

Background: Chyle leak is a common complication following pancreatic surgery. After failure of conservative treatment, lymphography is one of the last therapeutic options. The objective of this study was to evaluate whether lymphography represents an effective treatment for severe chyle leak (International study Group on Pancreatic Surgery, grade C) after pancreatic surgery. Methods: Patients with grade C chyle leak after pancreatic surgery who received transpedal or transnodal therapeutic lymphography between 2010 and 2020 were identified from a prospectively maintained database. Clinical success of the lymphography was evaluated according to percent decrease of drainage output after lymphography (> 50% decrease = partial success; > 85% decrease = complete success). Results: Of the 48 patients undergoing lymphography, 23 had a clinically successful lymphography: 14 (29%) showed partial and 9 (19%) complete success. In 25 cases (52%) lymphography did not lead to a significant reduction of chyle leak. Successful lymphography was associated with earlier drain removal and hospital discharge [complete clinical success: 7.1 days (+/- 4.1); partial clinical success: 12 days (+/- 9.1), clinical failure: 19 days (+/- 19) after lymphography; p = 0.006]. No serious adverse events were observed. Conclusion: Therapeutic lymphography is a feasible, safe, and effective option for treating grade C chyle leak after pancreatic surgery.

Purpose Report of experience from a single institution in treating postoperative lymphatic leakage (PLL) applying conventional transpedal lymphangiography (TL). Materials and Methods 453 patients with the initial diagnosis of PLL receiving TL between 03/1993 and 09/2018 were identified in the database. Only patients with confirmed PLL were included in the study. The technical success, safety, and treatment success of TL were evaluated. Independent predictors of TL treatment failure were examined using univariate and multivariate logistic regression analysis. Results 355 consecutive patients (218 men, 137 women; median age of 62 years) who underwent TL for PLL (e. g., chylothorax) after ineffective conservative treatment were included. The median time between causal surgery and TL was 27 days. The median technical success rate of TL was 88.5%, with a median volume of Lipiodol of 10.0 ml. No complication of TL was recorded. Three groups were defined according to the different clinical courses: group A (41/355, 11.5%) - TL with technical failure; group B (258/355, 72.7 %) - "therapeutic" TL alone with technical success; and group C (56/355, 15.8%) - "diagnostic" TL with simultaneously invasive treatment (incl. surgical revision and percutaneous sclerotherapy). Treatment success rate and median time to treatment success were higher in group C than in group B, but without significant differences (64.3% vs. 61.6%, p = 0.710; six vs. five days, p =0.065). Univariate and multivariate logistic regression analyses for group B confirmed drainage volume (>500 ml/d) and Lipiodol extravasation as independent predictors of TL clinical failure (odds ratios [ORs] of 2.128 and 2.372 [p = 0.005 and p = 0.003, respectively]). Conclusion TL is technically reliable, safe, and effective in treating PLL. When conservative treatment fails, TL can be regarded as the next treatment option.

C-arm cone-beam computed tomography (CBCT) virtual navigation-guided lung biopsy has been developed in the last decade as an alternative to conventional CT-guided lung biopsy. This study aims to compare the biopsy accuracy and safety between these two techniques and explores the risk factors of biopsy-related complications. A total of 217 consecutive patients undergoing conventional CT- or C-arm CBCT virtual navigation-guided lung biopsy from 1 June 2018 to 31 December 2019 in this single-center were retrospectively reviewed. Multiple factors (e.g., prior emphysema, lesion size, etc.) were compared between two biopsy techniques. The risk factors of complications were explored by using logistic regression. The patients' median age and male-to-female ratio were 63 years and 2.1:1, respectively. Eighty-two (82) patients (37.8%) underwent conventional CT-guided biopsies, and the other 135 patients (62.2%) C-arm CBCT virtual navigation-guided biopsies. Compared with patients undergoing C-arm CBCT virtual navigation-guided lung biopsies, patients undergoing conventional CT-guided lung biopsies showed higher needle repositioning rate, longer operation time, and higher effective dose of X-ray (52.4% vs. 6.7%, 25 min vs. 15 min, and 13.4 mSv vs. 7.6 mSv, respectively; p < 0.001, each). In total, the accurate biopsy was achieved in 215 of 217 patients (99.1%), without a significant difference between the two biopsy techniques (p = 1.000). The overall complication rates, including pneumothorax and pulmonary hemorrhage/hemoptysis, are 26.3% (57/217), with most minor complications (56/57, 98.2%). The needle repositioning was the only independent risk factor of complications with an odds ratio of 6.169 (p < 0.001). In conclusion, the C-arm CBCT virtual navigation is better in percutaneous lung biopsy than conventional CT guidance, facilitating needle positioning and reducing radiation exposure. Needle repositioning should be avoided because it brings about more biopsy-related complications.

This study aims to dynamically assess tumor changes after variable treatments with vascular endothelial growth factor (VEGF) inhibitor and/or immune checkpoint inhibitor (ICI) using multimodal imaging of MRI and 18F-FDG PET/CT in a hepatocellular carcinoma (HCC) mice model. Based on different treatments, 24 mice were randomly divided into four groups: control (isotype-matched IgG antibody 10 mg/kg), VEGF inhibitor (sorafenib 50 mg/kg), ICI (anti-PD-L1 antibody 10 mg/kg), and combination groups (sorafenib 50 mg/kg + anti-PD-L1 antibody 10 mg/kg). Quantitative imaging assessments, including volume transfer constant (Ktrans), apparent diffusion coefficient (ADC), lactate/choline ratio, and the maximum standardized 18F-FDG uptake value ratio of tumor to muscle (SUVtumor/SUVmuscle ratio), were acquired at different time points (before treatment and 7, 14, and 21 days after treatment). Quantitative data were presented as the mean ± standard errors and two-way repeated-measure ANOVA tests were performed for intergroup and intertime point comparisons. After 21 days from the initiation of therapies, combination group showed the lowest tumor volume and weight, followed by ICI, VEGF inhibitor, and control group, with no significance between the VEGF inhibitor and control groups. In addition, Ktrans values significantly decreased, and the lactate/choline ratio and SUVtumor/SUVmuscle ratio were significantly elevated in the VEGF inhibitor group. ADC significantly increased in the ICI and combination groups, with no significant differences in ADC observed between the control and VEGF inhibitor groups, which showed a similar dynamic change to the tumor volume. Furthermore, Ktrans, lactate/choline ratio, and ADC were significantly correlated with CD31+ area, hypoxyprobe+ area, and apoptosis, respectively. Our results suggest that the singular treatment and combination of the VEGF inhibitor and ICI treatments for HCC present different multimodal imaging changes in accordance with the specific histopathological features. These findings might facilitate the formulation of better treatment response criteria; besides, we find ADC is probably an indicator easily to obtain for treatment response evaluation.

Background/objectives: Irreversible electroporation (IRE) is an emerging treatment for locally advanced pancreatic cancer (LAPC) which in some cohorts has been associated with severe complications. Additionally, re-resection of isolated local recurrence (ILR) after pancreatic ductal adenocarcinoma (PDAC) can improve survival. We investigated safety, feasibility and oncologic outcomes in the first report on open IRE for unresectable ILR of PDAC in a staged surgical approach. Methods: Records of the prospectively documented institutional database were screened for patients undergoing laparotomy in IRE-standby due to questionable resectability. Endpoints were morbidity, mortality and overall (OS) and progression free survival (PFS). Data of LAPC and ILR were compared statistically for safety and feasibility analysis. Results: Intraoperative IRE was performed in 11 ILR and 14 LAPC. Six (54.5%) ILR and 10 (71.4%) LAPC patients had postoperative complications, type and frequency did not differ significantly. Major complications occurred in one ILR and two LAPC patients. Median OS was 20.0 months (95% CI: 2.7-37.3) after IRE for ILR and 28 (17.4-38.6) for LAPC. Median PFS after IRE was seven months for both ILR (4.1 -9.9; n = 9) and LAPC (2.3-11.7; n = 13). Conclusion: Open IRE for unresectable ILR was associated with acceptable perioperative risk. In this small, highly selected subset of patients with limited therapeutic options ancillary treatment with IRE might improve survival. Randomized treatment studies are required to establish the definitive role of IRE as compared to palliative standards of care in unresectable recurrence of PDAC and inconvertible LAPC. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

This study aimed to explore the associations between cerebral white matter (WM) alterations, mental health status, and metabolism in recovered COVID-19 patients. We included 28 recovered COVID-19 patients and 27 healthy controls between April 2020 and June 2020. Demographic data, the mental health scores, diffusion-tensor imaging (DTI) data, and plasma metabolomics were collected and compared between the two groups. Tract-based spatial statistics and graph theory approaches were used for DTI data analysis. Untargeted metabolomics analysis of the plasma was performed. Correlation analyses were performed between these characteristics. Recovered COVID-19 patients showed decreased fractional anisotropy, increased mean diffusivity and radial diffusivity values in widespread brain regions, and significantly lower global efficiency, longer shortest path length, and less nodal local efficiency in superior occipital gyrus (all, P < 0.05, Bonferroni corrected). Our results also demonstrated significantly different plasma metabolic profiling in recovered COVID-19 patients even at 3 months after their hospital discharge, which was mainly related to purine pathways, amino acids, lipids, and amine metabolism. Certain regions with cerebral WM alterations in the recovered patients showed significant correlations with different metabolites and the mental health scores. We observed multiple alterations in both WM integrity and plasma metabolomics that may explain the deteriorated mental health of recovered COVID-19 patients. These findings may provide potential biomarkers for the mental health evaluation for the recovered COVID-19 patients and potential targets for novel therapeutics.

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman’s correlation coefficient between these two estimation methods was 0.920 ( p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.