A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study.

OBJECTIVES: We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making. MATERIALS AND METHODS: This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models. RESULTS: Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model. CONCLUSIONS: The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients. CLINICAL RELEVANCE STATEMENT: The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS. KEY POINTS: Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.

Coronary inflammation based on pericoronary adipose tissue attenuation in type 2 diabetic mellitus: effect of diabetes management.

BACKGROUND: Coronary inflammation plays crucial role in type 2 diabetes mellitus (T2DM) induced cardiovascular complications. Both glucose-lowering drug interventions (GLDIS) and glycemic control (GC) status potentially correlate coronary inflammation, as indicated by changes in pericoronary adipose tissue (PCAT) attenuation, and thus influence cardiovascular risk. This study evaluated the impact of GLDIS and GC status on PCAT attenuation in T2DM patients. METHODS: This retrospective study collected clinical data and coronary computed tomography angiography (CCTA) images of 1,342 patients, including 547 T2DM patients and 795 non-T2DM patients in two tertiary hospitals. T2DM patients were subgroup based on two criteria: (1) GC status: well: HbA1c < 7%, moderate: 7 ≤ HbA1c ≤ 9%, and poor: HbA1c > 9%; (2) GLDIS and non-GLDIS. PCAT attenuations of the left anterior descending artery (LAD-PCAT), left circumflex artery (LCX-PCAT), and right coronary artery (RCA-PCAT) were measured. Propensity matching (PSM) was used to cross compare PCAT attenuation of non-T2DM and all subgroups of T2DM patients. Linear regressions were conducted to evaluate the impact of GC status and GLDIS on PCAT attenuation in T2DM patients. RESULTS: Significant differences were observed in RCA-PCAT and LCX-PCAT between poor GC-T2DM and non-T2DM patients (LCX: - 68.75 ± 7.59 HU vs. - 71.93 ± 7.25 HU, p = 0.008; RCA: - 74.37 ± 8.44 HU vs. - 77.2 ± 7.42 HU, p = 0.026). Higher PCAT attenuation was observed in LAD-PCAT, LCX-PCAT, and RCA-PCAT in non-GLDIS T2DM patients compared with GLDIS T2DM patients (LAD: - 78.11 ± 8.01 HU vs. - 75.04 ± 8.26 HU, p = 0.022; LCX: - 71.10 ± 8.13 HU vs. - 68.31 ± 7.90 HU, p = 0.037; RCA: - 78.17 ± 8.64 HU vs. - 73.35 ± 9.32 HU, p = 0.001). In the linear regression, other than sex and duration of diabetes, both metformin and acarbose were found to be significantly associated with lower LAD-PCAT (metformin: ß coefficient = - 2.476, p=0.021; acarbose: ß coefficient = - 1.841, p = 0.031). CONCLUSION: Inadequate diabetes management, including poor GC and lack of GLDIS, may be associated with increased coronary artery inflammation in T2DM patients, as indicated by PCAT attenuation on CCTA, leading to increased cardiovascular risk. This finding could help healthcare providers identify T2DM patients with increased cardiovascular risk, develop improved cardiovascular management programs, and reduce subsequent cardiovascular related mortality.

Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis.

Purpose To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. Targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts. The automated csPCa diagnostic models trained with and without TPAS were compared using multicenter validation datasets. The impact of rectal artifacts on the diagnostic performance of each model at the patient and lesion levels was compared using the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). The AUC between models was compared using the DeLong test, and the AUPRC was compared using the bootstrap method. Results The TPAS model exhibited diagnostic performance improvements of 6% at the patient level (AUC: 0.87 vs 0.81, P < .001) and 7% at the lesion level (AUPRC: 0.84 vs 0.77, P = .007) compared with the control model. The TPAS model demonstrated less performance decline in the presence of rectal artifact-pattern adversarial noise than the control model (ΔAUC: -17% vs -19%, ΔAUPRC: -18% vs -21%). The TPAS model performed better than the control model in patients with moderate (AUC: 0.79 vs 0.73, AUPRC: 0.68 vs 0.61) and severe (AUC: 0.75 vs 0.57, AUPRC: 0.69 vs 0.59) artifacts. Conclusion This study demonstrates that the TPAS model can reduce rectal artifact interference in MRI-based csPCa diagnosis, thereby improving its performance in clinical applications. Keywords: MR-Diffusion-weighted Imaging, Urinary, Prostate, Comparative Studies, Diagnosis, Transfer Learning Clinical trial registration no. ChiCTR23000069832 Supplemental material is available for this article. Published under a CC BY 4.0 license.

Synchronous Superficial Middle Cerebral Vein Outflow Correlates Favorable Tissue Fate After Mechanical Thrombectomy for Acute Ischemic Stroke.

RATIONALE AND OBJECTIVES: The purpose of this study was to determine the association between hemispheric synchrony in venous outflow at baseline and tissue fate after mechanical thrombectomy (MT) for acute ischemic stroke (AIS). MATERIALS AND METHODS: A two-center retrospective analysis involving AIS patients who underwent MT was performed. The four cortical veins of interest include the superficial middle cerebral vein (SMCV), sphenoparietal sinus (SS), vein of Labbé (VOL), and vein of Trolard (VOT). Baseline computed tomography perfusion data were used to compare the following outflow parameters between the hemispheres: first filling time (△FFT), time to peak (△TTP) and total filling time (△TFT). Synchronous venous outflow was defined as △FFT = 0. Multivariable regression analyses were performed to evaluate the association of venous outflow synchrony with penumbral salvage, infarct growth, and intracranial hemorrhage (ICH) after MT. RESULTS: A total of 151 patients (71.4 ± 13.2 years, 65.6% women) were evaluated. Patients with synchronous SMCV outflow demonstrated significantly greater penumbral salvage (41.3 mL vs. 33.1 mL, P = 0.005) and lower infarct growth (9.0 mL vs. 14.4 mL, P = 0.015) compared to those with delayed SMCV outflow. Higher △FFTSMCV (ß = -1.44, P = 0.013) and △TTPSMCV (ß = -0.996, P = 0.003) significantly associated with lower penumbral salvage, while higher △FFTSMCV significantly associated with larger infarct growth (ß = 1.09, P = 0.005) and increased risk of ICH (odds ratio [OR] = 1.519, P = 0.047). CONCLUSION: Synchronous SMCV outflow is an independent predictor of favorable tissue outcome and low ICH risk, and thereby carries the potential as an auxiliary radiological marker aiding the treatment planning of AIS patients.

The Value of Dual-Energy Computed Tomography Angiography-Derived Parameters in the Evaluation of Clot Composition.

OBJECTIVES: We aimed to assess the value of dual-energy computed tomography angiography (DE-CTA) derived parameters as a quantitative biomarker of thrombus composition in acute ischemic stroke (AIS). METHODS: AIS patients who underwent DE-CTA before thrombectomy between August 2016 and September 2022 were included in this study. We assessed the relative proportion of red blood cells (RBCs) and the fibrin/platelet ratio (F/P) of the retrieved clots and categorized the clots as RBC-dominant (RBCs > F/P) or F/P-dominant (F/P > RBCs). The thrombus based parameters were measured on polyenergetic images (PEI), virtual monoenergetic (VM), virtual non-contrast (VNC), iodine concentration (IC), and effective atomic number (Zeff) images respectively, and the slope of the spectral Hounsfield unit curve (λHU) was calculated. These parameters were compared in the DE-CTA images of RBC- and F/P-dominant thrombi. The diagnostic performance of the parameters was analyzed using the ROC curve. Correlations between thrombus composition and DE-CTA-derived parameters were assessed. RESULTS: The retrieved clots in 54 of 88 patients (61.36%) were RBC-dominant. The RBC-dominant thrombi showed significantly higher VNC values and lower IC, λHU, and Zeff values than the F/P-dominant thrombi (p < 0.05). The CT density measured on IC images showed the largest AUC value (AUC, 0.94; sensitivity, 77.78%; specificity, 100.00%). The Spearman rank-order correlation coefficient values showed that CT density measured on IC images of the thrombus showed the strongest association with the proportion of RBCs (r = -0.64, p < 0.001) and F/P (r = 0.65, p < 0.001). CONCLUSIONS: DE-CTA-derived parameters, especially the CT density measured on IC images, could be associated with thrombus composition and allow for personalized thrombectomy strategies.

Hypertension Exacerbates Severity and Outcomes of COVID-19 in Elderly Patients: A Retrospective Observational Study.

OBJECTIVE: To evaluate the impact of hypertension on the clinical outcome of COVID-19 patients aged 60 years old and older. METHODS: This single-center retrospective cohort study enrolled consecutive COVID-19 patients aged 60 years old and older, who were admitted to Liyuan Hospital from January 1, 2020 to April 25, 2020. All included patients were divided into two groups: hypertension and nonhypertension group. The baseline demographic characteristics, laboratory test results, chest computed tomography (CT) images and clinical outcomes were collected and analyzed. The prognostic value of hypertension was determined using binary logistic regression. RESULTS: Among the 232 patients included in the analysis, 105 (45.3%) patients had comorbid hypertension. Compared to the nonhypertension group, patients in the hypertension group had higher neutrophil-to-lymphocyte ratios, red cell distribution widths, lactate dehydrogenase, high-sensitivity C-reactive protein, D-dimer and severity of lung lesion, and lower lymphocyte counts (all P<0.05). Furthermore, the hypertension group had a higher proportion of intensive care unit admissions [24 (22.9%) vs. 14 (11.0%), P=0.02) and deaths [16 (15.2%) vs. 3 (2.4%), P<0.001] and a significantly lower probability of survival (P<0.001) than the nonhypertension group. Hypertension (OR: 4.540, 95% CI: 1.203-17.129, P=0.026) was independently correlated with all-cause in-hospital death in elderly patients with COVID-19. CONCLUSION: The elderly COVID-19 patients with hypertension tend to have worse conditions at baseline than those without hypertension. Hypertension may be an independent prognostic factor of poor clinical outcome in elderly COVID-19 patients.

Global Limb Anatomic Staging System Score correlates with the clinical outcomes in chronic limb threatening ischemia patients.

BACKGROUND: Chronic limb-threatening ischemia (CLTI) affects millions of people and causes health care burden around the globe. Global Limb Anatomic Staging System (GLASS) was proposed as a new anatomic system for integrating the complexity of threatened limb. METHODS: We retrospectively classified computed tomography angiography images of threatened limbs into GLASS stages between January 2018 and April 2020. Comorbidities, limb treatments, and outcomes including amputation-free survival (AFS), reintervention and mortality were compared and the likelihood of benefit from revascularization was estimated according to GLASS. Kaplan-Meier estimate was used to determine the rates of endpoint events at 1 year. Multivariate analysis was performed to identify predictors of those outcomes. RESULTS: In our study, 285 threatened limbs in 263 patients were stratified including GLASS stage I disease (N.=53, 19%), stage II (N.=129; 45%) and stage III (N.=103; 36%) disease. The percentage of limbs undergoing endovascular revascularization and minor amputation increased significantly with increasing GLASS stage. On Kaplan-Meier analysis, increasing GLASS stage was associated with 1-year reduced AFS (stage I: 96.1%, stage II: 94.1%, stage III: 83.9%; log rank P=0.016). The percentage of 1-year reintervention rate in infrapopliteal GLASS grade 3-4 (15%) was significantly higher than the percentage of reintervention in infrapopliteal GLASS grade 0-2 (5%) (Log rank P=0.002). Infrapopliteal GLASS grade 3 and 4 was the independent predictor of reduced AFS. CONCLUSIONS: GLASS stage correlated with intensity of limb treatment and with clinical outcomes at 1 year. Infrapopliteal GLASS grade 3 and 4 independently predicted the reduced amputation-free survival.

Assessment of peripheral neuropathy in type 2 diabetes by diffusion tensor imaging: A case-control study.

OBJECTIVES: This study aimed to evaluate diabetes peripheral neuropathy (DPN) by diffusion tensor imaging (DTI) and explore the correlation between DTI parameters and electrophysiological parameters. METHODS: We examined tibial nerve (TN) and common peroneal nerve (CPN) of 32 DPN patients and 23 healthy controls using T1-weighted magnetic resonance imaging and DTI. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) of TN and CPN were measured and compared between groups. Spearman correlation coefficient was used to explore the relationship between DTI parameters and electrophysiology parameters in the DPN group. Diagnostic value was assessed by receiver operating characteristic (ROC) analysis. RESULTS: In the DPN group, FA was decreased (p < 0.0001) and MD and RD were increased (p < 0.05, p < 0.001) in the TN and CPN compared with the values of healthy control group. Moreover, in the DPN group, FA was positively correlated with motor nerve conduction velocity (MCV) (p < 0.0001), and both MD and RD were negatively correlated with MCV (p < 0.05, p < 0.001). However, there was no correlation between AD and any electrophysiological parameters. Among all DTI parameters, FA displayed the best diagnostic accuracy, with an area under the ROC curve of 0.882 in TN and 0.917 in CPN. CONCLUSION: FA and RD demonstrate appreciable diagnostic accuracy. Furthermore, they both have a moderate correlation with MCV.

Joint Learning of 3D Lesion Segmentation and Classification for Explainable COVID-19 Diagnosis.

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy.

Dynamic Increase of Red Cell Distribution Width Predicts Increased Risk of 30-Day Readmission in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

PURPOSE: Studies have demonstrated that red blood cell distribution width (RDW) is closely associated with the prognosis of patients with chronic obstructive pulmonary disease (COPD). In addition, the dynamic changes in RDW appear to play an important role. Thus, we aimed to investigate the relationship between dynamic changes in RDW and 30-day all-cause readmission of patients with acute exacerbation of COPD (AECOPD). METHODS: In this retrospective cohort study, we enrolled patients with AECOPD hospitalized in the Department of Respiratory Medicine in Liyuan Hospital (Wuhan China), a tertiary, university-affiliated, public hospital. Patients with AECOPD were divided into three groups based on their RDW values after the first and fourth days of admission. The normal range for RDW is 10-15%. Patients with normal RDW values were included in the normal group. Patients with an RDW value >15% on the first day, which subsequently decreased by >2% on the fourth day was included in the decreased group. The increased group was comprised of patients with an RDW value >15% on the first day which continued to increase, or those with a normal RDW value on the first day which increased >15% on the fourth day. RESULTS: A total of 239 patients (age: 72 years [range: 64-81 years]; male: n=199 [83.3%]) were included. There were 108, 72, and 59 patients in the RDW normal, decreased, and increased groups, respectively; the 30-day all-cause readmission rate was 9.3%, 9.7%, 27.1%, respectively; (p=0.003), being noticeably higher in the RDW increased group. Dynamic increase of RDW (OR:3.45, 95% CI: 1.39-8.58, p= 0.008) was independently correlated with 30-day all-cause readmission of patients with AECOPD. CONCLUSION: The dynamic increase of RDW is an independent prognostic factor of 30-day all-cause readmission of patients with AECOPD.

Deep learning-based classification of lower extremity arterial stenosis in computed tomography angiography.

PURPOSE: The purpose of this study is to develop and evaluate a deep learning model to assist radiologists in classifying lower extremity arteries based on the degree of arterial stenosis caused by plaque in lower extremity computed tomography angiography (CTA) of patients with peripheral artery disease. METHODS: In this retrospective study, 265 patients who underwent lower-extremity CTA between January 1, 2016 and October 31, 2019 were selected. A total of 17050 axial images of iliac, femoropopliteal and infrapopliteal artery from these patients were used for the training and validation of the parallel efficient network (p-EffNet), a kind of supervised convolutional neural network, to classify the lower-extremity artery segments according to the degree of stenosis with digital subtraction angiography as reference standard. The classification results of the p-EffNet were then compared with those obtained from radiologists. Receiver operating characteristic curve (ROC) was used to evaluate the performance of the p-EffNet and accuracy, specificity, sensitivity and area under the curve (AUC) were used as measure metrics to compare the performance of the p-EffNet and that of radiologists. RESULTS: The p-EffNet exhibited a good performance of 91.5 % accuracy, 0.987 AUC and 90.2 % sensitivity and 97.7 % specificity in classifying above-knee artery and 90.9 % accuracy, 0.981 AUC, 91.3 % sensitivity and 95.2 % specificity in classifying below-knee artery. When compared with human readers, for both above-knee and below-knee artery, the p-EffNet had comparable accuracy (p = 0.266 and p = 0.808, respectively) and specificity (p = 0.118 and p = 0.971, respectively) but lower sensitivity (p < 0.001 and p = 0.022, respectively). CONCLUSIONS: The p-EffNet demonstrates promising diagnostic performance and has the potential to reduce the workload of radiologists and help to find the plaques that might otherwise have been missed or misjudged.

Brain structural and functional changes in patients with major depressive disorder: a literature review.

Depression is a mental disorder characterized by low mood and anhedonia that involves abnormalities in multiple brain regions and networks. Epidemiological studies demonstrated that depression has become one of the most important diseases affecting human health and longevity. The pathogenesis of the disease has not been fully elucidated. The clinical effect of treatment is not satisfactory in many cases. Neuroimaging studies have provided rich and valuable evidence that psychological symptoms and behavioral deficits in patients with depression are closely related to structural and functional abnormalities in specific areas of the brain. There were morphological differences in several brain regions, including the frontal lobe, temporal lobe, and limbic system, in people with depression compared to healthy people. In addition, people with depression also had abnormal functional connectivity to the default mode network, the central executive network, and the salience network. These findings provide an opportunity to re-understand the biological mechanisms of depression. In the future, magnetic resonance imaging (MRI) may serve as an important auxiliary tool for psychiatrists in the process of early and accurate diagnosis of depression and finding the appropriate treatment target for each patient to optimize clinical response.