Quantitative Imaging in Medicine and Surgery最新文献

Background: In the early stages of hyperthyroidism (HTH), cardiovascular symptoms are not prominent, and heart function impairment may occur before structural changes. The non-invasive left ventricular (LV) pressure-strain loop (PSL) allows for early, quantitative assessment of changes in LV systolic function. However, previous studies have not grouped hyperthyroid patients based on the presence or absence of tachycardia. This study aimed to assess the LV function in individuals diagnosed with HTH who exhibit varying heart rates, utilizing the PSL technique.

Methods: Seventy-eight hyperthyroid patients were recruited between December 2022 and September 2023 using a random method and then stratified into two groups based on the presence or absence of tachycardia, designated as HTH1 (tachycardia) group and HTH2 (non-tachycardia) group, respectively. Additionally, a control group comprising 38 healthy volunteers was included for comparison purposes. Standard echocardiographic parameters and LV global longitudinal strain (GLS) were quantified. Furthermore, LV myocardial work parameters, encompassing global work index (GWI), global constructive work (GCW), global wasted work (GWW), and global work efficiency (GWE), were assessed using the PSL technique. The main statistical methods included one-way analysis of variance, LSD-t test, Kruskal-Wallis H test, Bonferroni correction, and χ² test.

Results: Compared to the control group, the systolic blood pressure (SBP), pulse pressure differential, serum free triiodothyronine (FT3) and free thyroxine (FT4) levels of HTH1 and HTH2 subgroups demonstrated elevation, whereas serum thyroid-stimulating hormone (TSH) levels exhibited reduction (P<0.05). Moreover, comparisons between the HTH1 and HTH2 subgroups revealed significantly higher SBP, pulse pressure differential, FT3, and FT4 concentrations in the former relative to the latter (P<0.05). LV ejection fraction (LVEF) exhibited a statistically significant increase in the HTH group compared to the control group (P<0.05). GLS, GWI, and GWE in the HTH group were less compared to the control group, while GWW exhibited an increase in the HTH group relative to the control group (P<0.05). Within the HTH group, both GWI and GWE were significantly lower in the HTH1 subgroup compared to the HTH2 subgroup.

Conclusions: PSL method enables the quantitative assessment of LV myocardial work alterations in individuals with HTH exhibiting varying heart rates, which may help clinical physicians make an accurate early diagnosis and take timely treatment measures.

Background: The visibility and signal continuity of deep medullary veins (DMVs) play an important role in cerebral small vessel disease (CSVD). However, the relationship between DMV and gray-matter atrophy remains unclear. This study sought to investigate the link between DMV scores, extracellular fluid, and gray-matter atrophy in patients with CSVD.

Methods: We reviewed the clinical and multimodal magnetic resonance imaging data from 123 patients diagnosed with CSVD between January and December 2022. The DMV score was assessed using a scoring system (0 to 3 points) based on DMV visibility on susceptibility-weighted images across six anatomical regions, yielding a final score from 0 to 18. Extracellular fluid was assessed through the metric of free water (FW) in normal-appearing white matter (NAWM). Normalized gray-matter volume (GM_N) was used to quantify the gray-matter volume, defined as the ratio of gray-matter volume to intracranial volume. Spearman correlation, general linear model, and mediation analyses were employed to evaluate the relationships among variables.

Results: Spearman correlation analysis revealed a positive correlation between DMV score and FW in NAWM (r=0.603; P<0.001). General linear model analysis confirmed this association as independent [β=0.656, 95% confidence interval (CI) 0.521-0.790; P<0.001]. Conversely, FW in NAWM showed a negative correlation with GM_N (r=-0.485; P<0.001), with an independent association confirmed by general linear model analysis (β=-0.630, 95% CI: -0.769 to -0.491; P<0.001). Additionally, the DMV score was negatively correlated with GM_N (r=-0.390; P<0.001), as supported by a significant association in general linear model analysis (β=-0.502, 95% CI: -0.657 to 0.348; P<0.001). Mediation analysis indicated a significant indirect effect of FW in NAWM on the relationship between DMV score and GM_N (β=-0.346, 95% CI: -0.534 to -0.187; P<0.001). All associations were remained significant after adjustments were made for age, gender, vascular risk factors, normalized white-matter hyperintensity volume, and CSVD burden.

Conclusions: The observed link between DMV disruption and FW in NAWM-GM_N suggests that DMV dysfunction may contribute to gray-matter atrophy in CSVD by increasing extracellular fluid. This identifies DMV changes as a key factor in CSVD pathology and supports the potential of targeting extracellular fluid as a therapeutic strategy to mitigate gray-matter loss.

Background: Post-stroke epilepsy (PSE) is a common and significant complication that often occurs after stroke, and affects patients' prognosis and overall quality of life. In recent years, non-contrast computed tomography (NCCT) has become the preferred method for the clinical diagnosis of intracerebral hemorrhage (ICH). This study aimed to develop and validate a triple deep-learning model, simply named, the post-stroke epilepsy network (PSENet), to predict PSE in ICH patients based on NCCT.

Methods: A total of 1,130 patients (62 with PSE and 1,068 without PSE) who experienced an initial ICH at our hospital were enrolled in this study. Using five-fold cross-validation, all patients were randomly divided into training and validation sets at a ratio of 4:1. Next, the no-new-Net (nnU-Net) was used to automatically segment the ICH for the subsequent quantitative analysis. A triple deep-learning model was developed to extract the PSE-related features and incorporate the deep-learning features related to cortical involvement (FCI) and ICH volume to predict PSE. This model was compared with three clinical models constructed using random forest. Model performance was mainly evaluated using the area under the curve (AUC).

Results: The nnU-Net had a high Dice score of 0.923. The proposed PSENet, which incorporated multiple features, showed excellent diagnostic performance, and had an accuracy of 0.876, a F1-score of 0.621, a recall of 0.716, a specificity of 0.897, and an AUC of 0.840, which significantly surpassed the AUC of the baseline clinical model (AUC =0.787).

Conclusions: Based on our findings, the developed PSENet could be used to predict PSE quickly after the first ICH, especially in scenarios in which reliable clinical information is lacking on admission.

Background: The morphological differences in the pulmonary vascular tree between chronic thromboembolic pulmonary hypertension (CTEPH) and chronic thromboembolic disease (CTED) are not yet fully understood. This study aimed to use artificial intelligence (AI) segmentation technology to identify morphological markers that can be used to differentiate CTEPH from CTED using computed tomography pulmonary angiography (CTPA).

Methods: We conducted a retrospective cohort study with consecutive patients diagnosed with CTEPH, CTED, and control subjects at the China-Japan Friendship Hospital from January 2019 to October 2023. The study involved the automatic quantification of the pulmonary blood volume (BV), tortuosity, and fractal dimension (FD) from CTPA images using an AI workstation. These morphological metrics were compared among the three groups using the Kruskal-Wallis test. Correlations between these metrics and the hemodynamic parameters were evaluated using Spearman's rank correlation coefficients. Additionally, a receiver operating characteristic (ROC) curve analysis was conducted to assess the discriminative ability of pulmonary artery tortuosity to differentiate between each pair of groups.

Results: A total of 190 participants [57 years, interquartile range (IQR), 49-65 years, 97 men], including 116 CTEPH patients, 54 CTED patients, and 20 controls, were enrolled in this study. Pulmonary artery tortuosity in the control, CTED, and CTEPH groups showed a progressively increasing trend [1.07 (IQR, 1.06-1.10) vs. 1.10 (IQR, 1.07-1.14) vs. 1.14 (IQR, 1.10-1.18), P<0.01]. The area under the curve (AUC) values of pulmonary arterial tortuosity for differentiating between the CTEPH patients and controls, CTED patients and controls, and CTEPH patients and CTED patients were 0.859, 0.712, and 0.663, respectively. There was a positive correlation between pulmonary artery tortuosity and mean pulmonary arterial pressure (mPAP) (r=0.44, P<0.01), and pulmonary vascular resistance (PVR) (r=0.47, P<0.01). Additionally, the volume of the small- and medium-sized pulmonary arteries was significantly higher in the CTED patients than the CTEPH patients (P<0.01). The pulmonary arterial FD among the three groups was comparable (P=0.36).

Conclusions: Pulmonary arterial tortuosity on CTPA had auxiliary diagnostic value in differentiating between CTEPH patients and controls, but its value in differentiating between CTED and CTEPH patients requires further study. The reduced volume of small- and medium-sized pulmonary arteries in CTEPH patients could indicate impaired pulmonary hemodynamics.

Background: Much effort has been exerted to improve the vitreous cutter probe. This study sought to investigate the clinical outcomes of using a novel 25-gauge (25-G), 10,000-cuts-per-minute (cpm) beveled-tip cutter probe (BTCP) to treat complex rhegmatogenous retinal detachment (RRD).

Methods: The preoperative characteristics, intraoperative surgical information, and postoperative recovery data of a prospective case series of 60 consecutive eyes that underwent primary pars plana vitrectomy (PPV) for complex RRD between March and October 2021 were recorded, and the correlations among these factors were examined.

Results: A total of 60 patients (25 female) with primary complex RRD were included in the study. Of the 60 eyes, 6 (10.00%) had RRD with a giant retinal tear, 15 (25.00%) had RRD with multiple retinal breaks involving more than one quadrant, 12 (20.00%) had macular holes (MHs), 14 (23.33%) had choroidal detachment, and 32 (53.33%) had proliferative vitreoretinopathy (PVR) grade C. The mean total surgical time was 59.22±14.85 min. Iatrogenic breaks occurred in 15 eyes. The total surgical time was correlated with the severity of PVR (P<0.05). Iatrogenic breaks were more likely to be detected in eyes with severe PVR and those that underwent longer surgical time. During the follow-up period, retinal re-detachment occurred in six eyes, which were re-treated. After a mean follow-up period of 6.31±2.24 months, the retina was attached in all 60 eyes (13 of which still contained silicone oil tamponades). The best-corrected visual acuity (BCVA) improved from the preoperative level in all eyes (P<0.001). The final BCVA appeared to be correlated with the duration of retinal detachment, the preoperative BCVA, macular involvement, and MHs.

Conclusions: The primary results showed that the 25-G, 10,000-cpm BTCP combines the merits of high cutting speed, a large port, and a beveled-tip design, which allows the vitreous to be thoroughly cleared with great efficiency, and which is safe and effective for complex RRD surgery.

Background: Posterior lumbar interbody fusion (PLIF) is a surgical procedure to treat painful unstable spinal segments using bone graft filled interbody cages and instrumentation. The graft can be autologous bone or a bone substitute. RegenOss is a porous, 3-dimensional composite bone substitute consisting of collagen fibers including hydroxyapatite. The aim of this cohort study was to evaluate RegenOss as graft material in PLIF patients, as compared to a historical cohort grafted with autologous bone.

Methods: Fifteen patients were enrolled prospectively at the Maastricht University Medical Center between November 2016 and May 2019 in the RegenOss cohort (METC14-1-056) and were compared to 15 matched prospective patients from a historical autologous cohort collected at the Maastricht University Medical Center between October 2011 and January 2014 (METC10-1-025). During PLIF, interbody cages were filled with RegenOss bone substitute in the RegenOss cohort and with local autologous bone in the autologous cohort. Both cohorts were subjected to patient reported outcome measures (PROMs) [including Oswestry Disability Index (ODI), Visual Analogue Scale (VAS), EuroQol-5D (EQ-5D)], 18F-fluoride positron emission tomography/computed tomography (PET/CT) at six weeks and one year. Interbody fusion was assessed on CT at one year and bone metabolism on 18F-fluoride PET/CT was assessed at six weeks and one year. Differences in patient demographics, clinical outcome, interbody fusion and bone metabolism were assessed between graft cohorts. Bone metabolism was also compared between follow-up moments.

Results: Patient demographics were comparable between cohorts. At one-year follow-up, clinical outcome in both cohorts was similar (ODI: P=0.12, VAS: P=0.15, EQ-5D: P=0.22). Interbody fusion was considered comparable between cohorts; 7/15 RegenOss patients and 8/15 autologous patients were scored as completely fused on CT at one year. Similar findings in bone metabolism between follow-up moments were found for both cohorts; bone metabolism in the operated segment was significantly lower at one year compared to at six weeks (RegenOss cohort: P=0.003, P=0.005 for the upper and lower endplate respectively, autologous cohort: P=0.001, P=0.001). No differences in bone metabolism were observed between cohorts.

Conclusions: Bony bridging, clinical outcome, bone metabolism were comparable for the RegenOss and the autologous bone graft PLIF cohorts.

Background: The application of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is limited due to severe coronary calcium burden or stent implantation. This study aimed to explore the diagnostic value of subtraction CT-FFR with deep learning reconstruction (DLR) or hybrid iterative reconstruction (HIR) in detecting calcified-related hemodynamically significant stenosis, and the feasibility in the application of coronary stents.

Methods: Between March 2020 and January 2022, consecutive patients with calcified-related stenosis or previous stent treatment who had undergone subtraction coronary computed tomography angiography (CTA) and invasive fractional flow reserve (FFR) were included in this prospective study. CT image data were reconstructed using HIR and DLR. The diagnostic performance of CT-FFR, and subtraction CT-FFR were evaluated. An FFR value of 0.8 or less was considered hemodynamically significant.

Results: A total of 30 patients with 52 calcified-related lesions and 14 coronary stents were included in this study. Subtraction CT-FFR outperformed the corresponding CT-FFR in detecting calcified-related hemodynamically significant stenosis and in the application of coronary stents, while there was no significant difference when subtraction CT-FFR_DLR was compared with subtraction CT-FFR_HIR (P>0.05). Lesion-based analysis showed the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy for subtraction CT-FFR_DLR were 100.0%, 71.4%, 63.0%, 100% and 80.8%, respectively in detecting calcified-related hemodynamically significant stenosis, and were 100.0%, 83.3%, 88.9%, 100% and 92.9%, respectively in the application of coronary stents.

Conclusions: Subtraction CT-FFR yielded optimal diagnostic performance for hemodynamically significant calcified-related stenosis, and the application of subtraction CT-FFR in the evaluation of coronary stents was feasible. The diagnostic performance of subtraction CT-FFR_DLR was better than that of subtraction CT-FFR_HIR, but there was no significant difference.

Background: Thyroid nodules classified as American College of Radiology Thyroid Imaging Reporting and Data System category 4 (ACR-TR4) present a diagnostic challenge due to their undetermined nature. This study aimed to develop and validate nomogram models using magnetic resonance imaging (MRI) morphological features to enhance the diagnostic accuracy of ACR-TR4 thyroid nodules, thereby reducing unnecessary fine-needle aspiration (FNA) and minimizing missed cancers.

Methods: We retrospectively analyzed 229 ACR-TR4 nodules from 184 patients who underwent preoperative MRI and surgical thyroidectomy between January 2017 and December 2022 in Minhang Hospital, Fudan University. All nodules were pathologically confirmed and randomly divided into training (n=166) and validation (n=63) cohorts. We recorded MRI morphological features of the nodules, performed logistic regression analysis to identify independent predictors of malignancy, and developed a nomogram and improved models. The performance of the nomogram was assessed for discrimination, calibration, and clinical utility. The diagnostic performance of the improved models was compared with that of the ACR-TR4.

Results: Among the 229 ACR-TR4 thyroid nodules, there were 140 benign and 89 malignant nodules, with 46 males and 183 females, and a mean age of 51.2±13.5 years. Diffusion restriction and reversed halo sign in the delayed phase were identified as independent predictors of malignancy and included in the nomogram. The nomogram showed robust discrimination and calibration in distinguishing malignant and benign ACR-TR4 nodules in both the training and validation cohorts, with areas under the curve (AUC) of 0.928 [95% confidence interval (CI): 0.887-0.970] and 0.904 (95% CI: 0.825-0.984), respectively. Four improved models were constructed using the two independent predictors either individually or collectively (OR or AND). The unnecessary FNA (21.1%, 11.7%, 5%, and 23.4%, respectively) and missed cancer rates (12.9%, 13.8%, 18.9%, and 5.7, respectively) were significantly lower than those of the ACR-TR4 system (64% and 43%, respectively).

Conclusions: The nomogram model using MRI features such as restricted diffusion and reversed halo sign in the delayed phase improved the accuracy of diagnosing benign versus malignant ACR-TR4 thyroid nodules, potentially reducing unnecessary FNA and minimizing missed cancers.

Background: The progression of knee osteoarthritis is mainly characterized by the reduction in joint space width (JSW). The goal of this study was to build a knee joint space segmentation model through deep learning (DL) methods and develop a model for automatically measuring JSW. Furthermore, we predicted JSW changes in the sixth year based on regression models.

Methods: The data for this study was sourced from the Osteoarthritis Initiative database. We filtered knee X-ray images from 1,947 participants and tested six neural networks for segmentation to build an automatic JSW measurement model. Subsequently, we combined the clinical data with the JSW measurement results to predict the sixth-year knee JSW using six different regression models.

Results: The segmentation results showed that TransUNet performed the best, with an overall Dice coefficient of 0.889. The intraclass correlation coefficient (ICC) between manually measured and TransUNet's automatically measured JSW reached 0.927 (P<0.01). Among the regression models, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with a mean absolute error (MAE) of 0.48 and an ICC of 0.887 (P<0.01). To better align with clinical practice, we reduced the prediction model to utilize only 2 years of JSW images. The results showed that using the 0- and 12-month X-ray images still achieved high accuracy, with an MAE of 0.585 (P<0.05) and an ICC of 0.805 (P<0.01).

Conclusions: We developed a novel JSW measurement model that significantly improves accuracy compared to previous methods and identified the best prediction model by combining TransUNet and XGBoost. Additionally, in our built model, predicting the 72-month JSW using only 2 years of knee X-ray images and several clinical features achieved high accuracy.