Quantitative Imaging in Medicine and Surgery最新文献

Background: Synthetic magnetic resonance imaging (SyMRI) is a fast, standardized, and robust novel quantitative technique that has the potential to circumvent the subjectivity of interpretation in prostate multiparametric magnetic resonance imaging (mpMRI) and the limitations of existing MRI quantification techniques. Our study aimed to evaluate the potential utility of SyMRI in the diagnosis and aggressiveness assessment of prostate cancer (PCA).

Methods: We retrospectively analyzed 309 patients with suspected PCA who had undergone mpMRI and SyMRI, and pathologic results were obtained by biopsy or PCA radical prostatectomy (RP). Pathological types were classified as PCA, benign prostatic hyperplasia (BPH), or peripheral zone (PZ) inflammation. According to the Gleason Score (GS), PCA was divided into groups of intermediate-to-high risk (GS ≥4+3) and low-risk (GS ≤3+4). Patients with biopsy-confirmed low-risk PCA were further divided into upgraded and nonupgraded groups based on the GS changes of the RP results. The values of the apparent diffusion coefficient (ADC), T1, T2 and proton density (PD) of these lesions were measured on ADC and SyMRI parameter maps by two physicians; these values were compared between PCA and BPH or inflammation, between the intermediate-to-high-risk and low-risk PCA groups, and between the upgraded and nonupgraded PCA groups. The risk factors affecting GS grades were identified via univariate analysis. The effects of confounding factors were excluded through multivariate logistic regression analysis, and independent predictive factors were calculated. Subsequently, the ADC+Sy(T2+PD) combined models for predicting PCA risk grade or GS upgrade were constructed through data processing analysis. The diagnostic performance of each parameter and the ADC+Sy(T2+PD) model was analyzed. The calibration curve was calculated by the bootstrapping internal validation method (200 bootstrap resamples).

Results: The T1, T2, and PD values of PCA were significantly lower than those of BPH or inflammation (P≤0.001) in both the PZ or transitional zone. Among the 178 patients with PCA, intermediate-to-high-risk PCA group had significantly higher T1, T2, and PD values but lower ADC values compared with the low-risk group (P<0.05), and the diagnostic efficacy of each single parameter was similar (P>0.05). The ADC+Sy(T2+PD) model showed the best performance, with an area under the curve (AUC) 0.110 [AUC =0.818; 95% confidence interval (CI): 0.754-0.872] higher than that of ADC alone (AUC =0.708; 95% CI: 0.635-0.774) (P=0.003). Among the 68 patients initially classified as PCA in the low-risk group by biopsy, PCA in the postoperative upgraded GS group had significantly higher T1, T2, and PD values but lower ADC values than did those in the nonupgraded group (P<0.01). In addition, the ADC+Sy(T2+PD) model better predicted the upgrade of GS, with a significant increase in AUC of 0.

Background: Prognosis of twin-to-twin transfusion syndrome (TTTS) varies depending on the Quintero stage and fetal cardiac function. The purpose of our study was to evaluate fetal cardiac function before and after different intrauterine treatments of TTTS through myocardial performance index (MPI).

Methods: In this retrospective study, data were collected from August 2016 to December 2022. Totals of 68 cases of TTTS and 68 monochorionic diamniotic (MCDA) twins without TTTS were included. MPI was collected and compared between TTTS and MCDA twins without TTTS before intrauterine treatments. TTTS cases were divided into 3 groups according to different intrauterine treatments: (I) amnioreduction (34 cases), (II) fetoscopic laser photocoagulation (FLPC; 20 cases), and (III) selective reduction (14 cases). The MPI of the left ventricle (LV) and right ventricle (RV) in each surviving fetus were measured 48 hours before and after treatments by pulse Doppler ultrasound. One-way analysis of variance (ANOVA) was employed to assess whether there were statistical differences in LV-MPI and RV-MPI among the donors, recipients, and the control group. Paired t-test analysis was used to compare whether there were differences in MPI before and after intrauterine treatments.

Results: The MPIs of the LV and RV in the recipients were significantly higher than those in the MCDA twins without TTTS (P<0.05). After the amnioreduction treatment of TTTS, no significant differences were observed in the MPI of either the LV or the RV before and after treatment. At 48 hours after FLPC treatment, the value of the LV-MPI in donors was 0.25±0.08, and the value of the RV-MPI in recipients was 0.58±0.17. Both of them were significantly lower than those before the treatment (P<0.05). In the selective reduction group, the value of the RV-MPI in surviving recipients significantly decreased compared to that before treatment (P<0.05).

Conclusions: MPI is an effective indicator to evaluate fetal cardiac function of TTTS and assess the efficacy of intrauterine treatments of TTTS.

Background: Vulnerable carotid plaque is closely associated with ischemic stroke. Contrast-enhanced ultrasound (CEUS) and high-resolution magnetic resonance imaging (HR-MRI) are two imaging modalities capable of assessing the vulnerability of carotid plaques. This systematic review aimed to compare the diagnostic performance of CEUS and HR-MRI in the evaluation of histologically defined vulnerable carotid plaques.

Methods: A systematic literature search with predefined search terms was performed on PubMed, the Cochrane library, Embase, and Web of Science from January 2001 to December 2023. Studies that evaluated the diagnostic accuracy of vulnerable carotid plaques confirmed by histology with CEUS and/or HR-MRI were included. The pooled values were calculated using a random-effects meta-analysis to determine diagnostic power.

Results: This analysis included a total of 839 patients from 20 studies comprising 1,357 HR-MRI plaques and CEUS 504 plaques. With the reference to histological results, all nine CEUS studies focused on the detection of intraplaque neovascularization (IPN), and three studies also examined morphological changes or ulcerated plaques; meanwhile, among the HR-MRI studies, seven predominantly focused on identifying intraplaque hemorrhage (IPH) and three mainly examined lipid-rich necrotic cores (LRNCs). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the area under the curve (AUC) for CEUS studies were 0.85 [95% confidence interval (CI): 0.81-0.89], 0.76 (95% CI: 0.69-0.83), 3.41 (95% CI: 1.68-6.94), 0.14 (95% CI: 0.05-0.38), 27.68 (95% CI: 5.78-132.62), and 0.89 [standard error (SE) 0.06], respectively; for HR-MRI, these values were 0.88 (95% CI: 0.85-0.90), 0.89 (95% CI: 0.86-0.92), 7.49 (95% CI: 3.28-17.09), 0.17 (95% CI: 0.12-0.24), 49.13 (95% CI: 23.87-101.11), and 0.94 (SE 0.01), respectively. The difference in AUC between the two modalities was not statistically significant (Z=0.82; P=0.68).

Conclusions: CEUS and HR-MRI are valuable noninvasive diagnostic tools for identifying histologically confirmed vulnerable carotid plaques and demonstrate similar diagnostic performance. CEUS is more capable of detecting IPN and morphological changes, while HR-MRI is more suited to classifying IPH and LRNCs.

Background: Patients with lung cancer accompanied by sarcopenia may have a poor prognosis. Normally, low muscle mass associated with sarcopenia is assessed using the skeletal muscle index (SMI). It remains unclear whether the standardized skeletal muscle area (SMA) using 2-dimensional (2D) vertebral metrics (called the skeletal muscle vertebral related index, SMVI) could substitute for SMI when it is missing. The aim of this study was to investigate the feasibility of SMVI as an alternative to SMI, and their associations with overall survival (OS) in patients with non-small cell lung cancer (NSCLC).

Methods: In this single-center study, a retrospective analysis was conducted on 433 NSCLC patients who underwent computed tomography (CT) scans. At the third lumbar vertebra (L3) level, measurements were taken for SMA, vertebral body area, transverse vertebral diameter (TVD), longitudinal vertebral diameter (LVD), and vertebral height (VH). The 4 SMVIs were skeletal muscle vertebral ratio (SMVR) (SMA/vertebral body area), skeletal muscle transverse vertebral diameter index (SMTVDI) (SMA/TVD2), skeletal muscle longitudinal vertebral diameter index (SMLVDI) (SMA/LVD2), and skeletal muscle vertebral height index (SMVHI) (SMA/VH2). The patients were categorized into low and high muscle mass groups based on SMI, and the differences in SMVIs between the 2 groups were compared to assess their correlation with SMI. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were utilized to assess the discriminatory ability. Kaplan-Meier curves were employed to compare the survival disparity between the 2 groups.

Results: We included 191 male and 242 female patients in this study. Compared to the high muscle mass group, patients in the low muscle mass group exhibited significantly lower SMVR, SMTVDI, SMLVDI, and SMVHI (all P<0.05). All 4 SMVIs showed a positive correlation with SMI, with Spearman correlation coefficients of 0.83, 0.76, 0.75, and 0.67, respectively (all P<0.001). The AUC for diagnosing low muscle mass was higher than 0.8 for all 4 SMVI parameters. The Kaplan-Meier curve revealed that the low-risk group had a better survival probability than the high-risk group in the SMVR, SMTVDI, and SMLVDI.

Conclusions: The SMVI functions as an alternative metric for evaluating skeletal muscle mass in the assessment of NSCLC based on SMI.

Background: The measurement of posterior tibial slopes (PTS) can aid in the screening and prevention of anterior cruciate ligament (ACL) injuries and improve the success rate of some other knee surgeries. However, the circle method for measuring PTS on magnetic resonance imaging (MRI) scans is challenging and time-consuming for most clinicians to implement in practice, despite being highly repeatable. Currently, there is no automated measurement scheme based on this method. To enhance measurement efficiency, consistency, and reduce errors resulting from manual measurements by physicians, this study proposes two novel, precise, and computationally efficient pipelines for autonomous measurement of PTS.

Methods: The first pipeline employs traditional algorithms with experimental parameters to extract the tibial contour, detect adhesions, and then remove these adhesions from the extracted contour. A cyclic process is employed to adjust the parameters adaptively and generate a better binary image for the following tibial contour extraction step. The second pipeline utilizes deep learning models for classifying MRI slice images and segmenting tibial contours. The incorporation of deep learning models greatly simplifies the corresponding steps in pipeline 1.

Results: To evaluate the practical performance of the proposed pipelines, doctors utilized MRI images from 20 patients. The success rates of pipeline 1 for central, medial, and lateral slices were 85%, 100%, and 90%, respectively, while pipeline 2 achieved success rates of 100%, 100%, and 95%. Compared to the 10 minutes required for manual measurement, our automated methods enable doctors to measure PTS within 10 seconds.

Conclusions: These evaluation results validate that the proposed pipelines are highly reliable and effective. Employing these tools can effectively prevent medical practitioners from being burdened by monotonous and repetitive manual measurement procedures, thereby enhancing both the precision and efficiency. Additionally, this tool holds the potential to contribute to the researches regarding the significance of PTS, particularly those demanding extensive and precise PTS measurement outcomes.

Background: Noninvasively detecting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients before targeted therapy remains a challenge. This study aimed to develop a 3-dimensional (3D) convolutional neural network (CNN)-based deep learning model to predict EGFR mutation status using computed tomography (CT) images.

Methods: We retrospectively collected 660 patients from 2 large medical centers. The patients were divided into training (n=528) and external test (n=132) sets according to hospital source. The CNN model was trained in a supervised end-to-end manner, and its performance was evaluated using an external test set. To compare the performance of the CNN model, we constructed 1 clinical and 3 radiomics models. Furthermore, we constructed a comprehensive model combining the highest-performing radiomics and CNN models. The receiver operating characteristic (ROC) curves were used as primary measures of performance for each model. Delong test was used to compare performance differences between different models.

Results: Compared with the clinical [training set, area under the curve (AUC) =69.6%, 95% confidence interval (CI), 0.661-0.732; test set, AUC =68.4%, 95% CI, 0.609-0.752] and the highest-performing radiomics models (training set, AUC =84.3%, 95% CI, 0.812-0.873; test set, AUC =72.4%, 95% CI, 0.653-0.794) models, the CNN model (training set, AUC =94.3%, 95% CI, 0.920-0.961; test set, AUC =94.7%, 95% CI, 0.894-0.978) had significantly better predictive performance for predicting EGFR mutation status. In addition, compared with the comprehensive model (training set, AUC =95.7%, 95% CI, 0.942-0.971; test set, AUC =87.4%, 95% CI, 0.820-0.924), the CNN model had better stability.

Conclusions: The CNN model has excellent performance in non-invasively predicting EGFR mutation status in patients with lung adenocarcinoma and is expected to become an auxiliary tool for clinicians.

Background: Automated tumor segmentation and survival prediction are critical to clinical diagnosis and treatment. This study aimed to develop deep-learning models for automatic tumor segmentation and survival prediction in magnetic resonance imaging (MRI) of cervical cancer (CC) by combining deep neural networks and Transformer architecture.

Methods: This study included 406 patients with CC, each with comprehensive clinical information and MRI scans. We randomly divided patients into training, validation, and independent test cohorts in a 6:2:2 ratio. During the model training, we employed two architecture types: one being a hybrid model combining convolutional neural network (CNN) and ransformer (CoTr) and one of pure CNNs. For survival prediction, the hybrid model combined tumor image features extracted by segmentation models with clinical information. The performance of the segmentation models was evaluated using the Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). The performance of the survival models was assessed using the concordance index.

Results: The CoTr model performed well in both contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) imaging segmentation tasks, with average DSCs of 0.827 and 0.820, respectively, which outperformed other the CNN models such as U-Net (DSC: 0.807 and 0.808), attention U-Net (DSC: 0.814 and 0.811), and V-Net (DSC: 0.805 and 0.807). For survival prediction, the proposed deep-learning model significantly outperformed traditional methods, yielding a concordance index of 0.732. Moreover, it effectively divided patients into low-risk and high-risk groups for disease progression (P<0.001).

Conclusions: Combining Transformer architecture with a CNN can improve MRI tumor segmentation, and this deep-learning model excelled in the survival prediction of patients with CC as compared to traditional methods.

Background: Both intracranial atherosclerosis and white matter hyperintensity (WMH) are prevalent among the stroke population. However, the relationship between intracranial atherosclerosis and WMH has not been fully elucidated. Therefore, the aim of this study was to investigate the relationship between the characteristics of intracranial atherosclerotic plaques and the severity of WMH in patients with ischemic stroke using high-resolution magnetic resonance vessel wall imaging.

Methods: Patients hospitalized with ischemic stroke and concurrent intracranial atherosclerosis at Beijing Tsinghua Changgung Hospital, a tertiary comprehensive stroke center, who underwent high-resolution magnetic resonance vessel wall imaging and conventional brain magnetic resonance imaging were continuously recruited from January 2018 to December 2018. Both intracranial plaque characteristics (plaque number, maximum wall thickness, luminal stenosis, T1 hyperintensity, and plaque length) and WMH severity (Fazekas score and volume) were evaluated. Spearman correlation or point-biserial correlation analysis was used to determine the association between clinical characteristics and WMH volume. The independent association between intracranial plaque characteristics and the severity as well as WMH score was analyzed using logistic regression. The associations of intracranial plaque characteristics with total white matter hyperintensity (TWMH) volume, periventricular white matter hyperintensity (PWMH) volume and deep white matter hyperintensity (DWMH) volume were determined using multilevel mixed-effects linear regression.

Results: A total of 159 subjects (mean age: 64.0±12.5 years; 103 males) were included into analysis. Spearman correlation analysis indicated that age was associated with TWMH volume (r=0.529, P<0.001), PWMH volume (r=0.523, P<0.001) and DWMH volume (r=0.515, P<0.001). Point-biserial correlation analysis indicated that smoking (r=-0.183, P=0.021) and hypertension (r=0.159, P=0.045) were associated with DWMH volume. After adjusting for confounding factors, logistic regression analysis showed plaque number was significantly associated with the presence of severe WMH [odds ratio (OR), 1.590; 95% CI, 1.241-2.035, P<0.001], PWMH score of 3 (OR, 1.726; 95% CI, 1.074-2.775, P=0.024), and DWMH score of 2 (OR, 1.561; 95% CI, 1.150-2.118, P=0.004). Intracranial artery luminal stenosis was associated with presence of severe WMH (OR, 1.032; 95% CI, 1.002-1.064, P=0.039) and PWMH score of 2 (OR, 1.057; 95% CI, 1.008-1.109, P=0.023). Multilevel mixed-effects linear regression analysis showed that plaque number was associated with DWMH volume (β=0.128; 95% CI, 0.016-0.240; P=0.026) after adjusted for age and sex.

Conclusions: In ischemic stroke patients, intracranial atherosclerotic plaque characteristics as measured by plaque number and luminal stenosis were associated with WMH burd

Background: Currently, intensity-modulated radiation therapy (IMRT) is commonly used in radiotherapy clinics. However, designing a treatment plan with multiple beam angles depends on the experience of human planners, and is mostly achieved using a trial-and-error approach. It is preferrable but challenging to solve this issue automatically and mathematically using an optimization approach. The goal of this study is to develop a mixed-integer linear programming (MILP) approach for the beam angle optimization (BAO) of non-coplanar IMRT for liver cancer.

Methods: MILP models for the BAO of both coplanar and non-coplanar IMRT treatment plans were developed. The beam angles of the IMRT plans were first selected by the MILP model built using mathematical optimization software. Next, the IMRT plans with the selected beam angles was created in a commercial treatment planning system. Finally, the fluence map and dose distribution of the IMRT plans were generated under pre-defined dose-volume constraints. The IMRT plans of 10 liver cancer patients previously treated at our institute were used to assessed the proposed MILP models. For each patient, both coplanar and non-coplanar IMRT plans with beam angles optimized by the MILP models were compared with the IMRT plan clinically approved by physicians.

Results: The MILP model-guided IMRT plans showed reduced doses for most of the organs at risk (OARs). Compared with the IMRT plans clinically approved by physicians, the doses for the spinal cord (28.5 vs. 36.1, P=0.001<0.05) and liver (27.6 vs. 29.1, P=0.005<0.05) decreased significantly in the IMRT plans with non-coplanar beams selected by the MILP models.

Conclusions: The MILP model is an effective tool for the BAO in coplanar and non-coplanar IMRT treatment planning. It facilitates the automation of IMRT treatment planning for current high-precision radiotherapy.

Background: Lumbar paraspinal muscles (LPM) are a part of the deep spinal stabilisation system and play an important role in stabilising the lumbar spine and trunk. Inadequate function of these muscles is thought to be an essential aetiological factor in low back pain, and several neuromuscular diseases are characterised by dysfunction of LPM. The main aims of our study were to develop a methodology for LPM assessment using advanced magnetic resonance imaging (MRI) methods, including a manual segmentation process, to confirm the measurement reliability, to evaluate the LPM morphological parameters [fat fraction (FF), total muscle volume (TMV) and functional muscle volume (FMV)] in a healthy population, to study the influence of physiological factors on muscle morphology, and to build equations to predict LPM morphological parameters in a healthy population.

Methods: This prospective cross-sectional observational comparative single-centre study was conducted at the University Hospital in Brno, enrolling healthy volunteers from April 2021 to March 2023. MRI of the lumbar spine and LPM (erector spinae muscle and multifidus muscle) were performed using a 6-point Dixon gradient echo sequence. The segmentation of the LPM and the control muscle (psoas muscle) was done manually to obtain FF and TMV in a range from Th12/L1 to L5/S1. Intra-rater and inter-rater reliability were evaluated. Linear regression models were constructed to assess the effect of physiological factors on muscle FF, TMV and FMV.

Results: We enrolled 90 healthy volunteers (median age 38 years, 45 men). The creation of segmentation masks and the assessment of FF and TMV proved reliable (Dice coefficient 84% to 99%, intraclass correlation coefficient ≥0.97). The univariable models showed that FF of LPM was influenced the most by age (39.6% to 44.8% of variability, P<0.001); TMV and FMV by subject weight (34.9% to 67.6% of variability, P<0.001) and sex (24.7% to 64.1% of variability, P<0.001). Multivariable linear regression models for FF of LPM included age, body mass index and sex, with R-squared values ranging from 45.4% to 51.1%. Models for volumes of LPM included weight, age and sex, with R-squared values ranged from 37.4% to 76.8%. Equations were developed to calculate predicted FF, TMV and FMV for each muscle.

Conclusions: A reliable methodology has been developed to assess the morphological parameters (biomarkers) of the LPM. The morphological parameters of the LPM are significantly influenced by physiological factors. Equations were constructed to calculate the predicted FF, TMV and FMV of individual muscles in relation to anthropometric parameters, age, and sex. This study, which presented LPM assessment methodology and predicted values of LPM morphological parameters in a healthy population, could improve our understanding of diseases involving LPM (low back pain and some neuromuscular dis