Quantitative Imaging in Medicine and Surgery最新文献

Background: Obesity is commonly linked with heart failure (HF) with preserved ejection fraction, with diastolic dysfunction playing an important role in this type of HF. However, diastolic function has not been well clarified in obese patients free of overt comorbidities. We aimed to comprehensively assess diastolic function in adults with uncomplicated obesity by combining left atrial (LA) and left ventricular (LV) strain and ventricular volume-time curve based on cardiac magnetic resonance (CMR), and to evaluate its association with body fat distribution.

Methods: A cross-sectional study was conducted with 49 uncomplicated obese participants and 43 healthy controls who were continuously recruited in West China Hospital, Sichuan University from September 2019 to June 2022. LA strain indices [total, passive, and active strains (ε^s, ε^e, and ε^a) and peak positive, early negative, and late negative strain rates (SRs, SRe, and SRa)], LV strain rates [peak diastolic strain rate (PDSR) and peak systolic strain rate (PSSR)], and LV volume-time curve parameters [peak filling rate index (PFRI) and peak ejection rate index (PERI)] were measured. Body fat distribution was assessed by dual-energy X-ray absorptiometry. Correlation between body fat distribution and LA and LV function was evaluated by multiple linear regression.

Results: The obese participants had impaired diastolic function, manifested as lower LV circumferential and longitudinal PDSR (1.3±0.2 vs. 1.5±0.3 s^-1, P=0.014; 0.8±0.2 vs. 1.1±0.2 s^-1, P<0.001), LV PFRI (3.5±0.6 vs. 3.9±0.7 s^-1, P=0.012), and declined LA reservoir function [ε^s and SRs (46.4%±8.4% vs. 51%±12%, P=0.045; 1.9±0.5 vs. 2.3±0.5 s^-1, P<0.001)] and conduit function [ε^e and SRe (30.8%±8.0% vs. 35.5%±9.8%, P=0.019; -3.1±0.8 vs. -3.5±1.0 s^-1, P=0.030)] compared with controls. The LA pumping function (ε^a and SRa) and LV systolic function [LV ejection fraction (LVEF), PSSR and PERI] were not different between obese and control participants. Multivariable analysis indicated that trunk fat had independent relationships with LA ε^e (β=-0.520, P<0.001) and LV circumferential PDSR (β=-0.418, P=0.003); visceral fat and peripheral fat were associated with LV longitudinal PDSR (β=-0.342, P=0.038; β=0.376, P=0.024); gynoid fat was associated with LA ε^s (β=0.384, P=0.014) and PFRI (β=0.286, P=0.047) in obesity.

Conclusions: The obese participants (uncomplicated obese adults with preserved LVEF) had impaired subclinical diastolic function. Central adipose tissue deposits (trunk fat and visceral fat) may exhibit inverse relationships with LV and LA function in obesity. However, peripheral adipose tissue deposits (peripheral fat and gynoid fat) may show positive relation

Background: There is no unified scope for regional lymph node (LN) dissection in patients with pancreatic ductal adenocarcinoma (PDAC). Incomplete regional LN dissection can lead to postoperative recurrence, while blind expansion of the scope of regional LN dissection significantly increases the perioperative risk without significantly prolonging overall survival. We aimed to establish a noninvasive visualization tool based on dual-layer detector spectral computed tomography (DLCT) to predict the probability of regional LN metastasis in patients with PDAC.

Methods: A total of 163 regional LNs were reviewed and divided into a metastatic cohort (n=58 LNs) and nonmetastatic cohort (n=105 LNs). The DLCT quantitative parameters and the nodal ratio of the longest axis to the shortest axis (L/S) of the regional LNs were compared between the two cohorts. The DLCT quantitative parameters included the iodine concentration in the arterial phase (APIC), normalized iodine concentration in the arterial phase (APNIC), effective atomic number in the arterial phase (APZeff), normalized effective atomic number in the arterial phase (APNZeff), slope of the spectral attenuation curves in the arterial phase (APλHU), iodine concentration in the portal venous phase (PVPIC), normalized iodine concentration in the portal venous phase (PVPNIC), effective atomic number in the portal venous phase (PVPZeff), normalized effective atomic number in the portal venous phase (PVPNZeff), and slope of the spectral attenuation curves in the portal venous phase (PVPλHU). Logistic regression analysis based on area under the curve (AUC) was used to analyze the diagnostic performance of significant DLCT quantitative parameters, L/S, and the models combining significant DLCT quantitative parameters and L/S. A nomogram based on the models with highest diagnostic performance was developed as a predictor. The goodness of fit and clinical applicability of the nomogram were assessed through calibration curve and decision curve analysis (DCA).

Results: The combined model of APNIC + L/S (APNIC + L/S) had the highest diagnostic performance among all models, yielding an AUC, sensitivity, and specificity of 0.878 [95% confidence interval (CI): 0.825-0.931], 0.707, and 0.886, respectively. The calibration curve indicated that the APNIC-L/S nomogram had good agreement between the predicted probability and the actual probability. Meanwhile, the decision curve indicated that the APNIC-L/S nomogram could produce a greater net benefit than could the all- or-no-intervention strategy, with threshold probabilities ranging from 0.0 to 0.75.

Conclusions: As a valid and visual noninvasive prediction tool, the APNIC-L/S nomogram demonstrated favorable predictive efficacy for identifying metastatic LNs in patients with PDAC.

Background: A large number of studies related to ultrasound-based radiomics have been published in recent years; however, a systematic bibliometric analysis of this topic has not yet been conducted. In this study, we attempted to identify the hotspots and frontiers in ultrasound-based radiomics through bibliometrics and to systematically characterize the overall framework and characteristics of studies through mapping and visualization.

Methods: A literature search was carried out in Web of Science Core Collection (WoSCC) database from January 2016 to December 2023 according to a predetermined search formula. Bibliometric analysis and visualization of the results were performed using CiteSpace, VOSviewer, R, and other platforms.

Results: Ultimately, 466 eligible papers were included in the study. Publication trend analysis showed that the annual publication trend of journals in ultrasound-based radiomics could be divided into three phases: there were no more than five documents published in this field in any year before 2018, a small yearly increase in the number of annual publications occurred between 2018 and 2022, and a high, stable number of publications appeared after 2022. In the analysis of publication sources, China was found to be the main contributor, with a much higher number of publications than other countries, and was followed by the United States and Italy. Frontiers in Oncology was the journal with the highest number of papers in this field, publishing 60 articles. Among the academic institutions, Fudan University, Sun Yat-sen University, and the Chinese Academy of Sciences ranked as the top three in terms of the number of documents. In the analysis of authors and cocited authors, the author with the most publications was Yuanyuan Wang, who has published 19 articles in 8 years, while Philippe Lambin was the most cited author, with 233 citations. Visualization of the results from the cocitation analysis of the literature revealed a strong centrality of the subject terms papillary thyroid cancer, biological behavior, potential biomarkers, and comparative assessment, which may be the main focal points of research in this subject. Based on the findings of the keyword analysis and cluster analysis, the keywords can be categorized into two major groups: (I) technological innovations that enable the construction of radiomics models such as machine learning and deep learning and (II) applications of predictive models to support clinical decision-making in certain diseases, such as papillary thyroid cancer, hepatocellular carcinoma (HCC), and breast cancer.

Conclusions: Ultrasound-based radiomics has received widespread attention in the medical field and has been gradually been applied in clinical research. Radiomics, a relatively late development in medical technology, has made substantial contributions to the diagnosis, prediction, and prognos

Background: Cancer-associated fibroblasts (CAFs) within the tumor microenvironment (TME) can interact with tumor parenchymal cells to promote tumor growth and migration. Fibroblast activation protein (FAP) expressed by CAFs can be targeted with positron emission tomography (PET) tracers, but studies on FAP expression patterns in intracranial tumors remain scarce. We aimed to evaluate FAP expression patterns in intracranial tumors with gallium-68 FAP inhibitor-04 (⁶⁸Ga-FAPi-04) and immunohistochemical staining and to observe the interactions between CAFs and tumor cells with a head-to-head comparison of ⁶⁸Ga-FAPi-04 and fluoride-18 fluoroethyl-L-tyrosine (¹⁸F-FET) for PET quantification analysis.

Methods: We prospectively enrolled 22 adult patients with intracranial mass lesions. ⁶⁸Ga-FAPi-04 and ¹⁸F-FET PET-computed tomography (PET/CT) brain imaging were applied before surgery. Maximal tumor-to-brain ratio (TBRmax), metabolic tumor volume (MTV), and total lesion tracer uptake (TLU) was obtained, and different thresholds were used for ⁶⁸Ga-FAPi-04-positive lesion delineation owing to the lack of relevant guidelines. The MTV and TLU ratios of both tracers were calculated. Linear regression was applied to observe the differential efficacy of semiquantitative PET parameters.

Results: A total of 22 patients with a mean age of 50±13 years (range, 27-69 years) were enrolled. Heterogeneous patterns of ⁶⁸Ga-FAPi-04 uptake [median of maximal standardized uptake value (SUVmax) =3.8; range, 0.1-19.1] were found. More malignant tumors, including brain metastasis, glioblastoma, and medulloblastoma, generally exhibited more significant ⁶⁸Ga-FAPi-04 uptake than did the less malignant tumors, while the SUVmax and TBRmax exhibited nonsignificant differences across three intracranial lesion groups of primary brain tumor, brain metastasis, and noncancerous disease (SUVmax: P=0.092; TBRmax: P=0.189). Immunohistochemistry staining showed different stromal FAP expression status in various intracranial lesions. In 15 patients with positive ⁶⁸Ga-FAPi-04 intracranial tumor uptake, the MTV_FAPi:MTV_FET ratio had differential efficacy in various types of intracranial tumors [95% confidence interval (CI): 0.572-7.712; P=0.027], and further quantification analyses confirmed the differential ability of the MTV_FAPi:MTV_FET ratio (95% CI: -0.045 to 11.013, P=0.052; 95% CI: 0.044-17.903, P=0.049; 95% CI: -1.131 to 30.596, P=0.065) with different isocontour volumetric thresholds.

Conclusions: This head-to-head study demonstrated heterogeneous FAP expression in intracranial tumors. The FAP expression volume percentage in tumor parenchyma may therefore offer benefit with respect to differentiating between intracranial tumor types.

Background: The evaluation of brain tumor recurrence after surgery is based on the comparison between tumor regions on pre-operative and follow-up magnetic resonance imaging (MRI) scans in clinical practice. Accurate alignment of MRI scans is important in this evaluation process. However, existing methods often fail to yield accurate alignment due to substantial appearance and shape changes of tumor regions. The study aimed to improve this misalignment situation through multimodal information and compensation for shape changes.

Methods: In this work, a deep learning-based deformation registration method using bilateral pyramid to create multi-scale image features was developed. Moreover, morphology operations were employed to build correspondence between the surgical resection on the follow-up and pre-operative MRI scans.

Results: Compared with baseline methods, the proposed method achieved the lowest mean absolute error of 1.82 mm on the public BraTS-Reg 2022 dataset.

Conclusions: The results suggest that the proposed method is potentially useful for evaluating tumor recurrence after surgery. We effectively verified its ability to extract and integrate the information of the second modality, and also revealed the micro representation of tumor recurrence. This study can assist doctors in registering multiple sequence images of patients, observing lesions and surrounding areas, analyzing and processing them, and guiding doctors in their treatment plans.

Background: Whether to perform prophylactic central lymph node dissection for cN0 papillary thyroid carcinoma (PTC) patients is still controversial. This retrospective study aimed to develop and validate a nomogram based on ultrasound and dual-energy computed tomography (DECT) for the risk stratification of central lymph node metastasis (CLNM) in patients with PTC.

Methods: A total of 525 patients from 2017 to 2019 [Tianjin First Central Hospital (Hospital A)] were retrospectively analyzed to form the training cohort and to conduct internal validation. Another group of 204 patients in 2020 (Hospital A) formed the temporal validation cohort. A total of 107 patients in 2020 [Binzhou Medical University Hospital (Hospital B)] formed the geographic validation cohort, which was a retrospective cohort study. The area under the curve (AUC), calibration curve, and decision curve were used to evaluate the performance of the nomogram. The locally weighted regression curve was used for risk stratification.

Results: Diameter, taller-than-wide, calcification, capsular invasion, and iodine concentration in the arterial and venous phases were independent risk predictors of CLNM. The AUC of the nomogram was 0.922 (95% confidence interval: 0.895-0.943) in the training cohort. Two external validation cohorts demonstrated the good performance of the nomogram in predicting CLNM, with AUCs of 0.912 and 0.861. The significantly improved net reclassification index and integrated discriminatory improvement index indicated that DECT was a powerful supplement to ultrasound for predicting CLNM. The risk stratification system divided all patients into low-risk (0-50 points), intermediate-risk (51-100 points), and high-risk groups (>100 points).

Conclusions: The nomogram and risk stratification system estimated the utility of CLNM to guide individualized treatment of patients with PTC.

Background: The information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation.

Methods: We propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions.

Results: Evaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations.

Conclusions: The experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.

Background: Brain metastases present significant challenges in radiotherapy due to the need for precise tumor delineation. Traditional methods often lack the efficiency and accuracy required for optimal treatment planning. This paper proposes an improved U-Net model that uses a position attention module (PAM) for automated segmentation of gross tumor volumes (GTVs) in computed tomography (CT) simulation images of patients with brain metastases to improve the efficiency and accuracy of radiotherapy planning and segmentation.

Methods: We retrospectively collected CT simulation imaging datasets of patients with brain metastases from two centers, which were designated as the training and external validation datasets. The U-Net architecture was enhanced by incorporating a PAM into the transition layer, which improved the automated segmentation capability of the U-Net model. With cross-entropy loss employed as the loss function, the samples from the training dataset underwent training. The model's segmentation performance on the external validation dataset was assessed using metrics including the Dice similarity coefficient (DSC), intersection over union (IoU), accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and Hausdorff distance (HD).

Results: The proposed automated segmentation model demonstrated promising performance on the external validation dataset, achieving a DSC of 0.753±0.172. In terms of evaluation metrics (including the DSC, IoU, accuracy, sensitivity, MCC, and HD), the model outperformed the standard U-Net, which had a DSC of 0.691±0.142. The proposed model produced segmentation results that were closer to the ground truth and could reveal more detailed features of brain metastases.

Conclusions: The PAM-improved U-Net model offers considerable advantages in the automated segmentation of the GTV in CT simulation images for patients with brain metastases. Its superior performance in comparison with the standard U-Net model supports its potential for streamlining and improving the accuracy of radiotherapy. With its ability to produce segmentation results consistent with the ground truth, the proposed model holds promise for clinical adoption and provides a reference for radiation oncologists to make more informed GTV segmentation decisions.

Background: Early neurologic deterioration occurs in up to one-third of patients with acute ischemic stroke (IS), often leading to poor functional outcomes. At present, few studies have applied amide proton transfer (APT) imaging to the evaluation of early neurological deterioration (END). This study analyzed the value of computed tomography perfusion (CTP) combined with multimodal magnetic resonance imaging (MRI) in patients with acute IS with END.

Methods: This retrospective study included patients with acute IS who were admitted to the neurology inpatient department in a tertiary hospital from October 2021 to June 2023. Patients with acute IS underwent CTP within 24 hours of stroke onset and MRI [arterial spin labeling (ASL), susceptibility-weighted imaging (SWI), and APT] within 7 days. END was defined as an elevation of ≥2 points on the National Institute of Health Stroke Scale (NIHSS) within 7 days of stroke onset. Univariable and multivariable analyses were used to compare clinical and imaging biomarkers in patients with acute IS with and without END. The performance of potential biomarkers in distinguishing between the two groups was evaluated using receiver operating characteristic (ROC) curve analysis.

Results: Among the 70 patients with acute IS, 20 (29%) had END. After conducting univariable analysis, variables were selected for entry into a binary logistic regression analysis based on our univariable analysis results, previous research findings, clinical experience, and methodological standards. The results indicated that relative cerebral blood volume (CBV) on CTP, relative cerebral blood flow (CBF) on ASL, and relative signal intensity on amide proton transfer-weighted (APTw) imaging were independent risk factors for END. The areas under the ROC curves for these risk factors were 0.710 [95% confidence interval (CI): 0.559-0.861, P=0.006], 0.839 (95% CI: 0.744-0.933, P<0.001), and 0.804 (95% CI: 0.676-0.932, P<0.001), respectively. The combined area under the curve (AUC), sensitivity, and specificity of the four indices (0.941, 100%, and 78%, respectively) were higher than those of the four indices alone.

Conclusions: CTP combined with multi-modal MRI better evaluated hemodynamics, tissue metabolism, and other relevant patient information, providing an objective basis for the clinical assessment of patients with acute IS with END and facilitating the development of accurate and personalized treatment plans.