Background: The updated diagnostic framework prostate-specific membrane antigen Reporting and Data System 2.0 (PSMA-RADS 2.0) has been introduced as a standardized scoring system for PSMA positron emission tomography/computed tomography (PET/CT) structured reporting to enhance the accuracy and clinical utility of prostate cancer (PCa) lesion interpretation. This study aims to evaluate the reliability and identify potential limitations of PSMA-RADS 2.0 in clinical applications.
Methods: We conducted a comparative analysis between PSMA-RADS versions 1.0 and 2.0, followed by prospective evaluation of 109 lesions using PSMA-RADS 2.0 criteria. Inter- and intra-reader consistencies were analyzed statistically to evaluate the reliability and practicality of the scoring system. In the context of two independent readings, the inter-reader consistency between experienced readers (ERs) and inexperienced readers (IRs) was evaluated using the intra-class correlation coefficient (ICC).
Results: Compared to version 1.0, PSMA-RADS 2.0 simplified primary PSMA-RADS-1 classification for Category I lesions and introduced PSMA-RADS-5T for post-treatment assessment. Inter-reader correlation coefficients values demonstrated excellent consistency (ERs: 0.964-0.969; IRs: 0.929-0.932). Intra-reader correlation coefficients ranged from 0.920 to 0.985 across all readers. However, challenges persisted in lymph node interpretation (ICC: 0.797-0.823) and post-treatment classification.
Conclusions: PSMA-RADS 2.0 provides a reliable framework for PSMA PET/CT interpretation, even for IRs. Further refinement is needed for post-treatment categorization and lymph node differentiation. The proposed PSMA-RADS-5T sub-classification complete remission/partial remission/stability disease/progression disease (CR/PR/SD/PD) may enhance clinical utility for treatment monitoring.
Background: Synchronous microwave ablation (MWA) and biopsy are suitable for patients with high-risk pulmonary ground-glass nodules (GGNs) who are unfit for resection. The ablation accuracy is affected by hemorrhage and nodule displacement. We thus aimed to verify whether iodixanol localization could enhance the accuracy of the synchronous MWA and biopsy of GGNs.
Methods: A total of 211 continuous patients who underwent synchronous MWA and biopsy were enrolled in a multicenter retrospective study from January 1, 2021, to December 31, 2022. Patients undergoing synchronous MWA and biopsy under conventional computed tomography (CT) guidance were placed in the conventional CT guidance for synchronous MWA and biopsy (cSMB) group and those with iodixanol localization were placed in the iodixanol localization for synchronous MWA and biopsy (iSMB) group. The primary outcomes included primary technique efficacy rate (defined as complete ablation based on a CT scan at 3 months), positive biopsy rate, and complications. In addition to an analysis of the overall cohort, data were compared in propensity score matching for GGN characteristics to minimize the impact of confounding factors.
Results: Compared to the cSMB group (n=108), the iSMB group (n=103) had a higher primary technique efficacy rate (100.0% vs. 92.6%; P=0.007) and positive biopsy rate (96.1% vs. 88.0%, P=0.030). In the cSMB group, the rates of pneumothorax and pleural effusion were 49.1% (53/108) and 37.1% (40/108), respectively, while they were 38.8% (40/103) and 21.3% (22/103), respectively, in the iSMB group (P=0.134 and P=0.012, respectively). Analysis of the matched cohort (n=94 per group) confirmed that the iSMB group, as compared to the cSMB group, had a higher primary technique efficacy rate (100.0% vs. 91.5%; P=0.007) and a higher positive biopsy rate (97.9% vs. 89.4%; P=0.017).
Conclusions: Iodixanol localization during synchronous MWA and biopsy of GGNs is efficient and safe.
Background: Ultrasound (US)-guided microwave ablation (MWA) for primary hyperparathyroidism (PHPT) is a relatively novel minimally invasive treatment. However, definitive evidence for the efficacy of thermal ablation in treating PHPT is not well characterized. This work aimed to evaluate the effectiveness and safety of US-guided MWA in patients with PHPT.
Methods: This retrospective study analyzed the data of patients diagnosed with PHPT who underwent US-guided MWA at Peking University First Hospital between October 2020 and October 2024. Serum levels of parathyroid hormone (PTH), calcium, and phosphate were measured preoperatively and at 3, 6, and 12 hours post-MWA to assess immediate biochemical changes. Long-term therapeutic outcomes were evaluated by monitoring the volume of ablation areas and serum PTH, calcium, and phosphate levels at 1, 3, 6, and 12 months post-MWA or at the last follow-up.
Results: A total of 30 clinical records were reviewed, of which 25 (15 female and 10 male; mean age: 60.04±17.38 years) met the inclusion criteria and were included in the final analysis. No clinically significant complications were observed during or following the MWA procedure. Serum PTH levels showed a significant reduction at 3, 6, and 12 hours post-MWA compared to baseline. Serum calcium levels began to decrease significantly at 3 hours post-MWA, whereas serum phosphate levels showed no significant changes within the first 12 hours. Serum PTH levels were significantly lower at the last follow-up (mean duration: 7.48 ± 4.8 months) compared to baseline (P=0.002). Serum calcium levels were significantly lower at 1 month and at the last follow-up (P=0.0021) compared to baseline. At the last follow-up, the volume reduction ratio (VRR) of the ablated masses was 71.5%±29%.
Conclusions: Our study provides evidence that US-guided MWA is safe and effective for managing PHPT adenomas.
Background: Wall shear stress (WSS) is affected by a variety of hemodynamic factors and plays a role in the pathogenesis of many diseases, such as abnormal WSS is associated with local endothelial dysfunction and atherosclerosis (AS). Vector flow mapping (VFM) is a new tool developed to calculate WSS according to the mass conservation equation. The aim of this study was to evaluate the association between carotid WSS measured by the VFM technique and atherosclerotic cardiovascular disease (ASCVD) risk stratification.
Methods: A retrospective analysis was conducted on 155 individuals who were recruited from the Department of Cardiology at Qilu Hospital of Shandong University. Carotid WSS was measured via the VFM technique. The correlations between carotid intima-media thickness (CIMT) or ASCVD risk stratification and carotid WSS were assessed via Spearman analysis. Multiple linear regression was used to examine the correlation between traditional risk factors or biochemical indicators and carotid WSS. Receiver operating characteristic (ROC) analysis was performed to analyze the association between carotid WSS and ASCVD risk stratification.
Results: The mean age of all participants was 53.06±15.07 years, and 68.4% (n=106) of them were male. The mean carotid WSS of a cardiac cycle (WSSmean) for low, moderate, high, very high, and ultrahigh risk of ASCVD was 0.93±0.21, 0.76±0.20, 0.67±0.10, 0.63±0.18, and 0.52±0.18 Pa, respectively. Carotid WSS was negatively associated with CIMT and ASCVD risk stratification (all P values <0.001). Multiple linear regression analysis confirmed that the maximum WSS of a cardiac cycle (WSSmax) was correlated with age, body mass index (BMI), and heart rate (HR) (R2=0.460; P<0.001). The minimum WSS of a cardiac cycle (WSSmin) was correlated with age, BMI, systolic blood pressure (SBP), HR, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) (R2=0.472; P<0.001). WSSmean was correlated with age, BMI, HR, and LDL-C (R2=0.454; P<0.001). The difference between the WSSmax and WSSmin (WSS△) was correlated with age, BMI, and SBP (R2=0.316; P<0.001). The area under the curve (AUC) of WSSmax, WSSmin, and WSSmean for predicting very high or ultrahigh risk of ASCVD was larger than of CIMT (all P values <0.05).
Conclusions: Carotid WSS measured with the VFM technique was significantly correlated with CIMT and ASCVD risk stratification and could be used for screening and monitoring individuals with potential ASCVD risk.
Background: A deep learning (DL) model based on preoperative computed tomography (CT) has been proposed for estimating recurrence in patients with ovarian cancer. However, the inherent opacity of DL models complicates the interpretation of their output, limiting their clinical applicability. The aim of this study was thus to generate histopathologic evidence supporting such DL prediction models and to construct a clustering-based analytical framework for identifying patients with risk factors for ovarian cancer.
Methods: A retrospective study was conducted in which preoperative CT data were collected from patients with high-grade serous ovarian cancer treated with radical tumor resection from January 2013 to December 2019 at three tertiary care centers. Unsupervised clustering was performed with 1,280 DL model-driven features, and the associations between clusters and histopathological features were analyzed. Multivariate regression was used to investigate the added value of DL outputs for histopathologic correlations.
Results: A total of 418 patients [median age 55 years, interquartile range (IQR), 30-77 years] were evaluated. Unsupervised clusters 3 and 4 were associated with the positive status of P53, P16, and Ki-67, along with invasion of the omentum, rectum, and pelvic wall (P<0.05). In the multivariate logistic regression, the DL output, when adjusted for International Federation of Gynecology and Obstetrics (FIGO) stage, was independently associated with P53 [odd ratios (OR) 1.9642; 95% confidence interval (CI): 1.2412-3.1082; P=0.0039], P16 (OR 2.3446; 95% CI: 1.5445-3.5592; P=0.0001), Ki-67 (OR 10.0433; 95% CI: 5.3525-18.8450; P<0.001), invasion of the omentum (OR 2.5995; 95% CI: 1.7175-3.9342; P<0.001), invasion of the rectum (OR 2.3568; 95% CI: 1.5614-3.5574; P<0.001), and pelvic wall effusion (OR 2.0779; 95% CI: 1.3769-3.1360; P=0.0005). Unsupervised cluster 4 and patients with lower principal component analysis (PCA) scores were associated with worse survival (P<0.0001).
Conclusions: The DL model could effectively extract histopathological features of high-grade serous ovarian cancer from CT images.
Background: Deep learning excels at multi-modal medical image segmentation, but its performance often drops with incomplete modalities, a frequent challenge in clinical settings. Accurate brain tumor segmentation from magnetic resonance imaging (MRI) scans is vital for diagnosis, treatment planning, and therapy assessment, yet, incomplete MRI data are common due to various clinical factors. The aim of this study is to improve brain tumor segmentation of incomplete MRI data.
Methods: To tackle this, we introduce a novel Mamba fusion (MF) network specifically designed to maintain segmentation performance even when MRI modalities are absent or incomplete. Our network utilizes multi-modal encoders to extract features from all available modalities. We have developed cross-level MF blocks that leverage a contextual learning mechanism to capture global features from low-level data. Additionally, a cross-level uncertainty (CU) constraint is applied to each class of the final predicted tumor, enhancing reliability.
Results: Extensive experiments on the BraTS2018 and BraTS2020 datasets demonstrate that our method consistently outperforms existing state-of-the-art techniques across various incomplete multi-modal settings, and improves mean dice similarity coefficient (DSC) by about one point over the strongest baseline across whole tumor (WT)/tumor core (TC)/enhancing tumor (ET) on both datasets.
Conclusions: The proposed method allows to foster the learning of absent modality features, leading to a more comprehensive representation of multi-modal magnetic resonance (MR) images for tumor segmentation, mitigating the challenges associated with feature incompleteness due to absent modalities and enhancing the model's capability to navigate these complex situations.
Background: Patients with hydrocephalus require multiple cranial imaging sessions for visualization of the ventricles following the implantation of a ventriculoperitoneal shunt (VPS) valve. However, conventional imaging methods have certain limitations: high-field-strength magnetic resonance imaging (MRI) equipment can interfere with the pressure setting of a Codman Hakim programmable valve, while mobile computed tomography (CT) exposes patients and medical staff to radiation. Low-field-strength portable MRI (pMRI) offers a safer and more convenient method for the monitoring of patients with hydrocephalus after VPS surgery. This study aimed to evaluate the stability of valve pressure setting during 0.23 T pMRI and to assess the image quality in patients with hydrocephalus.
Methods: This prospective observational study involved 20 patients with hydrocephalus admitted to Zhujiang Hospital of Southern Medical University between September 2022 and September 2023. Eligible patients had hydrocephalus confirmed by recent routine imaging (1.5 T/3 T MRI or CT) and had undergone implantation of a non-MR-resistant Codman Hakim programmable valve. Participants were then subjected to pMRI (0.23 T), before and after which the valve pressure settings were verified via X-ray. Pressure stability was analyzed with the Wilcoxon signed-rank test. Image quality was evaluated through comparison of the Evans index (EI) measured on pMRI with that on standard CT (performed within 24 hours) via intraclass correlation coefficients (ICCs) and Bland-Altman analysis.
Results: In the in vitro pretest, no significant pressure changes were observed (P=0.161). In the clinical study involving 40 pMRI sessions, the valve pressure setting remained unchanged in 30 (75.0%) sessions. Pressure deviations occurred in 10 (25.0%) sessions, but no change exceeded 20 mmH2O. There was no statistically significant difference between pre- and post-pMRI pressure settings (P=0.5552). Regarding image quality (n=22 paired scans), pMRI showed excellent agreement with CT for EI assessment, with an ICC of 0.981 [95% confidence interval (CI): 0.955-0.992; P<0.001]. Bland-Altman analysis indicated a negligible bias of -0.0019 (limits of agreement -0.0283 to 0.0245).
Conclusions: The results indicate that 0.23T pMRI has minimal impact on the pressure setting of the Codman Hakim programmable valve. This study's findings support the safety and convenience of pMRI for VPS follow-up.
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