Quantitative Imaging in Medicine and Surgery最新文献

Background: Computed tomography (CT) offers detailed cross-sectional images of internal anatomy for disease detection but carries a risk of solid cancer or blood malignancies due to exposure to X-ray radiation. This study aimed to develop a new method to quickly predict patient-specific organ doses from CT examinations by training neural networks (NNs) based on radiomics features.

Methods: CT Digital Imaging and Communications in Medicine (DICOM) image data were exported to DeepViewer, a clinical autosegmentation software, to segment the regions of interest (ROIs) for patient organs. Radiomics feature extraction was performed based on the selected CT data and ROIs. Reference organ doses were computed using Monte Carlo (MC) simulations. Patient-specific organ doses were predicted by training a NN model based on radiomics features and reference doses. For the dose prediction performance, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) were evaluated on the test sets. The robustness of the NN model was evaluated via the random rearrangement of patient samples in the training and test sets.

Results: The maximal difference between the reference and predicted doses was less than 1 mGy for all investigated organs. The range of MAPE was 1.68% to 5.2% for head organs, 11.42% to 15.2% for chest organs, and 5.0% to 8.0% for abdominal organs; the maximal R² values were 0.93, 0.86, and 0.89 for the head, chest, and abdominal organs, respectively.

Conclusions: The radiomics feature-based NN model can achieve accurate prediction of patient-specific organ doses at a high speed of less than 1 second using a single central processing unit, which supports its use as a user-friendly online clinical application.

Background: Accurate delineation of knee bone boundaries is crucial for computer-aided diagnosis (CAD) and effective treatment planning in knee diseases. Current methods often struggle with precise segmentation due to the knee joint's complexity, which includes intricate bone structures and overlapping soft tissues. These challenges are further complicated by variations in patient anatomy and image quality, highlighting the need for improved techniques. This paper presents a novel semi-automatic segmentation method for extracting knee bones from sequential computed tomography (CT) images.

Methods: Our approach integrates the fuzzy C-means (FCM) algorithm with an adaptive region-based active contour model (ACM). Initially, the FCM algorithm assigns membership degrees to each voxel, distinguishing bone regions from surrounding soft tissues based on their likelihood of belonging to specific bone regions. Subsequently, the adaptive region-based ACM utilizes these membership degrees to guide the contour evolution and refine segmentation boundaries. To ensure clinical applicability, we further enhance our method using the marching cubes algorithm to reconstruct a three-dimensional (3D) model. We evaluated the method on six randomly selected knee joints.

Results: We evaluated the method using quantitative metrics such as the Dice coefficient, sensitivity, specificity, and geometrical assessment. Our method achieved high Dice scores for the femur (98.95%), tibia (98.10%), and patella (97.14%), demonstrating superior accuracy. Remarkably low root mean square distance (RSD) values were obtained for the tibia and femur (0.5±0.14 mm) and patella (0.6±0.13 mm), indicating precise segmentation.

Conclusions: The proposed method offers significant advancements in CAD systems for knee pathologies. Our approach demonstrates superior performance in achieving precise and accurate segmentation of knee bones, providing valuable insights for anatomical analysis, surgical planning, and patient-specific prostheses.

Background: White matter hyperintensity (WMH) is a common neuroimaging marker of cerebral small vessel disease (SVD) and a critical independent predictor of motor dysfunction, which increases the risk of disability, morbidity, and mortality. However, the mechanism underlying the relationship between WMH and motor function has not yet been fully clarified. It was hypothesized that cognitive impairment mediates the relationship between WMH and motor dysfunction in patients with SVD, which were considered predictor and outcome variables, respectively.

Methods: A total of 221 patients with SVD were enrolled in this study, and their magnetic resonance imaging (MRI), neuropsychological, and motor function data were collected. The MRI data were visually assessed to determine the WMH burden using the Fazekas scale. Cognition was evaluated using the Montreal Cognitive Assessment (MoCA). Motor function was assessed using the Tinetti Gait and Balance Scale and the Short Physical Performance Battery (SPPB). Finally, a bootstrap analysis was performed to determine whether cognition mediated the relationship between WMH and motor function.

Results: Of all the patients, 30.3% had mild WMH, 37.6% had moderate WMH, and 32.1% had severe WMH. Patients' cognition and motor function decreased as the WMH burden increased (P<0.01). The MoCA scores were associated with the Tinetti scale (r=0.545, P<0.01) and SPPB scores (r=0.365, P<0.01). Finally, multi-categorical mediation models confirmed our research hypothesis; the coefficients for the indirect effects had 95% confidence intervals (CIs) that excluded zero, indicating statistically significant mediation effects.

Conclusions: WMH is associated with motor dysfunction, and this association is mediated by cognition in patients with SVD. This finding highlights the importance of early interventions targeting cognitive function to reduce the risk of motor dysfunction.

Background: Many studies have explored the application of the magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score as a tool for opportunistic osteoporosis screening before spine surgery. We aimed to conduct a systematic review and meta-analysis of studies to determine the diagnostic value of the VBQ score for bone mineral density (BMD) assessment.

Methods: The PubMed, EBSCO, Ovid, Web of Science, Cochrane Library, Wanfang, China National Knowledge Infrastructure, Chinese Science and Technology Periodical databases were searched to retrieve original studies on VBQ and BMD published from inception to 31 December 2023 with no restrictions on language and the reference standard of BMD. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Summary accuracy metrics were obtained by bivariate random-effects meta-analysis. The area under the curve (AUC) of the summary receiver operating characteristic (SROC) analysis was used to evaluate the performance of the VBQ score. Meta-regression and subgroup analyses were performed to examine the source of heterogeneity.

Results: In total, 23 studies, comprising 2981 patients, from 2022 to 2023, of high- or medium-to-high scientific quality were included in the meta-analysis. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of the VBQ score in the assessment of BMD were 0.77 [95% confidence interval (CI), 0.73-0.81; P<0.001], 0.65 (95% CI, 0.59-0.71; P<0.001), and 6.49 (95% CI, 4.82-8.73; P<0.001), respectively, with an area under the SROC curve of 0.78 (95% CI, 0.74-0.82; P<0.001). The presence of heterogeneity was found to have a significant association with factors such as the reference standard, sex, mean age, region of publication, VBQ method, and magnet field strength.

Conclusions: The MRI-based VBQ score has some diagnostic value in detecting osteoporosis. The VBQ score could be used as a tool for opportunistic osteoporosis screening before spine surgery.

Background: Accurate preoperative risk stratification methods are important for clear cell renal cell carcinoma (ccRCC) patients to enable personalized treatment. However, there are still no accurate and quantitative prognostic factors. This study aimed to investigate the effectiveness of T1 mapping in predicting the progression-free survival (PFS) of ccRCC after nephrectomy.

Methods: A retrospective cohort study was performed in a China tertiary care hospital. This study reviewed the clinical and magnetic resonance imaging (MRI) data of consecutive inpatients with pathologically confirmed ccRCCs between September 2014 and September 2021. PFS was evaluated by following patients until the first adverse event. Radiological features including T1 relaxation time of tumors were assessed by 2 radiologists. Cox regression and visual nomogram, Kaplan-Meier survival, and log-rank test were utilized for survival analysis.

Results: A total of 195 patients with pathologically confirmed ccRCCs (mean age ± standard deviation, 56.0±12.0 years; 133 men) with eligible data were included in the study. The median follow-up was 27.6 months (range, 1-88 months), and 22 (11.3%) patients experienced metastasis or recurrence. Univariate and multivariate survival analysis showed the higher post-contrasted T1 relaxation time [P=0.001; hazard ratio (HR), 2.077; 95% confidence interval (CI): 1.350-3.196] and the incomplete tumor capsule (P<0.001; HR, 7.849; CI: 2.614-23.570) were independently associated with a shorter PFS of patients. Patients with ≥222.73 ms post-contrasted T1 relaxation time ccRCCs had worse PFS than the lower post-contrasted T1 relaxation time group.

Conclusions: The T1 mapping quantitative parameters may be a new potential biomarker for predicting PFS in patients with ccRCCs.

Background: Spontaneous renal artery dissection (SRAD) is a rare cause of renal failure and renovascular hypertension, with the diagnosis often being delayed and treatment varying across different centers. The objective of this retrospective cohort study was to scrutinize the characteristics, treatment modalities, and outcomes of patients with SRAD at our center over the past ten years. Furthermore, the study sought to identify the most suitable treatment options for different categories of patients with SRAD.

Methods: Data from 21 consecutive patients who presented with symptoms of SRAD from December 2013 to December 2023 were collected. Lesion characteristics, treatment options, blood pressure (BP) control, serum creatinine and estimated glomerular filtration rate (eGFR) were analyzed. A paired t-test was used for comparisons of BP, serum creatinine, and eGFR. An independent samples t-test was used to analyze baseline BP and BP change in different treatment groups.

Results: The mean age, weight, and height of patients with SRAD was 49.2±13.0 (range, 18-69) years, 69.0±9.7 (range, 50-80) kg, and 1.7±0.1 (range 1.6-1.8) m, respectively. New-onset hypertension was found in 8 (38.1%) patients. Renal artery dissecting aneurysm and renal artery stenosis were found in 1 (4.8%) and 4 (19.0%) patients, respectively. Supportive medical treatment alone, endovascular intervention, and nephrectomy were required in 15, 4 and 2 cases, respectively. Stable renal function and satisfactory hypertension control were obtained in all treatment groups, with a median follow-up of 18.1 (range, 12-32) months.

Conclusions: Medical management is a reasonable choice in most patients with SRAD. Interventional management is an efficacious strategy for the management of renovascular hypertension and the preservation of renal function.