Journal of Computer Assisted Tomography最新文献

Objective: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CAD v ) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR), mode-based iterative reconstruction (MBIR), and deep learning reconstruction (DLR) at a phantom study.

Methods: A commercially available anthropomorphic abdominal phantom was scanned five times with a 320-detector row CT at 600 mA, 400 mA, 200 mA, and 100 mA and reconstructed by four methods. Signal-to-noise ratios (SNRs) of all lesions within the arterial and portal-venous phase inserts were calculated, and SNR of the lesion phantom was compared with that of all reconstruction methods by means of Tukey's honestly significant difference (HSD) test. Then, tumor volume ( V ) of each nodule was automatically measured using commercially available CAD v software. To compare dose reduction capability for each reconstruction method at both phases, mean differences between measured V and standard references were compared by Tukey's honestly significant difference test among the four different reconstruction methods on CT obtained at each of the four tube currents.

Results: With each of the tube currents, SNRs for MBIR and DLR were significantly higher than those for FBP and hybrid-type IR ( p < 0.05). At the arterial phase, the mean difference in V for the CT protocol obtained at 600 or 100 mA and reconstructed with DLR was significantly smaller than that for others ( p < 0.05). At the portal-venous phase, the mean differences in V for the CT protocol obtained at 100 mA and reconstructed with hybrid-type IR, MBIR, and DLR were significantly smaller than that for FBP ( p < 0.05).

Conclusions: Findings of our phantom study show that reconstruction method had influence on CAD v merits for abdominal CT with not only standard but also reduced dose examinations and that DLR can potentially yield better image quality and CAD v measurements than FBP, hybrid-type IR, or MBIR in this setting.

Objective: To investigate the relationship between craniocervical morphology and the presence and level of cervical facet joint degeneration (FJD).

Methods: A total of 108 consecutive female patients aged 45-55 years who had undergone neck + brain CT angiography were included in this retrospective sectional study. Only patients of a certain age and of the same gender were included in order to eliminate the differences that create a disposition to the development of spinal degeneration. The presence of facet joint (FJ) arthritis (grade ≥2 degeneration in at least one affected facet joint) and the grade of the facet joint degeneration for each patient were recorded. A total of 20 lengths and 3 angles of craniocervical morphology were measured. The differences between the individuals with and without FJ arthritis were investigated with the independent-sample t test, and the relationship between the FJD grade and craniocervical morphology was investigated using the Spearman correlation test.

Results: Individuals with FJ arthritis were found to have longer Grabb-Oakes measurement, shorter FM AP length, lower ADI, lower EOP thickness, higher clivus length, higher crista gall-ATS distance, lower CCA angle, lower distance between the C1 vertebra lateral masses, and higher BAI than those without FJ arthritis ( P ˂ 0.05). Besides, we found that the FJD grade increased as the Grabb-Oakes measurement increased, ADI distance decreased, FM AP length decreased, EOP thickness decreased, clivus length increased, basal angle increased, distance between the C1 vertebra lateral masses decreased, and BAI increased ( P ˂ 0.05).

Conclusions: Differences in craniocervical morphology are statistically associated with degenerative processes that result in degenerative changes in the facet joint. Therefore, some morphological changes in craniocervical anatomy cause changes in the momentum and distribution of the load on the facet joints, predisposing the patient to facet arthropathy and osteoarthritis.

Objective: The aim of the study is to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging in assessing treatment response in cervical cancer patients.

Methods: A retrospective analysis was done for 50 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy and underwent magnetic resonance imaging and diffusion-weighted imaging. Treatment response was classified into 4 categories according to RECIST criteria 6 months after therapy completion. Apparent diffusion coefficient (ADC) values were measured using both region of interest (ROI) ADC and whole lesion (WL) ADC histogram for all cases at both baseline pretreatment and posttreatment Magnetic resonance imaging studies. Changes in ADC values were calculated and compared between groups.

Results: The percentage change of ROI-ADCmean at a cutoff value of >20 had excellent discrimination of responders versus nonresponders, while the percentage change of WL-ADCmean, ADCmin, and ADCmax at cutoff values of >12.5, >35.8, and > 19.6 had acceptable discrimination of responders versus nonresponders. Logistic regression analysis revealed that only baseline WL ADCmin was a statistically significant independent predictor of response. Cancer cervix patients with baseline ADCmin < or equal to 0.73 have 12.1 times higher odds of exhibiting a response.

Conclusions: The percentage change of ROI-ADCmean and WL histogram ADCmean values after concurrent chemoradiotherapy can predict response. Pretreatment WL histogram ADCmin was a statistically significant independent predictor of posttherapy response.

Objective: To investigate the incremental value of pericoronary fat attenuation index (FAI) in routine coronary artery computed tomography angiography (CCTA) to identify culprit lesions in acute coronary syndrome (ACS).

Methods: We reviewed the CCTA data from 80 ACS patients and 40 individuals with stable coronary atherosclerosis. ACS patient plaques were categorized into culprit and nonculprit groups. The plaque-specific pericoronary FAI was assessed using the Perivascular Fat Analysis Tool. We applied a default prespecified window of -190 to -30 Hounsfield units (HU) and a broader prespecified window of -190 to 20 HU. FAI values within these prespecified windows and the types and severity of plaque stenosis were compared across the 3 groups. Additionally, we investigated high-risk characteristics of plaques in the ACS group and their correlation with FAI. The effectiveness and worthiness of FAI in identifying culprit lesions were analyzed based on the receiver operating characteristic curve.

Results: The FAI values under the 2 prespecified windows were higher in the culprit group than in the nonculprit and control groups (all P < 0.001). The culprit group showed the most mixed plaques and the most severe stenosis (all P < 0.001). In the ACS group, the FAI value was significantly lower around calcified lesions (-85.00 ± 9.97 HU) than around noncalcified (-78.00 ± 11.52 HU) and mixed plaques (-78.00 ± 9.24 HU) (both P < 0.001). The culprit group had more high-risk plaques, and high-risk plaques had higher FAI values than those without high-risk characteristics (-70.00 ± 7.67 HU vs -82.00 ± 10.16 HU, P < 0.001). The efficacy of FAI under the default prespecified window in identifying culprit lesions was higher compared than that under the broader prespecified window (area under the curve = 0.799 vs 0.761, P = 0.042), and the diagnostic cutoff values were -77 versus -58 HU. The FAI under the default prespecified window exhibited an incremental value for identifying culprit lesions, as compared with stenosis severity (area under the curve = 0.970 vs 0.939, P < 0.001).

Conclusion: The culprit lesions have higher FAI than the nonculprit lesions and the controls. FAI is a worthy parameter for identifying culprit lesions in routine CCTA according to stenosis severity, and the default prespecified window is a better option.

Objective: To evaluate the methodological quality and the predictive performance of artificial intelligence (AI) for predicting programmed death ligand 1 (PD-L1) expression and epidermal growth factor receptors (EGFR) mutations in lung cancer (LC) based on systematic review and meta-analysis.

Methods: AI studies based on PET/CT, CT, PET, and immunohistochemistry (IHC)-whole-slide image (WSI) were included to predict PD-L1 expression or EGFR mutations in LC. The modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate the methodological quality. A comprehensive meta-analysis was conducted to analyze the overall area under the curve (AUC). The Cochrane diagnostic test and I2 statistics were used to assess the heterogeneity of the meta-analysis.

Results: A total of 45 AI studies were included, of which 10 were used to predict PD-L1 expression and 35 were used to predict EGFR mutations. Based on the analysis using the QUADAS-2 tool, 37 studies achieved a high-quality score of 7. In the meta-analysis of PD-L1 expression levels, the overall AUCs for PET/CT, CT, and IHC-WSI were 0.80 (95% confidence interval [CI], 0.77-0.84), 0.74 (95% CI, 0.69-0.77), and 0.95 (95% CI, 0.93-0.97), respectively. For EGFR mutation status, the overall AUCs for PET/CT, CT, and PET were 0.85 (95% CI, 0.81-0.88), 0.83 (95% CI, 0.80-0.86), and 0.75 (95% CI, 0.71-0.79), respectively. The Cochrane Diagnostic Test revealed an I2 value exceeding 50%, indicating substantial heterogeneity in the PD-L1 and EGFR meta-analyses. When AI was combined with clinicopathological features, the enhancement in predicting PD-L1 expression was not substantial, whereas the prediction of EGFR mutations showed improvement compared to the CT and PET models, albeit not significantly so compared to the PET/CT models.

Conclusions: The overall performance of AI in predicting PD-L1 expression and EGFR mutations in LC has promising clinical implications.

Purpose: This study aims to evaluate the diagnostic value of standardized semiquantitative scoring of color Doppler ultrasound combined with liver stiffness measurement (LSM) of sound touch elastography (STE) in chronic hepatitis B (CHB) patients, providing a reference for the liver fibrosis diagnosis.

Methods: We performed ultrasound and STE on CHB patients, with liver biopsies as the benchmark. We compared the differences in ultrasound standardized semiquantitative scoring and LSM among patients with different stages of liver fibrosis, and evaluated the diagnostic efficacy of significant liver fibrosis using receiver operating characteristic (ROC) curves and the area under the ROC curve alone or in combination.

Results: The total scores of ultrasound semiquantitative scoring and LSM showed statistically significant differences among patients with different stages of liver fibrosis (P < 0.05). There was no statistically significant difference in the total scores of S0 and S1 stages or in the LSM values (P > 0.05). However, the total scores and LSM values for patients at stages S2 and S3 were both higher than those at stage S0, and increased with the severity of fibrosis staging, with statistically significant differences (P < 0.05). The results of the ROC curve analysis showed that the combined diagnosis of significant liver fibrosis with ultrasound standardized semiquantitative scoring and STE had an area under the curve of 0.807, which was significantly greater than using ultrasound standardized semiquantitative scoring (0.694, P < 0.05) or shear wave elastography alone (0.706, P < 0.05).

Conclusions: Color Doppler ultrasound with standardized semiquantitative scoring combined with STE examination can detect significant liver fibrosis (≥S2) in CHB patients.

Objective: Inflammatory characteristics in pericoronary adipose tissue (PCAT) may enhance the diagnostic capability of radiomics techniques for identifying vulnerable plaques. This study aimed to evaluate the incremental value of PCAT radiomics scores in identifying vulnerable plaques defined by intravascular ultrasound imaging (IVUS).

Methods: In this retrospective study, a PCAT radiomics model was established and validated using IVUS as the reference standard. The dataset consisted of patients with coronary artery disease who underwent both coronary computed tomography angiography and IVUS examinations at a tertiary hospital between March 2023 and January 2024. The dataset was randomly assigned to the training and validation sets in a 7:3 ratio. The diagnostic performance of various models was evaluated on both sets using the area under the curve (AUC).

Results: From 88 lesions in 79 patients, we selected 9 radiomics features (5 texture features, 1 shape feature, 1 gray matrix feature, and 2 first-order features) from the training cohort (n = 61) to build the PCAT model. The PCAT radiomics model demonstrated moderate to high AUCs (0.847 and 0.819) in both the training and test cohorts. Furthermore, the AUC of the PCAT radiomics model was significantly higher than that of the fat attenuation index model (0.847 vs 0.659, P < 0.05). The combined model had a higher AUC than the clinical model (0.925 vs 0.714, P < 0.01).

Conclusions: The PCAT radiomics signature of coronary CT angiography enabled the detection of vulnerable plaques defined by IVUS.

Objective: This study aimed to assess the effectiveness of a dual digital alert system and automatic radiologist reassignment in improving the efficiency of quantitative imaging report delivery for clinical trials.

Materials and methods: Assessing tumor metrics is critical to oncologic disease management, informed treatment planning, and for monitoring therapeutic response and even more so in cancer clinical research trials. A collaborative effort with the Institutional Research Information Systems division led to developing a web-based system with a Java backend, tested using Agile methodology to improve patient care with improved turnaround time (TAT) of quantitative reports. The system sent dual digital alerts including a page and an email notification to the radiologist based on the last submitted date and time for each QIAC report and autoreassigned radiologists till report finalization. Data was extracted from the Quantitative Imaging Analysis Core database for comparing the TAT, calculated as time difference between the submission of preliminary reports by Imaging Research Specialists and the finalization by radiologists before and after the digital alert system implementation.

Results: Implementing the dual digital alert system significantly increased the number of cases finalized within 6 hours to 50%. For nonexpedited cases, the mean TAT decreased by 57.2% from 85.9 hours to 36.8 hours (P < 0.001). Expedited cases saw a reduction in mean TAT by 63.7% from 44.9 hours to 16.3 hours (P = 0.022). Baseline and follow-up cases also showed significantly reduced mean and median TAT after deployment (P < 0.001).

Conclusions: The dual digital alert system and automatic radiologist reassignment significantly improved the TAT for quantitative imaging reports in clinical trials. This enhancement in report delivery efficiency led to better therapeutic decision making and increased patient satisfaction in clinical settings.

Abstract: In 2021, the Human Rights Council declared that having a clean, healthy, and sustainable environment is a human right. According to the WHO, 24% of deaths are attributable to environmental health risks and are largely preventable. Current predictions show that rising emissions will be linked to an enormous healthcare burden, especially for high-risk populations and historically disadvantaged communities. The US healthcare industry accounts for nearly 18% of its GDP and is a major consumer of resources. The largest healthcare-related source of greenhouse gas emissions is from the supply chain, including pharmaceuticals, other chemicals, food, and the transportation required to mobilize them accounting for 80% of emissions, with only 20% of emissions from purchased energy and the facilities directly. As a field, radiology has historically monitored its impact in terms of radiation exposure and thermal effects but has not focused on other pollutants, greenhouse gas emissions, or waste. Although tackling large issues such as climate change and pollution seems daunting, we can start by raising awareness through education, investigation, and advocacy. In this review, we discuss a systems-based approach to addressing climate change from the federal to the local level focusing on the potential role of the radiologist.