Computed Tomography Parameters Research Articles

Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters

BackgroundInterstitial lung abnormalities (ILA) are a proposed imaging concept. Fibrous ILA have a higher risk of progression and death. Clinically, computed tomography (CT) examination is a frequently used and convenient method compared with pulmonary function tests. This study aimed to correlate quantitative CT airway parameters with pulmonary function parameters in patients with fibrous ILA, with the goal of establishing a prediction model for abnormal pulmonary function parameters in patients with fibrous ILA.MethodsNinety-five cases of fibrous ILA including CT images and 64 normal control cases were collected. All patients completed pulmonary function tests within one week. The airway parameters of the CT images of the two groups of cases were measured using a commercial software (Aview). Differences in airway parameters and lung function parameters between the two groups were analyzed by logistic multifactorial regression. The correlation between airway parameters and lung function parameters among 95 patients with fibrous ILA and a prediction model was determined for the decreased percentage forced vital capacity to predicted normal value (FVC%pred) in patients with fibrous ILA.ResultsLogistic multifactorial regression correlated FVC%pred and bronchial wall thickness (WT) were correlated with fibrous ILA. The 95 patients with fibrous ILA were divided into normal FVC%pred (n = 69) and decreased FVC%pred (n = 26) groups at the 80% cut-off. Logistic multifactorial regression revealed that FVC%pred decline in patients with fibrous ILA was effectively predicted by age (odds ratio [OR]: 1.11, 95% confidence interval [CI]: 1.02–1.21, p = 0.014), gender (OR: 4.16,95% CI: 1.27–13.71, p = 0.019), luminal area of the sixth generation brochi (LA6; OR: 0.87, 95%CI: 0.78–0.970,p = 0.015), and airway wall area (WA; OR: 1.12, 95%CI: 1.02–1.24, p = 0.020) were effective predictors of. The area under the curve of the prediction model based on the four parameters was 0.8428.ConclusionWT is a quantitative CT biomarker and FVC%pred is a valid lung function parameter in fibrous ILA patients. Age, gender, LA6, and WA are effective predictors of FVC%pred decline in fibrous ILA patients. The combined model has good predictive value.Clinical trial number2024K249.

A New Methodology for Selecting CT Scanning Parameters Depending on the Density of Materials.

The CT (computed tomography) scanner has been used for many years now not only for medical measurements but also in many industries, for example, in defectoscopy for measuring sheet thickness and checking the joining of materials, as well as for measuring the geometry of individual components. This type of scanner is a good complement to coordinate contact and non-contact measurements for intra-structural measurements and inaccessible places. The variety of materials, however, makes it very difficult to select individual CT parameters. In this paper, a curve for selecting the maximum and minimum voltage of the lamp depending on the density of a given material is determined and an interpolation polynomial (1d with a third-degree polynomial) is used, by defining third-degree glued functions (cubic spline) to determine intermediate voltage values to a given material density, so as to determine full data ranges. This approach can facilitate the work of selecting scanning parameters for non-destructive testing, as this is a difficult process and sometimes consumes half of the measurement time. The practical experiments were carried out at the Accredited Coordinate Metrology Laboratory to develop a multi-criteria matrix for selecting CT measurement parameters for measurement accuracy. This approach reduced the time by an average of half an hour and effectively optimized the selection of scanning parameters.

Exploring the use of biphasic quantitative thoracic computed tomography to evaluate halo-pelvic traction in managing severe adolescent idiopathic scoliosis.

Patients diagnosed with severe adolescent idiopathic scoliosis (AIS) often experience reduced pulmonary function and lung volume due to deformities in the spine and ribcage. Presurgical halo-pelvic traction (HPT) has been used to treat severe scoliosis before second-stage surgery. Nevertheless, there is a scarcity of studies utilizing computed tomography (CT) scans to evaluate changes in lung volume post-HPT. Therefore, this study aimed to investigate changes in lung volumes and anatomical parameters using biphasic CT scans following HPT in AIS patients. The hypothesis was that the lung tidal volume, as determined by biphasic CT scans, would exhibit changes in conjunction with pulmonary function. The study comprised a cohort of 24 patients with AIS scheduled for presurgical HPT intervention. Pre- and post-traction radiographs, biphasic CT scans, and pulmonary function tests (PFTs) were obtained. Then, 3-dimensional (3D) reconstructions were generated from the CT scans obtained at both end inspiration and end expiration to calculate various measurements of lung volumes, particularly the lung tidal volume. Additionally, various thoracic anatomical parameters and clinical parameters, including PFTs, were assessed and compared. Following an average of 91.7 days of HPT, a significant increase in CT total lung volume (TLV) was noted at both end inspiration (P<0.001) and expiration (P=0.020). Conversely, there was no significant change in lung tidal volume (P=0.115). The traction resulted in significant reductions in the thoracic Cobb angle, kyphosis, and hemithoracic symmetry ratio, with average decreases of 32.9%, 33.0%, and 18.8%, respectively, accompanied by notable enhancements in PFTs. Changes in radiographic and CT parameters, excluding TLV at end expiration and hemithoracic symmetry ratio, were not correlated with PFT changes. Significant correlations were found between HPT duration and changes in the Cobb angle (r=-0.688, P<0.001) and left lung volumes at end inspiration (r=0.518, P=0.010) and expiration (r=0.452, P=0.027). Despite the enhancement in pulmonary function after HPT, CT lung tidal volume remained unchanged. This discrepancy contradicts our initial hypothesis, suggesting that CT lung tidal volume is not a reliable indicator of pulmonary function. Preoperative HPT significantly improved spinal deformity, TLV, and pulmonary function. The alteration in CT lung tidal volume did not correlate with PFTs and was deemed an unreliable predictor of pulmonary function. The use of biphasic thoracic CT for calculating lung tidal volume did not demonstrate substantial benefits over monophasic CT and is not recommended for severe AIS.

Radiological Findings for Distinguishing Between Xanthogranulomatous Cholecystitis and Gallbladder Cancer.

Xanthogranulomatous cholecystitis (XGC) is a rare, chronic gallbladder inflammation often mistaken for gallbladder cancer (GBC) on imaging. Accurate differentiation is vital for appropriate treatment. This study aims to enhance computed tomography (CT) scan diagnostic accuracy for distinguishing XGC from GBC. This retrospective study included patients diagnosed with XGC and GBC between 2014 and 2023. CT images of 70 patients (16 GBC, 54 XGC) were reviewed. Radiologists assessed CT parameters: gallbladder wall thickening, intramural hypoattenuating nodules, enhancement characteristics, mucosal line continuity, pericholecystic fat stranding, presence of stones, bile duct dilatation, hepatic invasion, invasion to adjacent structures, and lymph node size. Among 70 patients, there were 38 males (54%) and 32 females (46%), with a median age of 62 years. GBC patients were significantly older (median age 72 years) compared to XGC patients (60 years) (P=0.001). Diffuse gallbladder wall thickening was more frequent in XGC (70%) than GBC (12.5%) (P<0.001). Continuous mucosal lines and intramural hypoattenuating nodules were more common in XGC (P<0.001 and P=0.010, respectively). Intrahepatic bile duct dilatation and invasion to adjacent structures were significantly linked with GBC (P<0.001 and P=0.043). Lymph nodes with a short axis>8 mm indicated GBC (P<0.001), with a cutoff providing 71.4% sensitivity and 84% specificity (AUC: 0.843, P<0.001). CT showed 75% sensitivity (95% CI: 48-93%), 74% specificity (95% CI: 60%-85%), and 74% accuracy (95% CI: 62%-84%). CT imaging can effectively differentiate XGC from GBC, and larger studies can further improve diagnostic accuracy.

Correlation between Clinical and Radiological Findings in Individuals with Patellofemoral Pain Syndrome

AIM: Firstly, this study aimed to compare between different clinical findings commonly measured in physical therapy among healthy individuals and those with patellofemoral pain syndrome (PFPS). Secondly, this study aimed to investigate the correlation between several clinical findings in individuals with PFPS including Craig’s test, quadriceps (Q) Angle and Kujula test and the findings of computed tomography (CT). METHODS: Twenty-nine healthy individuals and thirty individuals diagnosed with PFPS were enrolled in the study after signing a consent form. The examiner took the medical history and fills the data collection sheet for each participant and they were examined for the following measures Craig’s test, Q-Angle and Kujula test. The participants in the experimental (PFPS) group were further investigated using CT. RESULTS: There was no significant difference between patients and controls in the mean age, weight, height and BMI. Significant decrease of Kujula in patient group compared to control group (p=0.0001) and there was significant increase of Q angle and Criage test in patients than controls (P= 0.026 and P= 0.017 respectively). There was significant positive correlation between Kujula and congruence angle and patellar weber (P &lt; 0.01). Moreover, significant positive correlation was observed between Craig’s test and patellar tendon ratio. While, negative correlation was found between Q angle and patellar tendon ratio. CONCLUSIONS: Kujala Score, Q angle and Criage test were proved to be valid and sensitive to clinical changes in PFPS and have moderate to good concordance with CT parameters

CT parameters of psoas muscle predicts 28-day mortality in older patients with sepsis: a retrospective study

OdjectivesTo investigate the predictive value of computed tomography (CT)-based transverse diameter (TD) and longitudinal diameter (LD) of the psoas muscle at the level of third lumbar vertebra for 28-day mortality in older patients with sepsis.MethodsThis retrospective single-center cohort study included 115 septic patients aged over 65 years old who were admitted to ICU in Beijing-Chaoyang Hospital between July 2017 and July 2022. The TD and LD of psoas muscle were obtained by measurement on abdominal CT images. Logistic regression analysis was conducted to identify the prognostic factors for 28-day mortality in older patients with sepsis, and their predictive performances were evaluated using the receiver operating characteristic (ROC) curve.ResultsMultivariable logistic analysis indicated that TD (OR: 0.405, 95% CI: 0.190–0.864) was a protective factor for 28-day mortality in older patients with sepsis, whereas LD was not. The areas under the curve (AUCs) of TD, APACHE II, and SOFA were 0.666 (95% CI: 0.565–0.767), 0.660 (95% CI: 0.561–0.760), and 0.679 (95% CI: 0.581–0.777), respectively. Furthermore, the AUCs for the combination of TD with APACHE II or SOFA were 0.766 (95% CI: 0.679–0.853, P < 0.001) and 0.765 (95% CI: 0.679–0.852, P < 0.001).ConclusionsTD of psoas muscle was a predictor for 28-day mortality in older patients with sepsis. The combination of TD with APACHE II or SOFA scores enhances the early identification of patients at high risk for poor prognosis.Clinical trial numbernot applicable.

Predictive Value of Clinical and Dual-Energy Computed Tomography Parameters for Hemorrhagic Transformation and Long-Term Outcomes Following Endovascular Thrombectomy.

This study's objective was to explore whether certain parameters measurable by dual-energy computed tomography (DECT) performed 24 h after endovascular thrombectomy (EVT) can predict subsequent hemorrhagic transformation. We retrospectively reviewed patients with acute ischemic stroke (AIS) managed with EVT who had follow-up DECT within 24 h post-EVT between January 2019 and December 2023. Clinical and image parameters were recorded for predictive factor analysis. The primary outcome was hemorrhagic transformation, which was determined by using follow-up computed tomography (CT) or magnetic resonance imaging (MRI). The secondary outcomes were in-hospital mortality and 3-month post-EVT favorable functional outcome, as defined by a modified ranking scale (mRS) score of ≤2. A total of 152 patients were included in this study. Multivariable analysis showed that the VNC-ASPECT score (p = 0.002) and superior sagittal sinus density (p = 0.01) were significantly associated with hemorrhagic transformation. For in-hospital survival rate analysis, post-EVT NIHSS measured 24 h post-EVT was an effective predictor, with a cutoff value of 23 (≤23: 88% vs. >23: 52.1%; p < 0.001). For functional outcome analysis, age (p < 0.001), tPA prior to EVT (p = 0.017), NIHSS 24 h post-EVT (p = 0.001), and VNC-ASPECT score (p < 0.003) were associated with a favorable functional outcome 3 months after EVT. The VNC-ASPECT score was associated with both hemorrhagic transformation and a 3-month post-EVT favorable functional outcome, and could therefore be an useful predictor for the development of hemorrhagic transformation.

CT Image Parameters for Predicting Surgical Risk and Outcome in Wilms Tumor.

This study determined preoperative image parameters for predicting surgical risk and outcome in Wilms tumor (WT). A total of 55 patients with WT were enrolled and classified into surgically low-risk (SLR) and surgically high-risk (SHR) groups. The relationship between imaging findings and surgical risk factors was analyzed, and a survival analysis was performed. The number of patients in the SLR and SHR groups was 35 and 20, respectively. The abdominal aorta encasement, adrenal involvement, and tumor spillage of the computed tomography (CT) image parameters showed a statistically significant difference between the two groups (p-value = 0.021, 0.02, and < 0.01, respectively). Multivariable Cox regression analysis demonstrated that those three CT parameters significantly increased surgical risks (OR = 10.11 p-value = 0.043, OR = 7.61 p-value = 0.031, and OR = 55.57 p-value = < 0.001, respectively). The one-, two-, and five-year disease-free survival (DFS) rates were 83%, 78.2%, and 72.6%, respectively. The radiological parameters associated with poor survival were adrenal involvement and tumor spillage. The abdominal aorta encasement, adrenal involvement, and tumor spillage in the preoperative CT image were strong evidence for predicting surgical risk and outcome in WT. These parameters could be beneficial for the surgeon in preoperative preparation and during the procedure.

Computed tomography morphological assessments of central airways in interstitial lung abnormalities and idiopathic pulmonary fibrosis

BackgroundLittle is known about whether central airway morphological changes beyond traction bronchiectasis develop and affect clinical outcomes in patients with idiopathic pulmonary fibrosis (IPF). This study aimed to compare central airway structure comprehensively between patients with IPF, subjects with interstitial lung abnormality (ILA), and those without ILA (control) using computed tomography (CT). We further examined the prognostic impact of IPF-specific CT airway parameters in patients with IPF.MethodsThis retrospective study included male patients with IPF, and male health checkup subjects divided into those with ILA and control based on lung cancer screening CT. Using an artificial intelligence-based segmentation technique, the extent of fibrotic regions in the lung was quantified. After airway tree segmentation, CT parameters for central airway morphology, including the lumen area of the extrapulmonary airways (LAextra), wall and lumen area of the segmental/subsegmental intrapulmonary airways (WAintra and LAintra), tracheal distortion (tortuosity and curvature) and bifurcation angle of the main carina, were calculated.ResultsThere were 106 patients with IPF, 53 subjects with ILA, and 1295 controls. Multivariable models adjusted for age, height and smoking history revealed that LAintra and WAintra were larger in both ILA and IPF, and that tracheal tortuosity and curvature were higher in IPF, but not in ILA, than in the control, whereas the bifurcation angle did not differ between the 3 groups. According to multivariable Cox proportional hazards models including only patients with IPF, increased WAintra was significantly associated with greater mortality (standardized hazard ratio [95% confidence interval] = 1.58 [1.17, 2.14]), independent of the volume of fibrotic regions, normal-appearing regions, or the whole airway tree in the lung.ConclusionIncreased lumen area and wall thickening of the central airways may be involved in the pathogenesis of ILA and IPF, and wall thickening may affect the prognosis of patients with IPF.

Deep Learning for the Study of Urinary Stone Composition from Computed Tomography Images.

Urinary stones composed of uric acid can be treated with medicine. Computed tomography (CT) can diagnose urinary stone disease, but it is difficult to predict the type of uric stones. This study aims to develop a method to distinguish pure uric acid (UA) stones from non-uric acid (non-UA) stones by describing quantitative CT parameters of single-energy slices of urinary stones related to chemical stone types. Clinical data, CT images, and stone composition analysis results of patients with urinary stones clinically diagnosed at The Department of Urology, Affiliated Hospital of Qingdao University between 1 January 2018 and 31 December 2020 were collected and retrospectively analyzed. The above data were preprocessed and fed into a convolutional neural network to perform deep learning (DL) of the model, and the dataset was validated at a ratio of 4:1. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve and the confusion matrix were utilized to evaluate the predictive effect of the model. A retrospective analysis of 918 non-enhanced thin-slice single-energy CT images of known chemical stone types (124 with UA stones and 794 with non-UA stones) was conducted using a DL model. Compared with the results of ex vivo analysis by infrared spectroscopy, the prediction model obtained an AUC of 0.83 for the dichotomous classification of UA stones and non-UA stones. The accuracy of the model was 97.01%, with an F1 score of 89.04%, sensitivity of 84.62%, and specificity of 82.28%. This DL model constructed based on convolutional neural network analysis of thin-slice single-energy CT images is highly accurate in predicting the composition of pure UA and non-UA stones, providing a simple and rapid diagnosis method.

Role of CT-based body composition parameters in the course of COVID-19.

A significant correlation is observed between the course of coronavirus disease 2019 (COVID-19) and body composition parameters including visceral fat quantification, muscle mass, and hepatic attenuation. The aim of the study was to investigate the correlation between the extent of lung involvement and various computed tomography (CT) parameters, as well as laboratory findings in COVID-19 patients. A retrospective analysis was conducted on 72 adult patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection who underwent two consecutive thorax CT scans at least 2 weeks apart. The patients were divided into two groups, as progressive and nonprogressive, based on the presence of two consecutive CT scans. Skeletal muscle area (SMA), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), liver-to-spleen (L/S) density ratio, and laboratory findings were compared between the groups. The correlation between the extent of lung involvement and CT parameters, as well as the laboratory findings were assessed. A total of 72 patients were included in the study, with 34 (47.2%) females and 38 (52.8%) males. Hemoglobin levels were significantly lower in the progressive group compared to the nonprogressive group. C-reactive protein (CRP) values were higher in the progressive group at follow-up. The nonprogressive group exhibited decreases in the SFA, VFA, and TFA, while liver density increased. The progressive group showed a decrease in the twelfth thoracic vertebra (T12) paravertebral muscle area and muscle index. In the comparison of the laboratory and radiological data in the course of COVID-19, white blood cell (WBC) and neutrophil counts increased, SMA T12, and the skeletal muscle index (SMI) decreased in the lung progressive group. Hemoglobin and CRP levels at admission may indicate disease progression. Future studies are warranted to increase the reliability with larger series.

Combination of FDG PET/CT radiomics and clinical parameters for outcome prediction in patients with non-Hodgkin's lymphoma.

The purposes was to build model incorporating PET + computed tomography (CT) radiomics features from baseline PET/CT + clinical parameters to predict outcomes in patients with non-Hodgkin lymphomas. Cohort of 138 patients with complete clinical parameters and follow up times of 25.3 months recorded. Textural analysis of PET and manual correlating contouring in CT images analyzed using LIFE X software. Defined outcomes were overall survival (OS), disease free-survival, radiotherapy, and unfavorable response (defined as disease progression) assessed by end of therapy PET/CT or contrast CT. Univariable and multivariable analysis performed to assess association between PET, CT, and clinical. Male ( P = 0.030), abnormal lymphocytes ( P = 0.030), lower value of PET entropy ( P = 0.030), higher value of SHAPE sphericity ( P = 0.002) were significantly associated with worse OS. Advanced stage (III or IV, P = 0.013), abnormal lymphocytes ( P = 0.032), higher value of CT gray-level run length matrix (GLRLM) LRLGE mean ( P = 0.010), higher value of PET gray-level co-occurrence matrix energy angular second moment ( P < 0.001), and neighborhood gray-level different matrix (NGLDM) busyness mean ( P < 0.001) were significant predictors of shorter DFS. Abnormal lymphocyte ( P = 0.033), lower value of CT NGLDM coarseness ( P = 0.082), and higher value of PET GLRLM gray-level nonuniformity zone mean ( P = 0.040) were significant predictors of unfavorable response to chemotherapy. Area under the curve for the three models (clinical alone, clinical + PET parameters, and clinical + PET + CT parameters) were 0.626, 0.716, and 0.759, respectively.

Computed Tomography Parameters for Prognosis Prediction in Non-Occlusive Mesenteric Ischemia.

BACKGROUND Non-occlusive mesenteric ischemia (NOMI) is difficult to diagnose and has a high mortality rate. We aimed to determine the qualitative and quantitative parameters of computed tomography (CT) that can determine patient prognosis and contribute to early diagnosis in order to reduce mortality. MATERIAL AND METHODS The biphasic CT images of 40 patients, mean age 72.7±12 years, 24 men (60%), 14 women 14 (40%), with a diagnosis of NOMI were analyzed retrospectively. Patients were divided into survivor and non-survivor groups. Qualitative CT parameters, consisting of vascular, intestinal, and mesenteric and peritoneal findings, comorbidities, and surgical resection were compared using Fisher's exact test. Quantitative CT parameters of superior mesenteric artery (SMA), celiac trunk, inferior vena cava (IVC), superior mesenteric vein (SMV) diameters, and intestinal wall density difference (delta HU) in arterial and venous phases were compared with patient prognosis by using ANOVA. RESULTS Pneumatosis intestinalis (P=0.012), paper-thin bowel wall (P=0.015), and pale mesentery (P=0.008) were higher in the non-survivor group. In the survivor group, mesentery congestion (P=0.003), bowel wall thickening (P=0.001), bowel wall enhancement (P=0.044), and enhancing mucosa and submucosa of bowel wall (P=0.042) were higher. The celiac trunk, SMA, SMV, IVC, and IMA diameters, artery wall density, and delta HU were statistically significantly lower in the non-survivor group. The IVC diameter was correlated with patient prognosis (P=0.000). CONCLUSIONS A systematic evaluation of CT parameters can make important contributions to the early diagnosis and management of patients with NOMI.

Node Reporting and Data System Combined With Computed Tomography Radiomics Can Improve the Prediction of Nonenlarged Lymph Node Metastasis in Gastric Cancer.

To investigate the diagnostic performance of Node Reporting and Data System (Node-RADS) combined with computed tomography (CT) radiomics for assessing nonenlargement regional lymph nodes in gastric cancer (GC). Preoperative CT images were retrospectively collected from 376 pathologically confirmed of gastric adenocarcinoma from January 2019 to December 2023, with 605 lymph nodes included for analysis. They were divided into training (n = 362) and validation (n = 243) sets. Radiomics features were extracted from venous-phase, and the radiomics score was obtained. Clinical information, CT parameters, and Node-RADS classification were collected. A combined model was built using machine-learning approach and tested in validation set using receiver operating characteristic curve analysis. Further validation was conducted in different subgroups of lymph node short-axis diameter (SD) range. Node-RADS score, SD, maximum diameter of thickness of tumor, and radiomics were identified as the most predictive factors. The results demonstrated that the integrated model combining SD, maximum diameter of thickness of tumor, Node-RADS, and radiomics outperformed the model excluding radiomics, yielding an area under the receiver operating characteristic curve of 0.82 compared with 0.79, with a statistically significant difference (P < 0.001). Subgroup analysis based on different SDs of lymph nodes also revealed enhanced diagnostic accuracy when incorporating the radiomics score for the 4- to 7.9-mm subgroups, all P < 0.05. However, for the 8- to 9.9-mm subgroup, the combination of the radiomics did not significantly improve the prediction, with an area under the receiver operating characteristic curve of 0.85 versus 0.85, P = 0.877. The integration of radiomics scores with Node-RADS assessments significantly enhances the accuracy of lymph node metastasis evaluation for GC. This combined model is particularly effective for lymph nodes with smaller standard deviations, yielding a marked improvement in diagnostic precision. The findings of this study indicate that a composite model, which incorporates Node-RADS, radiomics features, and conventional parameters, may serve as an effective method for the assessment of nonenlarged lymph nodes in GC.

Value of spectral parameters in the differential diagnosis of benign and malignant breast nodules in non-enhanced chest CT.

Dual-layer spectral computed tomography (DSCT) is capable of acquiring both conventional and spectral images during one routine scan, and is widely used for the quantitative and qualitative analyses of substances, differential diagnosis, and disease staging. However, limited research has been conducted on its performance in the differential diagnosis of benign and malignant breast nodules using non-enhanced scans. This study aimed to assess the diagnostic performance of multiple quantitative parameters derived from non-enhanced DSCT in differentiating benign from malignant breast nodules. This retrospective cross-sectional study examined a total of 121 breast nodules from 114 patients (malignant group: n=68; benign group: n=53) identified during chest physical examination or routine admission for the treatment of breast diseases at The First Affiliated Hospital of Soochow University from March 2023 to December 2023. All the patients underwent DSCT scanning and pathological diagnosis. The DSCT quantitative parameters, including the effective atomic number (Zeff), computed tomography (CT) attenuation values at 40-70 keV, and the slope of the spectral Hounsfield unit curve (λHU), in non-enhanced images were measured. The λHU was calculated as follows: λHU = CT70 keV - CT40 keV/30 HU. Additionally, typical radiological features were analyzed. A DSCT parameter diagnostic model and a conventional CT diagnostic model were assessed using receiver operating characteristic (ROC) curves. The Delong test was used to assess and compare the diagnostic performance of each model. The DSCT parameters, including the Zeff (P<0.001), λHU (P<0.001), and CT attenuation values at 40 keV (P<0.001) and 50 keV (P=0.001), as well as the presence of the lobular sign (P<0.001) and spicule sign (P<0.001), exhibited statistically significant differences between the benign and malignant groups. The logistic regression analysis revealed that the Zeff [odds ratio (OR): 9.22; 95% confidence interval (CI): 2.11-40.35; P=0.003], λHU (OR: 0.64; 95% CI: 0.52-0.79; P<0.001), 40 keV CT attenuation value (OR: 8.69; 95% CI: 3.28-23.06; P<0.001), 50 keV CT attenuation value (OR: 0.01; 95% CI: 0.001-0.07; P<0.001), and lobular sign (OR: 3.95; 95% CI: 1.52-10.31; P=0.005) were independent predictors of malignancy. Compared to the benign group, the malignant group had a higher likelihood of presenting with the lobular sign and higher Zeff values but lower λHU values. The ROC curve indicated that the Zeff had the highest diagnostic efficacy [area under the curve (AUC) of the ROC =0.792, 95% CI: 0.71-0.87]. Further, the DSCT parameter diagnostic model had improved diagnostic efficacy with an AUC of 0.899 (95% CI: 0.84-0.96), which was higher than the AUC of the conventional CT diagnostic model (AUC =0.796, 95% CI: 0.72-0.87). The Delong test revealed a statistically significant difference between these two models (P=0.04). DSCT parameters derived from non-enhanced DSCT images, such as the Zeff value and λHU, can be used to differentiate benign and malignant breast nodules, and the differential diagnosis efficacy of the DSCT parameters is higher than that of conventional CT parameters.

Unveiling the Hidden Links: Anatomical and Radiological Insights into Primary Hip Osteoarthritis.

Hip osteoarthritis (HOA) is a disease with globally rising incidence that leads to disability and morbidity, overall, in older populations, and might be primary or secondary to numerous risk factors. The most common idiopathic HOA is generally a diagnosis of exclusion, with pathogenetic mechanisms largely still misunderstood. We aimed to investigate the correlation between femoral-acetabular and spinopelvic anatomical and computed tomography (CT) characteristics, and the presence of primary OA. We retrospectively analyzed CT scans from 2019 to 2021, excluding patients under 45 years or with conditions affecting the pelvis, sacrum, or lower limbs. Femoral, acetabular, and spinopelvic parameters were measured; signs of OA were analyzed in the hip and knee joints. Patients were categorized into two groups: A (isolated hip OA) and B (no OA); patients with hip OA, also presenting knee OA, were excluded from this study. In total, 232 cases were examined; statistical analyses compared CT parameters between 129 subjects from Group A and 103 patients of Group B. Group A showed a mean femoral version of 16 ± 4.53 degrees, significantly higher than Group B's 13.16 ± 4.37 degrees (p = 0.0001). Other parameters showed no significant differences. This study highlights an association between femoral version and primary hip OA.

Artificial intelligence-assisted quantitative CT parameters in predicting the degree of risk of solitary pulmonary nodules

Introduction Artificial intelligence (AI) shows promise for evaluating solitary pulmonary nodules (SPNs) on computed tomography (CT). Accurately determining cancer invasiveness can guide treatment. We aimed to investigate quantitative CT parameters for invasiveness prediction. Methods Patients with stage 0–IB NSCLC after surgical resection were retrospectively analysed. Preoperative CTs were evaluated with specialized software for nodule segmentation and CT quantification. Pathology was the reference for invasiveness. Univariate and multivariate logistic regression assessed predictors of high-risk SPN. Results Three hundred and fifty-five SPN were included. On multivariate analysis, CT value mean and nodule type (ground glass opacity vs. solid) were independent predictors of high-risk SPN. The area under the curve (AUC) was 0.811 for identifying high-risk nodules. Conclusions Quantitative CT measures and nodule type correlated with invasiveness. Software-based CT assessment shows potential for noninvasive prediction to guide extent of resection. Further prospective validation is needed, including comparison with benign nodules.

Predictive Value of Quantitative Parameters of 18F-FDG PET/CT in Patients with Liposarcoma.

The purpose of this study was to evaluate the predictive features of baseline F-18-fluorodeoxy-D-glucose positron emission tomography (18F-FDG PET)/computed tomography (CT) parameters in patients with dedifferentiated liposarcomas (DDLPSs) and well-differentiated liposarcomas (WDLPSs) receiving systemic treatment. A total of 24 patients with liposarcoma who underwent longitudinal 18F-FDG PET/CT in systemic therapy were included. All volumetric segmentation of each tumor section and semiquantitative imaging parameters were extracted from the axial field of view from both PET and CT images. Maximum, mean, and minimum standardized uptake values (SUVmax, SUVmean, and SUVmin), Hounsfield units (HUs), and their respective changes from baseline and posttreatment were calculated. The voxel values from unenhanced CT images were correlated with PET-derived parameters. The 18F-FDG uptake decreased by more than 56% on average in responders for both SUVmax and SUVmean in DDLPS. There was a decrease in HUmax in DDLPS among responders. Using AUC > 0.8 as a reasonable predictor, we found that the ratios of SUVmaxD/HUmean, SUVmaxD/HUmedian, and SUVmeanD/HUmedian at baseline were significant indicators of the response to treatment in patients with liposarcoma. The changes in SUVmean and not just SUVmax parameters could be considered as accurate tumor response indicators. For the first time, we introduced baseline SUV/HU ratios as a valuable diagnostic tool in predicting liposarcoma treatment outcomes. This ability was not revealed by classic semiquantitative PET or CT parameters at baseline.

Design of an experimental approach based on the contrast-to-noise ratio measurement for X-ray computed tomography parameters optimization applied to a carbon fiber-reinforced polymer materials scan

This paper proposes a systematic approach for the optimization of scan parameters for industrial X-ray computed tomography (XCT), as regard its specific application as diagnostic tool on carbon fiber-reinforced polymer materials (CFRP). This procedure allows the system operator to overcome suboptimal scan results due to a subjective choice of XCT parameters. In this work, XCT scan quality has been measured in terms of contrast-to-noise ratio (CNR) metric, by calculating it on collected XCT 2D projection images. A four-factor five-level central composite design (CCD) was implemented to perform experiments, and a quadratic polynomial model was chosen to describe the effects of XCT scanning parameters combination on CNR measurement and finally to predict optimal results. Analysis of variance was carried out to evaluate the significance of the model on the response, reporting a R2 of 97.1%, and response surface analyses were also performed for CNR optimization purposes. In order to validate the CCD results, different XCT parameters combinations, coming from the CCD analysis on projection images, were used to run different scans, and, as result, the optimal CNR predicted from the model was also reflected in an optimal CNR measured on the reconstructed XCT images.

Construction of a predictive model for gastric cancer neuroaggression and clinical validation analysis: A single-center retrospective study.

This study investigated the construction and clinical validation of a predictive model for neuroaggression in patients with gastric cancer. Gastric cancer is one of the most common malignant tumors in the world, and neuroinvasion is the key factor affecting the prognosis of patients. However, there is a lack of systematic analysis on the construction and clinical application of its prediction model. This study adopted a single-center retrospective study method, collected a large amount of clinical data, and applied statistics and machine learning technology to build and verify an effective prediction model for neuroaggression, with a view to providing scientific basis for clinical treatment decisions and improving the treatment effect and survival rate of patients with gastric cancer. To investigate the value of a model based on clinical data, spectral computed tomography (CT) parameters and image omics characteristics for the preoperative prediction of nerve invasion in patients with gastric cancer. A retrospective analysis was performed on 80 gastric cancer patients who underwent preoperative energy spectrum CT at our hospital between January 2022 and August 2023, these patients were divided into a positive group and a negative group according to their pathological results. Clinicopathological data were collected, the energy spectrum parameters of primary gastric cancer lesions were measured, and single factor analysis was performed. A total of 214 image omics features were extracted from two-phase mixed energy images, and the features were screened by single factor analysis and a support vector machine. The variables with statistically significant differences were included in logistic regression analysis to construct a prediction model, and the performance of the model was evaluated using the subject working characteristic curve. There were statistically significant differences in sex, carbohydrate antigen 199 expression, tumor thickness, Lauren classification and Borrmann classification between the two groups (all P < 0.05). Among the energy spectrum parameters, there were statistically significant differences in the single energy values (CT60-CT110 keV) at the arterial stage between the two groups (all P < 0.05) and statistically significant differences in CT values, iodide group values, standardized iodide group values and single energy values except CT80 keV at the portal vein stage between the two groups (all P < 0.05). The support vector machine model with the largest area under the curve was selected by image omics analysis, and its area under the curve, sensitivity, specificity, accuracy, P value and parameters were 0.843, 0.923, 0.714, 0.925, < 0.001, and c:g 2.64:10.56, respectively. Finally, based on the logistic regression algorithm, a clinical model, an energy spectrum CT model, an imaging model, a clinical + energy spectrum model, a clinical + imaging model, an energy spectrum + imaging model, and a clinical + energy spectrum + imaging model were established, among which the clinical + energy spectrum + imaging model had the best efficacy in diagnosing gastric cancer nerve invasion. The area under the curve, optimal threshold, Youden index, sensitivity and specificity were 0.927 (95%CI: 0.850-1.000), 0.879, 0.778, 0.778, and 1.000, respectively. The combined model based on clinical features, spectral CT parameters and imaging data has good value for the preoperative prediction of gastric cancer neuroinvasion.