MRI features and tumor-infiltrating CD8 + T cells-based nomogram for predicting meningioma recurrence risk.

OBJECTIVE: This study was based on MRI features and number of tumor-infiltrating CD8 + T cells in post-operative pathology, in predicting meningioma recurrence risk. METHODS: Clinical, pathological, and imaging data of 102 patients with surgically and pathologically confirmed meningiomas were retrospectively analyzed. Patients were divided into recurrence and non-recurrence groups based on follow-up. Tumor-infiltrating CD8 + T cells in tissue samples were quantitatively assessed with immunohistochemical staining. Apparent diffusion coefficient (ADC) histogram parameters from preoperative MRI were quantified in MaZda. Considering the high correlation between ADC histogram parameters, we only chose ADC histogram parameter that had the best predictive efficacy for COX regression analysis further. A visual nomogram was then constructed and the recurrence probability at 1- and 2-years was determined. Finally, subgroup analysis was performed with the nomogram. RESULTS: The risk factors for meningioma recurrence were ADCp1 (hazard ratio [HR] = 0.961, 95% confidence interval [95% CI]: 0.937 ~ 0.986, p = 0.002) and CD8 + T cells (HR = 0.026, 95%CI: 0.001 ~ 0.609, p = 0.023). The resultant nomogram had AUC values of 0.779 and 0.784 for 1- and 2-years predicted recurrence rates, respectively. The survival analysis revealed that patients with low CD8 + T cells counts or ADCp1 had higher recurrence rates than those with high CD8 + T cells counts or ADCp1. Subgroup analysis revealed that the AUC of nomogram for predicting 1-year and 2-year recurrence of WHO grade 1 and WHO grade 2 meningiomas was 0.872 (0.652) and 0.828 (0.751), respectively. CONCLUSIONS: Preoperative ADC histogram parameters and tumor-infiltrating CD8 + T cells may be potential biomarkers in predicting meningioma recurrence risk. CLINICAL RELEVANCE STATEMENT: The findings will improve prognostic accuracy for patients with meningioma and potentially allow for targeted treatment of individuals who have the recurrent form.

Risk factors associated with low-grade virulent infection in intervertebral disc degeneration: a systematic review and meta-analysis.

BACKGROUND: An increasing number of research indicates an association between low-grade bacterial infections, particularly those caused by Propionibacterium acnes (P. acnes), and the development of intervertebral disc degeneration (IDD). However, no previous meta-analysis has systematically assessed the risk factors for low-grade bacterial infections that cause IDD. PURPOSE: This study reviewed the literature to evaluate the risk factors associated with low-grade bacterial infection in patients with IDD. STUDY DESIGN: Systematic review and meta-analysis. METHODS: The systematic literature review was conducted using the PubMed, Web of Science, Embase, and Cochrane Library databases. Eligible articles explicitly identified the risk factors for low-grade bacterial infections in IDD patients. Patient demographics and total bacterial infection rates were extracted from each study. Meta-analysis was performed using random- or fixed-effects models, with statistical analyses conducted using Review Manager (RevMan) 5.4 software.aut. RESULTS: Thirty-three studies involving 4,109 patients were included in the meta-analysis. The overall pooled low-grade bacterial infection rate was 30% (range, 24%-37%), with P. acnes accounting for 25% (range, 19%-31%). P. acnes constituted 66.7% of bacteria-positive discs. Fourteen risk factors were identified, of which 8 were quantitatively explored. Strong evidence supported male sex (odds ratio [OR] = 2.15; 95% confidence interval [CI]=1.65-2.79; p<.00001) and Modic changes (MCs) (OR=3.59; 95% CI=1.68-7.76; p=.0009); moderate evidence of sciatica (OR=2.31; 95% CI=1.33-4.00; p=.003) and younger age (OR=-3.47; 95% CI=-6.42 to -0.53; p=.02). No evidence supported previous disc surgery, MC type, Pfirrmann grade, smoking, or diabetes being risk factors for low-grade bacterial infections in patients with IDD. CONCLUSIONS: Current evidence highlights a significant association between IDD and low-grade bacterial infections, predominantly P. acnes being the most common causative agent. Risk factors associated with low-grade bacterial infections in IDD include male sex, MCs, sciatica, and younger age.

Preoperative Prediction of Meningioma Subtype by Constructing a Clinical-Radiomics Model Nomogram Based on Magnetic Resonance Imaging.

OBJECTIVE: We sought to investigate the value of a clinical-radiomics model based on magnetic resonance imaging in differentiating fibroblastic meningiomas from non-fibroblastic meningiomas. METHODS: Clinical, imaging, and postoperative pathologic data of 423 patients (128 fibroblastic meningiomas and 295 non-fibroblastic meningiomas) were randomly categorized into training (n = 296) and validation (n = 127) groups at a 7:3 ratio. The Selectpercentile and LASSO were used to selected the highly correlated features from 3376 radiomics features. Different classifiers were used to train and verify the model. The receiver operating characteristic curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE) were drawn to evaluate the performance. The optimal radiomics model was selected. Calibration curves and decision curve analysis were used to verify the clinical utility and consistency of the nomogram constructed from the radiomics features and clinical factors. RESULTS: Thirteen radiomics features were selected from contrast-enhanced T1-weighted imaging and T2-weighted imaging after dimensionality reduction. The prediction performance of random forest radiomics model is slightly lower than that of the clinical-radiomics model. The area under the curve, SEN, SPE, and ACC of the clinical-radiomics model training set were 0.836 (95% confidence interval, 0.795-0.878), 0.922, 0.583, and 0.686, respectively. The area under the curve, SEN, SPE, and ACC of the validation set were 0.756 (95% confidence interval, 0.660-0.846), 0.816, 0.596, and 0.661, respectively. CONCLUSIONS: The diagnostic efficacy of the clinical-radiomics model of fibroblastic meningioma and non-fibroblastic meningioma was better than that of the radiomics prediction model alone and can be used as a potential tool for clinical surgical planning and evaluation of patient prognosis.

T2-Weighted Imaging and Apparent Diffusion Coefficient Histogram Parameters Predict Meningioma Consistency.

RATIONALE AND OBJECTIVES: Preoperative prediction of meningioma consistency is of great clinical value for risk stratification and surgical approach selection. However, to date, objective quantitative criteria for predicting meningioma consistency have not been developed. This study aimed to investigate the predictive value of magnetic resonance imaging (MRI) T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) histogram parameters for meningioma consistency. MATERIALS AND METHODS: We retrospectively analyzed the clinical, preoperative MRI, and pathological data of 103 patients with histopathologically confirmed meningiomas. Histogram parameters (mean, variance, skewness, kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%) were calculated automatically on the whole tumor using MaZda software. Chi-square test, Mann-Whitney's U test, or independent samples t-test was used to compare clinical, conventional MRI features, and histogram parameters between soft and hard meningiomas. Receiver operating characteristic curve and binary logistic regression analysis were employed to assess the predictive performance of T2WI and ADC histogram parameters. RESULTS: Tumor enhancement was the only conventional MRI feature that was statistically different between soft and hard meningiomas. ADCmean, ADCp1, ADCp10, and ADCp50 among ADC histogram parameters, and T2mean, T2p1, T2p10, T2p50, T2p90, and T2p99 among T2WI histogram parameters showed statistically significant differences between soft and hard meningiomas (all P < 0.05). We found that all combined variables (combinedall) had the best accuracy in predicting meningioma consistency, with area under the curve, sensitivity, specificity, accuracy, positive predictive, and negative predictive values of 0.873 (0.804-0.941), 88.89%, 67.50%, 80.58%, 81.20%, and 79.40%, respectively. Among them, combinedT2 is the most beneficial for predicting meningioma consistency. CONCLUSION: CombinedT2 demonstrated better predictive performance for meningioma consistency than combinedADC. T2WI and ADC histogram parameters may be imaging markers for predicting meningioma consistency.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas.

PURPOSE: The purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA. MATERIALS AND METHODS: Sixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29-78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models. RESULTS: The 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73-0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66-0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74-0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74-0.93) (P = 0.60). CONCLUSION: Both whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.

Apparent diffusion coefficient histogram analysis for differentiating fibroblastic meningiomas from non-fibroblastic WHO grade 1 meningiomas.

PURPOSE: To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM). METHODS: This retrospective study analyzed the histopathological and diagnostic imaging data of 220 patients with histopathologically confirmed FM and nFM. The whole tumors were delineated on axial ADC images, and histogram parameters (mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC [ADCp1, ADCp10, ADCp50, ADCp90, and ADCp99, respectively]) were obtained. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating FM from nFM WHO grade 1 meningiomas, and their diagnostic efficacy in differentiating FM from nFM before surgery was assessed using receiver operating characteristic (ROC) curves. RESULTS: The mean, variance, ADCp50, ADCp90, and ADCp99 of the FM group were all lower than those of the nFM group (P < 0.05), there was significant difference in location and sex (P < 0.05). Multivariate logistic regression showed ADCp99 (P < 0.001) and location (P = 0.007) were the most valuable parameters in the discrimination of FM and nFM WHO grade 1 meningiomas. The diagnostic efficacy was achieved an AUC of 0.817(95% CI, 0.759-0.866), the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 66.4%, 83.6%, 75.0%, 80.2%, and 71.3%, respectively. CONCLUSION: ADC histogram analysis is helpful in noninvasive differentiation of FM and nFM WHO grade 1 meningiomas, and combined ADCp99 and location have the best diagnostic efficacy.

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index.

BACKGROUND: Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment. PURPOSE: To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI). METHODS: Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann-Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed. RESULTS: All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = -0.012 to -0.293). CONCLUSION: ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI.

The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence.

OBJECTIVE: To investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence. MATERIALS AND METHODS: Seventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017-June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curve analysis, and receiver operating characteristic curve, respectively. RESULTS: Maximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892-0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curve analysis indicated good clinical availability of the model. CONCLUSION: This model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment.

Whole-tumor histogram analysis of multi-parametric MRI for differentiating brain metastases histological subtypes in lung cancers: relationship with the Ki-67 proliferation index.

This study aims to investigate the predictive value of preoperative whole-tumor histogram analysis of multi-parametric MRI for histological subtypes in patients with lung cancer brain metastases (BMs) and explore the correlation between histogram parameters and Ki-67 proliferation index. The preoperative MRI data of 95 lung cancer BM lesions obtained from 73 patients (42 men and 31 women) were retrospectively analyzed. Multi-parametric MRI histogram was used to distinguish small-cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC), and adenocarcinoma (AC) from squamous cell carcinoma (SCC), respectively. The T1-weighted contrast-enhanced (T1C) and apparent diffusion coefficient (ADC) histogram parameters of the volumes of interest (VOIs) in all BMs lesions were extracted using FireVoxel software. The following histogram parameters were obtained: maximum, minimum, mean, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, entropy, and 1st-99th percentiles. Then investigated their relationship with the Ki-67 proliferation index. The skewness-T1C, kurtosis-T1C, minimum-ADC, mean-ADC, CV-ADC and 1st - 90th ADC percentiles were significantly different between the SCLC and NSCLC groups (all p < 0.05). When the 10th-ADC percentile was 668, the sensitivity, specificity, and accuracy (90.80%, 76.70% and 86.32%, respectively) for distinguishing SCLC from NSCLC reached their maximum values, with an AUC of 0.895 (0.824 - 0.966). Mean-T1C, CV-T1C, skewness-T1C, 1st - 50th T1C percentiles, maximum-ADC, SD-ADC, variance-ADC and 75th - 99th ADC percentiles were significantly different between the AC and SCC groups (all p < 0.05). When the CV-T1C percentiles was 3.13, the sensitivity, specificity and accuracy (75.00%, 75.60% and 75.38%, respectively) for distinguishing AC and SCC reached their maximum values, with an AUC of 0.829 (0.728-0.929). The 5th-ADC and 10th-ADC percentiles were strongly correlated with the Ki-67 proliferation index in BMs. Multi-parametric MRI histogram parameters can be used to identify the histological subtypes of lung cancer BMs and predict the Ki-67 proliferation index.

Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram.

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann-Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753-0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.

Is there an association between coronary artery inflammation and coronary atherosclerotic burden?

Background: As for the coronary artery inflammation and coronary atherosclerotic burden, which are used to assess the risk of adverse cardiac events in patients, it is unclear whether there is any certain correlation between them. Therefore, the purpose of this study was to explore the potential relationship between coronary artery inflammation and coronary atherosclerotic burden. Methods: A total of 346 eligible patients underwent assessment of computed tomography (CT) attenuation values of pericoronary adipose tissue (PCAT) in the right coronary artery and Agatston coronary artery calcium (CAC) based on coronary CT angiography. These measurements were utilized to evaluate coronary inflammation and atherosclerotic burden, respectively. Patients with a CAC score of 0 were categorized into groups based on the presence or absence of coronary artery disease (CAD). CAC scores of 10, 100, and 400 were chosen as cutoff values to compare differences in PCAT attenuation values across different CAC scores. Results: When comparing all CAD patients to non-CAD patients, a significantly higher PCAT attenuation was observed in CAD patients (-87.54±9.39 vs. -93.45±7.42 HU, P=0.000). The PCAT attenuation in CAD patients with a CAC score of 0 was significantly higher than that in patients with a CAC score greater than 0 and in non-CAD patients with a CAC score of 0 (-82.63±8.70 vs. -90.38±8.59 vs. -93.45±7.42 HU, P=0.000). The PCAT attenuation values did not exhibit significant differences among different CAC scores (all P>0.05); however, it was highest in CAD patients with a CAC score of 0 (P<0.05). Body mass index, hyperlipidemia, hypertension, and PCAT attenuation were identified as independent risk factors in both CAD patients with a CAC score of 0 and patients with a CAC score greater than 0 (all P<0.05). Conclusions: The results of this study suggest that a direct relationship between coronary inflammation and coronary atherosclerotic burden is not evident. Nonetheless, it is noteworthy that coronary inflammation was most pronounced in CAD patients with a CAC score of 0, while CAC score did not demonstrate an association with inflammation.

Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging.

PURPOSE: To explore the clinical value of a clinical radiomics model nomogram based on magnetic resonance imaging (MRI) for preoperative meningioma grading. MATERIALS AND METHODS: We collected retrospectively 544 patients with pathological diagnosis of meningiomas were categorized into training (n = 380) and validation (n = 164) groups at the ratio of 7∶ 3. There were 3,376 radiomics features extracted from T2WI and T1C by shukun technology platform after manual segmentation using an independent blind method by two radiologists. The Selectpercentile and Lasso are used to filter the most strongly correlated features. Random forest (RF) radiomics model and clinical radiomics model nomogram were constructed respectively. The calibration, discrimination, and clinical validity were evaluated by using the calibration curve and decision analysis curve (DCA). RESULTS: The RF radiomics model based on T1C and T2WI was the most effective to predict meningioma grade before surgery among the six different classifiers. The predictive ability of clinical radiomics model was slightly higher than that of RF model alone. The AUC, SEN, SPE, and ACC of the training set were 0.949, 0.976, 0.785, and 0.826, and the AUC, SEN, SPE, and ACC of the validation set were 0.838, 0.829, 0.783, and 0.793, respectively. The calibration curve and Hosmer-Lemeshow test showed the predictive probability of the fusion model was similar to the actual differentiated LGM and HGM. The analysis of the decision curve showed that the clinical radiomics model could obtain the best clinical net profit. CONCLUSIONS: The clinical radiomics model nomogram based on T1C and T2WI has high accuracy and sensitivity for predicting meningioma grade.

Can spectral computed tomography (CT) replace perfusion CT to assess the histological classification of non-small cell lung cancer?

Background: Non-small cell lung cancer (NSCLC) accounts for 80% of total lung cancer cases, it is necessary to distinguish the histological types of NSCLC. This study set out to investigate the correlation between spectral computed tomography (CT) and CT perfusion parameters in patients with NSCLC and to compare the differential diagnostic efficacy of these two imaging modalities for the histological classification of NSCLC. Methods: A total of 62 eligible consecutive patients, including 32 with lung adenocarcinoma (LUAD) and 30 with lung squamous cell carcinoma (LUSC), who underwent "one-stop" spectral combined perfusion scan and pathologically confirmed NSCLC at Lanzhou University Second Hospital between September 2020 and December 2021 were prospectively enrolled. The spectral parameters of lesions in the arterial phase (AP) and venous phase (VP) [including iodine concentration (IC), effective atomic number (Zeff), CT40keV, and slope of the spectral curve (K70keV)] and perfusion parameters [blood flow (BF), blood volume (BV), surface permeability (PS), and mean transit time (MTT)] were assessed. Pearson or Spearman correlation analysis was performed to evaluate the correlation between the two imaging parameters, and the DeLong test was used to compare the diagnostic performance of the two imaging modalities. Results: BV and BF were strongly correlated with spectral parameters CT40keV, IC, Zeff, and K70keV in the AP and VP (0.6

Differentiating invasive thymic epithelial tumors from mediastinal lung cancer using spectral CT parameters.

PURPOSE: The purpose of the study was to explore the importance of quantitative characteristics of spectral CT between invasive thymic epithelial tumors (TETs) and mediastinal lung cancer. METHODS: We analyzed 54 patients (28 with invasive TETs and 26 with mediastinal lung cancer) who underwent spectral CT. During the arterial and venous phase, we measured the CT70keV, effective atomic number (Zeff), iodine concentration (IC), and water concentration (WC) and calculated the slope of the spectral curve (K100keV). We compared the clinical findings and spectral CT parameters of both groups and performed receiver operating characteristic analysis to evaluate the diagnostic efficacy and the optimal cutoff values of the spectral CT parameters. RESULTS: During the AP and VP, the CT70keV, Zeff, IC, and K100keV were significantly higher in patients with invasive TETs than those in patients with mediastinal lung cancer (p < 0.05). WC was not statistically significantly different between the two groups (p > 0.05). ROC curve analysis revealed that all quantitative parameters combined in the AP and VP provided the best diagnostic performance in identifying invasive TETs from mediastinal lung cancer (AUC = 0.88, p = 0.002, sensitivity = 0.89 and specificity = 0.77). The cutoff values in the AP for CT70keV, IC, Zeff, and K100keV to differentiate invasive TETs from mediastinal lung cancer were 75.55, 15.86, 8.45, and 1.71, respectively. The cutoff values in the VP for CT70keV, IC, Zeff, and K100keV to differentiate them were 67.06, 15.74, 8.50, and 1.81, respectively. CONCLUSIONS: Spectral CT imaging has potential value in the differential diagnosis of invasive TETs and mediastinal lung cancer.

Abdominal virtual non-contrast images derived from energy spectrum CT to evaluate chemotherapy-related fatty liver disease.

Background: Chemotherapy-related fatty liver disease (CRFLD) is an important evaluation in patients undergoing computed tomography (CT) for cancer follow-up. This study set out to explore the feasibility of using abdominal virtual non-contrast (VNC) images derived from energy spectrum CT to evaluate CRFLD and reduce the radiation dose. Methods: A total of 160 eligible consecutive patients who underwent energy spectrum CT at Lanzhou University Second Hospital between June 2020 and July 2021 were retrospectively enrolled. The average CT attenuation values of the liver and spleen and the liver-to-spleen ratio (LSR) were measured by two independent blinded radiologists on true non-contrast (TNC) images and three types of VNC image. The diagnostic performance of the LSR for CRFLD, image quality, and diagnostic confidence were compared between the two types of imaging. Results: The average CT attenuation values of the liver and spleen were significantly lower on VNC images than on TNC images (P<0.05), whereas the LSR showed good agreement between the two (P>0.05). The average CT attenuation values of the liver and the LSR measured on the TNC and three types of VNC image were significantly lower in patients with CRFLD than in those without CRFLD (P<0.001). The area under the curve (AUC) values of the LSR for the diagnosis of CRFLD calculated on TNC and three types of VNC image were 0.870 (95% CI: 0.808-0.918), 0.852 (95% CI: 0.787-0.903), 0.819 (95% CI: 0.750-0.875), and 0.851 (95% CI: 0.786-0.902), respectively. The DeLong test confirmed the consistency of TNC and VNC images of diagnostic efficacy (P>0.05). There were no significant differences in image quality or diagnostic confidence between the TNC and three types of VNC image (P>0.05). When VNC imaging was applied, the radiation dose was reduced by approximately 25.0%. Conclusions: VNC imaging could become a reliable alternative to TNC imaging for the clinical evaluation of patients with CRFLD and could reduce the radiation dose by up to 25%.

Combination of autologous osteochondral and periosteum transplantation effectively promotes fibrocartilage regeneration at the tendon-bone junction of the rotator cuff in rabbits.

PURPOSE: Rotator cuff tendon-bone healing often leads to scarring and low biomechanical strength, resulting in a tendency to re-tear. This study examined whether combining autologous osteochondral transplantation and periosteum transplantation increases fibrocartilage transition zone regeneration and improves biomechanical fixation. METHODS: A total of 48 New Zealand white rabbits were divided into the periosteum, autologous osteochondral, combination of autologous osteochondral and periosteum, and control groups. The supraspinatus tendon was cut from the greater tuberosity and repaired by different transplants. A total of 12 rabbits were used for histological examination (haematoxylin and eosin staining, Masson's staining and Safranin-O staining) at 4, 8 and 12 weeks after the repair, and 36 rabbits were used for biomechanical tests (maximal failure load and stiffness). RESULTS: At 4 weeks following the operation, each group had a large tendon-bone gap with a small number of disordered collagen fibres. At 8 weeks, the tendon-bone gap was smaller than that before the operation, and the tendon-bone gap in each experimental group was smaller with neater and denser collagen fibres and chondrocytes than in the control group, with the osteochondral combined periosteum group having the best results. At 12 weeks, the typical tendon-bone transitional structure was observed in the osteochondral combined periosteum group, and more collagen fibres and chondrocytes were generated in each group. The osteochondral combined periosteum group had the largest staining area and the largest amount of cartilage. The maximum tensile strength and stiffness of each group increased over time. There was no significant difference in each group's maximum tensile strength and stiffness at 4 weeks after the operation. However, the maximum tensile strength and stiffness of the osteochondral combined periosteum group at 8 and 12 weeks after operation were significantly higher than those of other groups (P < 0.05). CONCLUSION: Histological and biomechanical results show that autologous osteochondral transplantation combined with periosteum transplantation can effectively promote the regeneration of fibrous cartilage in the tendon-bone junction of the rotator cuff. It is concluded that this technique is a new treatment method to promote tendon-bone healing in the rotator cuff.

[Comparative Evaluation of the Value of Quantitative Parameters of Dual-energy CT and MRI for KRAS Mutation in Rectal Cancer].

Objective To evaluate and compare the value of quantitative parameters of preoperative dual-energy CT and MRI on KRAS mutation in rectal cancer,and to explore the correlations between postoperative pathological indicators and KRAS mutation. Methods This study retrospectively analyzed 50 patients with rectal cancer confirmed by surgery and pathology and receiving KRAS genetic testing in Lanzhou University Second Hospital from August 2017 to April 2021.According to the results of genetic testing,the patients were assigned into a wild-type group (29 patients) and a mutant type group (21 patients).The preoperative baseline data included sex,age,and serum tumor markers,and the postoperative pathological data included pathological stage,lymphovascular invasion,perineural invasion,and lymph node metastasis.The quantitative parameters of three-phase energy spectral CT included iodine (water) concentration,water (iodine) concentration,effective atomic number,and normalized iodine concentration.The quantitative parameters of apparent diffusion coefficient (ADC) included minimum ADC,average ADC,and relative ADC.In addition,the width of the superior rectal vein was obtained from the CT images of the venous phase,and the tumor segmentation,the maximum axial length of tumor,and the maximum longitudinal length of tumor were obtained from the MRI images.The qualitative and quantitative data were compared by χ2 test,t-test,and Mann-Whitney U test.The diagnostic efficacy of the two detection methods for KRAS mutations in rectal cancer was compared,and the receiver operating characteristic curve was employed to evaluate the diagnostic efficacy. Results The KRAS mutation rate was higher in the carbohydrate antigen 199 abnormal group than the normal group (P=0.036) and higher in the positive group of lymphovascular invasion (P=0.034).The KRAS mutant type group had higher normalized iodine concentration in the venous phase (P=0.016) and lower average ADC and relative ADC (P=0.008, P=0.002,respectively) than the wild-type group.Among them,relative ADC had the highest diagnostic efficiency (AUC=0.755). Conclusion The quantitative parameters of dual-energy CT and ADC have similar diagnostic efficiency for KRAS mutation in rectal cancer,and relative ADC is superior to other parameters.

Combination of clinical and spectral-CT parameters for predicting lymphovascular and perineural invasion in gastric cancer.

PURPOSE: The purpose of this study was to investigate the utility of combining clinical and spectral computed tomography (CT) parameters for the preoperative evaluation of lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancers (GCs). MATERIALS AND METHODS: Patients with gastric adenocarcinoma who underwent spectral-CT examination were retrospectively examined. All diagnoses were confirmed by pathology, and the patients were divided into positive and negative groups based on LVI/PNI occurrence. Clinical characteristics, including demographic information, serum tumor markers, and gastroscopic pathological information, were collected. The effective atomic number (Zeff), iodine concentration (IC), and water concentration were measured in the arterial (AP) and venous phase (VP). Differences between the two groups were searched for using independent sample t-test, Mann-Whitney U test, or chi-square (χ2) test and diagnostic performances of the different variables were evaluated using receiver operating characteristic (ROC) curve. RESULTS: A total of 121 patients (96 men, 25 women; mean age: 59 ± 8.7 [SD] years, range: 36-82 years) with gastric adenocarcinoma were included in the study. The serum level of the tumor marker CA125, as well as Zeff and IC in the LVI/PNI-positive group, were significantly higher than in the negative group, and the histological grade and Borrmann type differed between the two groups (all P < 0.05). The discriminating capability analysis demonstrated that CA125 exhibited a favorable performance, and the VP parameters' diagnostic efficacy was superior to that of the AP parameters. The efficacy of the combination of clinical and spectral-CT parameters was superior to that of individual parameters (all AUC > 0.85). The clinical parameters combined with Zeff and IC in the AP and VP exhibited a high evaluation efficacy (AUC = 0.890 [95% CI: 0.826-0.955]; F1 score = 0.888; accuracy = 84.3% [102/121; 95% CI: 76.7-89.8]; sensitivity = 86.2% [75/87; 95% CI: 76.8-92.4]; specificity = 79.4% [27/34; 95% CI: 61.6-90.1]). CONCLUSIONS: Clinical and spectral-CT parameters exhibit considerable capabilities in the preoperative evaluation of LVI and PNI in GCs. The combination of clinical and spectral-CT parameters effectively predicts LVI and PNI in GCs.

Development of a Nomogram Based on 3D CT Radiomics Signature to Predict the Mutation Status of EGFR Molecular Subtypes in Lung Adenocarcinoma: A Multicenter Study.

Background: This study aimed to noninvasively predict the mutation status of epidermal growth factor receptor (EGFR) molecular subtype in lung adenocarcinoma based on CT radiomics features. Methods: In total, 728 patients with lung adenocarcinoma were included, and divided into three groups according to EGFR mutation subtypes. 1727 radiomics features were extracted from the three-dimensional images of each patient. Wilcoxon test, least absolute shrinkage and selection operator regression, and multiple logistic regression were used for feature selection. ROC curve was used to evaluate the predictive performance of the model. Nomogram was constructed by combining radiomics features and clinical risk factors. Calibration curve was used to evaluate the goodness of fit of the model. Decision curve analysis was used to evaluate the clinical applicability of the model. Results: There were three, two, and one clinical factor and fourteen, thirteen, and four radiomics features, respectively, which were significantly related to each EGFR molecular subtype. Compared with the clinical and radiomics models, the combined model had the highest predictive performance in predicting EGFR molecular subtypes [Del-19 mutation vs. wild-type, AUC=0.838 (95% CI, 0.799-0.877); L858R mutation vs. wild-type, AUC=0.855 (95% CI, 0.817-0.894); and Del-19 mutation vs. L858R mutation, AUC=0.906 (95% CI, 0.869-0.943), respectively], and it has a stable performance in the validation set [AUC was 0.813 (95% CI, 0.740-0.886), 0.852 (95% CI, 0.790-0.913), and 0.875 (95% CI, 0.781-0.929), respectively]. Conclusion: Our combined model showed good performance in predicting EGFR molecular subtypes in patients with lung adenocarcinoma. This model can be applied to patients with lung adenocarcinoma.

Differentiating atypical meningioma from anaplastic meningioma using diffusion weighted imaging.

PURPOSE: To explore the value of MRI conventional features and apparent diffusion coefficient (ADC) on the differential diagnosis of atypical meningioma (AtM) and anaplastic meningioma (AnM). MATERIALS AND METHODS: This retrospective study analyzed the preoperative clinical data, MRI conventional features, and DWI data of 55 AtM and 25 AnM confirmed by pathology in our hospital. The clinical features, MRI conventional features, ADCmean, ADCmin, and relative ADC (rADC) values were compared between the two tumors by Chi-square test or an independent sample t-test. Receiver operating characteristic curve (ROC) and binary logistic regression analysis were used to evaluate the diagnostic efficacy of each parameter to differentiate between these tumors. RESULTS: The MRI conventional features had a certain ability to distinguish AnM and AtM, with an area under the curve value (AUC) of 0.824 (95% CI, 0.723-0.900). The ADCmean, ADCmin, and rADC values were significantly higher in AtM compared to AnM (all P < 0.05). ADCmean had the best identification effect with an AUC of 0.867 (95% CI, 0.772-0.933) among them, at an cut-off of 0.817 × 10-3 mm2/s, the sensitivity and specificity of distinguishing AtM from AnM were 78.18% and 88.00%, respectively. A combination of ADCmean and MRI conventional features showed the optimum discrimination ability for the two tumors, the AUC, sensitivity, specificity, and accuracy were 0.918 (95% CI, 0.835-0.967), 80.00%, 94.55%, and 90.00%, respectively. CONCLUSION: MRI conventional features combined with ADCmean, as a non-invasive method, has potential clinical value in the preoperative diagnosis of AtM and AnM.