Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma.

PURPOSE: To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC). METHODS: Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model. RESULTS: Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695). CONCLUSION: The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

Differentiation of acute coronary syndrome with radiomics of pericoronary adipose tissue.

OBJECTIVE: To assess the potential values of radiomics signatures of pericoronary adipose tissue (PCAT) in identifying patients with acute coronary syndrome (ACS). METHODS: In total, 149, 227, and 244 patients were clinically diagnosed with ACS, chronic coronary syndrome (CCS), and without coronary artery disease (CAD), respectively, and were retrospectively analysed and randomly divided into training and testing cohorts at a 2:1 ratio. From the PCATs of the proximal left anterior descending branch, left circumflex branch, and right coronary artery (RCA), the pericoronary fat attenuation index (FAI) value and radiomics signatures were calculated, among which features closely related to ACS were screened out. The ACS differentiation models AC1, AC2, AC3, AN1, AN2, and AN3 were constructed based on the FAI value of RCA and the final screened out first-order and texture features, respectively. RESULTS: The FAI values were all higher in patients with ACS than in those with CCS and no CAD (all P < .05). For the identification of ACS and CCS, the area-under-the-curve (AUC) values of AC1, AC2, and AC3 were 0.92, 0.94, and 0.91 and 0.91, 0.86, and 0.88 in the training and testing cohorts, respectively. For the identification of ACS and no CAD, the AUC values of AN1, AN2, and AN3 were 0.95, 0.94, and 0.94 and 0.93, 0.87, and 0.89 in the training and testing cohorts, respectively. CONCLUSIONS: Identification models constructed based on the radiomics signatures of PCAT are expected to be an effective tool for identifying patients with ACS. ADVANCES IN KNOWLEDGE: The radiomics signatures of PCAT and FAI values are expected to differentiate between patients with ACS, CCS and those without CAD on imaging.

The relationship between different exercise conditions and pericoronary inflammation as quantified by coronary CTA in coronary artery disease.

Objectives: The correlation between exercise type and intensity and coronary artery inflammation in patients with stable coronary artery disease (CAD) is unknown. Therefore, this study assessed the relationship between coronary inflammation quantified by coronary computed tomography angiography (CCTA) and exercise intensity and pattern in patients with CAD. Materials and methods: Patients who underwent CCTA between 2019 and 2023 in the second hospital of Lanzhou University were retrospectively examined. We calculated the pericoronary fat attenuation index (FAI) on the right coronary artery (RCA) as a marker of coronary inflammation. We compared basic information, exercise status, and RCA-FAI values between the two groups, and described the relationship between different exercise durations and RCA-FAI using analysis of variance and restricted cubic splines. Results: In total, 1222 patients were included: 774 had no CAD and 448 patients had CAD. Sex (P = 0.016; odds ratio [OR]: 0.673), high-density lipoprotein (P = 0.006; OR: 0.601), low-density lipoprotein (P = 0.001; OR. 0.762), hypertension (P = 0.000; OR: 0.762), smoking (P = 0.005; OR: 0.670), and postprandial glucose (P = 0.030; OR: 0.812), household income (P = 0.038; OR:1.117), and body mass index (P = 0.000; OR:1.084) were the risk factors for elevated RCA-FAI values in the patients with coronary artery disease group. And when the exercise modality was running and aerobics, the correlation between RCA-FAI values and exercise time showed a "U"-shaped relationship. Follow-up revealed that short periods of high-intensity exercise resulted in lower RCA-FAI values. Conclusion: RCA-FAI was significantly associated with coronary artery inflammation. Although appropriate physical activity reduced the risk of pericoronary inflammation and coronary atherosclerosis, overly prolonged exercise could exacerbate the coronary inflammatory response and increase the likelihood of CAD.

Association of mean pericoronary adipose tissue attenuation with different demographic factors in a subgroup of patients without coronary artery disease stratified by sex, body mass index, and age.

Background: In patients without coronary artery disease (CAD), few studies have evaluated the association between mean pericoronary adipose tissue attenuation (PCATMA) and patient-based demographic factors, for example, age or sex. Therefore, the purpose of this study is to investigate the association between PCATMA and various demographic factors in patients without CAD. Methods: In this case-control study, the 806 patients who underwent coronary computed tomography angiography and were not diagnosed with CAD between July 2020 and July 2022 were retrospectively enrolled. Their PCATMA values of the proximal right coronary artery were measured automatically. Patients without CAD were stratified according to sex, body mass index (BMI), and age, and the relationship between PCATMA and different clinical characteristics was explored using Fisher's exact test or Chi-squared test and independent t-tests or Wilcoxon Mann-Whitney U tests. Results: Compared to non-smoking women [-88.00 (-95.00, -81.00) HU], women who smoked [-84.00 (-94.00, -78.00) HU, P=0.037] had higher PCATMA values and a positive correlation with PCATMA (rs=0.101, P=0.036). Compared to non-hypertensive patients with BMI ≥24.91 kg/m2 [-87.00 (-95.00, -81.00) HU], hypertensive patients with BMI ≥24.91 kg/m2 [-84.00 (-92.00, -78.00) HU, P=0.004] had higher PCATMA values, and a positive correlation with PCATMA (rs=0.144, P=0.004). In a subgroup of patients without CAD stratified by sex, BMI, and age, PCATMA values were all higher in patients with dyslipidemia (women, men, BMI ≥24.91 kg/m2, BMI <24.91 kg/m2, age ≥55 years, and age <55 years: -82.00, -82.00, -81.50, -82.00, -81.00 and -83.50 HU, respectively) than in those without dyslipidemia (-89.00, -89.00, -89.00, -90.00, -90.00 and -88.00 HU, respectively; all P<0.001) and showed a positive relationship (rs=0.328, 0.339, 0.342, 0.326, 0.367, and 0.298, respectively; all P<0.001). Conclusions: Higher PCATMA attenuation values were observed in patients with dyslipidemia, smoking women, and hypertensive patients with BMI ≥24.91 kg/m2, suggesting that PCATMA values can be used to detect patients at high risk for future events with CAD even if they do not currently have atherosclerosis.

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.

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.

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.

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.

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.

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.

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.

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

Development of a nomogram based on pericoronary adipose tissue histogram parameters to differentially diagnose acute coronary syndrome.

PURPOSE: To develop a nomogram based on pericoronary adipose tissue (PCAT) histogram parameters to identify patients with acute coronary syndrome (ACS). MATERIALS AND METHODS: This study retrospectively enrolled 114 and 383 eligible patients with ACS and stable coronary artery disease (CAD), respectively, and divided them into training and testing cohorts in a 7:3 ratio. A blinded radiologist obtained PCAT histogram parameters from the right coronary artery's proximal segment using fully automated software and compared clinical characteristics and PCAT histogram parameters between the two patient groups. The binary logistic regression included significant parameters (P < 0.05), and a nomogram was constructed. RESULTS: In both the training and testing cohorts, the mean, 10th percentile, 90th percentile, median, and minimum values of PCAT were higher, and the interquartile range, skewness, and variance values of PCAT were lower in patients with ACS than in those with stable CAD (P ≤ 0.001). The mean (OR = 4.007), median (OR = 0.576), minimum (OR = 0.893), skewness (OR = 85,158.806) and variance (OR = 1.013) values of PCAT were independent risk factors for ACS and stable CAD in the training cohort. The nomogram was constructed using the five variables mentioned above with area under the curve values of 0.903 and 0.897, respectively, while the calibration and decision curves showed the nomogram's good clinical efficacy for the training and testing cohorts. CONCLUSIONS: The constructed nomogram had good discrimination and accuracy and can be a noninvasive tool to intuitively and individually distinguish between ACS and stable CAD.

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%.

Pericoronary adipose tissue differences among plaque types: a retrospective assessment.

PURPOSE: To assess differences in pericoronary adipose tissue (PCAT) in patients with different plaque types by using several quantitative parameters of PCAT and investigate the relationship between PCAT and different plaque types. MATERIALS AND METHODS: We retrospectively recruited 488 patients diagnosed with stable coronary artery disease (CAD) via coronary computed tomographic angiography, including 279 with calcified plaques (CP), 153 with non-calcified plaques (NCP), and 56 with mixed plaques (MP). Volume, fat attenuation index (FAI), and 10th percentile, 90th percentile, median, and minimum Hounsfield unit (HU) values of PCAT surrounding plaques were quantified. Clinical features and quantitative PCAT parameters were compared between different plaque types. RESULTS: No intergroup differences were observed for age, sex, body mass index, risk factors, and plaque location. Length and PCAT volume in the NCP group were lower than those of the CP and MP groups (P < 0.001), whereas there were no significant differences between the CP and MP groups (P > 0.05). Patients with NCP and MP had a higher FAI and 10th percentile, 90th percentile, median, and minimum HU values of PCAT than CP (P < 0.001); however these values were not significantly different between the NCP and MP groups (P > 0.05). CONCLUSION: The quantitative parameters of PCAT, as a biosensor for CAD, vary among the different plaque types.