Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas.

PURPOSE: To investigate the value of histogram analysis of postcontrast T1-weighted (T1C) and apparent diffusion coefficient (ADC) images in predicting the grade and proliferative activity of adult intracranial ependymomas. METHODS: Forty-seven adult intracranial ependymomas were enrolled and underwent histogram parameters extraction (including minimum, maximum, mean, 1st percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, Perc.99, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, and entropy of T1C and ADC) using FireVoxel software. Differences in histogram parameters between grade 2 and grade 3 adult intracranial ependymomas were compared. Receiver operating characteristic curves and logistic regression analyses were conducted to evaluate the diagnostic performance. Spearman's correlation analysis was used to evaluate the relationship between histogram parameters and Ki-67 proliferation index. RESULTS: Grade 3 intracranial ependymomas group showed significantly higher Perc.95, Perc.99, SD, variance, CV, and entropy of T1C; lower minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50 of ADC; and higher CV and entropy of ADC than grade 2 intracranial ependymomas group (all p < 0.05). Entropy (T1C) and Perc.10 (ADC) had a higher diagnostic performance with AUCs of 0.805 and 0.827 among the histogram parameters of T1C and ADC, respectively. The diagnostic performance was improved by combining entropy (T1C) and Perc.10 (ADC), with an AUC of 0.857. Significant correlations were observed between significant histogram parameters of T1C (r = 0.296-0.417, p = 0.001-0.044) and ADC (r = -0.428-0.395, p = 0.003-0.038). CONCLUSION: Whole-tumor histogram analysis of T1C and ADC may be a promising approach for predicting the grade and proliferative activity of adult intracranial ependymomas.

The role of apparent diffusion coefficient in the grading of adult isocitrate dehydrogenase-mutant astrocytomas: relationship with the Ki-67 proliferation index.

BACKGROUND: The grading of adult isocitrate dehydrogenase (IDH)-mutant astrocytomas is a crucial prognostic factor. PURPOSE: To investigate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) in the grading of adult IDH-mutant astrocytomas, and to analyze the correlation between ADC and the Ki-67 proliferation index. MATERIAL AND METHODS: The clinical and MRI data of 82 patients with adult IDH-mutant astrocytoma who underwent surgical resection and molecular genetic testing with IDH and 1p/19q were retrospectively analyzed. The conventional MRI features, ADCmin, ADCmean, and nADC of the tumors were compared using the Kruskal-Wallis single factor ANOVA and chi-square tests. Receiver operating characteristic (ROC) curves were drawn to evaluate conventional MRI and ADC accuracy in differentiating tumor grades. Pearson correlation analysis was performed to determine the correlation between ADC and the Ki-67 proliferation index. RESULTS: The difference in enhancement, ADCmin, ADCmean, and nADC among WHO grade 2, 3, and 4 tumors was statistically significant (all P <0.05). ADCmin showed the preferable diagnostic accuracy for grading WHO grade 2 and 3 tumors (AUC=0.724, sensitivity=63.4%, specificity=80%, positive predictive value (PPV)=62.0%; negative predictive value (NPV)=82.5%), and distinguishing grade 3 from grade 4 tumors (AUC=0.764, sensitivity=70%, specificity=76.2%, PPV=75.0%, NPV=71.4%). Enhancement + ADC model showed an optimal predictive accuracy (grade 2 vs. 3: AUC = 0.759; grade 3 vs. 4: AUC = 0.799). The Ki-67 proliferation index was negatively correlated with ADCmin, ADCmean, and nADC (all P <0.05), and positively correlated with tumor grade. CONCLUSION: Conventional MRI features and ADC are valuable to predict pathological grading of adult IDH-mutant astrocytomas.

Prediction of O(6)-methylguanine-DNA methyltransferase promoter methylation status in IDH-wildtype glioblastoma using MRI histogram analysis.

To evaluate the utility of magnetic resonance imaging (MRI) histogram parameters in predicting O(6)-methylguanine-DNA methyltransferase promoter (pMGMT) methylation status in IDH-wildtype glioblastoma (GBM). From November 2021 to July 2023, forty-six IDH-wildtype GBM patients with known pMGMT methylation status (25 unmethylated and 21 methylated) were enrolled in this retrospective study. Conventional MRI signs (including location, across the midline, margin, necrosis/cystic changes, hemorrhage, and enhancement pattern) were assessed and recorded. Histogram parameters were extracted and calculated by Firevoxel software based on contrast-enhanced T1-weighted images (CET1). Differences and diagnostic performance of conventional MRI signs and histogram parameters between the pMGMT-unmethylated and pMGMT-methylated groups were analyzed and compared. No differences were observed in the conventional MRI signs between pMGMT-unmethylated and pMGMT-methylated groups (all p > 0.05). Compared with the pMGMT-methylated group, pMGMT-unmethylated showed a higher minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50, and coefficient of variation (CV) (all p < 0.05). Among all significant CET1 histogram parameters, minimum achieved the best distinguishing performance, with an area under the curve of 0.836. CET1 histogram parameters could provide additional value in predicting pMGMT methylation status in patients with IDH-wildtype GBM, with minimum being the most promising parameter.

Development of an MRI-Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor.

BACKGROUND: Accurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear. PURPOSE: To investigate the feasibility of a CRDL model based on magnetic resonance imaging (MRI) in preoperative HS in ISFT. STUDY TYPE: Retrospective. POPULATION: Three hundred and ninety-eight patients from Beijing Tiantan Hospital, Capital Medical University (primary training cohort) and 49 patients from Lanzhou University Second Hospital (external validation cohort) with ISFT based on histopathological findings (237 World Health Organization [WHO] tumor grade 1 or 2, and 210 WHO tumor grade 3). FIELD STRENGTH/SEQUENCE: 3.0 T/T1-weighted imaging (T1) by using spin echo sequence, T2-weighted imaging (T2) by using fast spin echo sequence, and T1-weighted contrast-enhanced imaging (T1C) by using two-dimensional fast spin echo sequence. ASSESSMENT: Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the CRDL model and a clinical model (CM) in preoperative HS in the external validation cohort. The decision curve analysis (DCA) was used to evaluate the clinical net benefit provided by the CRDL model. STATISTICAL TESTS: Cohen's kappa, intra-/inter-class correlation coefficients (ICCs), Chi-square test, Fisher's exact test, Student's t-test, AUC, DCA, calibration curves, DeLong test. A P value <0.05 was considered statistically significant. RESULTS: The CRDL model had significantly better discrimination ability than the CM (AUC [95% confidence interval, CI]: 0.895 [0.807-0.912] vs. 0.810 [0.745-0.874], respectively) in the external validation cohort. The CRDL model can provide a clinical net benefit for preoperative HS at a threshold probability >20%. DATA CONCLUSION: The proposed CRDL model holds promise for preoperative HS in ISFT, which is important for predicting patient outcomes and developing personalized treatment plans. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Postoperative progression of intracranial grade II-III solitary fibrous tumor/hemangiopericytoma: predictive value of preoperative magnetic resonance imaging semantic features.

BACKGROUND: Preoperative prediction of postoperative tumor progression of intracranial grade II-III hemangiopericytoma is the basis for clinical treatment decisions. PURPOSE: To use preoperative magnetic resonance imaging (MRI) semantic features for predicting postoperative tumor progression in patients with intracranial grade II-III solitary fibrous tumor/hemangiopericytoma (SFT/HPC). MATERIAL AND METHODS: We retrospectively analyzed the preoperative MRI data of 42 patients with intracranial grade II-III SFT/HPC, as confirmed by surgical resection and pathology in our hospital from October 2010 to October 2017, who were followed up for evaluation of recurrence, metastasis, or death. We applied strict inclusion and exclusion criteria and finally included 37 patients. The follow-up time was in the range of 8-120 months (mean = 57.1 months). RESULTS: Single-factor survival analysis revealed that tumor grade (log-rank, P = 0.024), broad-based tumor attachment to the dura mater (log-rank, P = 0.009), a blurred tumor-brain interface (log-rank, P = 0.008), skull invasion (log-rank, P = 0.002), and the absence of postoperative radiotherapy (log-rank, P = 0.006) predicted postoperative intracranial SFT/HPC progression. Multivariate survival analysis revealed that tumor grade (P = 0.009; hazard ratio [HR] = 11.42; 95% confidence interval [CI] = 1.832-71.150), skull invasion (P = 0.014; HR = 5.72; 95% CI = 1.421-22.984), and the absence of postoperative radiotherapy (P = 0.001; HR = 0.05; 95% CI = 0.008-0.315) were independent predictors of postoperative intracranial SFT/HPC progression. CONCLUSION: Broad-based tumor attachment to the dura mater, skull invasion, and blurring of the tumor-brain interface can predict postoperative intracranial SFT/HPC progression.

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.

Apparent diffusion coefficient values effectively predict cell proliferation and determine oligodendroglioma grade.

This study aims to evaluate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) values in differentiating oligodendroglioma of various grades and explore the correlation between ADC and Ki-67. The preoperative MRI data of 99 patients with World Health Organization (WHO) grades 2 (n = 42) and 3 (n = 57) oligodendroglioma confirmed by surgery and pathology were retrospectively analyzed. Conventional MRI features, ADCmean, ADCmin, and normalized ADC (nADC) were compared between the two groups. A receiver operating characteristic curve was used to evaluate each parameter's diagnostic efficacy in differentiating the two tumor types. Each tumor's Ki-67 proliferation index was also measured to explore its relationship with the ADC value. Compared with WHO2 grade tumors, WHO3 grade tumors had a larger maximum diameter and more significant cystic degeneration/necrosis, edema, and moderate/severe enhancement (all P < 0.05). The ADCmin, ADCmean, and nADC values of the WHO3 and WHO2 grade tumors were significantly different, and the ADCmin value most accurately distinguished the two tumor types, yielding an area under the curve value of 0.980. When 0.96 × 10-3 mm2/s was used as the differential diagnosis threshold, the sensitivity, specificity, and accuracy of the two groups were 100%, 93.00%, and 96.96%, respectively. The ADCmin (r = -0.596), ADCmean (r = - 0.590), nADC (r = - 0.577), and Ki-67 proliferation index values had significantly negative correlations (all P < 0.05). Conventional MRI features and ADC values are beneficial in the noninvasive prediction of the WHO grade and tumor proliferation rate of oligodendroglioma.

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.

Finite element analysis of the mechanical strength of a new hip Spacer.

BACKGROUND AND OBJECTIVE: At present, the influence of the internal metallic endoskeleton of Spacer on the biomechanical strength of Spacer remains unclear. The aim of this study was to analyze the mechanical stability of a novel Spacer applying a annular skeleton that mimics the structure of trabecular bone using finite element methods. METHEDS: The femur models of three healthy individuals and skeletonless Spacer, K-Spacer, and AD-Spacer were assembled to create 15 3D models. Finite element analysis was performed in an Ansys Bench2022R1. Biomechanical parameters such as stress and strain of the Spacer, internal skeleton and femur were evaluated under three loads, which were applied with the maximum force borne by the hip joint (2100 N), standing on one leg (700 N), and standing on two legs (350 N). The mechanical properties of the new hip Spacer were evaluated. RESULT: The stresses on the medial and lateral surfaces of the AD-Spacer and K-Spacer were smaller than the stresses in the state without skeletal support. The maximum stresses on the medial and lateral surfaces of the AD-Spacer were smaller than those of the inserted K-Spacer, and the difference gradually increased with the increase of force intensity. When the skeleton diameter was increased from 3 to 4 mm, the stresses in the medial and lateral sides of the AD-Spacer and K-Spacer necks decreased. The stress of both skeletons was concentrated at the neck, but the stress of the annular skeleton was evenly distributed on the medial and lateral sides of the skeleton. The mean stress in the proximal femur was higher in femurs with K-Spacer than in femurs with AD-Spacer. CONCLUSIONS: AD-Spacer has lower stress and higher load-bearing capacity than K-Spacer, and the advantages of AD-Spacer are more obvious under the maximum load state of hip joint.

Preoperative surgical risk assessment of meningiomas: a narrative review based on MRI radiomics.

Meningiomas are one of the most common intracranial primary central nervous system tumors. Regardless of the pathological grading and histological subtypes, maximum safe resection is the recommended treatment option for meningiomas. However, considering tumor heterogeneity, surgical treatment options and prognosis often vary greatly among meningiomas. Therefore, an accurate preoperative surgical risk assessment of meningiomas is of great clinical importance as it helps develop surgical treatment strategies and improve patient prognosis. In recent years, an increasing number of studies have proved that magnetic resonance imaging (MRI) radiomics has wide application values in the diagnostic, identification, and prognostic evaluations of brain tumors. The vital importance of MRI radiomics in the surgical risk assessment of meningiomas must be apprehended and emphasized in clinical practice. This narrative review summarizes the current research status of MRI radiomics in the preoperative surgical risk assessment of meningiomas, focusing on the applications of MRI radiomics in preoperative pathological grading, assessment of surrounding tissue invasion, and evaluation of tumor consistency. We further analyze the prospects of MRI radiomics in the preoperative assessment of meningiomas angiogenesis and adhesion with surrounding tissues, while pointing out the current challenges of MRI radiomics research.

The relationship between the apparent diffusion coefficient and the Ki-67 proliferation index in intracranial solitary fibrous tumor/hemangiopericytoma.

This study evaluated the value of the apparent diffusion coefficient (ADC) in distinguishing grade II and III intracranial solitary fibrous tumors/hemangiopericytomas and explored the correlation between ADC and Ki-67. The preoperative MRIs of 37 patients treated for solitary fibrous tumor/hemangiopericytoma (grade II, n = 15 and grade III, n = 22) in our hospital from 2011 to October 2020 were retrospectively analyzed. We compared the difference between the minimum, average, maximum, and relative ADCs based on tumor grade and examined the correlation between ADC and Ki-67. Receiver operating characteristic curve analysis was used to analyze the diagnostic efficiency of the ADC. There were significant differences in the average, minimum, and relative ADCs between grade II and III patients. The optimal cutoff value for the relative ADC value to differentiate grade II and III tumors was 0.998, which yielded an area under the curve of 0.879. The Ki-67 proliferation indexes of grade II and III tumors were significantly different, and the average (r = - 0.427), minimum (r = - 0.356), and relative (r = - 0.529) ADCs were significantly negatively correlated with the Ki-67 proliferation index. ADC can be used to differentiate grade II and III intracranial solitary fibrous tumors/hemangiopericytomas. Our results can be used to formulate a personalized surgical treatment plan before surgery.

Differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from atypical meningioma using apparent diffusion coefficient histogram analysis.

This study aimed to investigate the value of apparent diffusion coefficient (ADC) histogram analysis in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from atypical meningioma (ATM). Retrospective analyzed the clinical, magnetic resonance imaging, and pathological data of 20 and 25 patients with SFT/HPC and ATM, respectively. Histogram analysis was performed on the axial ADC images using MaZda software, and nine histogram parameters were obtained, including mean, variance, skewness, kurtosis, and the 1st (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentile ADC. Differences in ADC histogram parameters between SFT/HPC and ATM were compared by an independent t test or Mann-Whitney U test, while the statistically significant histogram parameters were further analyzed by drawing receiver operating characteristic (ROC) curves to evaluate the differential diagnostic performance. Among the nine ADC histogram parameters we extracted, the mean, ADC1, ADC10, ADC50, and ADC90 in the SFT/HPC group were greater than those of ATM, and significant differences were observed (all P < 0.05). ROC analysis showed that the ADC1 generated the highest area under the curve (AUC) value of 0.920 in distinguishing the two tumors, when using 91.00 as the optimal threshold. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value in distinguishing between SFT/HPC and ATM were 84.00%, 85.00%, 84.44%, 87.50%, and 81.00%, respectively. ADC histogram analysis can be a reliable tool to differentiate between SFT/HPC and ATM, with the ADC1 being the most promising potential parameter.

Evaluation of the anti-angiogenic effect of bevacizumab on rat C6 glioma by spectral computed tomography.

BACKGROUND: Anti-angiogenic drugs have become a research hotspot in recent years. However, dynamically observing their therapeutic effect at different time points during treatment is a clinical problem. PURPOSE: To explore the feasibility of the quantitative parameters of spectral computed tomography (CT) in evaluating the anti-angiogenic effect of bevacizumab on rat C6 glioma. MATERIAL AND METHODS: Twenty-six male Sprague-Dawley rats were used to establish the C6 glioma model. The rats were randomly divided into the experimental group (n = 13) and control group (n = 13). The experimental group was intraperitoneally injected with 0.2 µL/g bevacizumab every day, whereas the control group was injected with the same dose of normal saline every day for one week. Spectral CT scanning was performed on the 4th and 8th days after treatment; meanwhile, the brain tissues were collected by heart perfusion for H&E staining, and VEGF and HIF-1α immunohistochemical staining. RESULTS: On the 4th and 8th days, significant differences in the 70-keV single-energy CT value, slope of the energy spectrum curve, and iodine concentration were found between the experimental group and the control group. Correlation analysis between immunohistochemistry and quantitative parameters of spectral CT showed that the single energy CT value of 70 keV, slope of the energy spectrum curve, and concentration of iodine were positively correlated with VEGF and HIF-1α at different time points in the experimental group and the control group. CONCLUSION: Spectral CT multi-parameter imaging can be employed as a new method to evaluate the anti-angiogenic effect of bevacizumab on rat C6 glioma.

Progression/Recurrence of Meningioma: An Imaging Review Based on Magnetic Resonance Imaging.

Meningiomas are the most common primary central nervous system tumors. The preferred treatment is maximum safe resection, and the heterogeneity of meningiomas results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma and is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and mortality. Early prediction of P/R plays an important role in postoperative management, further adjuvant therapy, and follow-up of patients. Therefore, it is essential to thoroughly analyze the heterogeneity of meningiomas and predict postoperative P/R with the aid of noninvasive preoperative imaging. In recent years, the development of advanced magnetic resonance imaging technology and machine learning has provided new insights into noninvasive preoperative prediction of meningioma P/R, which helps to achieve accurate prediction of meningioma P/R. This narrative review summarizes the current research on conventional magnetic resonance imaging, functional magnetic resonance imaging, and machine learning in predicting meningioma P/R. We further explore the significance of tumor microenvironment in meningioma P/R, linking imaging features with tumor microenvironment to comprehensively reveal tumor heterogeneity and provide new ideas for future research.

Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements.

PURPOSE: To evaluate and compare the diagnostic performances of whole-lesion iodine map (IM) histogram analysis and single-slice IM measurement in the risk classification of gastrointestinal stromal tumors (GISTs). METHODS: Thirty-seven patients with GISTs, including 19 with low malignant underlying GISTs (LG-GISTs) and 18 with high malignant underlying GISTs (HG-GISTs), were evaluated with dual-energy computed tomography (DECT). Whole-lesion IM histogram parameters (mean; median; minimum; maximum; standard deviation; variance; 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile; kurtosis, skewness, and entropy) were computed for each lesion. In other sessions, iodine concentrations (ICs) were derived from the IM by placing regions of interest (ROIs) on the tumor slices and normalizing them to the iodine concentration in the aorta. Both quantitative analyses were performed on the venous phase images. The diagnostic accuracies of the two methods were assessed and compared. RESULTS: The minimum, maximum, 1st, 10th, and 25th percentile of the whole-lesion IM histogram and the IC and normalized IC (NIC) of the single-slice IC measurement significantly differed between LG- and HG-GISTs (p < 0.001 - p = 0.042). The minimum value in the histogram analysis (AUC = 0.844) and the NIC in the single-slice measurement analysis (AUC = 0.886) showed the best diagnostic performances. The NIC of single-slice measurements had a diagnostic performance similar to that of the whole-lesion IM histogram analysis (p = 0.618). CONCLUSIONS: Both whole-lesion IM histogram analysis and single-slice IC measurement can differentiate LG-GISTs and HG-GISTs with similar diagnostic performances.

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.

Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study.

PURPOSE: This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis. METHODS: Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models. RESULTS: The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897. CONCLUSION: nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.

Extracellular volume fraction can predict the treatment response and survival outcome of colorectal cancer liver metastases.

OBJECTIVE: To assess the prognostic value of pre- and post-therapeutic changes in extracellular volume (ECV) fraction of liver metastases (LMs) for treatment response (TR) and survival outcomes in colorectal cancer liver metastases (CRLM). METHODS: 186 LMs were confirmed by pathology or follow-up (Training: 130; Test: 56). We analyzed the changes in ECV fraction of LMs before and after 2 cycles of chemotherapy combined with bevacizumab. After 12 cycles, we evaluated the TR on LMs based on the RECIST v1.1. Relative changes in ECV fraction and Hounsfield Units (HU), defined as ΔECV and ΔHU, were associated with progression-free survival (PFS), overall survival (OS), and TR. We identified TR predictors with multivariate logistic regression and PFS, OS risk factors with COX analysis. RESULTS: 186 LMs were classified as TR lesions (TR+: 84) and non-TR lesions (TR-:102). ΔECV, ΔHUA-E, and texture could distinguish the TR of LMs in training and test set (P < 0.05). ΔECV [Odds ratio (OR): 1.03; 95% Confidence interval (CI): 1.02-1.05, P < 0.01] was an independent predictor of TR-. Area under the curve (AUC), sensitivity and specificity of TR model in training and test set were 0.87, 0.84, 90.14%, 90.32%, 72.88%, 64.00%, respectively. High CRD_score indicates that patients have shorter PFS [Hazard ratio (HR): 2.01; 95%CI: 1.02-3.98, P = 0.045)] and OS (HR: 1.89, 95%CI: 1.04-3.42, P = 0.038). CONCLUSION: ΔECV can be used as an independent predictor of TR of CRLM chemotherapy combined with bevacizumab.

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.