Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.

Purpose: To develop and validate a radiomic nomogram based on texture features from out-of-phase T1W images and clinical biomarkers in prediction of liver fibrosis. Materials and Methods: Patients clinically diagnosed with chronic liver fibrosis who underwent liver biopsy and noncontrast MRI were enrolled. All patients were assigned to the nonsignificant fibrosis group with fibrosis stage <2 and the significant fibrosis group with stage ≥2. Texture parameters were extracted from out-of-phase T1-weighted (T1W) images and calculated using the Artificial Intelligent Kit (AK). Boruta and LASSO regressions were used for feature selection and a multivariable logistic regression was used for construction of a combinational model integrating radiomics and clinical biomarkers. The performance of the models was assessed by using the receiver operator curve (ROC) and decision curve. Results: ROC analysis of the radiomics model that included the most discriminative features showed AUCs of the training and test groups were 0.80 and 0.78. A combinational model integrating RADscore and fibrosis 4 index was established. ROC analysis of the training and test groups showed good to excellent performance with AUC of 0.93 and 0.86. Decision curves showed the combinational model added more net benefit than radiomic and clinical models alone. Conclusions: The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.

Predicting coronary artery calcified plaques using perivascular fat CT radiomics features and clinical risk factors.

OBJECTIVE: The purpose of this study was to develop a combined radiomics model to predict coronary plaque texture using perivascular fat CT radiomics features combined with clinical risk factors. METHODS: The data of 200 patients with coronary plaques were retrospectively analyzed and randomly divided into a training group and a validation group at a ratio of 7:3. In the training group, The best feature set was selected by using the maximum correlation minimum redundancy method and the least absolute shrinkage and selection operator. Radiomics models were built based on different machine learning algorithms. The clinical risk factors were then screened using univariate logistic regression analysis. and finally a combined radiomics model was developed using multivariate logistic regression analysis to combine the best performing radiomics model with clinical risk factors and validated in the validation group. The efficacy of the model was assessed by a receiver operating characteristic curve, the consistency of the nomogram was assessed using calibration curves, and the clinical usefulness of the nomogram was assessed using decision curve analysis. RESULTS: Twelve radiomics features were used by different machine learning algorithms to construct the radiomics model. Finally, the random forest algorithm built the best radiomics model in terms of efficacy, and this was combined with age to construct a combined radiomics model. The area under curve for the training and validation group were 0.98 (95% confidence interval, 0.95-1.00) and 0.97 (95% confidence interval, 0.92-1.00) with sensitivities of 0.92 and 0.86 and specificities of 0.99 and 1, respectively. The calibration curve demonstrated that the nomogram had good consistency, and the decision curve analysis demonstrated that the nomogram had high clinical utility. CONCLUSIONS: The combined radiomics model established based on CT radiomics features and clinical risk factors has high value in predicting coronary artery calcified plaque and can provide a reference for clinical decision-making.

The Value of MRI Findings Combined With Texture Analysis in the Differential Diagnosis of Primary Ovarian Granulosa Cell Tumors and Ovarian Thecoma-Fibrothecoma.

OBJECTIVE: This study aims to explore the value of magnetic resonance imaging (MRI) and texture analysis (TA) in the differential diagnosis of ovarian granulosa cell tumors (OGCTs) and thecoma-fibrothecoma (OTCA-FTCA). METHODS: The preoperative MRI data of 32 patients with OTCA-FTCA and 14 patients with OGCTs, confirmed by pathological examination between June 2013 and August 2020, were retrospectively analyzed. The texture data of three-dimensional MRI scans based on T2-weighted imaging and clinical and conventional MRI features were analyzed and compared between tumor types. The Mann-Whitney U-test, χ 2 test/Fisher exact test, and multivariate logistic regression analysis were used to identify differences between the OTCA-FTCA and OGCTs groups. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic curve analysis was carried out to evaluate diagnostic efficiency. RESULTS: A multivariate analysis of the imaging-based features combined with TA revealed that intratumoral hemorrhage (OR = 0.037), log-sigma-20mm-3D_glszm_SmallAreaEmphasis (OR = 4.40), and log-sigma-2-0mm-3D_glszm_SmallAreaHighGrayLevelEmphasis (OR = 1.034) were independent features for discriminating between OGCTs and OTCA-FTCA (P < 0.05). An imaging-based diagnosis model, TA-based model, and combination model were established. The areas under the curve of the three models in predicting OGCTs and OTCA-FTCA were 0.935, 0.944, and 0.969, respectively; the sensitivities were 93.75, 93.75, and 96.87%, respectively; and the specificities were 85.71, 92.86, and 92.86%, respectively. The DeLong test indicated that the combination model had the highest predictive efficiency (P < 0.05), with no significant difference among the three models in differentiating between OGCTs and OTCA-FTCA (P > 0.05). CONCLUSIONS: Compared with OTCA-FTCA, intratumoral hemorrhage may be characteristic MR imaging features with OGCTs. Texture features can reflect the microheterogeneity of OGCTs and OTCA-FTCA. MRI signs and texture features can help differentiate between OGCTs and OTCA-FTCA and provide a more comprehensive and accurate basis for clinical treatment.

Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer.

PURPOSE: To evaluate the predictive value of routine CT features combined with 3D texture analysis for prediction of BRCA gene mutation status in advanced epithelial ovarian cancer. METHOD: Retrospective analysis was performed on patients with masses occupying the pelvic space confirmed by pathology and complete preoperative images in our hospital, including 37 and 58 cases with mutant type and wild type BRCA, respectively (total: 95 cases). The enrolled patients' routine CT features were analyzed by two radiologists. Then, ROIs were jointly determined through negotiation, and the ITK-SNAP software package was used for 3D outlining of the third-stage images of the primary tumor lesions and obtaining texture features. For routine CT features and texture features, Mann-Whitney U tests, single-factor logistic regression analysis, minimum redundancy, and maximum correlation were used for feature screening, and the performance of individual features was evaluated by ROC curves. Multivariate logistic regression analysis was used to further screen features, find independent predictors, and establish the prediction model. The established model's diagnostic efficiency was evaluated by ROC curve analysis, and the histogram was obtained to conduct visual analysis of the prediction model. RESULTS: Among the routine CT features, the type of peritoneal metastasis, mesenteric involvement, and supradiaphragmatic lymph node enlargement were correlated with BRCA gene mutation (P < 0.05), whereas the location of the peritoneal metastasis (in the gastrohepatic ligament) was not significantly correlated with BRCA gene mutation (P > 0.05). Multivariate logistic regression analysis retained six features, including one routine CT feature and five texture features. Among them, the type of peritoneal metastasis was used as an independent predictor (P < 0.05), which had the highest diagnostic efficiency. Its AUC, accuracy, specificity, and sensitivity were 0.74, 0.79, 0.90, and 0.62, respectively. The prediction model based on the combination of routine CT features and texture features had an AUC of 0.86 (95% CI: 0.79-0.94) and accuracy, specificity, and sensitivity of 0.80, 0.76, and 0.81, respectively, indicating a better performance than that of any single feature. CONCLUSIONS: Both routine CT features and texture features had value for predicting the mutation state of the BRCA gene, but their predictive efficiency was low. When the two types of features were combined to establish a predictive model, the model's predictive efficiency was significantly higher than that of independent features.

Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Analysis of Microvascular Permeability in Peritumor Brain Edema of Fibrous Meningiomas.

INTRODUCTION: This study aims to analyze the permeability of intra- and peri-meningiomas regions and compare the microvascular permeability between peritumoral brain edema (PTBE) and non-PTBE using DCE-MRI. METHODS: This was a retrospective of patients with meningioma who underwent surgery. The patients were grouped as PTBE and non-PTBE. The DCE-MRI quantitative parameters, including volume transfer constant (Ktrans), rate constant (Kep), extracellular volume (Ve), and mean plasma volume (Vp), obtained using the extended Tofts-Kety 2-compartment model. Logistic regression analysis was conducted to explore the risk factor of PTBE. RESULTS: Sixty-three patients, diagnosed as fibrous meningioma, were included in this study. They were 17 males and 46 females, aged from 32 to 88 years old. Kep and Vp were significantly lower in patients with PTBE compared with those without (Kep: 0.1852 ± 0.0369 vs. 0.5087 ± 0.1590, p = 0.010; Vp: 0.0090 ± 0.0020 vs. 0.0521 ± 0.0262, p = 0.007), while there were no differences regarding Ktrans and Ve (both p > 0.05). The multivariable analysis showed that tumor size ≥10 cm3 (OR = 4.457, 95% CI: 1.322-15.031, p = 0.016) and Vp (OR = 0.572, 95%CI: 0.333-0.981, p = 0.044) were independently associated with PTBE in patients with meningiomas. CONCLUSION: DCE-magnetic resonance imaging·Meningioma·Blood vessel MRI can be used to quantify the microvascular permeability of PTBE in patients with meningioma.

High resolution MRI-based radiomic nomogram in predicting perineural invasion in rectal cancer.

BACKGROUND: To establish and validate a high-resolution magnetic resonance imaging (HRMRI)-based radiomic nomogram for prediction of preoperative perineural invasion (PNI) of rectal cancer (RC). METHODS: Our retrospective study included 140 subjects with RC (99 in the training cohort and 41 in the validation cohort) who underwent a preoperative HRMRI scan between December 2016 and December 2019. All subjects underwent radical surgery, and then PNI status was evaluated by a qualified pathologist. A total of 396 radiomic features were extracted from oblique axial T2 weighted images, and optimal features were selected to construct a radiomic signature. A combined nomogram was established by incorporating the radiomic signature, HRMRI findings, and clinical risk factors selected by using multivariable logistic regression. RESULTS: The predictive nomogram of PNI included a radiomic signature, and MRI-reported tumor stage (mT-stage). Clinical risk factors failed to increase the predictive value. Favorable discrimination was achieved between PNI-positive and PNI-negative groups using the radiomic nomogram. The area under the curve (AUC) was 0.81 (95% confidence interval [CI], 0.71-0.91) in the training cohort and 0.75 (95% CI, 0.58-0.92) in the validation cohort. Moreover, our result highlighted that the radiomic nomogram was clinically beneficial, as evidenced by a decision curve analysis. CONCLUSIONS: HRMRI-based radiomic nomogram could be helpful in the prediction of preoperative PNI in RC patients.

Radiomics Nomograms Based on Multi-Parametric MRI for Preoperative Differential Diagnosis of Malignant and Benign Sinonasal Tumors: A Two-Centre Study.

OBJECTIVES: To investigate the efficacy of multi-parametric MRI-based radiomics nomograms for preoperative distinction between benign and malignant sinonasal tumors. METHODS: Data of 244 patients with sinonasal tumor (training set, n=192; test set, n=52) who had undergone pre-contrast MRI, and 101 patients who underwent post-contrast MRI (training set, n=74; test set, n=27) were retrospectively analyzed. Independent predictors of malignancy were identified and their performance were evaluated. Seven radiomics signatures (RSs) using maximum relevance minimum redundancy (mRMR), and the least absolute shrinkage selection operator (LASSO) algorithm were established. The radiomics nomograms, comprising the clinical model and the RS algorithms were built: one based on pre-contrast MRI (RNWOC); the other based on pre-contrast and post-contrast MRI (RNWC). The performances of the models were evaluated with area under the curve (AUC), calibration, and decision curve analysis (DCA) respectively. RESULTS: The efficacy of the clinical model (AUC=0.81) of RNWC was higher than that of the model (AUC=0.76) of RNWOC in the test set. There was no significant difference in the AUC of radiomic algorithms in the test set. The RS-T1T2 (AUC=0.74) and RS-T1T2T1C (RSWC, AUC=0.81) achieved a good distinction efficacy in the test set. The RNWC and the RNWOC showed excellent distinction (AUC=0.89 and 0.82 respectively) in the test set. The DCA of the nomograms showed better clinical usefulness than the clinical models and radiomics signatures. CONCLUSIONS: The radiomics nomograms combining the clinical model and RS can be accurately, safely and efficiently used to distinguish between benign and malignant sinonasal tumors.

Use of Texture Analysis on Noncontrast MRI in Classification of Early Stage of Liver Fibrosis.

Purpose. To compare the diagnostic value of texture analysis- (TA-) derived parameters from out-of-phase T1W, in-phase T1W, and T2W images in the classification of the early stage of liver fibrosis. Methods. Patients clinically diagnosed with hepatitis B infection, who underwent liver biopsy and noncontrast MRI scans, were enrolled. TA parameters were extracted from out-of-phase T1-weighted (T1W), in-phase T1W, and T2-weighted (T2W) images and calculated using Artificial Intelligent Kit (AK). Features were extracted including first-order, shape, gray-level cooccurrence matrix, gray-level run-length matrix, neighboring gray one tone difference matrix, and gray-level differential matrix. After statistical analyses, final diagnostic models were constructed. Receiver operating curves (ROCs) and areas under the ROC (AUCs) were used to assess the diagnostic value of each final model and 100-time repeated cross-validation was applied to assess the stability of the logistic regression models. Results. A total of 57 patients were enrolled in this study, with 27 in the fibrosis stage < 2 and 30 in stages ≥ 2. Overall, 851 features were extracted per ROI. Eight features with high correlation were selected by the maximum relevance method in each sequence, and all had a good diagnostic performance. ROC analysis of the final models showed that all sequences had a preferable performance with AUCs of 0.87, 0.90, and 0.96 in T2W and in-phase and out-of-phase T1W, respectively. Cross-validation results reported the following values of mean accuracy, specificity, and sensitivity: 0.98 each for out-of-phase T1W; 0.90, 0.89, and 0.90 for in-phase T1W; and 0.86, 0.88, 0.84 for T2W in the training set, and 0.76, 0.81, and 0.72 for out-of-phase T1W; 0.74, 0.72, and 0.75 for in-phase T1W; and 0.63, 0.64, and 0.63 for T2W for the test group, respectively. Conclusion. Noncontrast MRI scans with texture analysis are viable for classifying the early stages of liver fibrosis, exhibiting excellent diagnostic performance.

High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer.

PURPOSE: To establish and validate two predictive radiomics models for preoperative prediction of lymph node metastases (LNMs) and tumor deposits (TDs) respectively in rectal cancer (RC) patients. METHODS: A total of 139 RC patients (98 in the training cohort and 41 in the validation cohort) were enrolled in the present study. High-resolution magnetic resonance images (HRMRI) were retrieved for tumor segmentation and feature extraction. HRMRI findings of RC were assessed by three experienced radiologists. Two radiomics nomograms were established by integrating the clinical risk factors, HRMRI findings and radiomics signature. RESULTS: The predictive nomogram of LNMs showed good predictive performance (area under the curve [AUC], 0.90; 95% confidence interval [CI] 0.83-0.96) which was better than clinico-radiological (AUC, 0.83; 95% CI 0.74-0.93; Delong test, p = 0.017) or radiomics signature-only model (AUC, 0.77; 95% CI 0.67-0.86; Delong test, p = 0.003) in training cohort. Application of the nomogram in the validation cohort still exhibited good performance (AUC, 0.87; 95% CI 0.76-0.98). The accuracy, sensitivity and specificity of the combined model in predicting LNMs was 0.86,0.79 and 0.91 in training cohort and 0.83,0.85 and 0.82 in validation cohort. As for TDs, the predictive efficacy of the nomogram (AUC, 0.82; 95% CI 0.71-0.93) was not significantly higher than radiomics signature-only model (AUC, 0.80; 95% CI 0.69-0.92; Delong test, p = 0.71). Radiomics signature-only model was adopted to predict TDs with accuracy=0.76, sensitivity=0.72 and specificity=0.94 in training cohort and 0.68, 0.62 and 0.97 in validation cohort. CONCLUSION: HRMRI-based radiomics models could be helpful for the prediction of LNMs and TDs preoperatively in RC patients.

Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics.

OBJECTIVE: To explore the value of CT texture analysis (CTTA) for determining coronavirus disease 2019 (COVID-19) severity. METHODS: The clinical and CT data of 81 patients with COVID-19 were retrospectively analyzed. The texture features were extracted using LK2.1. The two-sample t test or Mann-Whitney U test was used to find the significant features. Minimum redundancy and maximum relevance (MRMR) method was performed to find the features with maximum correlation and minimum redundancy. These features were then used to construct a radiomics texture model to discriminate the severe patients using multivariate logistic regression method. Besides, a clinical model was also built. ROC analyses were conducted to evaluate the performance of two models. The correlations of clinical features and textural features were analyzed using the Spearman correlation analysis. RESULTS: Of the total cases included, 60 were common and 21 were severe. (1) For textural features, 20 radiomics features selected by MRMR showed good performance in discriminating the two groups (AUC > 70%). (2) For clinical features, chi-square tests or Mann-Whitney U tests identified 16 clinical features as significant, and 12 were discriminative (p < 0.05) between two groups analyzed by univariate logistic analysis. Of these, 10 had an AUC > 70%. (3) Prediction models for textural features and clinical features were established, and both showed high predictive accuracy. The AUC values of textural features and clinical features were 0.93 (0.86-1.00) and 0.95 (0.95-0.99), respectively. (4) The Spearman correlation analysis showed that most textural and clinical features had above-moderate correlations with disease severity (> 0.4). CONCLUSION: Texture analysis can provide reliable and objective information for differential diagnosis of COVID-19. KEY POINTS: • CT texture analysis can well differentiate common and severe COVID-19 patients. • Some textural features showed above-moderate correlations with clinical factors. • CT texture analysis can provide useful information to judge the severity of COVID-19.