Preliminary study on CT contrast-enhanced radiomics for predicting central cervical lymph node status in patients with thyroid nodules.

Objective: To explore the feasibility of using a contrast-enhanced CT image-based radiomics model to predict central cervical lymph node status in patients with thyroid nodules. Methods: Pretreatment clinical and CT imaging data from 271 patients with surgically diagnosed and treated thyroid nodules were retrospectively analyzed. According to the pathological features of the thyroid nodules and central lymph nodes, the patients were divided into three groups: group 1: papillary thyroid carcinoma (PTC) metastatic lymph node group; group 2: PTC nonmetastatic lymph node group; and group 3: benign thyroid nodule reactive lymph node group. Radiomics models were constructed to compare the three groups by pairwise classification (model 1: group 1 vs group 3; model 2: group 1 vs group 2; model 3: group 2 vs group 3; and model 4: group 1 vs groups (2 + 3)). The feature parameters with good generalizability and clinical risk factors were screened. A nomogram was constructed by combining the radiomics features and clinical risk factors. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were performed to assess the diagnostic and clinical value of the nomogram. Results: For radiomics models 1, 2, and 3, the areas under the curve (AUCs) in the training group were 0.97, 0.96, and 0.93, respectively. The following independent clinical risk factors were identified: model 1, arterial phase CT values; model 2, sex and arterial phase CT values; model 3: none. The AUCs for the nomograms of models 1 and 2 in the training group were 0.98 and 0.97, respectively, and those in the test group were 0.95 and 0.87, respectively. The AUCs of the model 4 nomogram in the training and test groups were 0.96 and 0.94, respectively. Calibration curve analysis and DCA revealed the high clinical value of the nomograms of models 1, 2 and 4. Conclusion: The nomograms based on contrast-enhanced CT images had good predictive efficacy in classifying benign and malignant central cervical lymph nodes of thyroid nodule patients.

Predictive value of Gd-EOB-DTPA -enhanced magnetic resonance imaging for post-hepatectomy liver failure: a systematic review and meta-analysis.

BACKGROUND: Accurate preoperative diagnosis of post-hepatectomy liver failure (PHLF) is particularly important to improve the prognosis of patients. PURPOSE: To evaluate the predictive value of Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) for post-hepatectomy liver failure. MATERIAL AND METHODS: A systematic search was performed in the PubMed, Embase, the Cochrane Library, and Web of Science databases to find relevant original articles published up to December 2021. The included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The bivariate random-effects model was used to assess the diagnostic authenticity. Meta-regression analyses were performed to analyze the potential heterogeneity. RESULTS: In total, 13 articles were included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the area under the summary receiver operating characteristic curves were 88% (95% confidence interval [CI] = 0.80-0.94), 80% (95% CI = 0.73-0.86), 4.4 (95% CI = 3.3-5.9), 0.14 (95% CI = 0.08-0.25), 31 (95% CI = 17-57), and 0.91 (95% CI = 0.89-0.94), respectively. There was no publication bias and threshold effect in our study. CONCLUSION: Gd-EOB-DTPA-enhanced MRI is a potentially useful for the prediction of PHLF after major hepatectomy.

Qualitative (and Quantitative) Values of the Lung-RADS and Computed Tomography in Diagnosing Solitary Pulmonary Nodules.

Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.

Evaluation of Radiomics Models Based on Computed Tomography for Distinguishing Between Benign and Malignant Thyroid Nodules.

AIM: The aim of the study was to investigate the diagnostic value of radiomics models based on computed tomography (CT) in distinguishing between benign and malignant thyroid nodules. MATERIALS AND METHODS: We conducted a retrospective analysis of the clinical and imaging data of 172 patients with pathology-confirmed thyroid nodules (83 benign nodules and 89 malignant nodules). All patients underwent a plain CT scan + arterial and venous contrast enhancement before the operation. Using the stratified random sampling method, patients were divided into a training group (121 cases) and a test group (51 cases) at a ratio of 7:3. A.K. software was used to extract radiomics features from the preoperative CT images, and minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression analyses were then used for feature screening and model construction. Receiver operating characteristic (ROC) curves were constructed for the training and test groups to verify model performance and evaluate the efficacy of the radiomics features in identifying benign and malignant thyroid nodules. We then used the most efficient models to construct a nomogram. For the training group, 1-way analysis of variance and multivariate logistic regression analysis were used to screen statistically significant clinical features, and the radiomics scores were combined to construct a radiomics nomogram. We used ROC curve analysis to evaluate the predictive performance of the model. RESULTS: Screening yielded 21 radiomics features that were used to construct a model for differentiating between benign and malignant thyroid nodules. For the training group, the area under the ROC curve of the prediction models for the noncontrast, arterial phase, and venous phase scans were 0.86 (95% confidence interval [CI], 0.79-0.92), 0.89 (95% CI, 0.83-0.95), and 0.88 (95% CI, 0.82-0.94), respectively, and the corresponding diagnostic accuracy was 0.78, 0.84, and 0.83. For the test group, the corresponding 3-phase under the ROC curves for the test group were 0.76 (95% CI, 0.63-0.90), 0.78 (95% CI, 0.65-0.91), and 0.76 (95% CI, 0.62-0.90), and the corresponding accuracy was 0.63, 0.77, and 0.75. Thus, the arterial phase model exhibited the best diagnostic performance. The multivariate logistic regression results showed that morphology regularity and the cystic degeneration ratio were independent clinical risk factors for predicting benign and malignant thyroid nodules. The arterial phase radiomics score and clinically independent factors were then used to construct a nomogram. The nomogram had good discriminability for the training group (0.93; 95% CI, 0.88-0.98) and the test group (0.84; 95% CI, 0.73-0.95), achieving significantly higher accuracies than the radiomics score and clinical characteristics alone. CONCLUSIONS: The radiomics nomogram constructed by combining radiomics characteristics and clinical risk factors was efficacious for distinguishing benign and malignant thyroid nodules.

Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics.

OBJECTIVE: To investigate the relationship between CT radiomic features, pathological classification of pulmonary nodules, and evaluate the prediction effect of different stratified progressive radiomic models on the pathological classification of pulmonary nodules. METHODS: Altogether, 189 patients pathologically confirmed with pulmonary nodules from July 2017 to August 2019 who had complete data were enrolled, including 71 patients with benign nodules, 51 with malignant non-invasive nodules, and 67 with invasive nodules. Three CT radiomic models were established respectively. Model 1 classified benign and malignant nodules (including malignant non-invasive and invasive nodules). Model 2 classified malignant non-invasive and invasive nodules. Model 3 classified benign, malignant non-invasive, and invasive nodules. High-throughput feature collection was carried out for all delineated regions of interest (ROIs), and the best models were established by screening features and classifiers using intelligent methods. ROC curves and areas under the curve (AUCs) were used to evaluate the prediction efficacy of the models by calculating the sensitivity, specificity, accuracies, positive predictive values, and negative predictive values. RESULTS: Through Models 1, 2, and 3, we screened out 20, 2, and 20 radiomic features, respectively, and plotted the ROC curves. In the test group, the AUC values were 0.85, 0.89, and 0.84, respectively; the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 79.66 %, 70.42 %, 84.59 %, and 81.74 % and 67.57％ for Model 1, 88.06 %, 74.51 %, 82.2 %, 81.94 %, and 82.61 % for Model 2, and 71.34 %, 85.05 %, 70.37 %, 83.2 %, and 76.3 % for Model 3. CONCLUSION: The radiomic feature models based on CT images could well reflect the differences between benign nodules, malignant non-invasive nodules, and invasive nodules, and assist in their classification.

Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for malignant ovarian tumors: a systematic review and meta-analysis.

BACKGROUND: Accurate preoperative diagnosis of malignant ovarian tumors (MOTs) is particularly important for selecting the optimal treatment strategy and avoiding overtreatment. PURPOSE: To evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for MOTs. MATERIAL AND METHODS: A systematic search was performed in PubMed, Embase, the Cochrane Library, and Web of Science databases to find relevant original articles up to October 2019. The included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Studies on the diagnosis of MOTs with quantitative or semi-quantitative DCE-MRI were analyzed separately. The bivariate random-effects model was used to assess the diagnostic authenticity. Meta-regression analyses were performed to analyze the potential heterogeneity. RESULTS: For semi-quantitative DCE-MRI, the pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic curves (AUC) were 85% (95% confidence interval [CI] 0.75-0.92), 85% (95% CI 0.77-0.91), 5.8 (95% CI 3.8-8.8), 0.17 (95% CI 0.10-0.30), 33 (95% CI 18-61), and 0.92 (95% CI 0.89-0.94), respectively. For quantitative DCE-MRI, the pooled sensitivity, specificity, positive LR, negative LR, DOR, and AUC were 88% (95% CI 0.65-0.96), 93% (95% CI 0.78-0.98), 12.3 (95% CI 3.4-43.9), 0.13 (95% CI 0.04-0.45), 91 (95% CI 10-857), and 0.96 (95% CI 0.94-0.98), respectively. CONCLUSION: DCE-MRI has great diagnostic value for MOTs. Semi-quantitative DCE-MRI may be a relatively mature approach; however, quantitative DCE-MRI appears to be more promising than semi-quantitative DCE-MRI.

Correlation analysis of apparent diffusion coefficient value and P53 and Ki-67 expression in esophageal squamous cell carcinoma.

PURPOSES: To investigate the relationship between apparent diffusion coefficient (ADC) value and p53 and ki-67 expression in esophageal squamous cell carcinoma (ESCC) patients. MATERIALS AND METHODS: Clinical, pathologic and MRI findings of 55 ESCC patients were retrospectively analyzed. Immunohistochemical assay was used to determine the expression level of p53 and ki-67 in esophageal carcinoma tissues. The correlations between the ADC value (including ADCmax, ADCmean and ADCmin) and p53 and ki-67 expression level were explored. RESULTS: Significant differences of the ADCmean values were found between positive and negative expression of p53 and between high and low expression of ki-67 in 55 patients of ESCC (P = 0.008, P = 0.036). Receiver operation characteristic (ROC) curve analysis showed that the cutoff value of ADCmean value with positive expression of p53 was 1.475 × 10-3 mm2/s, the area under the curve (AUC) was 0.775, and the sensitivity and specificity were 80.0%, 70.0%, respectively. While the cutoff value for the ADCmean value with high expression of ki-67 was 1.590 × 10-3 mm2/s, the AUC was 0.713, and the sensitivity and specificity were 66.7%, 76.5%, respectively. The ADCmean values were significantly negatively correlated with the expression level of p53 and ki-67 (r = -0.403, P = 0.008; r = -0.329, P = 0.036). CONCLUSION: The ADCmean values of ESCC were related with the expression level of p53 and ki-67 in tumor tissue, which may be served as a non-invasive biological indicator to predict the proliferation of ESCC cells and judge the prognosis of patients.

Radiomic signature: a non-invasive biomarker for discriminating invasive and non-invasive cases of lung adenocarcinoma.

PURPOSE: We aimed to assess the classification performance of a computed tomography (CT)-based radiomic signature for discriminating invasive and non-invasive lung adenocarcinoma. PATIENTS AND METHODS: A total of 192 patients (training cohort, n=116; validation cohort, n=76) with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in the present study. Radiomic features were extracted from preoperative unenhanced chest CT images to build a radiomic signature. Predictive performance of the radiomic signature were evaluated using an intra-cross validation cohort. Diagnostic performance of the radiomic signature was assessed via receiver operating characteristic (ROC) analysis. RESULTS: The radiomic signature consisted of 14 selected features and demonstrated good discrimination performance between invasive and non-invasive adenocarcinoma. The area under the ROC curve (AUC) for the training cohort was 0.83 (sensitivity, 0.84 ; specificity, 0.78; accuracy, 0.82), while that for the validation cohort was 0.77 (sensitivity, 0.94; specificity, 0.52 ; accuracy, 0.82). CONCLUSION: The CT-based radiomic signature exhibited good classification performance for discriminating invasive and non-invasive lung adenocarcinoma, and may represent a valuable biomarker for determining therapeutic strategies in this patient population.