Development of clinical and magnetic resonance imaging-based radiomics nomograms for the differentiation of nodular fasciitis from soft tissue sarcoma.

BACKGROUND: Accurate differentiation of nodular fasciitis (NF) from soft tissue sarcoma (STS) before surgery is essential for the subsequent diagnosis and treatment of patients. PURPOSE: To develop and evaluate radiomics nomograms based on clinical factors and magnetic resonance imaging (MRI) for the preoperative differentiation of NF from STS. MATERIAL AND METHODS: This retrospective study analyzed the MRI data of 27 patients with pathologically diagnosed NF and 58 patients with STS who were randomly divided into training (n = 62) and validation (n = 23) groups. Univariate and multivariate analyses were performed to identify the clinical factors and semantic features of MRI. Radiomics analysis was applied to fat-suppressed T1-weighted (T1W-FS) images, fat-suppressed T2-weighted (T2W-FS) images, and contrast-enhanced T1-weighted (CE-T1W) images. The radiomics nomograms incorporating the radiomics signatures, clinical factors, and semantic features of MRI were developed. ROC curves and AUCs were carried out to compare the performance of the clinical factors, radiomics signatures, and clinical radiomics nomograms. RESULTS: Tumor location, size, heterogeneous signal intensity on T2W-FS imaging, heterogeneous signal intensity on CE-T1W imaging, margin definitions on CE-T1W imaging, and septa were independent predictors for differentiating NF from STS (P < 0.05). The performance of the radiomics signatures based on T2W-FS imaging (AUC = 0.961) and CE-T1W imaging (AUC = 0.938) was better than that based on T1W-FS imaging (AUC = 0.833). The radiomics nomograms had AUCs of 0.949, which demonstrated good clinical utility and calibration. CONCLUSION: The non-invasive clinical radiomics nomograms exhibited good performance in the differentiation of NF from STS, and they have clinical application in the preoperative diagnosis of diseases.

Prediction of Ki-67 of Invasive Ductal Breast Cancer Based on Ultrasound Radiomics Nomogram.

PURPOSE: The objective of this research was to develop and validate an ultrasound-based radiomics nomogram for the pre-operative assessment of Ki-67 in breast cancer (BC). MATERIALS AND METHODS: From December 2016 to December 2018, 515 patients with invasive ductal breast cancer who received two-dimensional (2D) ultrasound and Ki-67 examination were studied and analyzed retrospectively. The dataset was distributed at random into a training cohort (n = 360) and a test cohort (n = 155) in the ratio of 7:3. Each tumor region of interest was defined based on 2D ultrasound images and radiomics features were extracted. ANOVA, maximum correlation minimum redundancy (mRMR) algorithm, and minimum absolute shrinkage and selection operator (LASSO) were performed to pick features, and independent clinical predictors were integrated with radscore to construct the nomogram for predicting Ki-67 index by univariate and multivariate logistic regression analysis. The performance and utility of the models were evaluated by plotting receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. RESULTS: In the testing cohort, the area under the receiver characteristic curve (AUC) of the nomogram was 0.770 (95% confidence interval, 0.690-0.860). In both cohorts, the nomogram outperformed both the clinical model and the radiomics model (P < .05 according to the DeLong test). The analysis of DCA proved that the model has clinical utility. CONCLUSIONS: The nomogram based on 2D ultrasound images offered an approach for predicting Ki-67 in BC.

The diagnostic performance of conventional ultrasound and strain elastography in malignant soft tissue tumors.

OBJECTIVE: To investigate the diagnostic value of conventional ultrasound (US) and strain elastography (SE) in malignant soft tissue tumors. METHOD: A total of 83 soft tissue masses were included prospectively. US and SE imaging were performed at the same time. Two observers assessed the B mode, color Doppler, elastic scores (ES), strain ratio (SR), and SE size to B mode size (EI/B) ratio and compared the consistency of the data between the observers. According to the pathological diagnosis of resection, the cases were divided into malignant and nonmalignant groups. The diagnostic value of conventional US and SE in the prediction of malignant soft tissue tumors was assessed. RESULTS: The pathology results divided cases into 36 malignant lesions and 47 nonmalignant lesions. There was no statistically significant difference in gender, location, maximum diameter, echo, tail sign, cystic component, Doppler scores, or SR between the two groups (p > 0.05). However, significant differences between the two groups were found in age, depth, heterogeneity, edge, ES, and EI/B (p < 0.05). The biggest area under the receiver operating characteristics curve (0.934) was the combination model of age, heterogeneity, edge, ES, and EI/B, and the sensitivity and specificity were 0.861 and 0.957, respectively. CONCLUSIONS: Conventional US and SE are significant for the diagnosis of malignant soft tissue tumors, and SE can be used as a complementary technique to the characterization of STTs using conventional US.

Soft tissue sarcoma: can dynamic contrast-enhanced (DCE) MRI be used to predict the histological grade?

OBJECTIVE: To determine if dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters reflect histological grade of soft tissue sarcoma (STS) MATERIALS AND METHODS: The medical records of 50 patients diagnosed with pathologically confirmed STS were retrospectively reviewed. Each STS was assessed with conventional contrast-enhanced MRI and DCE-MRI using a 3.0-T MRI system. The conventional MRI characteristics of low-grade (grade 1) and high-grade (grade 2 and grade 3) tumors were analyzed. Semi-quantitative parameters, including iAUC and TTP, and quantitative parameters, including Ktrans, Kep, and Ve, were derived from DCE-MRI. The diagnostic performances and optimal thresholds of various combinations of DCE-MRI parameters for predicting histological grades of STS were investigated using receiver operator characteristic (ROC) curves. RESULTS: On conventional MRI, high-grade STSs were significantly larger (≥ 5 cm) and more likely to show a heterogeneous signal intensity on T2WI (> 75%), peritumoral hyperintensity on T2WI, or tumor necrosis (> 50%) compared with low-grade STS. On DCE-MRI, iAUC, TTP, Ktrans, and Kep were significant predictors of STS histological grade. Ktrans had a high diagnostic value for differentiating between high-grade and low-grade STSs. The combination of iAUC, TTP, and Ktrans yielded a higher AUC value (0.841) than the other models. CONCLUSION: High-grade STSs were usually larger than low-grade STSs, had unclear boundaries, a heterogeneous signal intensity on T2-weighted image (T2WI), and extensive necrosis. On DCE-MRI, iAUC, TTP, Ktrans, and Kep could differentiate between high-grade and low-grade STSs. The combination of iAUC, TTP, and Ktrans had a high diagnostic performance for differentiating between STS histological grades.