Nomogram Based on Ultrasonography and Clinical Features for Predicting Malignancy in Soft Tissue Tumors.

The objective of this study was to establish a predictive nomogram based on ultrasound (US) and clinical features for patients with soft tissue tumors (STTs). A total of 260 patients with STTs were enrolled in this retrospective study and were divided into a training cohort (n=200, including 110 malignant and 90 benign masses) and a validation cohort (n=60, including 30 malignant and 30 benign masses). Multivariate analysis was performed by binary logistic regression analysis to determine the significant factors predictive of malignancy. A simple nomogram was established based on these independent risk factors including US and clinical features. The predictive accuracy and discriminative ability of the nomogram were measured by the calibration curve and the concordance index (C-index). The nomogram, comprising US features (maximum diameter, margin and vascular density) and clinical features (sex, age, and duration of disease), showed a favorable performance for predicting malignancy, with a sensitivity of 88.2% and a specificity of 78.7%. The calibration curve for malignancy probability in the training cohort showed good agreement between the nomogram predictions and actual observations. The C-indexes of the training cohort and validation cohort for predicting malignancy were 0.89 (95% CI: 0.85-0.94) and 0.83 (95% CI: 0.73-0.94), respectively. The nomogram based on US and clinical features could be a simple, intuitive and reliable tool to individually predict malignancy in patients with STTs.

Wu M ，Hu Y ，Ren A ，Peng X ，Ma Q ，Mao C ，Hang J ，Li A ... -  《Cancer Management and Research》

Qualitative and Quantitative Contrast-Enhanced Ultrasound Combined with Conventional Ultrasound for Predicting the Malignancy of Soft Tissue Tumors.

This study was aimed at evaluating the performance of perfusion patterns and the quantitative parameters of contrast-enhanced ultrasound (CEUS) in the detection of soft tissue tumors (STTs) and establishing a US workflow for STTs to improve patient management. Conventional ultrasound (US) and CEUS data were retrospectively collected from 156 soft tissue masses. Six perfusion patterns (P1-P6) were applied for CEUS qualitative analysis. Multivariate logistic regression was used to evaluate the performance of conventional US and qualitative and quantitative CEUS in distinguishing benign and malignant STTs. The malignancy rates of P1-P6 in STTs were 0%, 50.0%, 9.1%, 33.3%, 73.4% and 61.0%, respectively. For "non-P1" STTs, the predictive model combining quantitative CEUS parameters with conventional US features, including margin (odds ratio [OR] = 4.490, p = 0.000), vascular density (OR = 2.307, p = 0.013), 50% wash-out intensity (OR = 1.904, p = 0.032) and 50% wash-out time (OR = 1.031, p = 0.019), performed favorably in predicting malignancy, with an accuracy of 81.0% and an area under the receiver operating characteristic curve of 0.868. Furthermore, a US workflow for the detection of STTs based on conventional US and CEUS was established. CEUS with qualitative and quantitative analyses could be an effective tool for STT diagnosis. The US workflow in this study may improve the management of STT patients.

Wu M ，Hu Y ，Hang J ，Peng X ，Mao C ，Ye X ，Li A ... -  《-》

A new nomogram for predicting the malignant diagnosis of Breast Imaging Reporting and Data System (BI-RADS) ultrasonography category 4A lesions in women with dense breast tissue in the diagnostic setting.

Biopsy has been recommended for Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. However, the malignancy rate of category 4A lesions is very low (2-10%). Therefore, most biopsies of category 4A lesions are benign, and the results will generally cause additional health care costs and patient anxiety. A prediction model was developed based on an analysis of 418 BI-RADS ultrasonography (US) category 4A patients at Sun Yat-sen Memorial Hospital. Univariate and multivariate logistic regression analyses were applied to identify significant variables for inclusion in the final nomogram. The predictive accuracy and discriminative ability were evaluated using the concordance index (C-index) and calibration curves. An independent cohort of 97 patients from the Second Affiliated Hospital of Guangzhou Medical University was used for external validation. The independent risk factors from the multivariate analysis for the training cohort were family history of breast cancer (OR =4.588, P=0.004), US features [margin (OR =2.916, P=0.019), shape (irregular vs. oval, OR =2.474, P=0.044; round vs. oval, OR =1.935, P=0.276), parallel orientation vs. not parallel (OR =2.204, P=0.040)], low suspicious lymph nodes (OR =7.664, P=0.019), and suspicious calcifications on mammography (MG) (OR =6.736, P=0.001). The C-index was good in the training [0.813, 95% confidence interval (95% CI), 0.733 to 0.893] and validation cohorts (0.765, 95% CI, 0.584 to 0.946). The calibration curves showed optimal agreement between the nomogram prediction and actual observations for the probability of malignancy. Also, the cutoff score was set to 100 for discriminating high and low risk. The model performed well in discerning different risk groups. We developed a well-discriminated and calibrated nomogram to predict the malignancy of BI-RADS US category 4A lesions in dense breast tissue, which may help clinicians identify patients at lower or higher risk.

Yang Y ，Hu Y ，Shen S ，Jiang X ，Gu R ，Wang H ，Liu F ，Mei J ，Liang J ，Jia H ，Liu Q ，Gong C ... -  《-》

Establishment and Validation of a Preoperative MRI-based Nomogram for Predicting the Risk of Malignancy in Patients with Breast Tumor.

Purpose: To establish a preoperative nomogram incorporating morphological and dynamic contrast-enhanced (DCE) features to individually predict the risk of malignancy in patients with breast tumor. Methods A total of 447 consecutive female patients who were divided into the primary cohort (n=326) and the validation cohort (n=121) were enrolled between March 2015 to January 2018. Univariate and multivariate logistic regression analyses were used to identify the potential independent indicators of malignancy. An MRI-based nomogram integrating morphological features and kinetic curves was developed to achieve individualized risk prediction of malignancy in patients with breast masses. The discrimination, calibration ability and clinical utility of the MRI-based model were assessed using C-index, calibration curve and decision curve analysis. Results: Age, tumor size, margin, internal enhancement characteristics, and kinetic curve were confirmed as the independent predictors of malignancy. The AUC of MRI-based nomogram was 0.940 (95% CI: 0.911-0.970) and 0.894 (95% CI: 0.816-0.974) in the primary cohort and validation cohort, respectively. 447 patients were subdivided into the low-risk group (n=107) and high-risk group (n=340) based on the optimal cut-off value of 21.704. The high-risk patients had a higher likelihood of harboring malignancy. Conclusion: The MRI-based nomogram can be used to achieve an accurate individualized risk prediction of malignancy and reduce unnecessary breast biopsy.

Lai J ，Lin J ，Wang H ，Sun Y ，Li Y ，Tian H ，Shen S ，Tan C ，Liu H ，Yu F ... -  《Journal of Cancer》

A novel diagnostic nomogram based on serological and ultrasound findings for preoperative prediction of malignancy in patients with ovarian masses.

To develop a novel diagnostic nomogram model to predict malignancy in patients with ovarian masses. In total, 1277 patients with ovarian masses were retrospectively analyzed. Receiver operating characteristic (ROC) analysis was performed to identify valuable predictive factors. Univariate and multivariate logistic regression analyses were used to identify risk factors for ovarian cancer. Subsequently, a predictive nomogram model was developed. The performance of the nomogram model was assessed by its calibration and discrimination in a validation cohort. Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model. Overall, 496 patients (38.8%) had ovarian cancer. Eighteen parameters were significantly different between the malignant and benign groups. Five parameters were identified as being most optimal for predicting malignancy, including age, carbohydrate antigen 125, fibrinogen-to-albumin ratio, monocyte-to-lymphocyte ratio, and ultrasound result. These parameters were incorporated to establish a nomogram model, and this model exhibited an area under the ROC curve (AUC) of 0.937 (95% confidence interval [CI], 0.920-0.954). The model was also well calibrated in the validation cohort and showed an AUC of 0.925 (95%CI, 0.896-0.953) at the cut-off point of 0.298. DCA confirmed that the nomogram model achieved the best clinical utility with almost the entire range of threshold probabilities. The model has demonstrated superior efficacy in predicting malignancy compared to currently available models, including the risk of ovarian malignancy algorithm, copenhagen index, and the risk of malignancy index. More importantly, the nomogram established here showed potential value in identification of early-stage ovarian cancer. The cost-effective and easily accessible nomogram model exhibited favorable accuracy for preoperative prediction of malignancy in patients with ovarian masses, even at early stages.

Guo Y ，Jiang T ，Ouyang L ，Li X ，He W ，Zhang Z ，Shen H ，You Z ，Yang G ，Lai H ... -  《-》