Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma

Abstract

Objective: This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differentiate triple-negative breast cancer (TNBC) from fibroadenoma.

Methods: A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs, which underwent preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by using the receiver operator characteristic curve, calibration curve, and decision curve analysis (DCA).

Results: The radiomics signature, composed of 12 selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models (p < 0.05). The calibration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram.

Conclusion: The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.

Advances in knowledge: Recent advances in radiomics-based US are increasingly showing potential for improved diagnosis, assessment of therapeutic response and disease prediction in oncology. Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models.

Figure 1.

Workflow of radiomics analysis and study flowchart. (a) Radiomics analysis was divided into three parts: imaging, ROI segmentation, and feature extraction. (b). In the study flowchart, LASSO logistic regression was used to construct radiomics signature and combine with multivariate analysis of the clinical data to develop a nomogram, and then the nomogram was validated in the testing set.

Figure 2.

Selection of the radiomics features by using a parametric algorithm was performed in the LASSO binary logistic regression model. (a) Selection of the tuning parameter (λ) was performed in the LASSO model by 10-fold cross-validation based on minimum criteria. A dotted vertical black line was depicted at the optimal value by using the one standard error of the minimum and minimum criteria. The number along the upper axis indicates the average number of predictors. The lower x-axis represents the log(λ), and the y-axis represents binomial deviances. The optimal λ value of 0.040 with log (λ) = −3.688 was selected. (b). LASSO coefficient profiles through the 1283 features. The dotted vertical line was shown at the optimal value using 10-fold cross-validation in A. Features with 12 non-zero coefficients are constructed by using the optimal λ in the plot.

Figure 3.

Result of feature selection and the verified set of Rad-score. Twelve non-zero coefficients features are presented, including first-order statistics, textural features, and wavelet-based features.

Figure 4.

Combined radiomics nomogram constructed by Rad-score and clinical characteristics for predicting TNBC in the training cohort. The predictors include age, BI-RADS, and Rad-score.

Figure 5.

(a, b)Comparison of the AUCs for the clinical model, Rad-score model, and radiomics nomogram in the training and validation cohorts. (c).Calibration curves of the radiomics nomogram in the training cohort. (d).Calibration curves of the radiomics nomogram in the validation cohort. The X-axis represents the predictive probability; Y-axis denotes the observed probability. The 45° blue diagonal line represents the perfect prediction of TNBC, and the red line indicates the prediction model of the radiomics nomogram. The closer the red line fits to the ideal line, the better the discrimination of the nomogram.

Figure 6.

Decision curve for the clinical findings, Rad-score, and radiomics nomogram. The gray line represents the assumption that all patients are TNBC cases; the black line refers to the assumption that all patients are fibroadenoma cases. The x-axis denotes the high-risk threshold, and the y-axis indicates the net benefit. The blue, orchid, and light-pink lines represent the clinical model, Rad-score model, and radiomics nomogram, respectively.