Predicting the pathological grade of breast phyllodes tumors: a nomogram based on clinical and magnetic resonance imaging features

Abstract

Objective: To explore the potential factors related to the pathological grade of breast phyllodes tumors (PTs) and to establish a nomogram to improve their differentiation ability.

Methods: Patients with PTs diagnosed by post-operative pathology who underwent pretreatment magnetic resonance imaging (MRI) from January 2015 to June 2020 were retrospectively reviewed. Traditional clinical features and MRI features evaluated according to the fifth BI-RADS were analyzed by statistical methods and introduced to a stepwise multivariate logistic regression analysis to develop a prediction model. Then, a nomogram was developed to graphically predict the probability of non-benign (borderline/malignant) PTs.

Results: Finally, 61 benign, 73 borderline and 48 malignant PTs were identified in 182 patients. Family history of tumor, diameter, lobulation, cystic component, signal on fat saturated T₂ weighted imaging (FS T₂WI), BI-RADS category and time-signal intensity curve (TIC) patterns were found to be significantly different between benign and non-benign PTs. The nomogram was finally developed based on five risk factors: family history of tumor, lobulation, cystic component, signal on FS T₂WI and internal enhancement. The AUC of the nomogram was 0.795 (95% CI: 0.639, 0.835).

Conclusion: Family history of tumor, lobulation, cystic components, signals on FS T₂WI and internal enhancement are independent predictors of non-benign PTs. The prediction nomogram developed based on these features can be used as a supplemental tool to pre-operatively differentiate PTs grades.

Advances in knowledge: More sample size and characteristics were used to explore the factors related to the pathological grade of PTs and establish a predictive nomogram for the first time.

Figure 1.

Benign phyllodes tumor in the left breast of a 16-year-old female. 1a FS T₂WI shows an irregular mass with high homogeneous signal intensity. 1b DCE-MRI shows homogeneous enhancement. 1c-d TIC shows a persistent pattern by contrast-enhanced dynamic imaging. DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; FS T₂WI, fat saturated T₂ weighted imaging; TIC, time–signal intensity curve.

Figure 2.

Borderline phyllodes tumor in the right breast of a 68-year-old female. 2a FS T₂WI shows an oval mass with high homogeneous signal intensity. 2b DCE-MRI shows homogeneous enhancement. 2c-d TIC shows a plateau pattern by contrast-enhanced dynamic imaging. DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; FS T₂WI, fat saturated T₂ weighted imaging; TIC, time–signal intensity curve.

Figure 3.

Malignant phyllodes tumor in the left breast of a 57-year-old female. 3a FS T₂WI shows an irregular mass with heterogeneous intensity. 3b DCE-MRI shows heterogeneous enhancement. 3c-d TIC shows a washout pattern by contrast-enhanced dynamic imaging. DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; FS T₂WI, fat saturated T₂ weighted imaging; TIC, time–signal intensity curve.

Figure 4.

ROC curve of the prediction model. The AUC was 0.795 (95% CI: 0.639, 0.835). AUC, area under the curve; CI, confidence interval; ROC, receiver operating chracteristic.

Figure 5.

The nomogram of the prediction model. The nomogram was developed based on family history of tumor, lobulation, cystic component, signal on FS T₂WI and internal enhancement. FS T₂WI, fat saturated T₂ weighted imaging.

Figure 6.

6a The 10-fold cross-validation calibration curves showed that the model has good calibration performance. 6b DCA showed this model had good clinical utility. DCA, decision curve analysis.