Evaluation of efficacy of chemotherapy for mucinous carcinoma: a surveillance, epidemiology, and end results cohort study

An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast

Objective

To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC).

Methods

One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models.

Results

Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram.

Conclusion

The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.

A novel nomogram and risk classification system for predicting lymph node metastasis of breast mucinous carcinoma: A SEER-based study

BACKGROUND

Mucinous breast cancer (MBC) is a rare disease, and patients with lymph node metastasis (LNM) have a poor prognosis. We aimed to explore the predictive factors of LNM and to construct a nomogram for predicting the risk of LNM and to identify the suitable axillary surgery for patients with diverse risks.

PATIENTS AND METHODS

Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Chi-square and rank-sum tests were used to analyze the differences between groups. Survival analysis was performed with Kaplan-Meier curves and log-rank tests. Independent factor identification and nomogram construction were performed with logistic regression analysis. The nomogram was qualified with a discrimination and calibration plot. Propensity score matching was performed to balance the disparities between groups.

RESULTS

Patients with metastatic lymph nodes have a worse prognosis. Univariate and multivariate analyses indicated that tumor size, grade, and age were independent risk factors for LNM. The nomogram constructed with these three factors can predict the risk of LNM with high accuracy (AUC: 0.767, 95% CI: 0.697-0.838) and good calibration. Based on the nomogram, a risk classification system satisfactorily stratified the patients into 3 groups with diverse risks of LNM. In the low-risk group, there were no significant differences between sentinel lymph node biopsy and no axillary surgery. In the middle- and high-risk groups, both SLNB and axillary lymph node dissection were superior to no axillary surgery, with similar survival benefits.

CONCLUSIONS

The nomogram based on tumor size, grade, and age could conveniently and accurately predict the risk of LNM in MBC and assist clinicians in optimizing surgical strategies.

Identifying Clinicopathological Risk Factors of the Regional Lymph Node Metastasis in Patients with T1-2 Mucinous Breast Cancer: A Population-Based Study

Background

Pure mucinous breast cancer (PMBC) has a better prognosis than other types of invasive breast cancer. However, regional lymph node metastasis (LNM) might reverse this outcome. We aim to determine the independent predictive factors for regional LNM and further develop a nomogram model for clinical practice.

Method

Data of PMBC patients from the Surveillance, Epidemiology, and End Results (SEER) program between Jan 2010 and Dec 2015 were retrospectively reviewed. Univariate and multivariate logistic regression analyses were used to determine the risk factors for LNM in T_1-2 MBC. The nomogram was constructed and further evaluated by an internal validation cohort. The receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were performed to evaluate the accuracy of this model.

Result

Five variables, including age, race, tumor size, grade, and breast subtype, were identified to be significantly associated with regional LNM in female patients with T_1-2 PMBC. A nomogram was successfully established with a favorable concordance index (C-index) of 0.780, supported by an internal validation cohort with a C-index of 0.767.

Conclusion

A nomogram for predicting regional LNM in female patients with T_1-2 PMBC was successfully established and validated via an internal cohort. This visualized model would assist surgeons to make appropriate clinical decisions in the management of primary PMBC, especially in terms of whether axillary lymph node dissection (ALND) is warranted.