Characteristics of clinically node negative breast cancer patients needing preoperative MRI

BACKGROUND

International guidelines do not recommend magnetic resonance imaging (MRI) for all breast cancer patients at primary diagnostics. This study aimed to understand which patient or tumor characteristics are associated with the use of MRI. The role of MRI among other preoperative imaging methods in clinically node negative breast cancer was studied.

MATERIAL AND METHODS

Patient and tumor characteristics were analyzed in association with the use of MRI by multivariable logistic regression analysis in 461 patients. Primary tumor size was compared between MRI, mammography (MGR), ultrasound (US) and histopathology by Spearman correlation. The delays in surgery and diagnosis were analyzed among patients with or without MRI, and axillary reoperations were evaluated.

RESULTS

Age (p < 0.0001), primary operation method (p < 0.0001), tumor histology (p < 0.0001) and HER2 status (p = 0.0064) were associated with the use of MRI. Spearman correlations between tumor size in histopathology and the difference in tumor size between histopathology and imaging methods were 0.52 in MGR, 0.66 in US and 0.36 in MRI (p < 0.0001 for all). A seven-day delay in surgical treatment was observed among patients with MRI compared to patients without MRI (p < 0.0001). Axillary reoperation rates were similar in patients with or without MRI (p = 0.57).

CONCLUSION

Patient selection through prearranged characterization is important in deciding on optimal candidates for preoperative MRI among breast cancer patients. MRI causes moderate delays in primary breast cancer surgery. Preoperative MRI is useful in the evaluation of tumor size but might be insufficient in detecting lymph node metastases.

Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Background: The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-making process between axillary treatment options. The aim of this study was to create a new model to predict the nodal stage N2-3 after a positive SLNB using machine learning methods that are rarely seen in nomogram development.Material and methods: Primary breast cancer patients who underwent SLNB and axillary lymph node dissection (ALND) between 2012 and 2017 formed cohorts for nomogram development (training cohort, N = 460) and for nomogram validation (validation cohort, N = 70). A machine learning method known as the gradient boosted trees model (XGBoost) was used to determine the variables associated with nodal stage N2-3 and to create a predictive model. Multivariate logistic regression analysis was used for comparison.Results: The best combination of variables associated with nodal stage N2-3 in XGBoost modeling included tumor size, histological type, multifocality, lymphovascular invasion, percentage of ER positive cells, number of positive sentinel lymph nodes (SLN) and number of positive SLNs multiplied by tumor size. Indicating discrimination, AUC values for the training cohort and the validation cohort were 0.80 (95%CI 0.71-0.89) and 0.80 (95%CI 0.65-0.92) in the XGBoost model and 0.85 (95%CI 0.77-0.93) and 0.75 (95%CI 0.58-0.89) in the logistic regression model, respectively.Conclusions: This machine learning model was able to maintain its discrimination in the validation cohort better than the logistic regression model. This indicates advantages in employing modern artificial intelligence techniques into nomogram development. The nomogram could be used to help identify nodal stage N2-3 in early breast cancer and to select appropriate treatments for patients.