Computed tomography reconstruction for evaluating response in axillary lymph nodes of breast cancer after neoadjuvant chemotherapy

Multiple-parameter MRI after neoadjuvant systemic therapy combining clinicopathologic features in evaluating axillary pathologic complete response in patients with clinically node-positive breast cancer

OBJECTIVE

This study aimed to evaluate axillary pathologic complete response (pCR) after neoadjuvant systemic therapy (NST) in clinically node-positive breast cancer (BC) patients based on post-NST multiple-parameter MRI and clinicopathological characteristics.

METHODS

In this retrospective study, females with clinically node-positive BC who received NST and followed by surgery between January 2017 and September 2021 were included. All axillary lymph nodes (ALNs) on MRI were matched with pathology by ALN markers or sizes. MRI morphological parameters, signal intensity curve (TIC) patterns and apparent diffusion coefficient (ADC) values of post-NST ALNs were measured. The clinicopathological characteristics was also collected and analyzed. Univariable and multivariable logistic regression analyses were performed to evaluate the independent predictors of axillary pCR.

RESULTS

Pathologically confirmed 137 non-pCR ALNs in 71 patients and 87 pCR ALNs in 87 patients were included in this study. Cortical thickness, fatty hilum, and TIC patterns of ALNs, hormone receptor, and human epidermal growth factor receptor 2 (HER2) status were significantly different between the two groups (all, p < 0.05). There was no significant difference for ADC values (p = 0.875). On multivariable analysis, TIC patterns (odds ratio [OR], 2.67, 95% confidence interval [CI]: 1.33, 5.34, p = 0.006), fatty hilum (OR, 2.88, 95% CI:1.39, 5.98, p = 0.004), hormone receptor (OR, 8.40, 95% CI: 2.48, 28.38, p = 0.001) and HER2 status (OR, 8.57, 95% CI: 3.85, 19.08, p < 0.001) were identified as independent predictors associated with axillary pCR. The area under the curve of the multivariate analysis using these predictors was 0.85 (95% CI: 0.79, 0.91).

CONCLUSION

Combining post-NST multiple-parameter MRI and clinicopathological characteristics allowed more accurate identification of BC patients who had received axillary pCR after NST.

ADVANCES IN KNOWLEDGE

A combined model incorporated multiple-parameter MRI and clinicopathologic features demonstrated good performance in evaluating axillary pCR preoperatively and non-invasively.

Evaluation of different imaging modalities for axillary lymph node staging in breast cancer patients to provide a personalized and optimized therapy algorithm

PURPOSE

The reliable detection of tumor-infiltrated axillary lymph nodes for breast cancer [BC] patients plays a decisive role in further therapy. We aimed to find out whether cross-sectional imaging techniques could improve sensitivity for pretherapeutic axillary staging in nodal-positive BC patients compared to conventional imaging such as mammography and sonography.

METHODS

Data for breast cancer patients with tumor-infiltrated axillary lymph nodes having received surgery between 2014 and 2020 were included in this study. All examinations (sonography, mammography, computed tomography [CT] and magnetic resonance imaging [MRI]) were interpreted by board-certified specialists in radiology. The sensitivity of different imaging modalities was calculated, and binary logistic regression analyses were performed to detect variables influencing the detection of positive lymph nodes.

RESULTS

All included 382 breast cancer patients had received conventional imaging, while 52.61% of the patients had received cross-sectional imaging. The sensitivity of the combination of all imaging modalities was 68.89%. The combination of MRI and CT showed 63.83% and the combination of sonography and mammography showed 36.11% sensitivity.

CONCLUSION

We could demonstrate that cross-sectional imaging can improve the sensitivity of the detection of tumor-infiltrated axillary lymph nodes in breast cancer patients. Only the safe detection of these lymph nodes at the time of diagnosis enables the evaluation of the response to neoadjuvant therapy, thereby allowing access to prognosis and improving new post-neoadjuvant therapies.