Improved Model for Predicting Axillary Response to Neoadjuvant Chemotherapy in Patients with Clinically Node-Positive Breast Cancer

Abstract

PurposePathological complete response (pCR) of axillary lymph node (LN) is frequently achieved in patients with clinically node-positive breast cancer after neoadjuvant chemotherapy (NAC). Treatment of the axilla after NAC is not well established and the value of sentinel LN biopsy following NAC remains unclear. This study investigated the predictive value of axillary response following NAC and evaluated the predictive value of a model based on axillary response.MethodsData prospectively collected on 201 patients with clinically node-positive breast cancer who were treated with NAC and underwent axillary LN dissection (ALND) were retrieved. A model predictive of axillary pCR was developed based on clinicopathologic variables. The overall predictive ability between models was compared by receiver operating characteristic (ROC) curve analysis.ResultsOf 201 patients who underwent ALND after NAC, 68 (33.8%) achieved axillary pCR. Multivariate analysis using axillary LN pCR after NAC as the dependent variable showed that higher histologic grade (p=0.031; odds ratio [OR], 2.537; 95% confidence interval [CI], 1.087–5.925) and tumor response rate ≥47.1% (p=0.001; OR, 3.212; 95% CI, 1.584–6.515) were significantly associated with an increased probability of achieving axillary pCR. The area under the ROC curve for estimating axillary pCR was significantly higher in the model that included tumor response rate than in the model that excluded this rate (0.732 vs. 0.649, p=0.022).ConclusionTumor response rate was the most significant independent predictor of axillary pCR in response to NAC. The model that included tumor response rate was a significantly better predictor of axillary pCR than the model that excluded tumor response rate.

Predicting Axillary Response to Neoadjuvant Chemotherapy: Breast MRI and US in Patients with Node-Positive Breast Cancer

“…To efficiently select the patients who would benefit from SLNB after NAC, many studies have been performed (8)(9)(10)(11)(12)(13)(14)(15). Some studies included initial clinical T or N stage in their model without consideration of residual LN metastasis after NAC (8)(9)(10)(11).…”

The role of magnetic resonance imaging on evaluating response of neoadjuvant therapy for breast cancer

“…According to the study by Osorio-Silla [77], 59.7% of patients with primary lesions complete response on MRI also achieved complete response on axillary lymph node, and 75.9% of patients with non-complete response of breast primary tumors on MRI had residual lymph node disease after surgery. Many studies [104,106,107] have similar results, indicating the importance of MRI complete response in primary lesion rate is an important independent for predicting of axillary pCR.…”

“…But the post-NAT MRI obtained a high negative predictive value (94% and 97.3%, respectively), which means that negative post-NAT MRI can accurately exclude the axillary lymph node diseases [101,102]. Before and during NAT, lymph node pCR can be predicted by negative hormone receptor and positive HER2 receptor status [103][104][105], lower clinical T and N stage [103], higher histological/nuclear grade [103], and treatment response to NAT of breast primary lesions [106,107]. According to the study by Osorio-Silla [77], 59.7% of patients with primary lesions complete response on MRI also achieved complete response on axillary lymph node, and 75.9% of patients with non-complete response of breast primary tumors on MRI had residual lymph node disease after surgery.…”

Role of MRI to Assess Response to Neoadjuvant Therapy for Breast Cancer

“…Several groups have found that adding MRI response of the primary tumor to clinicopathologic data achieves significantly better predictive power. [74][75][76][77] Ha et al trained an artificial intelligence algorithm to predict pCR in the axilla based on pretreatment MRI of the in-breast tumor alone, achieving an overall accuracy of 83%. 78 These models could improve prediction of axillary disease and allow better preoperative planning and patient counseling regarding surgical and radiation options.…”

“…Negative hormone receptor and positive HER2 receptor status, lower clinical T and N stages, high histologic/nuclear grade, and breast tumor response to neoadjuvant therapy are predictors for axillary pCR. Several groups have found that adding MRI response of the primary tumor to clinicopathologic data achieves significantly better predictive power 74–77 . Ha et al trained an artificial intelligence algorithm to predict pCR in the axilla based on pretreatment MRI of the in‐breast tumor alone, achieving an overall accuracy of 83% 78 .…”