Abstract P4-02-10: Generation of nomograms to predict non-sentinel node metastasis and poorer patient response to neoadjuvant chemotherapy in primary breast cancer: A 10-year study

Abstract

Abstract INTRODUCTION: The ability to predict non-sentinel node (non-SLN) metastasis in breast cancer patients has been an area of intense research for the past decade. This study aimed to identify predictors of non-SLN metastasis and level 3 node involvement. Further objectives included identifying factors which predicted poorer patient response to neoadjuvant chemotherapy (NAC). The final objective was to create mathematical models to predict non-SLN metastasis and level 3 disease. METHODS: Electronic patient records of 1088 patients who underwent ANC between 2007-2016 at the Royal Hallamshire Hospital, Sheffield, UK were reviewed. Clinicopathological characteristics were used to identify factors predicting lymph node metastasis. Significant predictors of axillary disease were incorporated into a nomogram using the first 75% of the cohort. Internal validation of the nomogram was performed using the remaining 25% of the study population. Four nomograms were created to predict non-SLN metastasis and level 3 disease in those who were had not received chemotherapy and those who underwent NAC. RESULTS: Larger tumour size (OR=1.025; CI=1.016-1.034; p<0.001), grade 3 (OR=3.706; CI=2.102-6.534) and grade 2 tumours (OR=2.174; CI=1.245-3.795) compared to grade 1 tumours (p<0.001), presence of lymphovascular invasion (LVI) (OR=2.832; CI=2.064-3.885; p<0.001), ER-negative tumours (OR=2.339; CI=1.472-3.717; p<0.001), and number of positive SLNs (OR=1.756; CI=1.333-2.313; p<0.001) were all significantly associated with non-SLN metastasis. In addition to these characteristics, lobular carcinomas (OR=1.832; CI=1.157-2.899; p=0.034) and multifocal tumours (OR=1.717; CI=1.108-2.662; p=0.016) were also significantly associated with level 3 disease. In patients who underwent NAC, larger tumour size (OR=1.040; CI=1.025-1.056; p<0.001), presence of LVI (OR=3.030; CI=1.673-5.488; p=0.001), and HER2-negative tumours (OR=1.983; CI=1.177-3.343; p=0.01) significantly predicted non-SLN metastasis, despite treatment. These same variables significantly predicted level 3 metastasis. In patients who did not receive NAC, the nomogram to predict non-SLN metastasis produced an AUC of 0.721 (CI=0.674-0.768). Internal validation produced an AUC of 0.781 (CI=0.708-0.854). A nomogram was produced to predict level 3 disease in non-NAC patients (AUC=0.771, CI=0.716-0.826). Validation provided accurate results (AUC=0.726, CI=0.625-0.826). A nomogram to predict non-SLN metastasis in NAC patients produced an AUC of 0.767 (CI=0.669-0.864). Validation produced an AUC of 0.838 (CI=0.666-1.000). An AUC of 0.806 (CI=0.0.711-0.901) was derived from the nomogram predicting level 3 disease in NAC patients. Validation produced an AUC of 0.879 (CI=0.742-1.000). CONCLUSION: Generation of a nomogram accurately predicted non-SLN metastasis and level 3 disease in those who did not receive NAC and those who underwent previous NAC. Incorporation of these mathematical models into the management of those with axillary disease will allow patients to make a more informed decision, whether they wish to proceed with full ANC, participate in a clinical trial, or choose to have their axilla re-staged following neoadjuvant chemotherapy. Citation Format: Sam Jenkins. Generation of nomograms to predict non-sentinel node metastasis and poorer patient response to neoadjuvant chemotherapy in primary breast cancer: A 10-year study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-02-10.