Clémentine Jankowski1,2,3, S Guiu4, M Cortet5, C Charon-Barra6, I Desmoulins7, V Lorgis7, L Arnould6, P Fumoleau6, B Coudert6, R Rouzier8, C Coutant5,9, F Reyal8. 1. Department of Surgical Oncology, Georges-François Leclerc Cancer Center, Dijon, France. cjankowski@cgfl.fr. 2. University of Burgundy, Dijon, France. cjankowski@cgfl.fr. 3. Department of Medical Oncology, Georges-François Leclerc Cancer Center, Dijon, France. cjankowski@cgfl.fr. 4. Department of Medical Oncology, Institut du cancer, Montpellier, France. 5. Department of Surgical Oncology, Georges-François Leclerc Cancer Center, Dijon, France. 6. Department on Pathology, Georges-François Leclerc Cancer Center, Dijon, France. 7. Department of Medical Oncology, Georges-François Leclerc Cancer Center, Dijon, France. 8. Department of Surgical Oncology, Curie Institute, Paris, France. 9. University of Burgundy, Dijon, France.
Abstract
PURPOSE: The aim of this study was to assess the Institut Gustave Roussy/M.D. Anderson Cancer Center (IGR/MDACC) nomogram in predicting pathologic complete response (pCR) to preoperative chemotherapy in a cohort of human epidermal growth factor receptor 2 (HER2)-positive tumors treated with preoperative chemotherapy with trastuzumab. We then combine clinical and pathological variables associated with pCR into a new nomogram specific to HER2-positive tumors treated by preoperative chemotherapy with trastuzumab. PATIENTS AND METHODS: Data from 270 patients with HER2-positive tumors treated with preoperative chemotherapy with trastuzumab at the Institut Curie and at the Georges François Leclerc Cancer Center were used to assess the IGR/MDACC nomogram and to subsequently develop a new nomogram for pCR based on multivariate logistic regression. Model performance was quantified in terms of calibration and discrimination. We studied the utility of the new nomogram using decision curve analysis. RESULTS: The IGR/MDACC nomogram was not accurate for the prediction of pCR in HER2-positive tumors treated by preoperative chemotherapy with trastuzumab, with poor discrimination (AUC = 0.54, 95% CI 0.51-0.58) and poor calibration (p = 0.01). After uni- and multivariate analysis, a new pCR nomogram was built based on T stage (TNM), hormone receptor status, and Ki67 (%). The model had good discrimination with an area under the curve (AUC) at 0.74 (95% CI 0.70-0.79) and adequate calibration (p = 0.93). By decision curve analysis, the model was shown to be relevant between thresholds of 0.3 and 0.7. CONCLUSION: To the best of our knowledge, ours is the first nomogram to predict pCR in HER2-positive tumors treated by preoperative chemotherapy with trastuzumab. To ensure generalizability, this model needs to be externally validated.
PURPOSE: The aim of this study was to assess the Institut Gustave Roussy/M.D. Anderson Cancer Center (IGR/MDACC) nomogram in predicting pathologic complete response (pCR) to preoperative chemotherapy in a cohort of human epidermal growth factor receptor 2 (HER2)-positive tumors treated with preoperative chemotherapy with trastuzumab. We then combine clinical and pathological variables associated with pCR into a new nomogram specific to HER2-positive tumors treated by preoperative chemotherapy with trastuzumab. PATIENTS AND METHODS: Data from 270 patients with HER2-positive tumors treated with preoperative chemotherapy with trastuzumab at the Institut Curie and at the Georges François Leclerc Cancer Center were used to assess the IGR/MDACC nomogram and to subsequently develop a new nomogram for pCR based on multivariate logistic regression. Model performance was quantified in terms of calibration and discrimination. We studied the utility of the new nomogram using decision curve analysis. RESULTS: The IGR/MDACC nomogram was not accurate for the prediction of pCR in HER2-positive tumors treated by preoperative chemotherapy with trastuzumab, with poor discrimination (AUC = 0.54, 95% CI 0.51-0.58) and poor calibration (p = 0.01). After uni- and multivariate analysis, a new pCR nomogram was built based on T stage (TNM), hormone receptor status, and Ki67 (%). The model had good discrimination with an area under the curve (AUC) at 0.74 (95% CI 0.70-0.79) and adequate calibration (p = 0.93). By decision curve analysis, the model was shown to be relevant between thresholds of 0.3 and 0.7. CONCLUSION: To the best of our knowledge, ours is the first nomogram to predict pCR in HER2-positive tumors treated by preoperative chemotherapy with trastuzumab. To ensure generalizability, this model needs to be externally validated.
Entities:
Keywords:
Breast cancer; HER2; Neoadjuvant chemotherapy; Predictive factors