Establishment of random forest model and its predictive value for pathologic complete response in breast cancer patients undergoing neoadjuvant chemotherapy / 肿瘤研究与临床

ABSTRACT

Objective:To investigate the predictive value of established random forest model for pathologic complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy.Methods:The clinicopathologic data of 142 primary breast cancer patients undergoing breast-conserving surgery or modified radical mastectomy after neoadjuvant chemotherapy from Cangzhou Central Hospital between January 2010 and October 2021 were retrospectively analyzed. Histologically, breast and axillary lymph node without residual infiltrated tumors was treated as pCR. The patients were divided into pCR group (23 cases) and non-pCR group (119 cases) according to whether patients achieved pCR or not, and the differences of clinicopathologic data between the two groups were compared. The risk factors affecting pCR were identified by using logistic regression analysis, random forest model was established by using random forest function of R statistical software, and Gini index of random forest algorithmic was used to order the importance of variables. The receiver operating characteristic (ROC) curve was used to assess the value of random forest model in predicting the efficacy of neoadjuvant chemotherapy.Results:The overall pCR ratio after neoadjuvant chemotherapy was 16.20% (23/142). The proportion of tumor diameter ≤5 cm, negative axillary lymph node, negative human epidermal growth factor receptor 2 (HER2), Ki-67 positive index >20%, histological grade 2, and neoadjuvant chemotherapy regimens including targeted therapy in pCR group was higher than that in non-pCR group, and the difference was statistically significant (all P < 0.05). Univariate logistic regression analysis showed that tumor diameter, axillary lymph node, HER2, Ki-67, histological grade, and neoadjuvant chemotherapy regimens were related with pCR (all P < 0.05). Multivariate logistic regression analysis showed that tumor diameter >5 cm ( OR = 5.85, 95% CI 1.28-26.67, P = 0.022), positive axillary lymph node ( OR = 11.22, 95% CI 1.84-68.42, P = 0.009), positive HER2 ( OR = 7.35, 95% CI 1.45-37.26, P = 0.016), Ki-67 positive index ≤20% ( OR = 1.03, 95% CI 1.01-1.06, P = 0.017), histological grade 3 ( OR = 7.37, 95% CI 1.24-43.86, P = 0.028), and non-targeted therapy ( OR = 0.02, 95% CI 0.00-0.25, P = 0.003) were independent risk factors of pCR. Random forest algorithm showed that the importance order of risk factors of pCR was successively Ki-67 low expression, positive axillary lymph node, tumor diameter >5 cm, positive HER2, non-targeted therapy and histological grade 3. The area under the ROC curve of random forest model for predicting pCR was 0.84 (95% CI 0.74-0.93); the sensitivity was 87.0% and specificity was 72.3% when the optimal cut-off value was 0.88. Conclusions:Low expression of Ki-67, positive axillary lymph node, tumor diameter >5cm, positive HER2, non-targeted therapy and histological grade 3 are risk factors of pCR in breast cancer patients after neoadjuvant chemotheapy. Random forest model helps to predict pCR in breast cancer patients after neoadjuvant chemotheapy.