Abstract
PurposePathologic complete response (pCR) after neoadjuvant therapy is an important indicator of long-term prognosis and the primary endpoint of many neoadjuvant studies. For breast cancer patients who do not achieve pCR, prognostic indicators related to prognosis are particularly important. This study is constructing a prediction model with more accurate and reliable prediction results by combining multiple clinicopathological factors, so as to provide a more accurate decision-making basis for subsequent clinical treatment.Patients and MethodsIn this study, 1,009 cases of invasive breast cancer and surgically resected after neoadjuvant therapy from 2010 to 2017. All indicators in this trial were interpreted in a double-blind manner by two pathologists with at least 10 years of experience, including histological grading, Tils, ER, PR, HER2, and Ki67. The prediction model used R language to calculate the calibration degree and ROC curve of the prediction model in the training set and validation set.ResultsThrough univariate survival analysis, the results showed histological grade (P=0.037), clinical stage (P<0.001), HER2 (P=0.044), RCB class (P<0.001), Tils (P<0.001), lymph node status (P =0.049), MP grade (P=0.013) are related to OS in non-PCR patients after neoadjuvant. Data were analyzed by substituting in a multivariate analysis, and the results were that clinical stage, HER2, RCB grading, and Tils grading were correlated with OS in non-PCR patients after neoadjuvant therapy for breast cancer. Among all cases in the training set, the prediction model predicted that the 3-year survival AUC value was 0.95 and 5-year survival AUC value was 0.79, and the RCB classification of 3-year survival and 5-year survival were 0.70 and 0.67, respectively, which proved that the prediction model could predict the OS of non-PCR patients after neoadjuvant therapy for breast cancer more accurately than the RCB classification, and showed the same results in HR, HER2+, and TN classifications. It also showed the same results in validation set.ConclusionThese data indicate that the predicted values of the prediction model developed in this study match the actual survival rates without underestimating the mortality risk and have a relatively accurate prediction effect.
Highlights
Neoadjuvant therapy (NAC) allows inoperable patients with advanced breast cancer to achieve downstaging, which in turn makes the patient operable and facilitates breast-conserving surgery [1]
The data were analyzed by substituting in a multivariate analysis, and the results were that clinical stage, HER2, residual cancer burden (RCB) grading, and tumor-infiltrating lymphocytes (Tils) grading were correlated with OS in non-PCR patients after neoadjuvant therapy for breast cancer
Neoadjuvant therapy for certain patients whose masses are too large or who are otherwise inoperable, so as to downstage their cancer to meet the criteria for surgery or to control disease progression and prolong survival, is widely applied in the treatment of breast cancer due to its remarkable results [18,19,20]. Pathologic complete response (pCR) is a recognized endpoint in clinical trials of neoadjuvant therapy, and many studies have shown that pCR is independently correlated with improved survival outcomes compared to non-pCR patients, and can serve as a useful prognostic indicator [21]
Summary
Neoadjuvant therapy (NAC) allows inoperable patients with advanced breast cancer to achieve downstaging, which in turn makes the patient operable and facilitates breast-conserving surgery [1]. In the ongoing study on prognostic factors correlated with breast cancer after neoadjuvant therapy, it has been shown that individual pathological factors have more or less limitations, and the combination of multiple prognostic indicators can provide a better prognosis and guide the decision-making for subsequent clinical treatment [8]. In order to better predict the prognosis of non-pCR patients after neoadjuvant therapy for breast cancer, this study counted the prognostic-related factors after neoadjuvant therapy for breast cancer and constructed a prediction model using R software, with the aim of constructing a prediction model with more accurate and reliable prediction results by combining multiple clinicopathological factors, so as to provide a more accurate decision-making basis for subsequent clinical treatment
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