Neoadjuvant Systemic Therapy For Breast Cancer Research Articles

Integration of Clinical and CT-Based Radiomic Features for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Systemic Therapy in Breast Cancer.

The purpose of the present study was to examine the potential of a machine learning model with integrated clinical and CT-based radiomics features in predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NST) in breast cancer. Contrast-enhanced CT was performed in 329 patients with breast tumors (n = 331) before NST. Pyradiomics was used for feature extraction, and 107 features of seven classes were extracted. Feature selection was performed on the basis of the intraclass correlation coefficient (ICC), and six ICC thresholds (0.7-0.95) were examined to identify the feature set resulting in optimal model performance. Clinical factors, such as age, clinical stage, cancer cell type, and cell surface receptors, were used for prediction. We tried six machine learning algorithms, and clinical, radiomics, and clinical-radiomics models were trained for each algorithm. Radiomics and clinical-radiomics models with gray level co-occurrence matrix (GLCM) features only were also built for comparison. The linear support vector machine (SVM) regression model trained with radiomics features of ICC ≥0.85 in combination with clinical factors performed the best (AUC = 0.87). The performance of the clinical and radiomics linear SVM models showed statistically significant difference after correction for multiple comparisons (AUC = 0.69 vs. 0.78; p < 0.001). The AUC of the radiomics model trained with GLCM features was significantly lower than that of the radiomics model trained with all seven classes of radiomics features (AUC = 0.85 vs. 0.87; p = 0.011). Integration of clinical and CT-based radiomics features was helpful in the pretreatment prediction of pCR to NST in breast cancer.

USP-11 as a Predictive and Prognostic Factor Following Neoadjuvant Therapy in Women With Breast Cancer

USP-11, a member of the ubiquitin-specific protease family, has emerged as an essential regulator of double-strand break repair. Few studies have shown that silencing USP-11 led to hypersensitivity to poly(ADP-ribose) polymerase inhibition, ionizing radiation, and DNA-damaging agents. We sought to examine the predictive and prognostic relevance of USP-11 in patients treated with neoadjuvant systemic therapy (NST) for breast cancer. Fifty-six women who were treated with NST for breast cancer between 1999 and 2004 were included in the study. The Kaplan-Meier product-limit method was used to estimate disease-free survival and overall survival rates. Logistic regression models were fit to determine the associations between USP-11 status, pathological complete response (pCR), and survival. Sixteen patients (29%) had high-USP-11-expressing tumors, and 40 (71%) patients had low-USP-11-expressing tumors. No significant differences were observed in pCR rates with respect to USP-11 status. At a median follow-up of 7.4 years, 33 patients (59%) experienced a disease recurrence or death. Patients with high-USP-11-expressing tumors had a higher risk of recurrence (odds ratio [OR], 3.87; 95% confidence interval [CI], 1.51-9.93; P = 0.005) and death (OR, 6.03; 95% CI, 2.00-18.17; P = 0.001) than those with low-USP-11-expressing tumors. Patients who did not achieve a pCR had an increased risk of recurrence (OR, 5.16; 95% CI, 1.16-23.07; P = 0.03). Our data indicate that USP-11 is not a predictor of a pCR after anthracycline-taxane-containing NST for breast cancer. Low USP-11 expression was independently correlated with better survival outcomes.