![]() ![]() Tumor morphology, vascularity, and cell density can be affected by NAC. Therefore, early and reliable predictors of tumor response are needed.īreast MRI is recommended to monitor therapy response during NAC, with dynamic contrast-enhanced MRI (DCE-MRI) providing vascular information to evaluate tumor presence with high sensitivity. The earlier accurate predictions are made, the more likely patients are to benefit. Thus, predicting the likelihood of pCR is important for the development of improved and personalized treatment plans. If we can confidently predict that a patient has a high probability of pCR, surgery can be safely postponed or even omitted. Achieving pCR can result in a more favorable long-term prognosis. Less than 10% to 50% of patients achieve pathological complete response (pCR) through NAC depending on receptor status and subtype. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.Īs a standard care for locally advanced breast cancer, neoadjuvant chemotherapy (NAC) is clinically useful to downsize and downstage tumors and reduce the extent of surgery from mastectomy to breast conservation. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. ![]() Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. Delta-radiomics features were computed in each contrast phases. Radiomic features were extracted from the postcontrast early, peak, and delay phases. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |