scholarly journals Clinicopathological and Prognostic Characteristics of Malaysian Triple Negative Breast Cancer Patients Undergoing TAC Chemotherapy Regimen

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ahmad Aizat Abdul Aziz ◽  
Md Salzihan Md Salleh ◽  
Ravindran Ankathil
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Triple Negative ◽  
Axillary Lymph Node Metastasis ◽  
Clinicopathological Features ◽  
Axillary Lymph ◽  
Node Metastasis ◽  
Positive Axillary Lymph Node ◽  
Tac Chemotherapy

Triple negative breast cancer (TNBC) is associated with aggressive tumour phenotype and early tumour relapse following diagnosis. Generally, clinicopathological features such as tumour size, patient’s age at diagnosis, tumour histology subtypes, grade and stage, involvement of lymph nodes, and menopausal status are commonly used for predicting disease progression, prospects of recurrence, and treatment response. Prognostic value of clinicopathological features on Malaysian TNBC patients is limited. Thus, this study is aimed at investigating the association of clinicopathological features on disease-free survival (DFS) and overall survival (OS) of Malaysian TNBC patients undergoing TAC chemotherapy. Seventy-six (76) immunohistochemistry-confirmed TNBC patients were recruited. The clinicopathological features of TNBC patients were collected and recorded. Kaplan-Meier and log-rank followed by a Cox proportional hazard regression model were performed to evaluate the TNBC patients’ survival. Out of 76 TNBC patients, 25 were chemoresistant and 51 were chemoresponders to the TAC chemotherapy regimen. The overall 5-year cumulative DFS and OS of TNBC patients were 63.5% and 76.3%, respectively. Multivariate Cox analysis demonstrated that medullary and metaplastic histology subtypes and positive axillary lymph node metastasis were significant prognostic factors associated with relapse with adjusted HR: 5.76, 95% CI: 2.35, 14.08 and adjusted HR: 3.55, 95% CI: 1.44, 8.74, respectively. Moreover, TNBC patients with medullary and metaplastic histology subtypes and positive axillary lymph node metastases had a higher risk to death than patients who had infiltrating ductal carcinoma and negative axillary lymph node metastasis (adjusted HR: 8.30, 95% CI: 2.38, 28.96 and adjusted HR: 6.12, 95% CI: 1.32, 28.42, respectively). Our results demonstrate the potential use of medullary and metaplastic histology subtype and positive axillary lymph node metastasis as a potential biomarker in predicting relapse and survival of the TNBC patients. This warrants further studies on intensification of chemotherapy and also identification and development of targeted therapy to reduce relapses and improve survival of TNBC patients.

scholarly journals Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 757
Author(s):  
Sanaz Samiei ◽  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey Primakov ◽  
Marc B. I. Lobbes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Mr Images ◽  
Node Metastasis

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51–68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41–0.74 and 0.48–0.89 in the training cohorts, respectively, and between 0.30–0.98 and 0.37–0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

Sign in / Sign up

Export Citation Format

Share Document