Abstract
Simple SummaryBreast cancer is the most common cancer in women worldwide. The axillary lymph node status is one of the main prognostic factors. Currently, the methods to define the lymph node status are invasive and not without sequelae (from biopsy to lymphadenectomy). Radiomics is a new tool, and highly varied, but with high potential that has already shown excellent results in numerous fields of application. In our study, we have developed a classifier validated on a relatively large number of patients, which is able to predict lymph node status using a combination of patients clinical features, primary breast cancer histological features and radiomics features based on 3 Tesla post contrast—MR images. This approach can accurately select breast cancer patients who may avoid unnecessary biopsy and lymphadenectomy in a non-invasive way.Background: axillary lymph node (LN) status is one of the main breast cancer prognostic factors and it is currently defined by invasive procedures. The aim of this study is to predict LN metastasis combining MRI radiomics features with primary breast tumor histological features and patients’ clinical data. Methods: 99 lesions on pre-treatment contrasted 3T-MRI (DCE). All patients had a histologically proven invasive breast cancer and defined LN status. Patients’ clinical data and tumor histological analysis were previously collected. For each tumor lesion, a semi-automatic segmentation was performed, using the second phase of DCE-MRI. Each segmentation was optimized using a convex-hull algorithm. In addition to the 14 semantics features and a feature ROI volume/convex-hull volume, 242 other quantitative features were extracted. A wrapper selection method selected the 15 most prognostic features (14 quantitative, 1 semantic), used to train the final learning model. The classifier used was the Random Forest. Results: the AUC-classifier was 0.856 (label = positive or negative). The contribution of each feature group was lower performance than the full signature. Conclusions: the combination of patient clinical, histological and radiomics features of primary breast cancer can accurately predict LN status in a non-invasive way.
Highlights
Breast cancer (BC) is the leading cause of death from cancer in women in Europe [1]
We have developed a classifier validated on a relatively large number of patients, which is able to predict lymph node status using a combination of patients clinical features, primary breast cancer histological features and radiomics features based on 3 Tesla post contrast—MR images
sentinel node biopsy (SNB) is an invasive procedure and with significant sequelae, including shoulder dysfunction, nerve damage, upper arm numbness, and lymphoedema [5,6]. It remains to be determined whether the emerging evidence for accurate diagnosis and adequate local control with SNB compared with ALN dissection (ALND), is accompanied by equivalent survival outcomes
Summary
Breast cancer (BC) is the leading cause of death from cancer in women in Europe [1]. This tumor is considered a pool of different kind of cancer, with various molecular subtypes and with distinct recurrence and survival rates. SNB is an invasive procedure and with significant sequelae, including shoulder dysfunction, nerve damage, upper arm numbness, and lymphoedema [5,6] It remains to be determined whether the emerging evidence for accurate diagnosis and adequate local control with SNB compared with ALN dissection (ALND), is accompanied by equivalent survival outcomes. It is well known that histopathological data of primary tumor, such as lymphovascular invasion, Ki-67 proliferation index, histological grade, estrogen receptor (ER) status and progesterone receptor (PgR) status, are predictors of SLN metastasis They are available postoperatively and cannot be used to guide decisions on performing SLN biopsy [7,8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
