Breast cancer is the most common tumour in women and it is characterized by a huge variety of clinical and histological scenarios and imaging pattern. The axillary lymph node metastases presence or absence is one of the most important prognostic factors affecting the loco-regional recurrence and the overall survival. The lymph node status is usually determined by an histological exam, an invasive procedure that could result in complications. This work aims to provide a safer and non-invasive prognostic approach by introducing a radiomics-based method that predicts axillary lymph node metastasis. It combines primary tumor histological features and patients clinical data with quantitative measures extracted from the MR images. To compute these latter quantities we determine the convex hull of the ROIs and we introduce the Three Orthogonal Planes-Local Binary Pattern (TOP-LBP). On 99 samples the approach achieves a promising AUC equal to 85.6%.
Radiomics-based non-invasive lymph node metastases prediction in breast cancer / Cordelli, E; Sicilia, R; Santucci, D; De Felice, C; Quattrocchi, Cc; Beomonte Zobel, B; Iannello, G; Soda, P. - (2020), pp. 486-491. (Intervento presentato al convegno IEEE Symposium on Computer-Based Medical Systems tenutosi a Rochester, MN, USA nel 28-30 July 2020) [10.1109/CBMS49503.2020.00098].
Radiomics-based non-invasive lymph node metastases prediction in breast cancer
Quattrocchi CC;
2020-01-01
Abstract
Breast cancer is the most common tumour in women and it is characterized by a huge variety of clinical and histological scenarios and imaging pattern. The axillary lymph node metastases presence or absence is one of the most important prognostic factors affecting the loco-regional recurrence and the overall survival. The lymph node status is usually determined by an histological exam, an invasive procedure that could result in complications. This work aims to provide a safer and non-invasive prognostic approach by introducing a radiomics-based method that predicts axillary lymph node metastasis. It combines primary tumor histological features and patients clinical data with quantitative measures extracted from the MR images. To compute these latter quantities we determine the convex hull of the ROIs and we introduce the Three Orthogonal Planes-Local Binary Pattern (TOP-LBP). On 99 samples the approach achieves a promising AUC equal to 85.6%.File | Dimensione | Formato | |
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2020 - Radiomics-based non-invasive lymph node metastases prediction in breast cancer.pdf
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Radiomics-Based_Non-Invasive_Lymph_Node_Metastases_Prediction_in_Breast_Cancer.pdf
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