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-:(2020), pp. 486-491. (Intervento presentato al convegno 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 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%.
2020
2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS),
Los Alamitos, California
IEEE Computer Society
Cordelli, E; Sicilia, R; Santucci, D; De Felice, C; Quattrocchi, Cc; Beomonte Zobel, B; Iannello, G; Soda, P
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-:(2020), pp. 486-491. (Intervento presentato al convegno 33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 tenutosi a Rochester, MN, USA nel 28-30 July 2020) [10.1109/CBMS49503.2020.00098].
File in questo prodotto:
File Dimensione Formato  
2020 - Radiomics-based non-invasive lymph node metastases prediction in breast cancer.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 337.57 kB
Formato Adobe PDF
337.57 kB Adobe PDF   Visualizza/Apri
Radiomics-Based_Non-Invasive_Lymph_Node_Metastases_Prediction_in_Breast_Cancer.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 338.39 kB
Formato Adobe PDF
338.39 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/372506
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
  • OpenAlex ND
social impact