| Autor: |
Andrea Delli Pizzi, Domenico Mastrodicasa, Antonio Maria Chiarelli, Piero Chiacchiaretta, Riccardo Luberti, Pierpaolo Croce, Mario Sguera, Concetta Torrione, Camilla Marinelli, Chiara Marchetti, Angelucci Domenico, Giulio Cocco, Angela Di Credico, Alessandro Russo, Claudia D'Eramo, Stefano Sensi, Marzia Muzi, Massimo Caulo |
| Rok vydání: |
2022 |
| Zdroj: |
SSRN Electronic Journal. |
| ISSN: |
1556-5068 |
| DOI: |
10.2139/ssrn.4034469 |
| Popis: |
Oncotype Dx is a genetic assay providing a recurrence score (RS) correlated with the risk of cancer recurrence and adjuvant treatment response in breast carcinoma. We investigated the ability of an MRI-based radiomics approach to predict the risk of tumor recurrence in breast cancer. A total of 62 patients with biopsy-proved ER+/HER2- early breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS>25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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