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Tesi etd-06302020-210240


Thesis type
Tesi di laurea specialistica LC6
Author
BORRI, FRANCESCO
URN
etd-06302020-210240
Title
Breast Cancer MRI and the molecular receptors status: does radiomics predict them?
Struttura
RICERCA TRASLAZIONALE E DELLE NUOVE TECNOLOGIE IN MEDICINA E CHIRURGIA
Corso di studi
MEDICINA E CHIRURGIA
Supervisors
relatore Prof. Neri, Emanuele
correlatore Dott.ssa Marini, Carolina
Parole chiave
  • breast
  • DCE-MRI
  • cancer subtype
  • hormonal receptors
  • radiomic
  • target therapy
Data inizio appello
20/07/2020;
Consultabilità
Secretata d'ufficio
Data di rilascio
20/07/2090
Riassunto analitico
Breast cancer is the second main cause of mortality for women in the world.
The knowledge of the presence or absence of specific molecular receptors in the tumor, such as the estrogen receptor (ER), the progesterone receptor (PR) and the human epidermal growth factor receptor 2 (HER2), is fundamental in order to set up a personalized therapy.
Radiomics is an interesting field of study that allows to extract and analyze, starting from biomedical images, data that can provide decision support in clinical practice.

The aim of our study was to verify if there are radiomic features that may predict, starting from magnetic resonance images, the state of the hormone receptors of the breast tumors analyzed in order to support decision-making and the therapeutic process.

The population of this study includes 100 patients having breast cancer subjected to magnetic resonance enhanced by dynamic contrast (DCE-MRI) at our center. All tumors were confirmed by a biopsy, and an immunohistochemical analysis was performed for each of them. A radiologist using QUIBIM PRECISION ® 2.3, identified, for each lesion, a region of interest, and segmented it manually. By the information contained in the segmented ROIs, 29 radiomic features were extracted. One feature (Sum Average Value) resulted statistically significant (p<0,05). We identified a Discriminant model that allowed us to find a value of D thanks to which we were able to stratify the risk that a patient, given the values of Sum Average Value extracted, results hormone receptors positive.
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