ETD

Archivio digitale delle tesi discusse presso l'Università di Pisa

Tesi etd-01092019-111908


Tipo di tesi
Tesi di laurea magistrale LM6
Autore
ROMANO, ALESSANDRA
URN
etd-01092019-111908
Titolo
Radiomic CT analysis in patients with non-small cell lung cancer: correlation with liquid biopsy
Dipartimento
RICERCA TRASLAZIONALE E DELLE NUOVE TECNOLOGIE IN MEDICINA E CHIRURGIA
Corso di studi
MEDICINA E CHIRURGIA
Relatori
relatore Prof. Neri, Emanuele
Parole chiave
  • radiomics
  • biopsia liquida
  • radiomica
  • immunoterapia.
  • tc
  • carcinoma polmonare non a piccole cellule
  • NSCLC
  • liquid biopsy
  • Ct
Data inizio appello
29/01/2019
Consultabilità
Non consultabile
Data di rilascio
29/01/2089
Riassunto
Radiomics is a process of extraction and analysis of quantitative features from diagnostic images. Liquid Biopsy is a test done on sample of blood to look for cells or pieces of tumourigenic DNA circulating in blood. Radiomic and liquid biopsy have a great potential in onclogy. They are minimally invasive,easy to perform and can be repeated in patient follow-up visits. In our study, we have analysed CT of 10 patients with NSCLC(3-4 stage) in immunotherapeutic treatmnet. Liquid biopsy was performed at basal and at first clinical reassessment. Analysed tc refer to the same time, they have been uploaded to QUIBIM software, and then ROI have been segmented and texture analysis has been started to extract radiomic features. Afterwards, statisctical analysis showed Cluster Prominance as the most predictive feature; the lower its values are, the more they seem to be predictive of non-response to treatment. From correlation analysis it was found that there is a positive correlation between Information measure of correlation 2 and TNF-A,and between Volume Max and INF-G. Instead between D2D value and INF-G and TNF-A there is negative correlation. Negative correlation between Information measure of correlation 1 and IFN-G and TNF-A. Our results showed that both radiomics and liquid biopsy could represent efficient diagnostic and predective methods.
File