The aim of this work is to implement an automatic method to predict and classify complete responders (CRs) patients, affected by rectal cancer and treated with neoadjuvant radiochemotherapy (RCT), by exploiting the tumor regression grade (MR-TRG) estimated by magnetic resonance imaging. For the purpose of the study, a total of 65 patients were enrolled and the magnetic resonance (MR) examinations to calculate TRG were performed using a 3.0 T scanner. By processing and testing patients’ data, the algorithm allows to determine the optimum threshold dividing CRs patients from patients that are considered non responders. The prediction accuracy of the classifier was investigated by using cross-validation statistical analysis in order to automatically determine the best testing rule. After collecting the outcomes of the performed cross-validation, the obtained results show the percentages of correct instances and misclassified patients. The automatic classification of CRs appears to be feasible and can be considered as a helpful method to predict CRs assisting clinicians to predict disease prognoses and patient survival prospects in order to provide treatments’ customization.
Computer aided effective prediction of complete responders after radiochemotherapy based on tumor regression grade estimated by MR imaging / Losquadro, C.; Conforto, S.; Schmid, M.; Giunta, G.; Rengo, M.; Caruso, D.; Laghi, A.. - (2019), pp. 257-266. - LECTURE NOTES IN COMPUTATIONAL VISION AND BIOMECHANICS. [10.1007/978-3-030-32040-9_27].
Computer aided effective prediction of complete responders after radiochemotherapy based on tumor regression grade estimated by MR imaging
Conforto S.Secondo
;Schmid M.;Giunta G.
;Rengo M.;Caruso D.Penultimo
;Laghi A.Ultimo
2019
Abstract
The aim of this work is to implement an automatic method to predict and classify complete responders (CRs) patients, affected by rectal cancer and treated with neoadjuvant radiochemotherapy (RCT), by exploiting the tumor regression grade (MR-TRG) estimated by magnetic resonance imaging. For the purpose of the study, a total of 65 patients were enrolled and the magnetic resonance (MR) examinations to calculate TRG were performed using a 3.0 T scanner. By processing and testing patients’ data, the algorithm allows to determine the optimum threshold dividing CRs patients from patients that are considered non responders. The prediction accuracy of the classifier was investigated by using cross-validation statistical analysis in order to automatically determine the best testing rule. After collecting the outcomes of the performed cross-validation, the obtained results show the percentages of correct instances and misclassified patients. The automatic classification of CRs appears to be feasible and can be considered as a helpful method to predict CRs assisting clinicians to predict disease prognoses and patient survival prospects in order to provide treatments’ customization.File | Dimensione | Formato | |
---|---|---|---|
Losquadro_Computer aided effective_2019.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
507.46 kB
Formato
Adobe PDF
|
507.46 kB | Adobe PDF | Contatta l'autore |
copertina_VipIMAGE2019_2019.pdf
solo gestori archivio
Tipologia:
Altro materiale allegato
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
181.49 kB
Formato
Adobe PDF
|
181.49 kB | Adobe PDF | Contatta l'autore |
frontespizio_VipIMAGE2019_2019.pdf
solo gestori archivio
Tipologia:
Altro materiale allegato
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
47.42 kB
Formato
Adobe PDF
|
47.42 kB | Adobe PDF | Contatta l'autore |
indice_VipIMAGE_2019.pdf
solo gestori archivio
Tipologia:
Altro materiale allegato
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
142.57 kB
Formato
Adobe PDF
|
142.57 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.