Background: Microvascular invasion (MVI) is a necessary step in the metastatic evolution of hepatocellular carcinoma liver tumors. Predicting the onset of MVI in the initial stages of the tumors could improve patient survival and the quality of life. In this study, the possibility of using radiomic features to predict the presence/absence of MVI was evaluated. Methods: Multiphase contrast-enhanced computed tomography (CECT) images were collected from 49 patients, and the radiomic features were extracted from the tumor region and the zone of transition. The most-relevant features were selected; the dataset was balanced, and the presence/absence of MVI was classified. The dataset was split into training and test sets in three ways using cross-validation: the first applied feature selection and dataset balancing outside cross-validation; the second applied dataset balancing outside and feature selection inside; the third applied the entire pipeline inside the cross-validation procedure. Results: The features from the tumor areas on CECT showed both the portal and the arterial phases to be the most predictive. The three pipelines showed receiver operating characteristic area under the curve (ROC AUC) scores of 0.89, 0.84, and 0.61, respectively. Conclusions: The results obtained confirmed the efficiency of multiphase CECT and the ZOT in detecting MVI. The results showed a significant difference in the performance of the three pipelines, highlighting that a non-rigorous pipeline design could lead to model performance and generalization capabilities that are too optimistic.

Biondi R., Renzulli M., Golfieri R., Curti N., Carlini G., Sala C., et al. (2023). Machine Learning Pipeline for the Automated Prediction of MicrovascularInvasion in HepatocellularCarcinomas. APPLIED SCIENCES, 13(3), 1-11 [10.3390/app13031371].

Machine Learning Pipeline for the Automated Prediction of MicrovascularInvasion in HepatocellularCarcinomas

Biondi R.;Renzulli M.;Golfieri R.;Curti N.;Carlini G.
;
Sala C.;Giampieri E.;Remondini D.;Vara G.;Cattabriga A.;Cocozza M. A.;Pastore L. V.;Brandi N.;Palmeri A.;Tanzarella G.;Cescon M.;Ravaioli M.;Castellani G.;
2023

Abstract

Background: Microvascular invasion (MVI) is a necessary step in the metastatic evolution of hepatocellular carcinoma liver tumors. Predicting the onset of MVI in the initial stages of the tumors could improve patient survival and the quality of life. In this study, the possibility of using radiomic features to predict the presence/absence of MVI was evaluated. Methods: Multiphase contrast-enhanced computed tomography (CECT) images were collected from 49 patients, and the radiomic features were extracted from the tumor region and the zone of transition. The most-relevant features were selected; the dataset was balanced, and the presence/absence of MVI was classified. The dataset was split into training and test sets in three ways using cross-validation: the first applied feature selection and dataset balancing outside cross-validation; the second applied dataset balancing outside and feature selection inside; the third applied the entire pipeline inside the cross-validation procedure. Results: The features from the tumor areas on CECT showed both the portal and the arterial phases to be the most predictive. The three pipelines showed receiver operating characteristic area under the curve (ROC AUC) scores of 0.89, 0.84, and 0.61, respectively. Conclusions: The results obtained confirmed the efficiency of multiphase CECT and the ZOT in detecting MVI. The results showed a significant difference in the performance of the three pipelines, highlighting that a non-rigorous pipeline design could lead to model performance and generalization capabilities that are too optimistic.
2023
Biondi R., Renzulli M., Golfieri R., Curti N., Carlini G., Sala C., et al. (2023). Machine Learning Pipeline for the Automated Prediction of MicrovascularInvasion in HepatocellularCarcinomas. APPLIED SCIENCES, 13(3), 1-11 [10.3390/app13031371].
Biondi R.; Renzulli M.; Golfieri R.; Curti N.; Carlini G.; Sala C.; Giampieri E.; Remondini D.; Vara G.; Cattabriga A.; Cocozza M.A.; Pastore L.V.; Br...espandi
File in questo prodotto:
File Dimensione Formato  
applsci-13-01371_red.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 427.41 kB
Formato Adobe PDF
427.41 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/11585/918681
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 2
social impact