Chemotherapy-associated liver injuries (CALI) have a major clinical impact, but their non-invasive diagnosis is still an unmet need. The present work aims at presenting a web-app for personalized risk prediction of developing CALI, elucidating the contribution of radiomic analysis. Patients undergoing liver resection for colorectal metastases after oxaliplatin-based or irinotecan-based chemotherapy between January 2018 and February 2020 were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma. Multivariate logistic regression models and CART were applied to identify predictors and were internally validated. Results show that radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves diagnosis of CALI.

Virtual biopsy in action: a radiomic-based model for CALI prediction

Francesca Ieva;Giulia Baroni;Lara Cavinato;Chiara Masci;
2021

Abstract

Chemotherapy-associated liver injuries (CALI) have a major clinical impact, but their non-invasive diagnosis is still an unmet need. The present work aims at presenting a web-app for personalized risk prediction of developing CALI, elucidating the contribution of radiomic analysis. Patients undergoing liver resection for colorectal metastases after oxaliplatin-based or irinotecan-based chemotherapy between January 2018 and February 2020 were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma. Multivariate logistic regression models and CART were applied to identify predictors and were internally validated. Results show that radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves diagnosis of CALI.
Book of short papers SIS 2021
9788891927361
Radiomics, Machine Learning, Personalized Medicine, Variable Selection, Virtual Biopsy
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1177646
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