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 = Biopsia virtuale basata su analisi radiomica per la previsione di CALI / F. Ieva, G. Baroni, L. Cavinato, C. Masci, G. Costa, F. Fiz, A. Chiti, L. Viganò - In: Book of short papers SIS 2021 / [a cura di] C. Perna, N. Salvati, F. Schirripa Spagnolo. - [s.l] : Pearson, 2021. - ISBN 9788891927361. - pp. 1438-1443 (( Intervento presentato al 50. convegno Meeting of the Italian Statistical Society tenutosi a Pisa nel 2021.

Virtual biopsy in action: a radiomic-based model for CALI prediction = Biopsia virtuale basata su analisi radiomica per la previsione di CALI

C. 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.
Radiomics; Machine Learning; Personalized Medicine; Variable Selection; Virtual Biopsy
Settore STAT-01/A - Statistica
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1151779
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