Cholangiocarcinoma (CCA) is the second most common liver malignancy and the incidence and mortality rates of this disease are worldwide increasing. This paper deals with the problem of Intrahepatic Cholangiocarcinoma (IH-CCA) classification using Computed Tomography (CT) images. Precisely, a radiomics-based approach is proposed by exploiting abdominal volumetric CT data in order to differentiate large bile duct from small bile duct IH-CCA. The developed method relies on the investigation of intrinsic discriminative properties of CT scans according to feature selection methods. The effectiveness of the proposed method is proved by enrolling in the study a total of 26 patients, including 16 patients with large bile duct and 10 with small bile duct pathological disease, respectively. The conducted tests have shown that our approach is a baseline to provide an efficient classification process with a low computational cost in order to facilitate clinical decision-making procedures.
Losquadro, C., Conforto, S., Schmid, M., Giunta, G., Rengo, M., Cardinale, V., et al. (2021). Small and Large Bile Ducts Intrahepatic Cholangiocarcinoma Classification: A Preliminary Feature-Based Study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 237-244). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-89128-2_23].
Small and Large Bile Ducts Intrahepatic Cholangiocarcinoma Classification: A Preliminary Feature-Based Study
Losquadro C.;Conforto S.;Schmid M.;Giunta G.;
2021-01-01
Abstract
Cholangiocarcinoma (CCA) is the second most common liver malignancy and the incidence and mortality rates of this disease are worldwide increasing. This paper deals with the problem of Intrahepatic Cholangiocarcinoma (IH-CCA) classification using Computed Tomography (CT) images. Precisely, a radiomics-based approach is proposed by exploiting abdominal volumetric CT data in order to differentiate large bile duct from small bile duct IH-CCA. The developed method relies on the investigation of intrinsic discriminative properties of CT scans according to feature selection methods. The effectiveness of the proposed method is proved by enrolling in the study a total of 26 patients, including 16 patients with large bile duct and 10 with small bile duct pathological disease, respectively. The conducted tests have shown that our approach is a baseline to provide an efficient classification process with a low computational cost in order to facilitate clinical decision-making procedures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
