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CT-based radiogenomics of intrahepatic cholangiocarcinoma.

作者信息

Viganò Luca, Zanuso Valentina, Fiz Francesco, Cerri Luca, Laino Maria Elena, Ammirabile Angela, Ragaini Elisa Maria, Viganò Samuele, Terracciano Luigi Maria, Francone Marco, Ieva Francesca, Di Tommaso Luca, Rimassa Lorenza

机构信息

Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Hepatobiliary Unit, Department of Minimally Invasive General & Oncologic Surgery, Humanitas Gavazzeni University Hospital, Bergamo, Italy.

Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Medical Oncology and Hematology Unit, Humanitas Cancer Center, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.

出版信息

Dig Liver Dis. 2025 Jan;57(1):118-124. doi: 10.1016/j.dld.2024.06.033. Epub 2024 Jul 12.

Abstract

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is an aggressive disease with increasing incidence and its genetic alterations could be the target of systemic therapies.

AIMS

To elucidate if radiomics extracted from computed tomography (CT) may non-invasively predict ICC genetic alterations.

METHODS

All consecutive patients with a diagnosis of a mass-forming ICC (01/2016-06/2022) were considered. Inclusion criteria were availability of a high-quality contrast-enhanced CT and molecular profiling by NGS or FISH for FGFR2 fusion/rearrangement. The CT scan at diagnosis was considered. Genetic analyses were performed on surgical specimens (resectable patients) or biopsies (unresectable ones). The radiomic features were extracted using the LifeX software. Multivariate predictive models of the commonest genetic alterations were built.

RESULTS

In the 90 enrolled patients (58 NGS/32 FISH, median age 65 years), the most common genetic alterations were FGFR2 (20/90), IDH1 (10/58), and KRAS (9/58). At internal validation, the combined clinical-radiomic models achieved the best performance for the prediction of FGFR2 (AUC = 0.892) and IDH1 status (AUC = 0.819), outperforming the pure clinical and radiomic models. The radiomic model for predicting KRAS mutations achieved an AUC = 0.767 (vs. 0.660 of the clinical model) without further improvements with the addition of clinical features.

CONCLUSIONS

CT-based radiomics provides a reliable non-invasive prediction of ICC genetic status with a major impact on therapeutic strategies.

摘要

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