Predictability of the radiological response to Yttrium-90 transarterial radioembolization by dynamic magnetic resonance imaging-based radiomics analysis in patients with intrahepatic cholangiocarcinoma


BALLI H. T., PİŞKİN F. C., YÜCEL S. P., Sözütok S., Özgül D., Aikimbaev K.

Diagnostic and Interventional Radiology, vol.30, no.3, pp.193-199, 2024 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 3
  • Publication Date: 2024
  • Doi Number: 10.4274/dir.2023.222025
  • Journal Name: Diagnostic and Interventional Radiology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.193-199
  • Keywords: intrahepatic cholangiocarcinoma, magnetic resonance imaging, radioembolization, radiological response, Radiomics
  • Çukurova University Affiliated: Yes

Abstract

PURPOSE The study aims to investigate the predictability of the radiological response in intrahepatic cholan-giocarcinoma (iCC) patients undergoing Yttrium-90 transarterial radioembolization (TARE) with a combined model built on dynamic magnetic resonance imaging (MRI)-based radiomics and clinical features. METHODS Thirty-six naive iCC patients who underwent TARE were included in this study. The tumor segmentation was performed on the axial T2-weighted (T2W) without fat suppression, axial T2W with fat suppression, and axial T1-weighted (T1W) contrast-enhanced (CE) sequence in equilibrium phase (Eq). At the sixth month MRI follow-up, all patients were divided into responders and non-respond-ers according to the modified Response Evaluation Criteria in Solid Tumors. Subsequently, a ra-diomics score (rad-score) and a combined model of the rad-score and clinical features for each sequence were generated and compared between the groups. RESULTS Thirteen (36.1%) patients were considered responders, and the remaining 23 (63.9%) were non-re-sponders. Responders exhibited significantly lower rad-scores than non-responders (P < 0.050 for all sequences). The radiomics models showed good discriminatory ability with an area under the curve (AUC) of 0.696 [95% confidence interval (CI), 0.522–0.870] for the axial T1W-CE-Eq, AUC of 0.839 (95% CI, 0.709–0.970) for the axial T2W with fat suppression, and AUC of 0.836 (95% CI, 0.678– 0.995) for the axial T2W without fat suppression. CONCLUSION Radiomics models created by pre-treatment MRIs can predict the radiological response to Yttri-um-90 TARE in iCC patients with high accuracy. Combining radiomics with clinical features could in-crease the power of the test. Large-scale studies of multi-parametric MRIs with internal and external validations are needed to determine the clinical value of radiomics in iCC patients.