Preoperative MRI radiomic analysis for predicting local tumor progression in colorectal liver metastases before microwave ablation

Della Corte, Angelo, Mori, Martina, Calabrese, Francesca, Palumbo, Diego, Ratti, Francesca, Palazzo, Gabriele, Pellegrini, Alessandro, Santangelo, Domenico, Ronzoni, Monica, Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813, Del Vecchio, Antonella, Fiorino, Claudio, Aldrighetti, Luca and De Cobelli, Francesco 2024. Preoperative MRI radiomic analysis for predicting local tumor progression in colorectal liver metastases before microwave ablation. International Journal of Hyperthermia 41 (1) , 2349059. 10.1080/02656736.2024.2349059

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Abstract

Purpose Radiomics may aid in predicting prognosis in patients with colorectal liver metastases (CLM). Consistent data is available on CT, yet limited data is available on MRI. This study assesses the capability of MRI-derived radiomic features (RFs) to predict local tumor progression-free survival (LTPFS) in patients with CLMs treated with microwave ablation (MWA). Methods All CLM patients with pre-operative Gadoxetic acid-MRI treated with MWA in a single institution between September 2015 and February 2022 were evaluated. Pre-procedural information was retrieved retrospectively. Two observers manually segmented CLMs on T2 and T1-Hepatobiliary phase (T1-HBP) scans. After inter-observer variability testing, 148/182 RFs showed robustness on T1-HBP, and 141/182 on T2 (ICC > 0.7). Cox multivariate analysis was run to establish clinical (CLIN-mod), radiomic (RAD-T1, RAD-T2), and combined (COMB-T1, COMB-T2) models for LTPFS prediction. Results Seventy-six CLMs (43 patients) were assessed. Median follow-up was 14 months. LTP occurred in 19 lesions (25%). CLIN-mod was composed of minimal ablation margins (MAMs), intra-segment progression and primary tumor grade and exhibited moderately high discriminatory power in predicting LTPFS (AUC = 0.89, p = 0.0001). Both RAD-T1 and RAD-T2 were able to predict LTPFS: (RAD-T1: AUC = 0.83, p = 0.0003; RAD-T2: AUC = 0.79, p = 0.001). Combined models yielded the strongest performance (COMB-T1: AUC = 0.98, p = 0.0001; COMB-T2: AUC = 0.95, p = 0.0003). Both combined models included MAMs and tumor regression grade; COMB-T1 also featured 10th percentile of signal intensity, while tumor flatness was present in COMB-T2. Conclusion MRI-based radiomic evaluation of CLMs is feasible and potentially useful for LTP prediction. Combined models outperformed clinical or radiomic models alone for LTPFS prediction.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Taylor and Francis
ISSN:	0265-6736
Date of First Compliant Deposit:	30 May 2024
Date of Acceptance:	25 April 2024
Last Modified:	30 May 2024 16:01
URI:	https://orca.cardiff.ac.uk/id/eprint/169319

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