医学
烧蚀
无线电技术
一致性
核医学
逻辑回归
放射科
比例危险模型
一致相关系数
内科学
数学
统计
作者
Femke C.R. Staal,Marjaneh Taghavi,Denise J. van der Reijd,Fernando Gómez,Farshad Imani,Elisabeth G. Klompenhouwer,David B. Meek,Sander Roberti,Myrte de Boer,Doenja M J Lambregts,R. G. H. Beets-Tan,Monique Maas
标识
DOI:10.1016/j.ejrad.2021.109773
摘要
Purpose To assess whether CT-based radiomics of the ablation zone (AZ) can predict local tumour progression (LTP) after thermal ablation for colorectal liver metastases (CRLM). Materials and methods Eighty-two patients with 127 CRLM were included. Radiomics features (with different filters) were extracted from the AZ and a 10 mm periablational rim (PAR)on portal-venous-phase CT up to 8 weeks after ablation. Multivariable stepwise Cox regression analyses were used to predict LTP based on clinical and radiomics features. Performance (concordance [c]-statistics) of the different models was compared and performance in an ‘independent’ dataset was approximated with bootstrapped leave-one-out-cross-validation (LOOCV). Results Thirty-three lesions (26 %) developed LTP. Median follow-up was 21 months (range 6−115). The combined model, a combination of clinical and radiomics features, included chemotherapy (HR 0.50, p = 0.024), cT-stage (HR 10.13, p = 0.016), lesion size (HR 1.11, p = <0.001), AZ_Skewness (HR 1.58, p = 0.016), AZ_Uniformity (HR 0.45, p = 0.002), PAR_Mean (HR 0.52, p = 0.008), PAR_Skewness (HR 1.67, p = 0.019) and PAR_Uniformity (HR 3.35, p < 0.001) as relevant predictors for LTP. The predictive performance of the combined model (after LOOCV) yielded a c-statistic of 0.78 (95 %CI 0.65−0.87), compared to the clinical or radiomics models only (c-statistic 0.74 (95 %CI 0.58−0.84) and 0.65 (95 %CI 0.52−0.83), respectively). Conclusion Combining radiomics features with clinical features yielded a better performing prediction of LTP than radiomics only. CT-based radiomics of the AZ and PAR may have potential to aid in the prediction of LTP during follow-up in patients with CRLM.
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