Predicting local tumour progression after ablation for colorectal liver metastases: CT-based radiomics of the ablation zone

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). Eighty-two patients with 127 CRLM were included. Radiomics features (with different filters) were extracted from the AZ and a 10 mm...

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Published in:European journal of radiology 2021-08, Vol.141, p.109773-109773, Article 109773
Main Authors: Staal, F.C.R., Taghavi, M., van der Reijd, D.J., Gomez, F.M., Imani, F., Klompenhouwer, E.G., Meek, D., Roberti, S., de Boer, M., Lambregts, D.M.J., Beets-Tan, R.G.H., Maas, M.
Format: Article
Language:English
Subjects:
Ablation techniques
Colorectal neoplasms
Liver
Local
Neoplasm metastasis
Neoplasm recurrence
Radiofrequency ablation
Tomography
X-ray computed
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container_title European journal of radiology
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creator Staal, F.C.R.
Taghavi, M.
van der Reijd, D.J.
Gomez, F.M.
Imani, F.
Klompenhouwer, E.G.
Meek, D.
Roberti, S.
de Boer, M.
Lambregts, D.M.J.
Beets-Tan, R.G.H.
Maas, M.
description 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). 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). 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 = 
doi_str_mv 10.1016/j.ejrad.2021.109773
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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). 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 = &lt;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 &lt; 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). Combining radiomics features with clinical features yielded a better performing prediction of LTP than radiomics only. 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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 = &lt;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 &lt; 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). Combining radiomics features with clinical features yielded a better performing prediction of LTP than radiomics only. 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subjects Ablation techniques
Colorectal neoplasms
Liver
Local
Neoplasm metastasis
Neoplasm recurrence
Radiofrequency ablation
Tomography
X-ray computed
title Predicting local tumour progression after ablation for colorectal liver metastases: CT-based radiomics of the ablation zone
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