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Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?

OBJECTIVES: To determine whether radiomics data can predict local tumor progression (LTP) following radiofrequency ablation (RFA) of colorectal cancer (CRC) lung metastases on the first revaluation chest CT. METHODS: This case–control single-center retrospective study included 95 distinct lung metas...

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Autores principales: Crombé, Amandine, Palussière, Jean, Catena, Vittorio, Cazayus, Maxime, Fonck, Marianne, Béchade, Dominique, Buy, Xavier, Markich, Romane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The British Institute of Radiology. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230393/
https://www.ncbi.nlm.nih.gov/pubmed/37066833
http://dx.doi.org/10.1259/bjr.20201371
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author Crombé, Amandine
Palussière, Jean
Catena, Vittorio
Cazayus, Maxime
Fonck, Marianne
Béchade, Dominique
Buy, Xavier
Markich, Romane
author_facet Crombé, Amandine
Palussière, Jean
Catena, Vittorio
Cazayus, Maxime
Fonck, Marianne
Béchade, Dominique
Buy, Xavier
Markich, Romane
author_sort Crombé, Amandine
collection PubMed
description OBJECTIVES: To determine whether radiomics data can predict local tumor progression (LTP) following radiofrequency ablation (RFA) of colorectal cancer (CRC) lung metastases on the first revaluation chest CT. METHODS: This case–control single-center retrospective study included 95 distinct lung metastases treated by RFA (in 39 patients, median age: 63.1 years) with a contrast-enhanced CT-scan performed 3 months after RFA. Forty-eight radiomics features (RFs) were extracted from the 3D-segmentation of the ablation zone. Several supervised machine-learning algorithms were trained in 10-fold cross-validation on reproducible RFs to predict LTP, with/without denoising CT-scans. An unsupervised classification based on reproducible RFs was built with k-means algorithm. RESULTS: There were 20/95 (26.7%) relapses within a median delay of 10 months. The best model was a stepwise logistic regression on raw CT-scans. Its cross-validated performances were: AUROC = 0.72 (0.58–0.86), area under the Precision-Recall curve (AUPRC) = 0.44. Cross-validated balanced-accuracy, sensitivity and specificity were 0.59, 0.25 and 0.93, respectively, using p = 0.5 to dichotomize the model predicted probabilities (vs 0.71, 0.70 and 0.72, respectively using p = 0.188 according to Youden index). The unsupervised approach identified two clusters, which were not associated with LTP (p = 0.8211) but with the occurrence of per-RFA intra-alveolar hemorrhage, post-RFA cavitations and fistulizations (p = 0.0150). CONCLUSION: Predictive models using RFs from the post-RFA ablation zone on the first revaluation CT-scan of CRC lung metastases seemed moderately informative regarding the occurrence of LTP. ADVANCES IN KNOWLEDGE: Radiomics approach on interventional radiology data is feasible. However, patterns of heterogeneity detected with RFs on early re-evaluation CT-scans seem biased by different healing processes following benign RFA complications.
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spelling pubmed-102303932023-06-01 Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression? Crombé, Amandine Palussière, Jean Catena, Vittorio Cazayus, Maxime Fonck, Marianne Béchade, Dominique Buy, Xavier Markich, Romane Br J Radiol Full Paper OBJECTIVES: To determine whether radiomics data can predict local tumor progression (LTP) following radiofrequency ablation (RFA) of colorectal cancer (CRC) lung metastases on the first revaluation chest CT. METHODS: This case–control single-center retrospective study included 95 distinct lung metastases treated by RFA (in 39 patients, median age: 63.1 years) with a contrast-enhanced CT-scan performed 3 months after RFA. Forty-eight radiomics features (RFs) were extracted from the 3D-segmentation of the ablation zone. Several supervised machine-learning algorithms were trained in 10-fold cross-validation on reproducible RFs to predict LTP, with/without denoising CT-scans. An unsupervised classification based on reproducible RFs was built with k-means algorithm. RESULTS: There were 20/95 (26.7%) relapses within a median delay of 10 months. The best model was a stepwise logistic regression on raw CT-scans. Its cross-validated performances were: AUROC = 0.72 (0.58–0.86), area under the Precision-Recall curve (AUPRC) = 0.44. Cross-validated balanced-accuracy, sensitivity and specificity were 0.59, 0.25 and 0.93, respectively, using p = 0.5 to dichotomize the model predicted probabilities (vs 0.71, 0.70 and 0.72, respectively using p = 0.188 according to Youden index). The unsupervised approach identified two clusters, which were not associated with LTP (p = 0.8211) but with the occurrence of per-RFA intra-alveolar hemorrhage, post-RFA cavitations and fistulizations (p = 0.0150). CONCLUSION: Predictive models using RFs from the post-RFA ablation zone on the first revaluation CT-scan of CRC lung metastases seemed moderately informative regarding the occurrence of LTP. ADVANCES IN KNOWLEDGE: Radiomics approach on interventional radiology data is feasible. However, patterns of heterogeneity detected with RFs on early re-evaluation CT-scans seem biased by different healing processes following benign RFA complications. The British Institute of Radiology. 2023-06-01 2023-04-17 /pmc/articles/PMC10230393/ /pubmed/37066833 http://dx.doi.org/10.1259/bjr.20201371 Text en © 2023 The Authors. Published by the British Institute of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 Unported License http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial reuse, provided the original author and source are credited.
spellingShingle Full Paper
Crombé, Amandine
Palussière, Jean
Catena, Vittorio
Cazayus, Maxime
Fonck, Marianne
Béchade, Dominique
Buy, Xavier
Markich, Romane
Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?
title Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?
title_full Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?
title_fullStr Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?
title_full_unstemmed Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?
title_short Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?
title_sort radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?
topic Full Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10230393/
https://www.ncbi.nlm.nih.gov/pubmed/37066833
http://dx.doi.org/10.1259/bjr.20201371
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