Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1785051517693198336 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10230393 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The British Institute of Radiology. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT crombeamandine radiofrequencyablationoflungmetastasesofcolorectalcancercouldearlyradiomicsanalysisoftheablationzonehelpdetectlocaltumorprogression AT palussierejean radiofrequencyablationoflungmetastasesofcolorectalcancercouldearlyradiomicsanalysisoftheablationzonehelpdetectlocaltumorprogression AT catenavittorio radiofrequencyablationoflungmetastasesofcolorectalcancercouldearlyradiomicsanalysisoftheablationzonehelpdetectlocaltumorprogression AT cazayusmaxime radiofrequencyablationoflungmetastasesofcolorectalcancercouldearlyradiomicsanalysisoftheablationzonehelpdetectlocaltumorprogression AT fonckmarianne radiofrequencyablationoflungmetastasesofcolorectalcancercouldearlyradiomicsanalysisoftheablationzonehelpdetectlocaltumorprogression AT bechadedominique radiofrequencyablationoflungmetastasesofcolorectalcancercouldearlyradiomicsanalysisoftheablationzonehelpdetectlocaltumorprogression AT buyxavier radiofrequencyablationoflungmetastasesofcolorectalcancercouldearlyradiomicsanalysisoftheablationzonehelpdetectlocaltumorprogression AT markichromane radiofrequencyablationoflungmetastasesofcolorectalcancercouldearlyradiomicsanalysisoftheablationzonehelpdetectlocaltumorprogression |