Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study
BackgroundCardiac disease (CD) is a primary long-term diagnosed pathology among childhood cancer survivors. Dosiomics (radiomics extracted from the dose distribution) have received attention in the past few years to assess better the induced risk of radiotherapy (RT) than standard dosimetric feature...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1241221/full |
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| author | Mahmoud Bentriou Véronique Letort Stefania Chounta Stefania Chounta Stefania Chounta Stefania Chounta Brice Fresneau Brice Fresneau Brice Fresneau Brice Fresneau Duyen Do Duyen Do Duyen Do Nadia Haddy Nadia Haddy Nadia Haddy Ibrahima Diallo Ibrahima Diallo Neige Journy Neige Journy Neige Journy Monia Zidane Monia Zidane Monia Zidane Thibaud Charrier Thibaud Charrier Thibaud Charrier Naila Aba Naila Aba Naila Aba Claire Ducos Claire Ducos Claire Ducos Vincent S. Zossou Vincent S. Zossou Vincent S. Zossou Florent de Vathaire Florent de Vathaire Florent de Vathaire Rodrigue S. Allodji Rodrigue S. Allodji Rodrigue S. Allodji Rodrigue S. Allodji Sarah Lemler |
| author_facet | Mahmoud Bentriou Véronique Letort Stefania Chounta Stefania Chounta Stefania Chounta Stefania Chounta Brice Fresneau Brice Fresneau Brice Fresneau Brice Fresneau Duyen Do Duyen Do Duyen Do Nadia Haddy Nadia Haddy Nadia Haddy Ibrahima Diallo Ibrahima Diallo Neige Journy Neige Journy Neige Journy Monia Zidane Monia Zidane Monia Zidane Thibaud Charrier Thibaud Charrier Thibaud Charrier Naila Aba Naila Aba Naila Aba Claire Ducos Claire Ducos Claire Ducos Vincent S. Zossou Vincent S. Zossou Vincent S. Zossou Florent de Vathaire Florent de Vathaire Florent de Vathaire Rodrigue S. Allodji Rodrigue S. Allodji Rodrigue S. Allodji Rodrigue S. Allodji Sarah Lemler |
| author_sort | Mahmoud Bentriou |
| collection | DOAJ |
| description | BackgroundCardiac disease (CD) is a primary long-term diagnosed pathology among childhood cancer survivors. Dosiomics (radiomics extracted from the dose distribution) have received attention in the past few years to assess better the induced risk of radiotherapy (RT) than standard dosimetric features such as dose-volume indicators. Hence, using the spatial information contained in the dosiomics features with machine learning methods may improve the prediction of CD.MethodsWe considered the 7670 5-year survivors of the French Childhood Cancer Survivors Study (FCCSS). Dose-volume and dosiomics features are extracted from the radiation dose distribution of 3943 patients treated with RT. Survival analysis is performed considering several groups of features and several models [Cox Proportional Hazard with Lasso penalty, Cox with Bootstrap Lasso selection, Random Survival Forests (RSF)]. We establish the performance of dosiomics compared to baseline models by estimating C-index and Integrated Brier Score (IBS) metrics with 5-fold stratified cross-validation and compare their time-dependent error curves.ResultsAn RSF model adjusted on the first-order dosiomics predictors extracted from the whole heart performed best regarding the C-index (0.792 ± 0.049), and an RSF model adjusted on the first-order dosiomics predictors extracted from the heart’s subparts performed best regarding the IBS (0.069 ± 0.05). However, the difference is not statistically significant with the standard models (C-index of Cox PH adjusted on dose-volume indicators: 0.791 ± 0.044; IBS of Cox PH adjusted on the mean dose to the heart: 0.074 ± 0.056).ConclusionIn this study, dosiomics models have slightly better performance metrics but they do not outperform the standard models significantly. Quantiles of the dose distribution may contain enough information to estimate the risk of late radio-induced high-grade CD in childhood cancer survivors. |
| format | Article |
| id | doaj-art-8258bcdae299469f8934d3f290f31e94 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-8258bcdae299469f8934d3f290f31e942024-12-02T06:23:37ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-12-011410.3389/fonc.2024.12412211241221Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor StudyMahmoud Bentriou0Véronique Letort1Stefania Chounta2Stefania Chounta3Stefania Chounta4Stefania Chounta5Brice Fresneau6Brice Fresneau7Brice Fresneau8Brice Fresneau9Duyen Do10Duyen Do11Duyen Do12Nadia Haddy13Nadia Haddy14Nadia Haddy15Ibrahima Diallo16Ibrahima Diallo17Neige Journy18Neige Journy19Neige Journy20Monia Zidane21Monia Zidane22Monia Zidane23Thibaud Charrier24Thibaud Charrier25Thibaud Charrier26Naila Aba27Naila Aba28Naila Aba29Claire Ducos30Claire Ducos31Claire Ducos32Vincent S. Zossou33Vincent S. Zossou34Vincent S. Zossou35Florent de Vathaire36Florent de Vathaire37Florent de Vathaire38Rodrigue S. Allodji39Rodrigue S. Allodji40Rodrigue S. Allodji41Rodrigue S. Allodji42Sarah Lemler43Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Gif-sur-Yvette, FranceUniversité Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Gif-sur-Yvette, FranceUniversité Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Gif-sur-Yvette, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Pediatric Oncology, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceDepartment of Radiation Oncology, Gustave Roussy, Paris, FranceGustave Roussy, Institut national de la santé et de la recherche médicale (INSERM), Radiothérapie Moléculaire et Innovation Thérapeutique, Paris-Saclay University, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), U900, Institut Curie, PSL Research University, Saint-Cloud, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FranceUniversité Paris-Saclay, Université Versailles - Saint Quentin en Yvelines (UVSQ), Institut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Villejuif, FranceInstitut national de la santé et de la recherche médicale (INSERM), CESP-U1018, Radiation Epidemiology Team, Villejuif, FranceGustave Roussy, Department of Clinical Research, Radiation Epidemiology Team, Villejuif, FrancePolytechnic School of Abomey-Calavi (EPAC), University of Abomey-Calavi, Cotonou, BeninUniversité Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Gif-sur-Yvette, FranceBackgroundCardiac disease (CD) is a primary long-term diagnosed pathology among childhood cancer survivors. Dosiomics (radiomics extracted from the dose distribution) have received attention in the past few years to assess better the induced risk of radiotherapy (RT) than standard dosimetric features such as dose-volume indicators. Hence, using the spatial information contained in the dosiomics features with machine learning methods may improve the prediction of CD.MethodsWe considered the 7670 5-year survivors of the French Childhood Cancer Survivors Study (FCCSS). Dose-volume and dosiomics features are extracted from the radiation dose distribution of 3943 patients treated with RT. Survival analysis is performed considering several groups of features and several models [Cox Proportional Hazard with Lasso penalty, Cox with Bootstrap Lasso selection, Random Survival Forests (RSF)]. We establish the performance of dosiomics compared to baseline models by estimating C-index and Integrated Brier Score (IBS) metrics with 5-fold stratified cross-validation and compare their time-dependent error curves.ResultsAn RSF model adjusted on the first-order dosiomics predictors extracted from the whole heart performed best regarding the C-index (0.792 ± 0.049), and an RSF model adjusted on the first-order dosiomics predictors extracted from the heart’s subparts performed best regarding the IBS (0.069 ± 0.05). However, the difference is not statistically significant with the standard models (C-index of Cox PH adjusted on dose-volume indicators: 0.791 ± 0.044; IBS of Cox PH adjusted on the mean dose to the heart: 0.074 ± 0.056).ConclusionIn this study, dosiomics models have slightly better performance metrics but they do not outperform the standard models significantly. Quantiles of the dose distribution may contain enough information to estimate the risk of late radio-induced high-grade CD in childhood cancer survivors.https://www.frontiersin.org/articles/10.3389/fonc.2024.1241221/fullsurvival analysisdosiomicscardiac diseasechildhood cancermachine learningFCCSS |
| spellingShingle | Mahmoud Bentriou Véronique Letort Stefania Chounta Stefania Chounta Stefania Chounta Stefania Chounta Brice Fresneau Brice Fresneau Brice Fresneau Brice Fresneau Duyen Do Duyen Do Duyen Do Nadia Haddy Nadia Haddy Nadia Haddy Ibrahima Diallo Ibrahima Diallo Neige Journy Neige Journy Neige Journy Monia Zidane Monia Zidane Monia Zidane Thibaud Charrier Thibaud Charrier Thibaud Charrier Naila Aba Naila Aba Naila Aba Claire Ducos Claire Ducos Claire Ducos Vincent S. Zossou Vincent S. Zossou Vincent S. Zossou Florent de Vathaire Florent de Vathaire Florent de Vathaire Rodrigue S. Allodji Rodrigue S. Allodji Rodrigue S. Allodji Rodrigue S. Allodji Sarah Lemler Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study Frontiers in Oncology survival analysis dosiomics cardiac disease childhood cancer machine learning FCCSS |
| title | Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study |
| title_full | Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study |
| title_fullStr | Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study |
| title_full_unstemmed | Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study |
| title_short | Combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors: the French Childhood Cancer Survivor Study |
| title_sort | combining dosiomics and machine learning methods for predicting severe cardiac diseases in childhood cancer survivors the french childhood cancer survivor study |
| topic | survival analysis dosiomics cardiac disease childhood cancer machine learning FCCSS |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1241221/full |
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