Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas
Abstract Introduction The delineation of organs-at-risk and lymph node areas is a crucial step in radiotherapy, but it is time-consuming and associated with substantial user-dependent variability in contouring. Artificial intelligence (AI) appears to be the solution to facilitate and standardize thi...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | Radiation Oncology |
| Online Access: | https://doi.org/10.1186/s13014-024-02554-y |
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| author | Céline Meyer Sandrine Huger Marie Bruand Thomas Leroy Jérémy Palisson Paul Rétif Thomas Sarrade Anais Barateau Sophie Renard Maria Jolnerovski Nicolas Demogeot Johann Marcel Nicolas Martz Anaïs Stefani Selima Sellami Juliette Jacques Emma Agnoux William Gehin Ida Trampetti Agathe Margulies Constance Golfier Yassir Khattabi Olivier Cravéreau Alizée Renan Jean-François Py Jean-Christophe Faivre |
| author_facet | Céline Meyer Sandrine Huger Marie Bruand Thomas Leroy Jérémy Palisson Paul Rétif Thomas Sarrade Anais Barateau Sophie Renard Maria Jolnerovski Nicolas Demogeot Johann Marcel Nicolas Martz Anaïs Stefani Selima Sellami Juliette Jacques Emma Agnoux William Gehin Ida Trampetti Agathe Margulies Constance Golfier Yassir Khattabi Olivier Cravéreau Alizée Renan Jean-François Py Jean-Christophe Faivre |
| author_sort | Céline Meyer |
| collection | DOAJ |
| description | Abstract Introduction The delineation of organs-at-risk and lymph node areas is a crucial step in radiotherapy, but it is time-consuming and associated with substantial user-dependent variability in contouring. Artificial intelligence (AI) appears to be the solution to facilitate and standardize this work. The objective of this study is to compare eight available AI software programs in terms of technical aspects and accuracy for contouring organs-at-risk and lymph node areas with current international contouring recommendations. Material and methods From January–July 2023, we performed a blinded study of the contour scoring of the organs-at-risk and lymph node areas by eight self-contouring AI programs by 20 radiation oncologists. It was a single-center study conducted in radiation department at the Lorraine Cancer Institute. A qualitative analysis of technical characteristics of the different AI programs was also performed. Three adults (two women and one man) and three children (one girl and two boys) provided six whole-body anonymized CT scans, along with two other adult brain MRI scans. Using a scoring scale from 1 to 3 (best score), radiation oncologists blindly assessed the quality of contouring of organs-at-risk and lymph node areas of all scans and MRI data by the eight AI programs. We have chosen to define the threshold of an average score equal to or greater than 2 to characterize a high-performing AI software, meaning an AI with minimal to moderate corrections but usable in clinical routine. Results For adults CT scans: There were two AI programs for which the overall average quality score (that is, all areas tested for OARs and lymph nodes) was higher than 2.0: Limbus (overall average score = 2.03 (0.16)) and MVision (overall average score = 2.13 (0.19)). If we only consider OARs for adults, only Limbus, Therapanacea, MVision and Radformation have an average score above 2. For children CT scan, MVision was the only program to have a average score higher than 2 with overall average score = 2.07 (0.19). If we only consider OARs for children, only Limbus and MVision have an average score above 2. For brain MRIs: TheraPanacea was the only program with an average score over 2, for both brain delineation (2.75 (0.35)) and OARs (2.09 (0.19)). The comparative analysis of the technical aspects highlights the similarities and differences between the software. There is no difference in between senior radiation oncologist and residents for OARs contouring. Conclusion For adult CT-scan, two AI programs on the market, MVision and Limbus, delineate most OARs and lymph nodes areas that are useful in clinical routine. For children CT-scan, only one IA, MVision, program is efficient. For adult brain MRI, Therapancea,only one AI program is efficient. Trial registration: CNIL-MR0004 Number HDH434. |
| format | Article |
| id | doaj-art-f2e7ed9042f649a69dd42e1a61a5cd58 |
| institution | Kabale University |
| issn | 1748-717X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj-art-f2e7ed9042f649a69dd42e1a61a5cd582025-01-26T12:46:02ZengBMCRadiation Oncology1748-717X2024-11-0119111010.1186/s13014-024-02554-yArtificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areasCéline Meyer0Sandrine Huger1Marie Bruand2Thomas Leroy3Jérémy Palisson4Paul Rétif5Thomas Sarrade6Anais Barateau7Sophie Renard8Maria Jolnerovski9Nicolas Demogeot10Johann Marcel11Nicolas Martz12Anaïs Stefani13Selima Sellami14Juliette Jacques15Emma Agnoux16William Gehin17Ida Trampetti18Agathe Margulies19Constance Golfier20Yassir Khattabi21Olivier Cravéreau22Alizée Renan23Jean-François Py24Jean-Christophe Faivre25Academic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerMedical Physics Department, Institut de Cancérologie de Lorraine - Alexis-VautrinAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerRadiation departmentMedical Physics DepartmentMedical Physics Department, CHR Metz-ThionvilleRadiation Department, AP-HP, Hôpital TenonMedical Physics Department, Centre Eugène MarquisAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAcademic Department of Radiation Therapy & Brachytherapy, Institut de Cancérologie de Lorraine – Alexis-Vautrin CLCC – UnicancerAbstract Introduction The delineation of organs-at-risk and lymph node areas is a crucial step in radiotherapy, but it is time-consuming and associated with substantial user-dependent variability in contouring. Artificial intelligence (AI) appears to be the solution to facilitate and standardize this work. The objective of this study is to compare eight available AI software programs in terms of technical aspects and accuracy for contouring organs-at-risk and lymph node areas with current international contouring recommendations. Material and methods From January–July 2023, we performed a blinded study of the contour scoring of the organs-at-risk and lymph node areas by eight self-contouring AI programs by 20 radiation oncologists. It was a single-center study conducted in radiation department at the Lorraine Cancer Institute. A qualitative analysis of technical characteristics of the different AI programs was also performed. Three adults (two women and one man) and three children (one girl and two boys) provided six whole-body anonymized CT scans, along with two other adult brain MRI scans. Using a scoring scale from 1 to 3 (best score), radiation oncologists blindly assessed the quality of contouring of organs-at-risk and lymph node areas of all scans and MRI data by the eight AI programs. We have chosen to define the threshold of an average score equal to or greater than 2 to characterize a high-performing AI software, meaning an AI with minimal to moderate corrections but usable in clinical routine. Results For adults CT scans: There were two AI programs for which the overall average quality score (that is, all areas tested for OARs and lymph nodes) was higher than 2.0: Limbus (overall average score = 2.03 (0.16)) and MVision (overall average score = 2.13 (0.19)). If we only consider OARs for adults, only Limbus, Therapanacea, MVision and Radformation have an average score above 2. For children CT scan, MVision was the only program to have a average score higher than 2 with overall average score = 2.07 (0.19). If we only consider OARs for children, only Limbus and MVision have an average score above 2. For brain MRIs: TheraPanacea was the only program with an average score over 2, for both brain delineation (2.75 (0.35)) and OARs (2.09 (0.19)). The comparative analysis of the technical aspects highlights the similarities and differences between the software. There is no difference in between senior radiation oncologist and residents for OARs contouring. Conclusion For adult CT-scan, two AI programs on the market, MVision and Limbus, delineate most OARs and lymph nodes areas that are useful in clinical routine. For children CT-scan, only one IA, MVision, program is efficient. For adult brain MRI, Therapancea,only one AI program is efficient. Trial registration: CNIL-MR0004 Number HDH434.https://doi.org/10.1186/s13014-024-02554-y |
| spellingShingle | Céline Meyer Sandrine Huger Marie Bruand Thomas Leroy Jérémy Palisson Paul Rétif Thomas Sarrade Anais Barateau Sophie Renard Maria Jolnerovski Nicolas Demogeot Johann Marcel Nicolas Martz Anaïs Stefani Selima Sellami Juliette Jacques Emma Agnoux William Gehin Ida Trampetti Agathe Margulies Constance Golfier Yassir Khattabi Olivier Cravéreau Alizée Renan Jean-François Py Jean-Christophe Faivre Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas Radiation Oncology |
| title | Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas |
| title_full | Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas |
| title_fullStr | Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas |
| title_full_unstemmed | Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas |
| title_short | Artificial intelligence contouring in radiotherapy for organs-at-risk and lymph node areas |
| title_sort | artificial intelligence contouring in radiotherapy for organs at risk and lymph node areas |
| url | https://doi.org/10.1186/s13014-024-02554-y |
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