Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up
Rationale and objectives: The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently time-consuming. Consequently, in clinical practice,...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925000023 |
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| author | Loïse Dessoude Raphaëlle Lemaire Romain Andres Thomas Leleu Alexandre G. Leclercq Alexis Desmonts Typhaine Corroller Amirath Fara Orou-Guidou Luca Laduree Loic Le Henaff Joëlle Lacroix Alexis Lechervy Dinu Stefan Aurélien Corroyer-Dulmont |
| author_facet | Loïse Dessoude Raphaëlle Lemaire Romain Andres Thomas Leleu Alexandre G. Leclercq Alexis Desmonts Typhaine Corroller Amirath Fara Orou-Guidou Luca Laduree Loic Le Henaff Joëlle Lacroix Alexis Lechervy Dinu Stefan Aurélien Corroyer-Dulmont |
| author_sort | Loïse Dessoude |
| collection | DOAJ |
| description | Rationale and objectives: The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently time-consuming. Consequently, in clinical practice, monitoring patients in clinical trials in compliance with the RANO-BM criteria is rarely achieved. The objective of this study was to develop and validate an AI solution capable of delineating brain metastases (BM) on MRI to easily obtain, using an in-house solution, RANO-BM criteria as well as BM volume in a routine clinical setting. Materials (patients) and methods: A total of 27,456 post-Gadolinium-T1 MRI from 132 patients with BM were employed in this study. A deep learning (DL) model was constructed using the PyTorch and PyTorch Lightning frameworks, and the UNETR transfer learning method was employed to segment BM from MRI. Results: A visual analysis of the AI model results demonstrates confident delineation of the BM lesions. The model shows 100 % accuracy in predicting RANO-BM criteria in comparison to that of an expert medical doctor. There was a high degree of overlap between the AI and the doctor's segmentation, with a mean DICE score of 0.77. The diameter and volume of the BM lesions were found to be concordant between the AI and the reference segmentation. The user interface developed in this study can readily provide RANO-BM criteria following AI BM segmentation. Conclusion: The in-house deep learning solution is accessible to everyone without expertise in AI and offers effective BM segmentation and substantial time savings. |
| format | Article |
| id | doaj-art-1fccb1f0a34041cb880b9365e64b0a34 |
| institution | Kabale University |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-1fccb1f0a34041cb880b9365e64b0a342025-01-23T05:26:21ZengElsevierNeuroImage1095-95722025-02-01306121002Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-upLoïse Dessoude0Raphaëlle Lemaire1Romain Andres2Thomas Leleu3Alexandre G. Leclercq4Alexis Desmonts5Typhaine Corroller6Amirath Fara Orou-Guidou7Luca Laduree8Loic Le Henaff9Joëlle Lacroix10Alexis Lechervy11Dinu Stefan12Aurélien Corroyer-Dulmont13Radiotherapy Department, Centre François Baclesse, Caen 14000, FranceMedical Physics Department, Centre François Baclesse, Caen 14000, FranceMedical Physics Department, Centre François Baclesse, Caen 14000, FranceRadiotherapy Department, Centre François Baclesse, Caen 14000, FranceMedical Physics Department, Centre François Baclesse, Caen 14000, FranceRadiotherapy Department, Centre François Baclesse, Caen 14000, FranceMedical Physics Department, Centre François Baclesse, Caen 14000, FranceMedical Physics Department, Centre François Baclesse, Caen 14000, FranceMedical Physics Department, Centre François Baclesse, Caen 14000, FranceRadiology Department, Centre François Baclesse, Caen 14000, FranceRadiology Department, Centre François Baclesse, Caen 14000, FranceENSICAEN, CNRS, GREYC UMR6072, Normandie Université, Université Caen Normandie, Caen F-14000, FranceRadiotherapy Department, Centre François Baclesse, Caen 14000, FranceMedical Physics Department, Centre François Baclesse, Caen 14000, France; CNRS, ISTCT UMR6030, GIP CYCERON, Normandie Université, Université de Caen Normandie, Caen 14000, France; Corresponding author at: Medical Physics Department, CLCC François Baclesse, Caen 14000, France.Rationale and objectives: The RANO-BM criteria, which employ a one-dimensional measurement of the largest diameter, are imperfect due to the fact that the lesion volume is neither isotropic nor homogeneous. Furthermore, this approach is inherently time-consuming. Consequently, in clinical practice, monitoring patients in clinical trials in compliance with the RANO-BM criteria is rarely achieved. The objective of this study was to develop and validate an AI solution capable of delineating brain metastases (BM) on MRI to easily obtain, using an in-house solution, RANO-BM criteria as well as BM volume in a routine clinical setting. Materials (patients) and methods: A total of 27,456 post-Gadolinium-T1 MRI from 132 patients with BM were employed in this study. A deep learning (DL) model was constructed using the PyTorch and PyTorch Lightning frameworks, and the UNETR transfer learning method was employed to segment BM from MRI. Results: A visual analysis of the AI model results demonstrates confident delineation of the BM lesions. The model shows 100 % accuracy in predicting RANO-BM criteria in comparison to that of an expert medical doctor. There was a high degree of overlap between the AI and the doctor's segmentation, with a mean DICE score of 0.77. The diameter and volume of the BM lesions were found to be concordant between the AI and the reference segmentation. The user interface developed in this study can readily provide RANO-BM criteria following AI BM segmentation. Conclusion: The in-house deep learning solution is accessible to everyone without expertise in AI and offers effective BM segmentation and substantial time savings.http://www.sciencedirect.com/science/article/pii/S1053811925000023Deep learningRadiologyBrain metastasesRANO-BMClinical routine |
| spellingShingle | Loïse Dessoude Raphaëlle Lemaire Romain Andres Thomas Leleu Alexandre G. Leclercq Alexis Desmonts Typhaine Corroller Amirath Fara Orou-Guidou Luca Laduree Loic Le Henaff Joëlle Lacroix Alexis Lechervy Dinu Stefan Aurélien Corroyer-Dulmont Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up NeuroImage Deep learning Radiology Brain metastases RANO-BM Clinical routine |
| title | Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up |
| title_full | Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up |
| title_fullStr | Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up |
| title_full_unstemmed | Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up |
| title_short | Development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on MRI for RANO-BM criteria follow-up |
| title_sort | development and routine implementation of deep learning algorithm for automatic brain metastases segmentation on mri for rano bm criteria follow up |
| topic | Deep learning Radiology Brain metastases RANO-BM Clinical routine |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925000023 |
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