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200 results on '"Bourbonne, V."'

12. CT respiratory motion synthesis using joint supervised and adversarial learning.

Author

Cao, Y-H, Bourbonne, V, Lucia, F, Schick, U, Bert, J, Jaouen, V, and Visvikis, D

Subjects
*FOUR-dimensional imaging, *RADIATION exposure, *RADIOTHERAPY treatment planning, *VECTOR fields, *COMPUTED tomography, *VENTILATION monitoring, *DEEP learning
Abstract

Objective. Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. Approach. In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude. Main results. Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim). Significance. This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found at https://github.com/cyiheng/Dynagan. [ABSTRACT FROM AUTHOR]

Published
2024
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15. MO-0226 Functional lung preservation during stereotactic body radiotherapy guided by perfusion PET/CT

Author

Lucia, F., primary, Hamya, M., additional, Pinot, F., additional, Goasduff, G., additional, Blanc-Béguin, F., additional, Bourhis, D., additional, Pradier, O., additional, Lucia, A., additional, Hennebicq, S., additional, Mauguen, M., additional, Floch, R., additional, Schick, U., additional, Bourbonne, V., additional, Salaun, P., additional, and Le Roux, P., additional

Published
2023
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26. Prédiction de la réponse pathologique complète à la chimioradiothérapie néoadjuvante à l’aide d’une analyse radiomique basée sur l’imagerie par résonance magnétique pour le cancer du rectum localement évolué

Author

Bordron, A., primary, Bourbonne, V., additional, Rio, E., additional, Badic, B., additional, Miranda, O., additional, Pradier, O., additional, Hatt, M., additional, Visvikis, D., additional, Lucia, F., additional, and Schick, U., additional

Published
2021
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28. Réponses abscopales chez les patients atteints de mélanome métastatique : étude de cohorte multicentrique nationale

Author

Ollivier, L., primary, Orione, C., additional, Champiat, S., additional, Lucia, F., additional, Misery, L., additional, Legoupil, D., additional, Pradier, O., additional, Bourbonne, V., additional, Kacperek, C., additional, Stefan, A., additional, Stefan, D., additional, Thillays, F., additional, Rio, E., additional, Vaugier, L., additional, Lesueur, P., additional, Supiot, S., additional, Schick, U., additional, and Kao, W., additional

Published
2021
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32. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images

Author

Sellami, S., Bourbonne, V., Hatt, M., Tixier, F., Bouzid, D., Lucia, F., Pradier, O., Goasduff, G., Visvikis, D., and Schick, U.

Abstract

Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 �� SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p p p We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models.

Published
2021
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44. Impact pronostique d’un « sous-dosage » des zones avides de fluorodésoxyglucose sur la tomographie par émission de positrons préthérapeutique pour les patientes prises en charge par chimioradiothérapie et curiethérapie pour un cancer du col de l’utérus

Author

Lucia, F., primary, Bourbonne, V., additional, Dissaux, G., additional, Miranda, O., additional, Mauguen, M., additional, Pradier, O., additional, Abgral, R., additional, and Schick, U., additional

Published
2020
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48. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images.

Author

Sellami, S., Bourbonne, V., Hatt, M., Tixier, F., Bouzid, D., Lucia, F., Pradier, O., Goasduff, G., Visvikis, D., and Schick, U.

Subjects
*CONFIDENCE intervals, *HEMOGLOBINS, *HEAD & neck cancer, *CANCER patients, *CHEMORADIOTHERAPY, *DESCRIPTIVE statistics, *COMPUTED tomography, *PREDICTION models, *RECEIVER operating characteristic curves, *SQUAMOUS cell carcinoma, *LONGITUDINAL method
Abstract

Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p =.006), 0.85 (p <.001), and 0.99 (p <.001) for clinical, CBCT4-radiomics (Coarseness) and clinical + radiomics based models respectively. The mean AUC of this last model on a 5-fold cross-validation was 0.80 (±0.09). On the testing cohort, the best prediction was given by the combined model (balanced accuracy [BAcc] 0.67 , p <.001). We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models. [ABSTRACT FROM AUTHOR]

Published
2022
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49. Validation externe d’un modèle radiomique dérivé de l’IRM pour guider la sélection des patients en vue d’une radiothérapie adjuvante après prostatectomie dans le cadre d’un adénocarcinome prostatique à haut risque

Author

Bourbonne, V., primary, Vallières, M., additional, Lucia, F., additional, Doucet, L., additional, Tissot, V., additional, Cuvelier, G., additional, Hue, S., additional, Le Penn Du, H., additional, Perdriel, L., additional, Bertrand, N., additional, Starroz, F., additional, Visvikis, D., additional, Pradier, O., additional, Hatt, M., additional, and Schick, U., additional

Published
2019
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50. Validation of an MRI-Derived Radiomics Model to Guide Patients Selection for Adjuvant Radiotherapy after Prostatectomy for High-Risk Prostate Cancer

Author

Bourbonne, V., primary, Vallieres, M., additional, Lucia, F., additional, Doucet, L., additional, Visvikis, D., additional, Tissot, V., additional, Cuvelier, G., additional, Hue, S., additional, Prigent, L., additional, Bertrand, N., additional, Staroz, F., additional, Pradier, O., additional, Hatt, M., additional, and Schick, U., additional

Published
2019
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