Development and prospective validation of a spatial dose pattern based model predicting acute pulmonary toxicity in patients treated with volumetric arc-therapy for locally advanced lung cancer

Bourbonne, Vincent; Lucia, François; Jaouen, Vincent; Bert, Julien; Rehn, Matilda; Pradier, Olivier; Visvikis, Dimitris; Schick, Ulrike

doi:10.1016/j.radonc.2021.09.008

Cited by 10 publications

(28 citation statements)

References 28 publications

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“…The second cohort consisted of the patients treated within the prospective trial TEFRARC (NCT03931356), evaluating the functional impact of lung radiotherapy. Characteristics of these 207 patients (165 from the retrospective and 42 from the prospective cohorts, respectively) were previously presented [ 9 , 17 ].…”

Section: Methodsmentioning

confidence: 99%

“…The segmentation used for registration was a hybrid volume of interest (VOI) consisting of the union of the lungs and the heart. The previously identified Pmap sub-region [ 17 ] was then transferred to the patient’s CT using the same elastic registration matrix but in reverse. The mean DICE coefficient between the phantom’s segmentation and each patient’s segmentation was evaluated.…”

Section: Methodsmentioning

confidence: 99%

“…The 8% threshold was based on a previous study [ 17 ], defining this cut-off as the most effective for the stratification of patients based on their APT risk. Dose constraints for the initial and optimized plannings are presented in Supplementary Table S1 .…”

Section: Methodsmentioning

confidence: 99%

“…A Pmap model was previously developed combining 11 features, among which the Dmean Pmap achieved the highest importance (35.8%) [ 17 ]. The remaining features were ranked by order of importance: Dmean 2Lungs , V30 2Lungs , Smoking Status, Mean Expiratory Volume/Second (MEVS), Chronic Obstructive Pulmonary Disease (COPD), V10 LungH , AJCC Stage (American Joint Cancer Committee), V5 LungH , Dmean LungH , andV40 Heart .…”

Section: Methodsmentioning

confidence: 99%

“…Patients with a high mean dose to this sub-region (DMean Pmap ≥ 30.3Gy) were 4.7 times more likely to present with APT ≥ grade 2 when compared to the low-risk patients. Furthermore, when combining the Dmean Pmap with ten other clinical and dosimetric features, a model was previously developed for the prediction of APT [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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VMAT-Based Planning Allows Sparing of a Spatial Dose Pattern Associated with Radiation Pneumonitis in Patients Treated with Radiotherapy for a Locally Advanced Lung Cancer

Bourbonne

Lucia

Jaouen

et al. 2022

Cancers

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Introduction: In patients treated with radiotherapy for locally advanced lung cancer, respect for dose constraints to organs at risk (OAR) insufficiently protects patients from acute pulmonary toxicity (APT), such toxicities being associated with a potential impact on the treatment’s completion and the patient’s quality of life. Dosimetric planning does not take into account regional lung functionality. An APT prediction model combining usual dosimetry features with the mean dose (DMeanPmap) received by a voxel-based volume (Pmap) localized in the posterior right lung has been previously developed. A DMeanPmap of ≥ 30.3Gy or a predicted APT probability (ProbAPT) of ≥ 8% were associated with a higher risk of APT. In the present study, the authors aim to demonstrate the possibility of decreasing the DMeanPmap via a volumetric arctherapy (VMAT)-based adapted planning and evaluate the impact on the risk of APT. Methods: Among the 207 patients included in the initial study, only patients who presented with APT of ≥ grade 2 and with a probability of APT ≥ 8% based on the prediction model were included. Dosimetry planning was optimized with a new constraint (DMeanPmap < 30.3Gy) added to the usual constraints. The initial and optimized treatment plans were compared using the t-test for the independent variables and the non-parametric Mann–Whitney U test otherwise, regarding both doses to the OARs and PTV (Planning Target Volume) coverage. Conformity and heterogeneity indexes were also compared. The risk of APT was recalculated using the new dosimetric features and the APT prediction model. Results: Dosimetric optimization was considered successful for 27 out of the 44 included patients (61.4%), meaning the dosimetric constraint on the Pmap region was achieved without compromising the PTV coverage (p = 0.61). The optimization significantly decreased the median DMeanPmap from 28.8Gy (CI95% 24.2–33.4) to 22.1Gy (CI95% 18.3–26.0). When recomputing the risk of APT using the new dosimetric features, the optimization significantly reduced the risk of APT (p < 0.0001) by reclassifying 43.2% (19/44) of the patients. Conclusion: Our approach appears to be both easily implementable on a daily basis and efficient at reducing the risk of APT. Regional radiosensitivity should be considered in usual lung dose constraints, opening the possibility of new treatment strategies, such as dose escalation or innovative treatment associations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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VMAT-Based Planning Allows Sparing of a Spatial Dose Pattern Associated with Radiation Pneumonitis in Patients Treated with Radiotherapy for a Locally Advanced Lung Cancer

Bourbonne

Lucia

Jaouen

et al. 2022

Cancers

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Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer

Lucia

Bourbonne

Pleyers³

et al. 2023

Eur J Nucl Med Mol Imaging

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Purpose To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18 F-FDG PET/CT and MRI radiomics combined with clinical parameters. Methods We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18 F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared. Results In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively. Conclusions Radiomic features extracted from pre-CRT analog and digital 18 F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.

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Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy

Lucia,

Louis,

Cousin

et al. 2023

Eur J Nucl Med Mol Imaging

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Purpose To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [ 18 F]FDG PET/CT and CT radiomics combined with clinical and dosimetric parameters. Methods We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [ 18 F]FDG PET/CT and planning CT. Radiomic features were extracted using the PyRadiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Clinical, radiomic, and combined models were trained and tested using a neural network approach to predict regional and/ or distant recurrence. Results In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (origi-nal_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). Conclusion Radiomic features extracted from pre-SBRT analog and digital [ 18 F]FDG PET/CT outperform clinical parameters in the prediction of regional and/or distant recurrence and to discuss an adjuvant systemic treatment in ES-NSCLC. Prospective validation of our models should now be carried out.

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Development and prospective validation of a spatial dose pattern based model predicting acute pulmonary toxicity in patients treated with volumetric arc-therapy for locally advanced lung cancer

Cited by 10 publications

References 28 publications

VMAT-Based Planning Allows Sparing of a Spatial Dose Pattern Associated with Radiation Pneumonitis in Patients Treated with Radiotherapy for a Locally Advanced Lung Cancer

VMAT-Based Planning Allows Sparing of a Spatial Dose Pattern Associated with Radiation Pneumonitis in Patients Treated with Radiotherapy for a Locally Advanced Lung Cancer

Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer

Multicentric development and evaluation of [18F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy

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