Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy
Introduction: The standard of care for people with locally advanced lung cancer (LALC) who cannot be operated on is (chemo)-radiation. Despite the application of dose constraints, acute pulmonary toxicity (APT) still often occurs. Prediction of APT is of paramount importance for the development of i...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Journal of Personalized Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4426/12/11/1926 |
| _version_ | 1797464904765014016 |
|---|---|
| author | Vincent Bourbonne François Lucia Vincent Jaouen Olivier Pradier Dimitris Visvikis Ulrike Schick |
| author_facet | Vincent Bourbonne François Lucia Vincent Jaouen Olivier Pradier Dimitris Visvikis Ulrike Schick |
| author_sort | Vincent Bourbonne |
| collection | DOAJ |
| description | Introduction: The standard of care for people with locally advanced lung cancer (LALC) who cannot be operated on is (chemo)-radiation. Despite the application of dose constraints, acute pulmonary toxicity (APT) still often occurs. Prediction of APT is of paramount importance for the development of innovative therapeutic combinations. The two models were previously individually created. With success, the Rad-model incorporated six radiomics functions. After additional validation in prospective cohorts, a Pmap-model was created by identifying a specific region of the right posterior lung and incorporating several clinical and dosimetric parameters. To create and test a novel model to forecast the risk of APT in two cohorts receiving volumetric arctherapy radiotherapy (VMAT), we aimed to include all the variables in this study. Methods: In the training cohort, we retrospectively included all patients treated by VMAT for LALC at one institution between 2015 and 2018. APT was assessed according to the CTCAE v4.0 scale. Usual clinical and dosimetric features, as well as the mean dose to the pre-defined Pmap zone (DMean<sub>Pmap</sub>), were processed using a neural network approach and subsequently validated on an observational prospective cohort. The model was evaluated using the area under the curve (AUC) and balanced accuracy (Bacc). Results: 165 and 42 patients were enrolled in the training and test cohorts, with APT rates of 22.4 and 19.1%, respectively. The AUCs for the Rad and Pmap models in the validation cohort were 0.83 and 0.81, respectively, whereas the AUC for the combined model (Comb-model) was 0.90. The Bacc for the Rad, Pmap, and Comb models in the validation cohort were respectively 78.7, 82.4, and 89.7%. Conclusion: The accuracy of prediction models were increased by combining radiomics, DMean<sub>Pmap</sub>, and common clinical and dosimetric features. The use of this model may improve the evaluation of APT risk and provide access to novel therapeutic alternatives, such as dose escalation or creative therapy combinations. |
| first_indexed | 2024-03-09T18:13:50Z |
| format | Article |
| id | doaj.art-720860ffbc67497394ea6c8a408268e3 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T18:13:50Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-720860ffbc67497394ea6c8a408268e32023-11-24T08:54:37ZengMDPI AGJournal of Personalized Medicine2075-44262022-11-011211192610.3390/jpm12111926Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-RadiotherapyVincent Bourbonne0François Lucia1Vincent Jaouen2Olivier Pradier3Dimitris Visvikis4Ulrike Schick5Radiation Oncology Department, University Hospital, 29200 Brest, FranceRadiation Oncology Department, University Hospital, 29200 Brest, FranceLaTIM, INSERM, UMR 1101, University of Western Brittany, 29200 Brest, FranceRadiation Oncology Department, University Hospital, 29200 Brest, FranceLaTIM, INSERM, UMR 1101, University of Western Brittany, 29200 Brest, FranceRadiation Oncology Department, University Hospital, 29200 Brest, FranceIntroduction: The standard of care for people with locally advanced lung cancer (LALC) who cannot be operated on is (chemo)-radiation. Despite the application of dose constraints, acute pulmonary toxicity (APT) still often occurs. Prediction of APT is of paramount importance for the development of innovative therapeutic combinations. The two models were previously individually created. With success, the Rad-model incorporated six radiomics functions. After additional validation in prospective cohorts, a Pmap-model was created by identifying a specific region of the right posterior lung and incorporating several clinical and dosimetric parameters. To create and test a novel model to forecast the risk of APT in two cohorts receiving volumetric arctherapy radiotherapy (VMAT), we aimed to include all the variables in this study. Methods: In the training cohort, we retrospectively included all patients treated by VMAT for LALC at one institution between 2015 and 2018. APT was assessed according to the CTCAE v4.0 scale. Usual clinical and dosimetric features, as well as the mean dose to the pre-defined Pmap zone (DMean<sub>Pmap</sub>), were processed using a neural network approach and subsequently validated on an observational prospective cohort. The model was evaluated using the area under the curve (AUC) and balanced accuracy (Bacc). Results: 165 and 42 patients were enrolled in the training and test cohorts, with APT rates of 22.4 and 19.1%, respectively. The AUCs for the Rad and Pmap models in the validation cohort were 0.83 and 0.81, respectively, whereas the AUC for the combined model (Comb-model) was 0.90. The Bacc for the Rad, Pmap, and Comb models in the validation cohort were respectively 78.7, 82.4, and 89.7%. Conclusion: The accuracy of prediction models were increased by combining radiomics, DMean<sub>Pmap</sub>, and common clinical and dosimetric features. The use of this model may improve the evaluation of APT risk and provide access to novel therapeutic alternatives, such as dose escalation or creative therapy combinations.https://www.mdpi.com/2075-4426/12/11/1926radiation pneumonitislung cancerpredictioncluster of voxelradiomicspersonalized medicine |
| spellingShingle | Vincent Bourbonne François Lucia Vincent Jaouen Olivier Pradier Dimitris Visvikis Ulrike Schick Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy Journal of Personalized Medicine radiation pneumonitis lung cancer prediction cluster of voxel radiomics personalized medicine |
| title | Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy |
| title_full | Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy |
| title_fullStr | Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy |
| title_full_unstemmed | Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy |
| title_short | Combination of Radiomics Features and Functional Radiosensitivity Enhances Prediction of Acute Pulmonary Toxicity in a Prospective Validation Cohort of Patients with a Locally Advanced Lung Cancer Treated with VMAT-Radiotherapy |
| title_sort | combination of radiomics features and functional radiosensitivity enhances prediction of acute pulmonary toxicity in a prospective validation cohort of patients with a locally advanced lung cancer treated with vmat radiotherapy |
| topic | radiation pneumonitis lung cancer prediction cluster of voxel radiomics personalized medicine |
| url | https://www.mdpi.com/2075-4426/12/11/1926 |
| work_keys_str_mv | AT vincentbourbonne combinationofradiomicsfeaturesandfunctionalradiosensitivityenhancespredictionofacutepulmonarytoxicityinaprospectivevalidationcohortofpatientswithalocallyadvancedlungcancertreatedwithvmatradiotherapy AT francoislucia combinationofradiomicsfeaturesandfunctionalradiosensitivityenhancespredictionofacutepulmonarytoxicityinaprospectivevalidationcohortofpatientswithalocallyadvancedlungcancertreatedwithvmatradiotherapy AT vincentjaouen combinationofradiomicsfeaturesandfunctionalradiosensitivityenhancespredictionofacutepulmonarytoxicityinaprospectivevalidationcohortofpatientswithalocallyadvancedlungcancertreatedwithvmatradiotherapy AT olivierpradier combinationofradiomicsfeaturesandfunctionalradiosensitivityenhancespredictionofacutepulmonarytoxicityinaprospectivevalidationcohortofpatientswithalocallyadvancedlungcancertreatedwithvmatradiotherapy AT dimitrisvisvikis combinationofradiomicsfeaturesandfunctionalradiosensitivityenhancespredictionofacutepulmonarytoxicityinaprospectivevalidationcohortofpatientswithalocallyadvancedlungcancertreatedwithvmatradiotherapy AT ulrikeschick combinationofradiomicsfeaturesandfunctionalradiosensitivityenhancespredictionofacutepulmonarytoxicityinaprospectivevalidationcohortofpatientswithalocallyadvancedlungcancertreatedwithvmatradiotherapy |