Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy for lung cancer

Bourbonne, Vincent; Da-ano, Ronrick; Jaouen, Vincent; Lucia, François; Dissaux, G.; Bert, Julien; Pradier, Olivier; Visvikis, Dimitris; Hatt, M.; Schick, Ulrike

doi:10.1016/j.radonc.2020.10.040

Cited by 39 publications

(34 citation statements)

References 34 publications

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“…The results of dosiomic studies relative to RP were also difficult to compare due to differences in the extracted features. For example, Liang et al found “contrast” from the GLCM and “low grey level run emphasis” from the GLRLM as the most predictive features of RP ≥2 in lung cancer patients treated with volumetric modulated arc therapy (VMAT) ( 34 ), while the study of dosiomics in lung cancer patients treated with VMAT by Bourbonne et al investigated acute and late lung toxicity separately, which was different approach from that of Liang et al ( 35 ). Adachi et al made a study of dosiomics that utilized different modalities (SBRTs) and different techniques for feature extraction ( 33 ).…”

Section: Discussionmentioning

confidence: 99%

Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer

et al. 2022

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PurposeThe aim was to investigate the advantages of dosiomic and radiomic features over traditional dose-volume histogram (DVH) features for predicting the development of radiation pneumonitis (RP), to validate the generalizability of dosiomic and radiomic features by using features selected from an esophageal cancer dataset and to use these features with a lung cancer dataset.Materials and MethodsA dataset containing 101 patients with esophageal cancer and 93 patients with lung cancer was included in this study. DVH and dosiomic features were extracted from 3D dose distributions. Radiomic features were extracted from pretreatment CT images. Feature selection was performed using only the esophageal cancer dataset. Four predictive models for RP (DVH, dosiomic, radiomic and dosiomic + radiomic models) were compared on the esophageal cancer dataset. We further used a lung cancer dataset for the external validation of the selected dosiomic and radiomic features from the esophageal cancer dataset. The performance of the predictive models was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROCAUC) and the AUC of the precision recall curve (PRAUC) metrics.ResultThe ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on esophageal cancer dataset were 0.67 ± 0.11 and 0.75 ± 0.10, 0.71 ± 0.10 and 0.77 ± 0.09, 0.71 ± 0.11 and 0.79 ± 0.09, and 0.75 ± 0.10 and 0.81 ± 0.09, respectively. The predictive performance of the dosiomic- and radiomic-based models was significantly higher than that of the DVH-based model with respect to esophageal cancer. The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on the lung cancer dataset were 0.64 ± 0.18 and 0.37 ± 0.20, 0.67 ± 0.17 and 0.37 ± 0.20, 0.67 ± 0.16 and 0.45 ± 0.23, and 0.68 ± 0.16 and 0.44 ± 0.22, respectively. On the lung cancer dataset, the predictive performance of the radiomic and dosiomic + radiomic models was significantly higher than that of the DVH-based model. However, the PRAUC of the dosiomic-based model showed no significant difference relative to the corresponding RP prediction performance on the lung cancer dataset.ConclusionThe results suggested that dosiomic and CT radiomic features could improve RP prediction in thoracic radiotherapy. Dosiomic and radiomic feature knowledge might be transferrable from esophageal cancer to lung cancer.

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Section: Discussionmentioning

confidence: 99%

Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer

et al. 2022

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“…Previous studies demonstrated that the use of dosiomic features can improve the performance of predictive models in RP [ 8 – 10 ]. In our study, dosiomic features also showed significant improvement from dosimetric features.…”

Section: Discussionmentioning

confidence: 99%

“…TA has also been applied to dose distribution in radiotherapy, referred to as dosiomics. Several researchers have reported improved toxicity prediction performance after radiation therapy by dosiomics [ 5 – 7 ], including for RP [ 8 – 10 ]. However, studies on dosiomics as features for the prediction of RP were performed in lung cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

et al. 2021

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Objective The purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2). Materials and methods DVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs). Result The AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2. Conclusion Dosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.

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“…Features with a higher frequency were retained for downstream model development. The maximum number of features in the final model was set to 10% of the training sample size [39][40][41]. In the case of exceeding the amount of features, features that had the least frequency of occurrence were excluded from the final R model.…”

Section: Model Development and Evaluationmentioning

confidence: 99%

A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma

Lam

Zhang

et al. 2022

Life

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Significant lymph node shrinkage is common in patients with nasopharyngeal carcinoma (NPC) throughout radiotherapy (RT) treatment, causing ill-fitted thermoplastic masks (IfTMs). To deal with this, an ad hoc adaptive radiotherapy (ART) may be required to ensure accurate and safe radiation delivery and to maintain treatment efficacy. Presently, the entire procedure for evaluating an eligible ART candidate is time-consuming, resource-demanding, and highly inefficient. In the artificial intelligence paradigm, the pre-treatment identification of NPC patients at risk for IfTMs has become greatly demanding for achieving efficient ART eligibility screening, while no relevant studies have been reported. Hence, we aimed to investigate the capability of computed tomography (CT)-based neck nodal radiomics for predicting IfTM-triggered ART events in NPC patients via a multi-center setting. Contrast-enhanced CT and the clinical data of 124 and 58 NPC patients from Queen Elizabeth Hospital (QEH) and Queen Mary Hospital (QMH), respectively, were retrospectively analyzed. Radiomic (R), clinical (C), and combined (RC) models were developed using the ridge algorithm in the QEH cohort and evaluated in the QMH cohort using the median area under the receiver operating characteristics curve (AUC). Delong’s test was employed for model comparison. Model performance was further assessed on 1000 replicates in both cohorts separately via bootstrapping. The R model yielded the highest “corrected” AUC of 0.784 (BCa 95%CI: 0.673–0.859) and 0.723 (BCa 95%CI: 0.534–0.859) in the QEH and QMH cohort following bootstrapping, respectively. Delong’s test indicated that the R model performed significantly better than the C model in the QMH cohort (p < 0.0001), while demonstrating no significant difference compared to the RC model (p = 0.5773). To conclude, CT-based neck nodal radiomics was capable of predicting IfTM-triggered ART events in NPC patients in this multi-center study, outperforming the traditional clinical model. The findings of this study provide valuable insights for future study into developing an effective screening strategy for ART eligibility in NPC patients in the long run, ultimately alleviating the workload of clinical practitioners, streamlining ART procedural efficiency in clinics, and achieving personalized RT for NPC patients in the future.

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Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy for lung cancer

Cited by 39 publications

References 34 publications

Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer

Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients

A Multi-Center Study of CT-Based Neck Nodal Radiomics for Predicting an Adaptive Radiotherapy Trigger of Ill-Fitted Thermoplastic Masks in Patients with Nasopharyngeal Carcinoma

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