Dosiomics and radiomics-based prediction of pneumonitis after radiotherapy and immune checkpoint inhibition: The relevance of fractionation

OBJECTIVES

Post-therapy pneumonitis (PTP) is a relevant side effect of thoracic radiotherapy and immunotherapy with checkpoint inhibitors (ICI). The influence of the combination of both, including dose fractionation schemes on PTP development is still unclear. This study aims to improve the PTP risk estimation after radio(chemo)therapy (R(C)T) for lung cancer with and without ICI by investigation of the impact of dose fractionation on machine learning (ML)-based prediction.

MATERIALS AND METHODS

Data from 100 patients who received fractionated R(C)T were collected. 39 patients received additional ICI therapy. Computed Tomography (CT), RT segmentation and dose data were extracted and physical doses were converted to 2-Gy equivalent doses (EQD2) to account for different fractionation schemes. Features were reduced using Pearson intercorrelation and the Boruta algorithm within 1000-fold bootstrapping. Six single (clinics, Dose Volume Histogram (DVH), ICI, chemotherapy, radiomics, dosiomics) and four combined models (radiomics + dosiomics, radiomics + DVH + Clinics, dosiomics + DVH + Clinics, radiomics + dosiomics + DVH + Clinics) were trained to predict PTP. Dose-based models were tested using physical dose and EQD2. Four ML-algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using 5-fold nested cross validation and Synthetic Minority Oversampling Technique (SMOTE) for resampling in R. Prediction was evaluated using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds.

RESULTS

The combined model of all features using EQD2 surpassed all other models (AUC = 0.77, Confidence Interval CI 0.76-0.78). DVH, clinical data and ICI therapy had minor impact on PTP prediction with AUC values between 0.42 and 0.57. All EQD2-based models outperformed models based on physical dose.

CONCLUSIONS

Radiomics + dosiomics based ML models combined with clinical and dosimetric models were found to be suited best for PTP prediction after R(C)T and could improve pre-treatment decision making. Different RT dose fractionation schemes should be considered for dose-based ML approaches.

The Value of Equivalent Dose Calculation for Dosiomics and Radiomics-Based Prediction of Pneumonitis after Thoracic Radiotherapy with Immune Checkpoint Inhibition

PURPOSE/OBJECTIVE(S)

Post-therapy pneumonitis (PTP) is a relevant side effect after thoracic radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICI). The impact of the combination of both is unclear. We aim to improve risk estimation by prediction of PTP with and without ICI therapy. To analyze the influence of different fractionation schemes, the value of voxel-wise 2 Gy equivalent dose (EQD2) is investigated.

MATERIALS/METHODS

Clinical data from 100 patients who received fractionated RT (single dose ≤ 3Gy) RT were collected. 36 patients received additional ICI therapy. PTP of all grades were monitored. Planning Computed tomographies (CTs), segmentations and 3D dose data were extracted and converted to EQD2. Dosiomics and radiomics features were extracted using 1000-fold bootstrapping using Pearson intercorrelation and the Boruta algorithm for 5 single and 4 combined predictive models. Machine learning algorithms (random forest (rf), logistic elastic net regression, support vector machine, logitBoost) were trained and tested using a 5-fold nested cross validation approach and Synthetic Minority Oversampling Technique resampling in R. Analysis was performed using the area under the receiver operating characteristic curve (AUC) on the test sets of the outer folds.

RESULTS

All investigated models predicted PTP better than random (AUC>.5) (Table 1). Dosiomics+Radiomics models based on EQD2 using rf classifier resulted in the highest predictive performance (AUC = .83 (95% Confidence Interval .83-.84)) and performed worse on physical dose data (AUC = .72 (.71-.73)). For single models, radiomics and dosiomics achieved the best prediction (AUC = .73 (.72-.74), AUC = .8 (.79-.81)) for physical dose and EQD2, respectively. Clinical factors and ICI therapy (AUC = .6 (.59-.62)) had minor impact on PTP prediction. Table 1: AUC and 95% confidence intervals (CI) for all investigated Machine Learning models for EQD2 and physical doses (D).

CONCLUSION

Dosiomics+Radiomics machine learning models have strong capability of PTP prediction and could contribute to pre-treatment decision making. Fractionation schemes should be considered for dose-based prediction strategies. Additional ICI therapy has limited impact on PTP prediction.

Dosiomics and radiomics to predict pneumonitis after thoracic stereotactic body radiotherapy and immune checkpoint inhibition

Introduction

Pneumonitis is a relevant side effect after radiotherapy (RT) and immunotherapy with checkpoint inhibitors (ICIs). Since the effect is radiation dose dependent, the risk increases for high fractional doses as applied for stereotactic body radiation therapy (SBRT) and might even be enhanced for the combination of SBRT with ICI therapy. Hence, patient individual pre-treatment prediction of post-treatment pneumonitis (PTP) might be able to support clinical decision making. Dosimetric factors, however, use limited information and, thus, cannot exploit the full potential of pneumonitis prediction.

Methods

We investigated dosiomics and radiomics model based approaches for PTP prediction after thoracic SBRT with and without ICI therapy. To overcome potential influences of different fractionation schemes, we converted physical doses to 2 Gy equivalent doses (EQD2) and compared both results. In total, four single feature models (dosiomics, radiomics, dosimetric, clinical factors) were tested and five combinations of those (dosimetric+clinical factors, dosiomics+radiomics, dosiomics+dosimetric+clinical factors, radiomics+dosimetric+clinical factors, radiomics+dosiomics+dosimetric+clinical factors). After feature extraction, a feature reduction was performed using pearson intercorrelation coefficient and the Boruta algorithm within 1000-fold bootstrapping runs. Four different machine learning models and the combination of those were trained and tested within 100 iterations of 5-fold nested cross validation.

Results

Results were analysed using the area under the receiver operating characteristic curve (AUC). We found the combination of dosiomics and radiomics features to outperform all other models with AUCradiomics+dosiomics, D = 0.79 (95% confidence interval 0.78-0.80) and AUCradiomics+dosiomics, EQD2 = 0.77 (0.76-0.78) for physical dose and EQD2, respectively. ICI therapy did not impact the prediction result (AUC ≤ 0.5). Clinical and dosimetric features for the total lung did not improve the prediction outcome.

Conclusion

Our results suggest that combined dosiomics and radiomics analysis can improve PTP prediction in patients treated with lung SBRT. We conclude that pre-treatment prediction could support clinical decision making on an individual patient basis with or without ICI therapy.