Abstract 195P
Background
Radiation pneumonitis (RP) is one of the common side effects of radiotherapy in the thoracic region. Radiomics and dosiomics quantifies information implicit within medical images and radiotherapy dose distributions. In this study we demonstrated the prognostic potential of radiomics, dosiomics, and clinical features for RP prediction.
Methods
Radiomics, dosiomics, dose-volume histogram (DVH) metrics, and clinical parameters were obtained on 314 retrospectively-collected and 35 prospectively-enrolled patients diagnosed with lung cancer between 2013 to 2019. A radiomics risk score (R-score) and dosiomics risk score (D-score) and DVH-score were calculated based on logistic regression after feature selection. Seven models were built using different combinations of R-score, D-score, and clinical parameters to evaluate their added prognostic power. The DVH model was built as a classical model for comparison with the dosiomics model. Over-optimism was evaluated by bootstrap resampling from the training set, and the prospectively-collected cohort was used as the external test set. Model calibration and decision-curve characteristics of the best-performing models were evaluated. For ease of further evaluation, nomograms were constructed for selected models.
Results
A model built by integrating all of R-score, D-score, and clinical parameters had the best discriminative ability with area under the curves (AUCs) of 0.793 (95%CI 0.735-0.851),0.774 (95%CI 0.762-0.786), and 0.855 (95%CI 0.719-0.990) in the training set, bootstrapping set, and external test set, respectively. The results of the calibration curve and decision curve analysis showed that the final model of the nomogram has potential for future clinical application. Table: 195P
| Model | Train (95%CI) | Validation by bootstrapping (95%CI) | Testing (95%CI) |
| R-score | 0.676 (0.606-0.745) | 0.619 (0.592-0.646) | 0.671 (0.558-0.899) |
| D-score | 0.728 (0.665-0.790) | 0.687 (0.667-0.706) | 0.684 (0.573-0.883) |
| DVH-score | 0.637 (0.570-0.705) | 0.628 (0.613-0.642) | 0.661 (0.551-0.856) |
| Clinical parameters | 0.664 (0.594-0.735) | 0.654 (0.628-0.680) | 0.709 (0.509-0.91) |
| R-score + D-score | 0.735 (0.673-0.796) | 0.729 (0.720-0.736) | 0.739 (0.553-0.926) |
| R-score + C | 0.717 (0.652-0.782) | 0.701 (0.683-0.719) | 0.771 (0.585-0.962) |
| D-score + C | 0.770 (0.710-0.830) | 0.755 (0.744-0.765) | 0.756 (0.559-0.954) |
| R-score + D-score + C | 0.793 (0.735-0.851) | 0.774 (0.762-0.786) | 0.855 (0.719-0.990) |
Conclusions
Radiomics and dosiomics features have potential to assist with the prediction of RP.
Legal entity responsible for the study
Zhen Zhang.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.