195P Radiomics and dosiomics signature from whole lung predicts radiation pneumonitis: A model development study with prospective external validation and decision-curve analysis

无线电技术 医学 放射治疗 列线图 布里氏评分 判别式 自举(财务) 逻辑回归 放射科 接收机工作特性 核医学 统计 人工智能 内科学 数学 计算机科学 计量经济学
作者
Z. Zhang,Ziqi Wang,André Dekker,Leonard Wee
出处
期刊:Annals of Oncology [Elsevier BV]
卷期号:33: S119-S119 被引量:2
标识
DOI:10.1016/j.annonc.2022.02.120
摘要

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. 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. 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: 195PModelTrain (95%CI)Validation by bootstrapping (95%CI)Testing (95%CI)R-score0.676 (0.606-0.745)0.619 (0.592-0.646)0.671 (0.558-0.899)D-score0.728 (0.665-0.790)0.687 (0.667-0.706)0.684 (0.573-0.883)DVH-score0.637 (0.570-0.705)0.628 (0.613-0.642)0.661 (0.551-0.856)Clinical parameters0.664 (0.594-0.735)0.654 (0.628-0.680)0.709 (0.509-0.91)R-score + D-score0.735 (0.673-0.796)0.729 (0.720-0.736)0.739 (0.553-0.926)R-score + C0.717 (0.652-0.782)0.701 (0.683-0.719)0.771 (0.585-0.962)D-score + C0.770 (0.710-0.830)0.755 (0.744-0.765)0.756 (0.559-0.954)R-score + D-score + C0.793 (0.735-0.851)0.774 (0.762-0.786)0.855 (0.719-0.990) Open table in a new tab Radiomics and dosiomics features have potential to assist with the prediction of RP.
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