Affiliation:
1. Department of Radiation Oncology and Image‐Applied Therapy Graduate School of Medicine Kyoto University Kyoto Japan
2. Department of Advanced Medical Physics Graduate School of Medicine Kyoto University Kyoto Japan
3. Division of Radiation Oncology Department of Radiology Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital Tokyo Japan
4. Department of Radiation Oncology Kobe City Medical Center General Hospital Hyogo Japan
5. Department of Radiation Oncology Kyoto Katsura Hospital Kyoto Japan
6. Department of Radiation Oncology Japanese Red Cross Society Wakayama Medical Center Wakayama Japan
Abstract
AbstractBackground and purposeThis study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)‐based radiomics features in prospectively enrolled non‐small‐cell lung cancer patients undergoing dynamic tumor‐tracking stereotactic body radiation therapy (DTT‐SBRT).Materials and methodsThe study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions as the external test dataset. Four clinical features were collected, and 944 CT‐based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high‐ and low‐risk groups based on medians of risk scores. Model performance was assessed using the concordance index (C‐index), and the statistical significance between groups was evaluated using Gray's test.ResultsIn the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C‐indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively.The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (p = 0.116).ConclusionAlthough predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT‐lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.