Incorporating intra‐fractional delta‐radiomics from 4DCT improves local recurrence prediction

Background Respiratory motion of tumors during radiotherapy can compromise dose delivery and further may impact tumor control efficacy. Developing a predictive model that integrates tumor heterogeneity with respiratory motion may enable the identification of high‐risk patients, thereby guiding adaptive radiotherapy strategies and refining follow‐up protocols. Purpose To propose a novel prognosis prediction method utilizing intra‐fractional temporal signatures and validate preliminarily on the prediction of local recurrence (LR) for patients with non‐small cell lung cancer (NSCLC). Methods A cohort of 100 NSCLC patients were retrospectively analyzed. The intra‐fractional temporal changes of 1321 radiomics features between phase 0% and i were calculated from 4DCT ( Delta_RF i , i = 10%,.90%) and stacked as accumulative delta‐radiomics features ( Delta_RF acc ). The delta‐radiomics signatures (DRS i , i = 10%,.90%) and the novel accumulative delta‐radiomics signature (ADRS) were developed by applying principal component analysis (PCA) to Delta_RF i and Delta_RF acc . The conventional radiomics signature (RS) was built from 3DCT. The predicting performance using RS, DRSs, and ADRS were compared through univariate Logistic regression. LR risk stratification using ADRS and RS were also evaluated. Results Based on the mean performance from 7:3 random‐split cross‐validations with 50 random seeds, the best area under the receiver operating characteristic curve (AUC) and area under the precision‐recall curve (AUPRC) were observed in ADRS model (AUC = 0.8391, AUPRC = 0.8043), which were significantly (all p < 0.05) higher than that of RS model (AUC = 0.7021, AUPRC = 0.5260) and the models based on DRS of any single phase (AUC ranging 0.6470∼0.7693, AUPRC ranging 0.4571∼0.6047). Significantly worse local progression‐free survival was associated with higher signature values of both ADRS and RS, with hazard ratios (HR) of 6.74 for ADRS (95% confidence interval (CI): 2.79‐16.24, p < 0.0001) and 3.28 for RS (95% CI: 1.39‐7.75, p = 0.0043). The motion‐sensitive feature was found contributive to the superior performance of ADRS. Conclusions The incorporation of temporal information from 4DCT significantly improved the model performance than that of the conventional method based on 3DCT. A novel ADRS approach based on intra‐fractional delta‐radiomics was proposed and validated on the LR risk prediction for patients with NSCLC. The improved accuracy of risk management can be beneficial to assist the personalized optimization of treatment and follow‐up protocols, achieving a balance among cost, efficiency, and outcome.