ArticleTranslational cancer research2025
Development and validation of a prediction model based on two-dimensional dose distribution maps fused with computed tomography images for noninvasive prediction of radiochemotherapy resistance in non-small cell lung cancer.
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: There are individualized differences in the prognosis of radiochemotherapy for non-small cell lung cancer (NSCLC), and accurate prediction of prognosis is essential for individualized treatment. This study proposes to explore the potential of multiregional two-dimensional (2D) dosiomics combined with radiomics as a new imaging marker for prognostic risk stratification of NSCLC patients receiving radiochemotherapy. Methods: In this study, 365 patients with histologically confirmed NSCLC, who had computed tomography (CT) scans before treatment, received standard radiochemotherapy, and had Karnofsky Performance Scale (KPS) scores ≥70 were included in three medical institutions, and 145 cases were excluded due to surgery, data accuracy, poor image quality, and the presence of other tumors. Finally, 220 patients were included in the study. Efficacy evaluation criteria for solid tumors are used to evaluate efficacy. Complete and partial remission indicate the radiochemotherapy-sensitive group, and disease stability and progression indicate the radiochemotherapy-resistant group. We combined all the data and then randomised them into a training cohort (154 cases) and a validation cohort (66 cases) in a 7:3 ratio. Radiomics and dosiomics features were extracted for gross tumor volume (GTV), GTV-heat, and 50 Gy-heat and screened. 2D dosiomics model (DM Results: DM Conclusions: Compared to the traditional radiomics model, the 2D dosiomics model demonstrates superior predictive performance. The combined model based on clinical data, radiomics, and dosiomics has improved the prediction of radiochemotherapy resistance in NSCLC and effectively performed survival stratification. Through precise risk assessment, doctors can better understand which patients may develop resistance to treatment and optimize treatment plans accordingly.
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