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
Year: 2025
Type: article
Study design: Observational Study
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: ), 2D radiomics-dosiomics model (RDM), and combined models were constructed, and the predictive performances for radiochemotherapy resistance were compared. Subsequently, the predictive performance of various models for radiochemotherapy resistance was compared by receiver operating characteristic (ROC) curves and calculating accuracy, sensitivity and specificity. The multi-omics and clinical models were integrated for patient risk stratification. Results: were 0.764 [95% confidence interval (CI): 0.687-0.841] and 0.729 (95% CI: 0.568-0.889). And the RDM performed significantly better than the single radiomics and dosiomics models, with AUC of 0.836 (95% CI: 0.773-0.899) and 0.748 (95% CI: 0.617-0.879), respectively. Hemoglobin level and T stage were independent predictors in the clinical model. The combined model containing independent predictors further improved the predictive performance in both the training and validation cohorts, with AUC of 0.844 (95% CI: 0.781-0.907) and 0.753 (95% CI: 0.618-0.887). Grouping of patients according to the critical value of the combined model revealed significant differences in progression-free survival (PFS) and overall survival (OS) between the high-risk and low-risk groups (P<0.05). 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.
Source: Translational Cancer Research
DOI: 10.21037/tcr-24-1897
Authors: Min Zhang, Ya Li, Yong Hu, Bo Du, Youlong Mo, Tianchu He, Yang Yang, Benlan Li, Ji Xia, Zhongjun Huang, Fangyang Lu, Bing Lu, Jie Peng
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4408685410