Multi‐omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited‐stage small cell lung cancer to definitive chemoradiotherapy

Abstract

Dear Editor,The efficacy of definitive concurrent or sequential chemoradiotherapy (dCRT) varies significantly among limited-stage small-cell lung cancer (LS-SCLC) patients, with about 10%-13% of patients achieving 5-year survival, while 58% of patients die within 1 year. 1-3 Therefore, there is an urgent need to find biomarkers for early prediction of the efficacy of dCRT in LS-SCLC in support of risk stratification. Tumourigenesis and progression are heterogeneous at the phenotypic, physiologic and genomic levels, making predictive information obtained via radiomic or genomic profiling alone of limited value for clinical decision making. [4][5][6] The present study aimed to develop a combination of genomic, radiomic and fused radiogenomic biomarkers for predicting the response of LS-SCLC to dCRT in training and validation cohorts, and to provide optimised multi-omics prediction models based on their predictive power for LS-SCLC.Totally 154 patients with LS-SCLC who received dCRT in Shandong Cancer Hospital and Institute were included, and were randomly divided into a training group and test group at a ratio of 7:3. No significant differences in clinical or genomic characteristics were found between the two cohorts (Table S1). The median PFS (progression free survival, mPFS) among all patients was 12.7 months (range, 2.4−60.5 months).In the training cohort, LASSO regression 7 was performed to obtain the most significant radiomic features related to PFS according to a λ min of .046 (Figure 1A,B). The radiomic signature (Rad-score) was then constructed by linearly combining the 10 selected features and corresponding weighting coefficients, as listed in Table S2. The best threshold was .35, 8 which divided patients into a high-risk group (Rad-score ≥ .35) and a low-risk group (Rad-score < .35). Rad-score was identified as an independent biomarker for PFS on both univariate and mul-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.