Patients with locally advanced non-small cell lung cancer (LA-NSCLC) exhibit heterogeneous prognoses despite receiving standard treatments, highlighting the need for more reliable prognostic biomarkers. This study aims to develop and validate OmicsMap model, a deep radiomics biomarkers derived from computed tomography (CT) images for the prediction of progression-free survival (PFS) in LA-NSCLC patients. Approach: We retrospectively analyzed data from 329 LA-NSCLC patients who underwent definitive radiotherapy. The cohort was randomly divided into development (N=220) and independent testing set (N=109). The prognostic signature was derived from integrated radiomics features extracted from both the primary tumor and involved lymph nodes, and inter-patient radiomics feature interactions. To achieve this, high-dimensional radiomics data from all patients were transformed into structured 2D representations, termed OmicsMap, wherein radiomics feature interactions were encoded within the pixelated configuration. Deep radiomics features from the OmicsMaps were then extracted using a convolutional neural network for prognostic prediction. Model performance was evaluated by time-dependent area under the receiver operating characteristic curves (AUC). Kaplan-Meier (KM) curves were plotted and Hazard ratios (HR) were calculated via Cox proportional hazards model. Main results: The OmicsMap model achieved time-dependent AUCs of 0.76, 0.78 and 0.76 at 1, 2 and 3 years in the independent testing set, significantly outperforming the clinical model (AUC: 0.57, 0.57, 0.64; p < 0.05). The proposed model improved predictive discrimination with 7.69% increase in C-index over conventional radiomics approaches. It effectively stratified patients into high-risk and low-risk subgroups for both PFS (p < 0.001, HR = 0.380) and OS (p = 0.0021, HR = 0.525) in the testing set. Significance: The proposed OmicsMap model provides a novel paradigm for enhancing prognostic prediction in patients with LA-NSCLC. By improving risk stratification, the framework may help inform clinical decision-making and support future efforts toward more individualized management strategies. .
Hou et al. (Wed,) studied this question.