Cross-Scale Integration of Nano-Sized Extracellular Vesicle-Based Biomarker and Radiomics Features for Predicting...

Sub-solid nodules (SSN) are common radiographic findings. Due to possibility of malignancy, further evaluation is urgentlyneeded for prevention and management of lung cancer (LC). This current study enrolled patients with SSN, including LC, benign nodules (BN), and healthy individuals as a control, to discover small extracellular vesicles (sEVs) differentially expressed miRNAs (DEMs) as biomarker by next-generation sequencing (NGS) and validation by RT-qPCR. Through cross-scale integration of validated small-molecule and macro-imaging, the prediction model was developed by logistic algorithms and further interpreted into an easy-to-use Nomogram by Cox-proportional hazards modeling. Present study has discovered various sEVs DEMs and sEVs-miR-424-5p that were selected and validated as novel potential biomarkers for cancerous nodule, namely LC. Furthermore, the 10 radiomics signs and 4 clinical features of SSN were merged with sEVs-miR-424-5p and proceeded in multivariate logistic regression analysis to develop the cross-scale integrated modeling, which yielded a significantly higher area under the curve (AUC). Finally, visualization of an easy-to-use nomogram was invented to potentially predict suspected SSN. sEVs-miR-424-5p could be a novel biomarker for distinguishing SSN from LC and BN populations. Its association with cross-scale fusion of radiomics-clinical features will provide great potential to be an errorless prediction of malignant SSN.