Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis

background: methods to improve stratification of small (≤15 mm) lung nodules are needed. we aimed to develop a radiomics model to assist lung cancer diagnosis. methods: patients were retrospectively identified using health records from January 2007 to december 2018. the external test set was obtained from the national LIBRA study and a prospective lung cancer screening programme. radiomics features were extracted from multi-region CT segmentations using texlab2.0. LASSO regression generated the 5-feature small nodule radiomics-predictive-vector (SN-RPV). K-means clustering was used to split patients into risk groups according to SN-RPV. model performance was compared to 6 thoracic radiologists. SN-RPV and radiologist risk groups were combined to generate “safety-net” and “early diagnosis” decision-support tools. results: In total, 810 patients with 990 nodules were included. the AUC for malignancy prediction was 0.85 (95% CI: 0.82–0.87), 0.78 (95% CI: 0.70–0.85) and 0.78 (95% CI: 0.59–0.92) for the training, test and external test datasets, respectively. the test set accuracy was 73% (95% CI: 65–81%) and resulted in 66.67% improvements in potentially missed [8/12] or delayed [6/9] cancers, compared to the radiologist with performance closest to the mean of six readers. conclusions: SN-RPV may provide net-benefit in terms of earlier cancer diagnosis.