Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical–radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study

Abstract

To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features for the preoperative prediction of invasiveness in patients with clinical stage IA lung adenocarcinoma manifesting as part-solid nodules (PSNs). This study was conducted from January 2015 to October 2021 at three centres: 355 patients with 355 PSN lung adenocarcinomas who underwent surgical resection were included and classified into the training (n=222) and validation (n=133) cohorts. PSN segmentation on CT images was performed automatically with a commercial deep-learning algorithm, and CT texture features were extracted. The least absolute shrinkage and selection operator was used for feature selection and transformed into a DL-TA score. The combined nomogram that incorporated the DL-TA score and identified clinical-radiological features was developed for the prediction of pathological invasiveness of the PSNs and validated in terms of discrimination and calibration. The present study generated a combined nomogram for predicting the invasiveness of PSNs that included age, consolidation-to-tumour ratio, smoking status, and DL-TA score, with a C-index of 0.851 (95% confidence interval: 0.826-0.877) for the training cohort and 0.854 (95% confidence interval: 0.817-0.891) for the validation cohort, indicating good discrimination. Furthermore, the model had a Brier score of 0.153 for the training cohort and 0.135 for the validation cohort, indicating good calibration. The developed combined nomogram consisting of the DL-TA score and clinical-radiological features and has the potential to predict the individual risk for the invasiveness of stage IA PSN lung adenocarcinomas.