Development and validation of an LDCT-based deep learning radiomics nomogram for predicting postoperative recurrence of stage Ia lung adenocarcinoma

Haimei Lan^*Chaosheng WeiMingzhuang LiaoHongfeng LiangJianli QinJixing YiFengming XuDandan HuangMeiqing ZhangFeng QingTao Li^*

• Liuzhou Workers' Hospital, Liuzhou, China

Objective: This study wanted to use low-dose computed tomography (LDCT) plain scan images to create a deep learning radiomic nomogram (DLRN) to accurately predict the likelihood of recurrence after surgery in patients with stage Ia lung adenocarcinoma (LUAD). Methods: We collected cases from January 2010 to December 2020 at Center 1 who underwent surgery and were pathologically diagnosed with stage Ia LUAD, and additionally collected patients with the same criteria at Center 2 from January 2015 to December 2018 for external validation. Deep learning and radiomic feature extraction were performed on LDCT images of all patients. In the deep learning and radiomics methods, we tested multiple different models and selected the best model based on the results of the internal validation cohort. Finally, we construct a nomogram by combining deep learning features, radiomics features and clinical data. Subsequently, We used the receiver operating characteristic (ROC) curve to check how well these models performed in terms of diagnosis. The calibration degree of each model was evaluated using calibration curves, while the clinical value of each model was assessed through decision curve analysis (DCA). Results: In Center 1, we collected a total of 233 eligible patients, who were randomly divided into a training cohort (163 patients) and an internal validation cohort (70 patients) at a 7:3 ratio. And we collected included a total of 89 patients in Center 2. Internal validation results showed Resnet50 and Logistic Regression (LR) as optimal models for deep learning and radiomics approaches, respectively. The area under the curve (AUC) values for this combined model were 0.972 (95% CI: 0.949-0.995) in the training cohort, 0.925 (95% CI: 0.845-1.000) in the internal validation cohort, and 0.915 (95% CI: 0.853-0.976) in the external validation cohort. Compared with other single models, it demonstrated the best performance. Conclusion: Preoperative DLRN based on LDCT plain scan images exhibit good predictive value for postoperative recurrence in patients with stage Ia LUAD. The present study developed a novel prognostic assessment method with the objective of assisting clinicians in refining adjuvant treatment plans for patients with stage Ia LUAD, thus facilitating personalised prognostic management.