Radiomics Nomogram Based on Optimal Volume of Interest Derived from High-Resolution CT for Preoperative Prediction of IASLC Grading in Clinical IA Lung Adenocarcinomas: A Multi-Center, Large-Population Study
Authors: Zuo Zhichao , Zhang Guochao , Lin Shanyue , Xue Qi , Qi Wanyin , Zhang Wei , Fan Xiaohong 
Published: November 2024

The novel grading system developed by the International Association for the Study of Lung Cancer (IASLC) for clinical stage IA lung adenocarcinomas has demonstrated remarkable prognostic capabilities. Notably, tumors classified as grade 3 have been associated with poor prognostic outcomes, thereby playing a crucial role in the formulation of personalized surgical strategies. The objective of this study is to develop a radiomics nomogram that utilizes the optimal volume of interest (VOI) derived from high-resolution CT (HRCT) scans to accurately predict the presence of grade 3 tumors in patients with clinical IA lung adenocarcinomas. In this multi-center, large-population study, clinical, pathological, and HRCT imaging data from 1418 patients who were pathologically diagnosed with lung adenocarcinomas were retrospectively collected. The data was obtained from four hospital databases between January 2018 and May 2022. From this patient cohort, 1206 individuals were screened from three databases and randomly divided into training and internal validation datasets in a 7:3 ratio. An additional dataset consisting of 212 individuals was used for external validation dataset. Radiomics features were extracted from HRCT images at various scales, including VOI−2mm, VOI entire, VOI +2mm, and VOI +4mm. To reduce dimensionality, select relevant features, and build radiomics signatures, the maximal redundancy minimal relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were utilized. Univariate and multivariate logistic regression analyses were conducted to identify independent clinic-radiological (Clin-Rad) predictors. Receiver operating characteristic (ROC) curves and corresponding area under the curve (AUC) were used to evaluate the diagnostic efficiency. A nomogram predicting the risk of grade 3 in clinical stage IA lung adenocarcinoma was constructed based on multivariate logistic regression, combining independent predictors and the optimal radiomics signatures. Multivariate logistic regression revealed that males exhibited a higher prevalence of grade 3 tumors, and solid nodules were frequently observed through radiological assessments. The utilization of radiomics features extracted from the VOI entire resulted in significant improvements in predictive performance, as evidenced by AUC values of 0.900 (0.880-0.942), 0.885 (0.824-0.946), and 0.888 (0.782-0.993) for the training, internal validation, and external validation datasets, respectively. Furthermore, the nomogram that combined VOI entire -based radiomics signatures and Clin-Rad characteristics, exhibited remarkable predictive performance. This was indicated by AUC values of 0.910(0.873-0.942), 0.891 (0.845-0.937), and 0.905 (0.846-0.964) for the training, internal validation, and external validation datasets, respectively. The extraction of radiomics features from both the indented and peri-tumoral regions does not offer any additional benefits in predicting grade 3 tumors according to the IASLC system. However, when combining the VOI entire-based radiomics model with Clin-Rad characteristics, the resulting integrated nomogram exhibited remarkable predictive performance.