Preoperative CT–based Radiomics Model for Predicting Micropapillary/Solid Patterns in Stage I Peripheral Lung Invasive Adenocarcinoma

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Abstract
Purpose: To develop and validate an accurate computed tomography–based radiomics model for predicting high-grade (micropapillary/solid) patterns in T1-stage lung invasive adenocarcinoma (IAC) after propensity score matching (PSM). Materials and Methods: We enrolled 546 participants from 2 cohorts with histologically diagnosed lung IAC after complete surgical resection between January 2020 and August 2021. The patients were divided into high-grade and non–high-grade groups and matched using PSM. Matched patient HRCT images were used to delineate regions of interest from tumors and extract radiomics features, and the random forest method was used to construct a radiomics model. The area under the receiver operating characteristic curve (area under the curve) was used to evaluate the model’s performance, and external validation was performed to assess the model’s generalizability. Results: Before PSM, there was no statistically significant difference in age between the two groups, though nodule type and sex exhibited significant differences (P < 0.05) in both cohorts. After PSM, we matched 176 and 97 pairs of patients in the 2 cohorts. In both cohorts, sex and nodule type were equal between the two groups, with a higher percentage of males and solid nodules in both groups. Our model exhibited moderate predictive performance after PSM, with area under the curve values of 0.75 (95% CI: 0.70-0.80) and 0.71 (95% CI: 0.63-0.80) for the development and external validation cohorts, respectively. Conclusion: Although the nodule type compromised the validity of the model’s performance, our results suggest that our acute computed tomography–based radiomics model could preoperatively predict micropapillary/solid patterns in patients with stage I lung IAC after PSM.

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