Deciphering the intratumoral histologic heterogeneity of lung adenocarcinoma using radiomics
Authors
Jae Mo Koo, Jonghoon Kim, Junghee Lee, Soohyun Hwang, Hyo Sup Shim, Tae Hee Hong, Yu Jin Oh, Hong Kwan Kim, Chang Young Lee, Byung Jo Park and Ho Yun Lee
This study utilized radiomics to predict high-risk histologic patterns in lung adenocarcinoma (LUAD) and analyze their association with occult lymph node metastasis and recurrence rate. Data from 777 patients (Institution A: 528, Institution B: 249) across two medical institutions were collected, and high-resolution preoperative CT images were analyzed to extract radiomic features. Using data from Institution A, a logistic regression-based model was developed to predict micropapillary and solid patterns, and external validation was conducted with Institution B data. A composite model incorporating six CT-based radiomic features and clinical variables outperformed single models (AUC = 0.84–0.91). Lung nodule segmentation was performed using AVIEW software from Coreline Soft, followed by expert modification. This study demonstrates the potential of preoperative, non-invasive prediction of high-risk histologic patterns in LUAD.