Multimodal modeling based on DNA methylation analysis in bronchoalveolar lavage fluid for early lung cancer detection

Abstract

Lung cancer diagnosis poses a significant clinical challenge, with emphasis on enhancing the positivity rate and accuracy of early detection. The use of bronchoalveolar lavage fluid (BALF) for detecting the methylation of ras-association domain family member 1 A (RASSF1A) and short stature homeobox 2 (SHOX2) genes has emerged as a novel molecular diagnostic technique for lung cancer. Nonetheless, this method’s positivity rate can vary due to factors such as BALF quality, and its diagnostic consistency is uncertain. It was a prospective diagnostic study with randomized sampling. In this study, 310 patients with lung lesions detected by computed tomography (CT) imaging were enrolled, and they were randomized 1:1 into pre-biopsy BALF group and post-biopsy BALF group. RASSF1A and SHOX2 methylation in BALF were detected, and CT images and tumor markers of patients were collected to develop a multimodal model based on BALF methylation for predicting malignant lung lesions. An internal validation set was employed to gauge the model’s effectiveness. The findings revealed a statistically significant increase in gene methylation positivity rate and pathological cytology rates in the post-biopsy BALF group compared to the pre-biopsy BALF group (P < 0.05). The model demonstrated an area under the curve (AUC) of 0.985 for predicting malignant lung masses and 0.903 for lung nodules in the training set. When tested on the validation set, the AUC for predicting malignant lung masses and lung nodules was 0.930 and 0.811, respectively. The multimodal prediction model constructed based on RASSF1A and SHOX2 methylation of post-biopsy BALF demonstrates a high predictive value for identifying malignant lung lesions.