RFDFM: A Deep Factorization Machine Network Model for Invasive Lung Adenocarcinoma Screening in CT Images

Jing Zhou, Chengcheng Guo, Ying Ji

Abstract: As a common subtype of lung cancer, the diagnosis of lung adenocarcinoma has significant importance in clinical practice, particularly in distinguishing between pre-invasive adenocarcinoma (Pre-IA) and invasive adenocarcinoma (IAC). This distinction is critical because the two types of lesions correspond to different clinical treatment strategies: Pre-IAs typically require only regular observation, while IACs necessitate immediate surgical removal. In this article, we propose a novel deep learning model, the Radiomic Feature Deep Factorization Machine (RFDFM) network model, for distinguishing IACs from Pre-IAs in CT images, leveraging both radiomic features and deep learning features. Our novelty resides in pioneering the application of recommendation system model structures to computer-aided diagnosis of pulmonary nodules, demonstrating feasibility and effectively addressing the limitations of traditional methods in handling radiomic features. Moreover, the use of low-level feature fusion convolutional neural networks minimizes the information loss, and an element-wise attention mechanism in feature fusion stage to accentuate key features and improve model fitting. For extensive validation, 1,052 nodule samples were gathered from a total of 791 patients that were diagnosed with lung adenocarcinoma across two top-tier hospitals. The proposed RFDFM method can achieve a sota performance of 94.2% in terms of AUC. Results of extensive ablation studies demonstrate its contribution to improved performance. Finally, to promote more efficient academic communication, the analysis code is publicly available at unmapped: uri https://github.com/Chengcheng-Guo/RFDFM.

External IDs:dblp:conf/ecai/ZhouGJ24