LPNScreen: a longitudinal pulmonary nodule malignancy risk prediction model based on temporal interval and feature evolution modeling
Year: 2026
Type: article
Abstract: Introduction Longitudinal CT provides valuable information for assessing pulmonary nodule malignancy risk and may improve access to lung cancer screening and follow-up care in resource-constrained primary-care settings. However, single-time-point CT-based methods cannot fully exploit temporal evolution across serial examinations, while the limited computational resources available in primary care also hinder the deployment of large-scale models. Methods To address these challenges, we propose LPNScreen, a lightweight longitudinal framework for pulmonary nodule malignancy prediction. LPNScreen consists of three complementary components: centercontext spatial attention, which enhances discriminative nodule and surrounding contextual representations; interval-aware temporal modeling, which incorporates actual follow-up intervals to characterize irregular temporal dependencies; and tri-pair change modeling, which explicitly captures both adjacent and long-range nodule evolution across longitudinal scans Results Experimental results showed that LPNScreen achieved AUCs of 82.13% and 94.11% on the NLSTLPN and DMULPN datasets, respectively. Extensive comparative experiments and ablation studies further demonstrated the effectiveness of the proposed framework and its robustness across datasets with different follow-up patterns. In addition, computational complexity analysis confirmed the lightweight nature of LPNScreen, which requires only 0.434M parameters and 0.577G FLOPs.
Source: Frontiers in Medicine
Authors: Mingyu Wang, Xiangrui Li, Min Feng, Yanxia Li
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7225824632