Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis

Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis

Abstract Objective In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven rad...

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Bibliographic Details
Main Authors: Wensong Shi, Yuzhui Hu, Guotao Chang, He Qian, Yulun Yang, Yinsen Song, Zhengpan Wei, Liang Gao, Hang Yi, Sikai Wu, Kun Wang, Huandong Huo, Shuaibo Wang, Yousheng Mao, Siyuan Ai, Liang Zhao, Xiangnan Li, Huiyu Zheng
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Medical Imaging
Subjects:
Artificial intelligence
Radiomics
3D CTR ≥ 50%
Benign and malignant
Clinical prediction model
Online Access:https://doi.org/10.1186/s12880-024-01533-9
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