Improvement of machine learning models for predicting high-grade subtypes of lung adenocarcinoma based on delta radiomics: A multicenter cohort study.

Feiyang Zhong^1,2, Ting Li³, Wenping Li⁴, Lijun Wu², Pengju Zhang⁵, Pengxin Yu⁶, Yuan Fang⁷, Meiyan Liao³, Shaohong Zhao^1,2

¹Nankai University, Tianjin, China.

²Department of Radiology, the First Medical Center of the Chinese PLA General Hospital, Beijing, China.

³Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.

⁴Department of Radiology, the Sixth Medical Center of the Chinese PLA General Hospital, Beijing, China.

⁵Department of Radiology, the Fourth Medical Center of the Chinese PLA General Hospital, Beijing, China.

⁶Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China.

⁷Department of Radiology, PLA Air Force Medical Center, Beijing, China.

PubMed PMC DOI PDF

Abstract

OBJECTIVES: To evaluate the effectiveness of delta radiomics in predicting high-grade components in lung adenocarcinoma and to develop a robust machine learning model for clinical application. METHODS: This retrospective multi-center cohort study included lung cancer patients from three hospitals who had pre-surgery CT follow-up scans. Training (n = 491) and validation (n = 210) were performed us…

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