3D deep learning model to predict the recurrence of stage IA invasive lung adenocarcinoma after sub-lobar resection: a multicenter retrospective cohort study.

Xin Fan¹, Chen Liang², Xue-Qin Ma³, Yi-Bo Feng⁴, Qian-Rui Fan⁴, Da-Wei Wang⁴, Tian-You Luo¹, Fa-Jin Lv⁵, Qi Li⁶

¹Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

²Department of Radiology, Second Affiliated Xinqiao Hospital of Army Medical University, No.83 Xinqiao Main Street, Shaping District, Chongqing, China.

³Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.

⁴Institute of Research, Infervision Medical Technology Co, Ltd, 25 F Building E, Yuanyang International Center, Chaoyang District, Beijing, China.

⁵Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. lvfj2020@sina.com.

⁶Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China. 202770@hospital.cqmu.edu.cn.

PubMed DOI

Abstract

The purpose of this study was to investigate the efficacy of a three-dimensional (3D) deep learning (DL) model in predicting recurrence risk of stage IA invasive lung adenocarcinoma (ILADC) after sub-lobar resection (SLR). A total of 287 stage IA ILADC patients were assigned to training and internal validation sets (4:1), with an external test cohort of 112 patients from two institutions. Three cl…

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