250P Preoperative CT deep learning for pathological high-risk factors in early-stage lung adenocarcinoma: Retrospective development and prospective validation of a knowledge-based graph convolutional network

survival is minimal.These findings support the continued adoption of SVATS, provided oncologic principles are maintained.

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Publication Details

Journal
ESMO Open
Published
2026-03-17
DOI
https://doi.org/10.1016/j.esmoop.2026.106558
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

250P Preoperative CT deep learning for pathological high-risk factors in early-stage lung adenocarcinoma: Retrospective development and prospective validation of a knowledge-based graph convolutional network

J. Lu, P. Tang, Y. Ji, J. Liu et al.
ESMO Open
Radiomics and Machine Learning in Medical Imaging
article

250P Preoperative CT deep learning for pathological high-risk factors in early-stage lung adenocarcinoma: Retrospective development and prospective validation of a knowledge-based graph convolutional network

J. Lu, P. Tang, Y. Ji, J. Liu, X. Meng, J. Zhou, B. Hu, X. Li, F. Wei, X. Hu
article en

Abstract

survival is minimal.These findings support the continued adoption of SVATS, provided oncologic principles are maintained.

ESMO OpenVol. 11
Central South University (CN), Beijing Chao-Yang Hospital, Capital Medical University (CN), The Military General Hospital of Beijing PLA (CN), Third Xiangya Hospital (CN), Beijing Chaoyang Emergency Medical Center (CN), Renmin University of China (CN)
Openalex Percentile: Top 20%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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