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

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

doi:10.1016/j.esmoop.2026.106558

March 17, 2026ESMO OpenOpen Access

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

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Key Points

To develop and validate a deep learning model for predicting pathological high-risk factors in early-stage lung adenocarcinoma using preoperative CT scans.
Developed a knowledge-based graph convolutional network for analysis of CT images.
Utilized retrospective data for initial model training and prospective data for validation.
Incorporated oncologic principles in the modeling process.
The deep learning model accurately identified pathological high-risk factors with high sensitivity.
Validation results showed an improvement in predictive ability compared to traditional methods.
Findings support the integration of advanced imaging techniques in clinical settings.

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Implication

Retrospective development and prospective validation of a deep learning model predicting high-risk factors in lung adenocarcinoma, indicating potential clinical applications.

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69be34886e48c4981c672b24 https://doi.org/10.1016/j.esmoop.2026.106558

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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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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