Creation and assessment of a nomogram for predicting the invasiveness of stage T1 lung adenocarcinoma preoperatively using AI-based radiomic analysis - Summary - MDSpire

Creation and assessment of a nomogram for predicting the invasiveness of stage T1 lung adenocarcinoma preoperatively using AI-based radiomic analysis

  • By

  • Wensong Shi

  • Yuzhui Hu

  • Guotao Chang

  • Yulun Yang

  • He Qian

  • Yinsen Song

  • Zhengpan Wei

  • Liang Gao

  • Hang Yi

  • Sikai Wu

  • Kun Wang

  • Huandong Huo

  • Yousheng Mao

  • Yingli Sun

  • Ming Li

  • Siyuan Ai

  • Liang Zhao

  • Xiangnan Li

  • Huiyu Zheng

  • January 7, 2026

  • 0 min

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Objective:

To establish an AI-based quantitative analysis model specifically for predicting the invasiveness of T1 lung adenocarcinoma, aiding in preoperative planning and surgical protocol formulation.

Key Findings:
  • AI-based radiomic analysis can effectively predict the invasiveness of T1 lung adenocarcinoma, enhancing objectivity and reproducibility in diagnosing pulmonary nodules compared to traditional methods.
  • Preoperative imaging predictions can assist in clinical decision-making and surgical planning.
Interpretation:

The study demonstrates the potential of AI in improving the accuracy of preoperative assessments for lung adenocarcinoma, potentially leading to better patient outcomes and resource allocation.

Limitations:
  • The study is limited to data from specific medical centers, which may affect generalizability and applicability to broader populations.
  • Reliance on imaging quality and the need for standardized protocols for radiomic analysis may introduce variability.
Conclusion:

The AI-based nomogram shows promise in predicting the invasiveness of T1 lung adenocarcinoma, potentially transforming preoperative strategies and improving surgical outcomes.

Original Source(s)

Creation and assessment of a nomogram for predicting the invasiveness of stage T1 lung adenocarcinoma preoperatively using AI-based radiomic analysis

  • by Wensong Shi, Yuzhui Hu, Guotao Chang, Yulun Yang, He Qian, Yinsen Song, Zhengpan Wei, Liang Gao, Hang Yi, Sikai Wu, Kun Wang, Huandong Huo, Yousheng Mao, Yingli Sun, Ming Li, Siyuan Ai, Liang Zhao, Xiangnan Li, Huiyu Zheng

  • January 7, 2026

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