Creation and assessment of a nomogram for predicting the invasiveness of stage T1 lung adenocarcinoma preoperatively using AI-based radiomic analysis - Takeaways - 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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  • 1

    The study develops a nomogram using AI-based radiomic analysis to predict the invasiveness of T1 lung adenocarcinoma preoperatively.

  • 2

    It utilizes data from six medical centers, analyzing 1523 nodules with confirmed postoperative pathology and thin-section chest CT imaging.

  • 3

    The classification distinguishes between non-invasive groups (AAH, AIS, MIA) and the invasive group (IAC) to aid in clinical decision-making.

  • 4

    AI-enhanced radiomic analysis addresses variability in manual imaging assessments, improving objectivity and reproducibility in lung cancer diagnosis.

  • 5

    The findings aim to support preoperative planning and enhance the differentiation of T1-stage invasive lung cancer for better 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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