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

At a Glance

CategoryDetail
ConditionStage T1 lung adenocarcinoma
Key MechanismsAI-based radiomic analysis for predicting invasiveness
Target PopulationPatients with solitary or dominant pulmonary nodules ≤ 3 cm
Care SettingMulti-center surgical settings

Key Highlights

  • AI improves objectivity and reproducibility in lung nodule assessment.
  • Predictive models outperform individual clinical experience in diagnosis.
  • Non-invasive carcinomas can achieve outcomes comparable to lobectomy with proper margins.

Guideline-Based Recommendations

Diagnosis

  • Utilize AI and radiomic features for non-invasive diagnosis of pulmonary nodules.

Management

  • Consider sublobar resection for non-invasive carcinomas with adequate margins.

Monitoring & Follow-up

  • Follow-up imaging should be conducted to assess nodule characteristics.

Risks

  • Intraoperative frozen section may lead to clinical decision-making dilemmas.

Patient & Prescribing Data

694 male and 1391 female patients, average age 56.60 years.

Surgical resection confirmed by postoperative pathology is essential.

Clinical Best Practices

  • Incorporate AI-based tools in the evaluation of pulmonary nodules.
  • Ensure imaging is free of artifacts for accurate assessment.
  • Adhere to inclusion criteria for optimal patient selection.

References

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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