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Objective <p>The current BTS guidelines recommend evaluation of suspicious pulmonary nodules using [<sup>18</sup>F]FDG-PET/CT imaging, followed by Herder model risk stratification. However, it is based on limited imaging features, which may limit diagnostic accuracy. This study aims to develop a PET/CT-based deep learning (DL) model for malignancy probability estimation (AITO-PETCT-MP) and compare its performance to the Herder model and clinician performance.</p> Materials and methods <p>In a single-center retrospective study, we collected 533 indeterminate pulmonary nodules (268 malignant) with a mean diameter of 18.4 mm (SD ± 12.1) in 436 patients. Histopathological malignancy confirmation or a minimum 2-year benign national cancer registry follow-up served as the reference standard. Model diagnostic performance was compared against the Herder model and seven clinicians in a reader study on a test set of 161 nodules (80 malignant).</p> Results <p>AITO-PETCT-MP achieved an AUC of 0.78 [95% CI: 0.70–0.85] compared to the Herder model: AUC = 0.73 [0.65–0.80] (non-inferiority: <i>p</i> = 0.005). On average, experienced clinicians achieved an AUC of 0.80 [0.75–0.85]. Stratifying into BTS follow-up categories, the Herder model referred more benign nodules for potential direct treatment (26/81) than AITO-PETCT-MP and clinicians (both 3/81), while AITO-PETCT-MP and clinicians assigned more malignant cases to CT surveillance instead of direct treatment.</p> Conclusion <p>AITO-PETCT-MP demonstrated non-inferior performance to the guideline-recommended Herder model, while only using imaging data. Diagnostic performance fell in the performance range of seven clinicians. Differences in BTS follow-up recommendations between the Herder model and clinicians suggest a difference in patient management compared to current clinical practice.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis><i> How well can an imaging-only deep learning model estimate pulmonary nodule malignancy probability on [</i><sup><i>18</i></sup><i>F]FDG-PET/CT compared to the established Herder model and expert clinicians?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis><i> The model (AITO-PETCT-MP) performed non-inferior to the Herder model (AUC 0.78 vs 0.73, p = 0.005) and comparably to seven expert readers (AUC 0.74–0.87).</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis><i> BTS-based follow-up stratification showed the Herder model referred more benign nodules to potential direct treatment than clinicians and AITO-PETCT-MP, while assigning fewer malignant cases to surveillance. This suggests a difference between Herder recommendations and current clinical practice.</i></p> Graphical Abstract <p></p>

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Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月11日

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  • Lars Leijten,
  • Erik H. J. G. Aarntzen,
  • Roel L. J. Verhoeven,
  • Adrienne H. Brouwers,
  • Bram Geurts,
  • Johannes A. van der Heide,
  • Klaas Pieter Koopmans,
  • Walter Noordzij,
  • Gilles N. Stormezand,
  • Erik H. F. M. van der Heijden,
  • Colin Jacobs

摘要

Objective

The current BTS guidelines recommend evaluation of suspicious pulmonary nodules using [18F]FDG-PET/CT imaging, followed by Herder model risk stratification. However, it is based on limited imaging features, which may limit diagnostic accuracy. This study aims to develop a PET/CT-based deep learning (DL) model for malignancy probability estimation (AITO-PETCT-MP) and compare its performance to the Herder model and clinician performance.

Materials and methods

In a single-center retrospective study, we collected 533 indeterminate pulmonary nodules (268 malignant) with a mean diameter of 18.4 mm (SD ± 12.1) in 436 patients. Histopathological malignancy confirmation or a minimum 2-year benign national cancer registry follow-up served as the reference standard. Model diagnostic performance was compared against the Herder model and seven clinicians in a reader study on a test set of 161 nodules (80 malignant).

Results

AITO-PETCT-MP achieved an AUC of 0.78 [95% CI: 0.70–0.85] compared to the Herder model: AUC = 0.73 [0.65–0.80] (non-inferiority: p = 0.005). On average, experienced clinicians achieved an AUC of 0.80 [0.75–0.85]. Stratifying into BTS follow-up categories, the Herder model referred more benign nodules for potential direct treatment (26/81) than AITO-PETCT-MP and clinicians (both 3/81), while AITO-PETCT-MP and clinicians assigned more malignant cases to CT surveillance instead of direct treatment.

Conclusion

AITO-PETCT-MP demonstrated non-inferior performance to the guideline-recommended Herder model, while only using imaging data. Diagnostic performance fell in the performance range of seven clinicians. Differences in BTS follow-up recommendations between the Herder model and clinicians suggest a difference in patient management compared to current clinical practice.

Key Points

Question How well can an imaging-only deep learning model estimate pulmonary nodule malignancy probability on [18F]FDG-PET/CT compared to the established Herder model and expert clinicians?

Findings The model (AITO-PETCT-MP) performed non-inferior to the Herder model (AUC 0.78 vs 0.73, p = 0.005) and comparably to seven expert readers (AUC 0.74–0.87).

Clinical relevance BTS-based follow-up stratification showed the Herder model referred more benign nodules to potential direct treatment than clinicians and AITO-PETCT-MP, while assigning fewer malignant cases to surveillance. This suggests a difference between Herder recommendations and current clinical practice.

Graphical Abstract