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Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images

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Abstract

Objectives

To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.

Materials and methods

Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians.

Results

There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician.

Conclusion

The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules.

Clinical relevance statement

The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis.

Key Points

• According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images.

• The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%).

• The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions.

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Abbreviations

AUC:

Area under the curve

CT:

Computed tomography

DCA:

Decision curve analysis

KMU:

First Affiliated Hospital of Kunming Medical University

NSMC:

Affiliated Hospital of North Sichuan Medical College

ROC curve:

Receiver operating characteristic curve

SCH:

Suining Central Hospital

WCH:

West China Hospital of Sichuan University

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Funding

This research is supported by Key R & D plan of Sichuan Provincial Department of science and technology (2021YFS0072), Natural Science Foundation of Sichuan Province (2022NSFSC0785), Key R & D program of Sichuan-Chongqing of the Chongqing Science and Technology Commission (CSTB2022TIAD-CUX0001), and Full-time Postdoctoral Program of Sichuan University (2023SCU12048).

Author information

Authors and Affiliations

  1. Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China

    Rui Zhang, Bojiang Chen & Weimin Li

  2. General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, China

    Rui Zhang & Yi Lei

  3. Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China

    Ying Wei, Qing Zhou & Feng Shi

  4. Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China

    Denian Wang

  5. Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China

    Huaiqiang Sun

  6. Department of Pulmonary and Critical Care Medicine, the First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China

    Zhuang Luo

  7. Department of Respiratory and Critical Care Medicine, the Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China

    Li Jiang

  8. Department of Respiratory and Critical Care Medicine, Suining Central Hospital, Suining, Sichuan, China

    Rong Qiu

Authors
  1. Rui Zhang
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  2. Ying Wei
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  3. Denian Wang
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  4. Bojiang Chen
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  7. Qing Zhou
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  9. Li Jiang
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  10. Rong Qiu
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  11. Feng Shi
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  12. Weimin Li
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Corresponding authors

Correspondence to Feng Shi or Weimin Li.

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Guarantor

The scientific guarantor of this publication is Weimin Li.

Conflict of interest

The authors declare no conflict of interest. Qing Zhou is an employee of Shanghai United Imaging Intelligence Co., Ltd.

Statistics and biometry

The authors Zhang Rui and Ying Wei have significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board because this was a retrospective study and patients’ anonymity was carefully protected.

Ethical approval

All methods were performed in accordance with the Declaration of Helsinki. The study was approved by the institutional review boards of the West China Hospital of Sichuan University, Affiliated Hospital of North Sichuan Medical College, First Affiliated Hospital of Kunming Medical University, and Suining Central Hospital.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• retrospective

• diagnostic study

• multicenter study

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Publisher's Note

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Rui Zhang, Ying Wei, and Denian Wang contributed equally to this work.

Supplementary information

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Supplementary file1 (PDF 244 KB)

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Cite this article

Zhang, R., Wei, Y., Wang, D. et al. Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10518-1

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  • DOI: https://doi.org/10.1007/s00330-023-10518-1

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