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Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies

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Abstract

Objective

To construct a new pulmonary nodule diagnostic model with high diagnostic efficiency, non-invasive and simple to measure.

Methods

This study included 424 patients with radioactive pulmonary nodules who underwent preoperative 7-autoantibody (7-AAB) panel testing, CT-based AI diagnosis, and pathological diagnosis by surgical resection. The patients were randomly divided into a training set (n = 212) and a validation set (n = 212). The nomogram was developed through forward stepwise logistic regression based on the predictive factors identified by univariate and multivariate analyses in the training set and was verified internally in the verification set.

Results

A diagnostic nomogram was constructed based on the statistically significant variables of age as well as CT-based AI diagnostic, 7-AAB panel, and CEA test results. In the validation set, the sensitivity, specificity, positive predictive value, and AUC were 82.29%, 90.48%, 97.24%, and 0.899 (95%[CI], 0.851–0.936), respectively. The nomogram showed significantly higher sensitivity than the 7-AAB panel test result (82.29% vs. 35.88%, p < 0.001) and CEA (82.29% vs. 18.82%, p < 0.001); it also had a significantly higher specificity than AI diagnosis (90.48% vs. 69.04%, p = 0.022). For lesions with a diameter of ≤ 2 cm, the specificity of the Nomogram was higher than that of the AI diagnostic system (90.00% vs. 67.50%, p = 0.022).

Conclusions

Based on the combination of a 7-AAB panel, an AI diagnostic system, and other clinical features, our Nomogram demonstrated good diagnostic performance in distinguishing lung nodules, especially those with ≤ 2 cm diameters.

Key Points

• A novel diagnostic model of lung nodules was constructed by combining high-specific tumor markers with a high-sensitivity artificial intelligence diagnostic system.

• The diagnostic model has good diagnostic performance in distinguishing malignant and benign pulmonary nodules, especially for nodules smaller than 2 cm.

• The diagnostic model can assist the clinical decision-making of pulmonary nodules, with the advantages of high diagnostic efficiency, noninvasive, and simple measurement.

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Abbreviations

3D:

Three-dimensional

7-AAB:

7-Autoantibody

AAH:

Atypical adenomatous hyperplasia

AI:

Artificial intelligence

AIS:

Adenocarcinoma in situ

CEA:

Carcinoembryonic antigen

CI:

Confidence interval

DF:

Degree of freedom

DICOM:

Digital Imaging and Communications in Medicine

ELISA:

Enzyme-linked immunosorbent assay

GGO:

Ground-glass opacity

GPPH:

Guangdong Provincial People’s Hospital

IA:

Invasive adenocarcinoma

LDCT:

Low-dose computed tomography

MIA:

Minimally invasive adenocarcinoma

SCC:

Squamous cell carcinoma

SD:

Standard deviation

SE:

Standard error

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Acknowledgements

We would like to thank Xiaosong Ben, Zihao Zhou, Liang Xie, Jiming Tang, and Haiyu Zhou for their help with the data collection. We would like to thank the Dianei Technology and AME Lung Cancer Collaborative Group for the academic support and to thank the Editage (www.editage. com) for English language edits.

Funding

The authors state that this work has not received any funding.

Author information

Author notes

  1. Yu Ding and Jingyu Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, No.106, Zhongshan 2nd Road, Guangzhou, 510080, China

    Yu Ding, Weitao Zhuang, Dan Tian, Cheng Deng, Gang Chen, Yong Tang & Guibin Qiao

  2. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China

    Yu Ding, Zhen Gao, Hansheng Wu & Guibin Qiao

  3. State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, No. 1, Medical College Road, Yuzhong District, Chongqing, 400016, China

    Jingyu Zhang & Qiuling Shi

  4. Dianei Technology, Shanghai, China

    Kaiming Kuang & Jiancheng Yang

  5. Department of Thoracic Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China

    Hansheng Wu

  6. Research Center of Medical Sciences, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Rixin Chen

  7. Department of Thoracic Surgery, Guangzhou Panyu Central Hospital, Guangzhou, China

    Guojie Lu

  8. Thoracic Surgery and Lung Transplant Unit, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy

    Paolo Mendogni

  9. Thoracic Surgery, Cardio-Thoracic Department, University Hospital of Wales, Cardiff, UK

    Marcello Migliore

  10. Minimally Invasive Surgery and New Technology, University Hospital of Catania, Department of Surgery and Medical Specialties, University of Catania, Catania, Italy

    Marcello Migliore

  11. Department of Thoracic and Cardiovascular Surgery, Chungnam National University School of Medicine, Daejeon, South Korea

    Min-Woong Kang

  12. Department of General Thoracic Surgery, Osaka University Graduate School of Medicine, Osaka, Japan

    Ryu Kanzaki

  13. Computer Vision Laboratory (CVLab), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Jiancheng Yang

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  1. Yu Ding
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Corresponding authors

Correspondence to Qiuling Shi or Guibin Qiao.

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Guarantor

The scientific guarantor of this publication is Guibin Qiao (MD, Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Email: guibinqiao@126.com).

Conflict of interest

The authors (Kaiming Kuang and Jiancheng Yang) of this manuscript declare relationships with the following companies: Diannei Technology Co. Ltd (Shanghai, China).

Statistics and biometry

Yu Ding did the statistical analysis. Qiuling Shi has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained from the Medical Ethics Committee and Institutional Review Board of Guangdong Provincial People’s Hospital (GPPH).

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• prospective

• diagnostic or prognostic study

• performed at one institution

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Ding, Y., Zhang, J., Zhuang, W. et al. Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies. Eur Radiol 33, 3092–3102 (2023). https://doi.org/10.1007/s00330-022-09317-x

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