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LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images

  • Chest Radiology
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Background

The accurate identification and evaluation of lymph nodes by CT images is of great significance for disease diagnosis, treatment, and prognosis.

Purpose

To assess the lymph nodes’ segmentation, size, and station by artificial intelligence (AI) for unenhanced chest CT images and evaluate its value in clinical scenarios.

Material and methods

This retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS) consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal Organ Segmentation model (MOS), and the Lymph Node Station Registration model (LNR). We selected a healthy chest CT image as the template image and annotated 14 lymph node station masks according to the IASLC to build the lymph node station mapping template. The exact contours and stations of the lymph nodes were annotated by two junior radiologists and reviewed by a senior radiologist. Patients aged 18 and above, who had undergone unenhanced chest CT and had at least one suspicious enlarged mediastinal lymph node in imaging reports, were included. Exclusions were patients who had thoracic surgeries in the past 2 weeks or artifacts on CT images affecting lymph node observation by radiologists. The system was trained on 6725 consecutive chest CTs that from Tianjin Medical University General Hospital, among which 6249 patients had suspicious enlarged mediastinal lymph nodes. A total of 519 consecutive chest CTs from Qilu Hospital of Shandong University (Qingdao) were used for external validation. The gold standard for each CT was determined by two radiologists and reviewed by one senior radiologist.

Results

The patient-level sensitivity of the LNAS system reached of 93.94% and 92.89% in internal and external test dataset, respectively. And the lesion-level sensitivity (recall) reached 89.48% and 85.97% in internal and external test dataset. For man–machine comparison, AI significantly apparently shortened the average reading time (p < 0.001) and had better lesion-level and patient-level sensitivities.

Conclusion

AI improved the sensitivity lymph node segmentation by radiologists with an advantage in reading time.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (82071907, 82271937), Natural Science Foundation of Tianjin (18JCYBJC25100), Health science and Technology project of Tianjin (MS20022), Wu Jieping Medical Foundation-special Fund for Clinical Research (320.6750.2022-3-5), China International Medical Foundation Sky Imaging Research Fund (Z-2014-07-2003-05), Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-001A), Tianjin University of Science and Technology Development Projects Fund (20140115), and Zhangjiakou City Self-financing Project of the 2019 Scientific Research Plan (1921131H).

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Author notes

  1. Yang Cao, Jintang Feng and Cheng Wang have equally contributed to this work.

Authors and Affiliations

  1. Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China

    Yang Cao, Jintang Feng, Fan Yang, Xiaomeng Wang, Tianzhu Zhang, Bingzhen Jia, Tongli Li, Dong Li, Ningnannan Zhang & Zhang Zhang

  2. Department of Radiology, Tianjin Chest Hospital, Tianjin, China

    Jintang Feng

  3. Deepwise AI Lab, Beijing, China

    Cheng Wang, Jingxu Xu, Chencui Huang, Shu Zhang, Zihao Li, Li Mao & Yizhou Yu

  4. Department of Computer Science, The University of Hong Kong, Hong Kong, China

    Yizhou Yu

  5. Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China

    Hui Li, Bingjin Zhang, Hongmei Shi & Xiangshui Meng

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Correspondence to Zhang Zhang.

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Cao, Y., Feng, J., Wang, C. et al. LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images. Radiol med 129, 229–238 (2024). https://doi.org/10.1007/s11547-023-01747-x

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