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
© 2023. Italian Society of Medical Radiology..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:129 |
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| Enthalten in: |
La Radiologia medica - 129(2024), 2 vom: 01. Feb., Seite 229-238 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cao, Yang [VerfasserIn] |
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| Links: |
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| Themen: |
AI |
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| Anmerkungen: |
Date Completed 21.02.2024 Date Revised 21.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s11547-023-01747-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM365996815 |
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| 100 | 1 | |a Cao, Yang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a LNAS |b a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Completed 21.02.2024 | ||
| 500 | |a Date Revised 21.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. Italian Society of Medical Radiology. | ||
| 520 | |a BACKGROUND: The accurate identification and evaluation of lymph nodes by CT images is of great significance for disease diagnosis, treatment, and prognosis | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a CONCLUSION: AI improved the sensitivity lymph node segmentation by radiologists with an advantage in reading time | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a AI | |
| 650 | 4 | |a Lymph node | |
| 650 | 4 | |a Segmentation | |
| 650 | 4 | |a Station mapping | |
| 650 | 4 | |a Unenhanced CT | |
| 700 | 1 | |a Feng, Jintang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Fan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xiaomeng |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Jingxu |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Chencui |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shu |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zihao |e verfasserin |4 aut | |
| 700 | 1 | |a Mao, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Tianzhu |e verfasserin |4 aut | |
| 700 | 1 | |a Jia, Bingzhen |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Tongli |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Bingjin |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Hongmei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Ningnannan |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Yizhou |e verfasserin |4 aut | |
| 700 | 1 | |a Meng, Xiangshui |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhang |e verfasserin |4 aut | |
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