Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies
© 2022. The Author(s), under exclusive licence to European Society of Radiology..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:33 |
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| Enthalten in: |
European radiology - 33(2023), 5 vom: 08. Mai, Seite 3092-3102 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ding, Yu [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 25.04.2023 Date Revised 25.04.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s00330-022-09317-x |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM349923779 |
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| 100 | 1 | |a Ding, Yu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Completed 25.04.2023 | ||
| 500 | |a Date Revised 25.04.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022. The Author(s), under exclusive licence to European Society of Radiology. | ||
| 520 | |a OBJECTIVE: To construct a new pulmonary nodule diagnostic model with high diagnostic efficiency, non-invasive and simple to measure | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Randomized Controlled Trial | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Autoantibodies | |
| 650 | 4 | |a Lung neoplasms | |
| 650 | 4 | |a Nomograms | |
| 650 | 7 | |a Autoantibodies |2 NLM | |
| 700 | 1 | |a Zhang, Jingyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhuang, Weitao |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Zhen |e verfasserin |4 aut | |
| 700 | 1 | |a Kuang, Kaiming |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Dan |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Hansheng |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Rixin |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Guojie |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Gang |e verfasserin |4 aut | |
| 700 | 1 | |a Mendogni, Paolo |e verfasserin |4 aut | |
| 700 | 1 | |a Migliore, Marcello |e verfasserin |4 aut | |
| 700 | 1 | |a Kang, Min-Woong |e verfasserin |4 aut | |
| 700 | 1 | |a Kanzaki, Ryu |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Jiancheng |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Qiuling |e verfasserin |4 aut | |
| 700 | 1 | |a Qiao, Guibin |e verfasserin |4 aut | |
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