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Objectives <p>Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizing airway nodules.</p> Materials and methods <p>At a single academic hospital, we retrospectively analyzed cancer diagnoses and radiology reports from patients who received a chest or chest–abdomen CT scan between 2004 and 2020 to find cases presenting as airway nodules. Primary cancers were verified through bronchoscopy with biopsy or cytologic testing. The malignancy status of other nodules was confirmed with bronchoscopy only or follow-up CT scans if such evidence was unavailable. An AI system was trained and evaluated with a ten-fold cross-validation procedure. The performance of the system was assessed with a free-response receiver operating characteristic curve.</p> Results <p>We identified 160 patients with airway nodules (median age of 64 years [IQR: 54–70], 58 women) and added a random sample of 160 patients without airway nodules (median age of 60 years [IQR: 48–69], 80 women). The sensitivity of the AI system was 75.1% (95% CI: 67.6–81.6%) for detecting all nodules with an average number of false positives per scan of 0.25 in negative patients and 0.56 in positive patients. At the same operating point, the sensitivity was 79.0% (95% CI: 70.4–86.6%) for the subset of tumors. A subgroup analysis showed that the system detected the majority of subtle tumors.</p> Conclusion <p>The AI system detects most airway nodules on chest CT with an acceptable false positive rate.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Incidental airway tumors are rare and are susceptible to being overlooked on chest CT</i>.</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>An AI system can detect most benign and malignant airway nodules with an acceptable false positive rate, including nodules that have very subtle features</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>An AI system shows potential for supporting radiologists in detecting airway tumors</i>.</p>

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Artificial intelligence for the detection of airway nodules in chest CT scans

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月5日

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  • Ward Hendrix,
  • Nils Hendrix,
  • Ernst T. Scholten,
  • Bram van Ginneken,
  • Mathias Prokop,
  • Matthieu Rutten,
  • Colin Jacobs

摘要

Objectives

Incidental airway tumors are rare and can easily be overlooked on chest CT, especially at an early stage. Therefore, we developed and assessed a deep learning-based artificial intelligence (AI) system for detecting and localizing airway nodules.

Materials and methods

At a single academic hospital, we retrospectively analyzed cancer diagnoses and radiology reports from patients who received a chest or chest–abdomen CT scan between 2004 and 2020 to find cases presenting as airway nodules. Primary cancers were verified through bronchoscopy with biopsy or cytologic testing. The malignancy status of other nodules was confirmed with bronchoscopy only or follow-up CT scans if such evidence was unavailable. An AI system was trained and evaluated with a ten-fold cross-validation procedure. The performance of the system was assessed with a free-response receiver operating characteristic curve.

Results

We identified 160 patients with airway nodules (median age of 64 years [IQR: 54–70], 58 women) and added a random sample of 160 patients without airway nodules (median age of 60 years [IQR: 48–69], 80 women). The sensitivity of the AI system was 75.1% (95% CI: 67.6–81.6%) for detecting all nodules with an average number of false positives per scan of 0.25 in negative patients and 0.56 in positive patients. At the same operating point, the sensitivity was 79.0% (95% CI: 70.4–86.6%) for the subset of tumors. A subgroup analysis showed that the system detected the majority of subtle tumors.

Conclusion

The AI system detects most airway nodules on chest CT with an acceptable false positive rate.

Key Points

Question Incidental airway tumors are rare and are susceptible to being overlooked on chest CT.

Findings An AI system can detect most benign and malignant airway nodules with an acceptable false positive rate, including nodules that have very subtle features.

Clinical relevance An AI system shows potential for supporting radiologists in detecting airway tumors.