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

被引:0
作者
Hendrix, Ward [1 ,2 ]
Hendrix, Nils [1 ,2 ,3 ]
Scholten, Ernst T. [1 ]
van Ginneken, Bram [1 ]
Prokop, Mathias [1 ,4 ]
Rutten, Matthieu [1 ,2 ]
Jacobs, Colin [1 ]
机构
[1] Radboud Univ Nijmegen Med Ctr, Dept Med Imaging, Diagnost Image Anal Grp, Nijmegen, Netherlands
[2] Jeroen Bosch Hosp, Dept Radiol, sHertogenbosch, Netherlands
[3] Jheronimus Acad Data Sci, sHertogenbosch, Netherlands
[4] Univ Med Ctr Groningen, Dept Radiol, Groningen, Netherlands
关键词
Thorax; Lung neoplasms; Tracheal neoplasms; Tomography (X-ray computed); Artificial intelligence; CANCER; TUMORS;
D O I
10.1007/s00330-025-11468-6
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
ObjectivesIncidental 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 methodsAt 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.ResultsWe 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.ConclusionThe AI system detects most airway nodules on chest CT with an acceptable false positive rate.Key PointsQuestionIncidental airway tumors are rare and are susceptible to being overlooked on chest CT.FindingsAn AI system can detect most benign and malignant airway nodules with an acceptable false positive rate, including nodules that have very subtle features.Clinical relevanceAn AI system shows potential for supporting radiologists in detecting airway tumors.Key PointsQuestionIncidental airway tumors are rare and are susceptible to being overlooked on chest CT.FindingsAn AI system can detect most benign and malignant airway nodules with an acceptable false positive rate, including nodules that have very subtle features.Clinical relevanceAn AI system shows potential for supporting radiologists in detecting airway tumors.Key PointsQuestionIncidental airway tumors are rare and are susceptible to being overlooked on chest CT.FindingsAn AI system can detect most benign and malignant airway nodules with an acceptable false positive rate, including nodules that have very subtle features.Clinical relevanceAn AI system shows potential for supporting radiologists in detecting airway tumors.
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页数:11
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