{
    "abstract": "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.\nAt 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.\nWe 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.\nThe AI system detects most airway nodules on chest CT with an acceptable false positive rate.\nQuestion 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.",
    "authors": [
        {
            "affiliation": "Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands. ward.hendrix@radboudumc.nl.\nDepartment of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands. ward.hendrix@radboudumc.nl.",
            "firstname": "Ward",
            "initials": "W",
            "lastname": "Hendrix"
        },
        {
            "affiliation": "Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.\nDepartment of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands.\nJheronimus Academy of Data Science, 's-Hertogenbosch, The Netherlands.",
            "firstname": "Nils",
            "initials": "N",
            "lastname": "Hendrix"
        },
        {
            "affiliation": "Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.",
            "firstname": "Ernst T",
            "initials": "ET",
            "lastname": "Scholten"
        },
        {
            "affiliation": "Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.",
            "firstname": "Bram",
            "initials": "B",
            "lastname": "van Ginneken"
        },
        {
            "affiliation": "Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.\nDepartment of Radiology, University Medical Center Groningen, Groningen, The Netherlands.",
            "firstname": "Mathias",
            "initials": "M",
            "lastname": "Prokop"
        },
        {
            "affiliation": "Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.\nDepartment of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands.",
            "firstname": "Matthieu",
            "initials": "M",
            "lastname": "Rutten"
        },
        {
            "affiliation": "Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.",
            "firstname": "Colin",
            "initials": "C",
            "lastname": "Jacobs"
        }
    ],
    "conclusions": "The AI system detects most airway nodules on chest CT with an acceptable false positive rate.",
    "copyrights": "\u00a9 2025. The Author(s).",
    "doi": "10.1007/s00330-025-11468-6\n10.1245/s10434-006-9229-z\n10.2147/OTT.S142847\n10.21037/JTD.2016.11.24\n10.21037/acs.2018.03.09\n10.1016/j.chest.2022.10.039\n10.3322/CAAC.21708\n10.1016/S1092-9134(97)80007-X\n10.2214/AJR.04.1893\n10.2214/AJR.12.9354\n10.1148/radiology.199.1.8633131\n10.1007/s00330-014-3394-4\n10.1016/j.ejrex.2008.06.004\n10.1016/j.compbiomed.2021.104806\n10.1016/J.CRAD.2021.07.012\n10.1038/s43856-023-00388-5\n10.1016/j.acra.2020.07.030\n10.1371/journal.pone.0266799\n10.1186/s12880-022-00938-8\n10.1016/j.jacr.2023.09.009\n10.1016/j.media.2017.06.015\n10.1183/13993003.01992-2015\n10.1001/jama.2023.2065\n10.1164/rccm.202006-2248OC\n10.1109/TMI.2006.871547\n10.2214/AJR.13.11392\n10.1148/rg.2018170107",
    "journal": "European radiology",
    "keywords": [
        "Artificial intelligence",
        "Lung neoplasms",
        "Thorax",
        "Tomography (X-ray computed)",
        "Tracheal neoplasms"
    ],
    "methods": null,
    "publication_date": "2025-03-05",
    "pubmed_id": "40042650\n17139460\n28066620\n29707497\n36521560\n35020204\n9869824\n16632723\n8633131\n25187382\n34461501\n34404517\n37891360\n35511758\n36419044\n37820837\n28732268\n27030677\n37210745\n33180550\n16608058\n24758650\n29194009",
    "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.",
    "title": "Artificial intelligence for the detection of airway nodules in chest CT scans.",
    "xml": "<Element 'PubmedArticle' at 0x788502ab0310>"
}