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Updated: Sep 2, 2026
Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
Published on: October 13, 2023
Utility of artificial intelligence for identifying thoracic lymphadenopathy in lung cancer
Muhiddin Dervis1, Anushree Burade1, Michael Lanuti2
1Department of Radiology, Massachusetts General Hospital, Harvard Medical School 55 Fruit Street, Boston, MA 02114, USA.
Purpose:
To evaluate the performance of an artificial intelligence (AI) system for detecting, classifying, and measuring AI-detected thoracic lymph node candidates on CT and compare AI detection with routine radiology reporting and histopathologic findings.
Methods:
In this HIPAA-compliant, IRB-approved multicenter study, 169 patients with operable thoracic malignancies underwent invasive mediastinal or hilar lymph node sampling and chest CT within 6 weeks (mean, 16 days ± 10). Patients were identified using a query of the EPIC database. CT images were processed using a commercially available AI that detected lymph nodes, measured bidimensional diameters, and assigned nodal stations. An expert thoracic radiologist reviewed all AI-generated candidates, with discrepancies resolved by a second radiologist to establish the reference standard. Performance metrics were calculated using a one-vs-rest framework. Radiology reports and pathology results were reviewed for lymph node detection and classification.
Results:
A total of 3,520 lymph nodal candidates. The AI achieved sensitivity of 96.1 %, specificity of 99.7 %, positive predictive value of 95.8 %, negative predictive value of 99.7 %, and accuracy of 99.5 % for station classification. Overall, 3,374 nodes (95.9 %) were correctly classified; 138 (3.9 %) were misclassified and 8 (0.2 %) were false-positive non-lymph-node detections. Lower performance was observed in stations 8 (sensitivity, 81.6 %) and 9 (positive predictive value, 81.5 %). After excluding non-lymph-node findings, 3,512 lymph nodes were analyzed; 3,203 (91.2 %) measured less than 10 mm. AI-radiologist agreement for size categorization was 100 %. Among 169 biopsied lymph nodes, routine radiology reports documented 36 (21.3 %), whereas AI detected 135 (79.9 %). AI detected 87.6 % of benign and 69.4 % of malignant nodes.
Conclusion:
In this radiologist-adjudicated evaluation of AI-detected thoracic lymph node candidates, AI demonstrated high performance in station classification and measurement. AI also identified a greater proportion of pathologically sampled stations than were documented in routine radiology reports, suggesting a potential future role for AI in supporting systematic lymph node assessment and reporting.
