Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT

Research output: Contribution to journalJournal articleResearchpeer-review

  1. Dual-Energy CT for Suspected Radiographically Negative Wrist Fractures: A Prospective Diagnostic Test Accuracy Study

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Patient Preferences for Coronary CT Angiography with Stress Perfusion, SPECT, or Invasive Coronary Angiography

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Measurement of Whole-Brain and Gray Matter Atrophy in Multiple Sclerosis: Assessment with MR Imaging

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Given overdiagnosis, recall reduction should trump DCIS detection

    Research output: Contribution to journalLetterResearchpeer-review

  1. Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Clinical Applicability of Lung Ultrasound Methods in the Emergency Department to Detect Pulmonary Congestion on Computed Tomography

    Research output: Contribution to journalJournal articleResearchpeer-review

  3. Risk of Chronic Obstructive Pulmonary Disease Exacerbation in Patients Who Use Methotrexate-A Nationwide Study of 58,580 Outpatients

    Research output: Contribution to journalJournal articleResearchpeer-review

  4. Incidental discovery of multiple tracheal diverticula

    Research output: Contribution to journalJournal articleResearchpeer-review

View graph of relations

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. Purpose To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT. Materials and Methods In this retrospective study, the DL algorithm was developed with 16 077 nodules (1249 malignant) collected -between 2002 and 2004 from the National Lung Screening Trial. External validation was performed in the following three -cohorts -collected between 2004 and 2010 from the Danish Lung Cancer Screening Trial: a full cohort containing all 883 nodules (65 -malignant) and two cancer-enriched cohorts with size matching (175 nodules, 59 malignant) and without size matching (177 -nodules, 59 malignant) of benign nodules selected at random. Algorithm performance was measured by using the area under the receiver operating characteristic curve (AUC) and compared with that of the Pan-Canadian Early Detection of Lung Cancer (PanCan) model in the full cohort and a group of 11 clinicians composed of four thoracic radiologists, five radiology residents, and two pulmonologists in the cancer-enriched cohorts. Results The DL algorithm significantly outperformed the PanCan model in the full cohort (AUC, 0.93 [95% CI: 0.89, 0.96] vs 0.90 [95% CI: 0.86, 0.93]; P = .046). The algorithm performed comparably to thoracic radiologists in cancer-enriched cohorts with both random benign nodules (AUC, 0.96 [95% CI: 0.93, 0.99] vs 0.90 [95% CI: 0.81, 0.98]; P = .11) and size-matched benign nodules (AUC, 0.86 [95% CI: 0.80, 0.91] vs 0.82 [95% CI: 0.74, 0.89]; P = .26). Conclusion The deep learning algorithm showed excellent performance, comparable to thoracic radiologists, for malignancy risk estimation of pulmonary nodules detected at screening CT. This algorithm has the potential to provide reliable and reproducible malignancy risk scores for clinicians, which may help optimize management in lung cancer screening. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Tammemägi in this issue.

Original languageEnglish
JournalRadiology
Volume300
Issue number2
Pages (from-to)438-447
Number of pages10
ISSN0033-8419
DOIs
Publication statusPublished - Aug 2021

ID: 66633832