European journal of radiology · 2025
Reference standard methodology in the clinical evaluation of AI chest X-ray algorithms for lung cancer detection: A systematic review.
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Duncan Sean F, Kidd Andrew C, Lampignano Jesus Perdomo, Cannon Paul, Hall Mark, Stobo David B, Maclay John D, Blyth Kevin G, Lowe David J
Аннотация
BACKGROUND: Lung cancer remains the leading cause of cancer death worldwide, with early diagnosis linked to improved survival. Artificial intelligence (AI) holds promise for augmenting radiologists' workflows in chest X-ray (CXR) interpretation, particularly for detecting thoracic malignancies. However, clinical implementation of this technology relies on robust and standardised reference standard methodology at the patient-level.
PURPOSE: This systematic review aims to describe reference standard methodology in the clinical evaluation of CXR algorithms for lung cancer detection.
MATERIALS AND METHODS: Searches targeted studies on AI CXR analysis across MEDLINE, Embase, CENTRAL, and trial registries. 2 reviewers independently screened titles and abstracts, with disagreements resolved by a 3rd reviewer. Studies lacking external validation in real-world cohorts were excluded. Bias was assessed using a modified QUADAS-2 tool, and data synthesis followed SWiM guidelines.
RESULTS: 1,679 papers were screened with 46 papers included for full paper review. 24 different AI solutions were evaluated across a broad range of research questions. We identified significant heterogeneity in reference standard methodology, including variations in target abnormalities, reference standard modality, expert panel composition, and arbitration techniques. 25 % of reference standard parameters were inadequately reported. 66 % of included studies demonstrated high risk of bias in at least one domain.
DISCUSSION: To our knowledge, this is the first systematic description of patient-level reference standard methodology in CXR AI analysis of thoracic malignancy. To facilitate translational progress in this field, researchers undertaking evaluations of diagnostic algorithms at the patient-level should ensure that reference standards are aligned with clinical workflows and adhere to reporting guidelines. Limitations include a lack of prospective studies.
PURPOSE: This systematic review aims to describe reference standard methodology in the clinical evaluation of CXR algorithms for lung cancer detection.
MATERIALS AND METHODS: Searches targeted studies on AI CXR analysis across MEDLINE, Embase, CENTRAL, and trial registries. 2 reviewers independently screened titles and abstracts, with disagreements resolved by a 3rd reviewer. Studies lacking external validation in real-world cohorts were excluded. Bias was assessed using a modified QUADAS-2 tool, and data synthesis followed SWiM guidelines.
RESULTS: 1,679 papers were screened with 46 papers included for full paper review. 24 different AI solutions were evaluated across a broad range of research questions. We identified significant heterogeneity in reference standard methodology, including variations in target abnormalities, reference standard modality, expert panel composition, and arbitration techniques. 25 % of reference standard parameters were inadequately reported. 66 % of included studies demonstrated high risk of bias in at least one domain.
DISCUSSION: To our knowledge, this is the first systematic description of patient-level reference standard methodology in CXR AI analysis of thoracic malignancy. To facilitate translational progress in this field, researchers undertaking evaluations of diagnostic algorithms at the patient-level should ensure that reference standards are aligned with clinical workflows and adhere to reporting guidelines. Limitations include a lack of prospective studies.