ArticleBMC pulmonary medicine2025
Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China.
Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundDeep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed to improve the DL's specificity for the efficiency of LC screening in China. PURPOSE: To develop and evaluate a risk model combining 7-TAAbs test and DL scores for diagnosing LC with pulmonary lesions < 70 mm. MATERIALS AND
methodsFour hundreds and six patients with 406 lesions were enrolled and assigned into training set (n = 313) and test set (n = 93) randomly. The malignant lesions were defined as those lesions with high malignant risks by DL or those with positive expression of 7-TAAbs panel. Model performance was assessed using the area under the receiver operating characteristic curves (AUC).
resultsIn the training set, the AUCs for DL, 7-TAAbs, combined model (DL and 7-TAAbs) and combined model (DL or 7-TAAbs) were 0.771, 0.638, 0.606, 0.809 seperately. In the test set, the combined model (DL or 7-TAAbs) achieved achieved the highest sensitivity (82.6%), NPV (81.8%) and accuracy (79.6%) among four models, and the AUCs of DL model, 7-TAAbs model, combined model (DL and 7-TAAbs), and combined model (DL or 7-TAAbs) were 0.731, 0.679, 0.574, and 0.794, respectively.
conclusionThe 7-TAAbs test significantly enhances DL performance in predicting LC with pulmonary leisons < 70 mm in China.
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