ArticleAIMS public health2025
Diagnostic value of combined CT artificial intelligence (AI) system and lung cancer biomarkers in pulmonary nodule evaluation.
Article in AIMS public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To analyze the diagnostic value of a Computed Tomography (CT) artificial intelligence (AI) system combined with lung cancer biomarkers for pulmonary nodules. Methods: A retrospective analysis was conducted on 200 patients with pulmonary nodules treated at our hospital from February 2021 to January 2025. Based on pathological results, patients were divided into a benign group and a malignant group. The two groups were compared in terms of baseline data and lung cancer biomarkers, including carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin 19 fragment 21-1 (CYFRA 21-1), squamous cell carcinoma antigen (SCCA), and pro-gastrin-releasing peptide (ProGRP). The sensitivity, specificity, accuracy, misdiagnosis rate, and missed diagnosis rate of the CT/AI system alone and in combination with lung cancer biomarkers were analyzed. Results: There were no statistically significant differences between the benign group (134 cases) and malignant group (66 cases) regarding sex, lobulation sign, spiculation sign, solitary pulmonary nodule (SPN), or mean CT value (P > 0.05). However, the benign group had significantly lower age, years of smoking, chronic lung disease, pure ground-glass nodules (pGGN), nodule diameter, irregular nodules, bronchial changes, and vascular changes compared to the malignant group (P < 0.05). Levels of CEA, NSE, CYFRA 21-1, SCCA, and ProGRP were also significantly lower in the benign group than in the malignant group (P < 0.05). Taking pathology as the reference standard, the CT/AI system alone had a sensitivity of 71.21% (47/66), specificity of 85.07% (114/134), accuracy of 80.50% (161/200), misdiagnosis rate of 19.50% (39/200), and missed diagnosis rate of 28.79% (19/66). In contrast, the CT/AI system combined with lung cancer biomarkers had a sensitivity of 92.42% (61/66), specificity of 93.28% (125/134), accuracy of 93.00% (186/200), misdiagnosis rate of 7.00% (14/200), and missed diagnosis rate of 7.58% (5/66), with all diagnostic parameters significantly improved compared with the CT/AI system alone (P < 0.05). Logistic regression analysis showed that age, smoking for >20 years, chronic lung disease, nodule diameter, irregular nodules, bronchial changes, vascular changes, NSE, CYFRA 21-1, and SCCA were all risk factors for malignant pulmonary nodules (P < 0.05). Receiver operating characteristic (ROC) curve analysis demonstrated that age, nodule type, chronic lung disease, nodule morphology, bronchial changes, and vascular changes had modest value for predicting malignant pulmonary nodules, with AUCs of 0.586, 0.750, 0.707, 0.601, 0.580, and 0.565, respectively. Smoking, nodule diameter, CEA, NSE, CYFRA 21-1, SCCA, and ProGRP had better predictive value, with AUCs of 0.840, 0.944, 0.958, 0.922, 0.856, 0.978, and 0.990, respectively. The combined diagnosis of all indicators achieved an AUC of 0.993. Conclusion: The CT/AI system combined with lung cancer biomarkers demonstrates high sensitivity and specificity in diagnosing the nature of pulmonary nodules. Moreover, the occurrence of malignant pulmonary nodules is significantly associated with factors such as age, smoking, and chronic lung disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.