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Cited by 13Lung Cancer Diagnosis and TreatmentAI in cancer detectionRadiomics and Machine Learning in Medical Imaging

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Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules

Open access
Yang, Han, Chen, Hongjie, Zhang, Guorui, Li, Hongyi, Ni, Ran, Yu, Yali, Zhang, Yepeng, Wu, Yongjun, Liu, Hong·2022·BMC Cancer
DOI 10.1186/s12885-022-09472-wPMC PMC8994303
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Abstract

Background: The accuracy of CT and tumour markers in screening lung cancer needs to be improved. Computer-aided diagnosis has been reported to effectively improve the diagnostic accuracy of imaging data, and recent studies have shown that circulating genetically abnormal cell (CAC) has the potential to become a novel marker of lung cancer. The purpose of this research is explore new ways of lung cancer screening. Methods: From May 2020 to April 2021, patients with pulmonary nodules who had received CAC examination within one week before surgery or biopsy at First Affiliated Hospital of Zhengzhou University were enrolled. CAC counts, CT scan images, serum tumour marker (CEA, CYFRA21–1, NSE) levels and demographic characteristics of the patients were collected for analysis. CT were uploaded to the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) to assess the malignancy probability of nodules. We compared diagnosis based on PNAIDS, CAC, Mayo Clinic Model, tumour markers alone and their combination. The combination models were built through logistic regression, and was compared through the area under (AUC) the ROC curve. Results: A total of 93 of 111 patients were included. The AUC of PNAIDS was 0.696, which increased to 0.847 when combined with CAC. The sensitivity (SE), specificity (SP), and positive (PPV) and negative (NPV) predictive values of the combined model were 61.0%, 94.1%, 94.7% and 58.2%, respectively. In addition, we evaluated the diagnostic value of CAC, which showed an AUC of 0.779, an SE of 76.3%, an SP of 64.7%, a PPV of 78.9%, and an NPV of 61.1%, higher than those of any single serum tumour marker and Mayo Clinic Model. The combination of PNAIDS and CAC exhibited significantly higher AUC values than the PNAIDS (P = 0.009) or the CAC (P = 0.047) indicator alone. However, including additional tumour markers did not significantly alter the performance of CAC and PNAIDS. Conclusions: CAC had a higher diagnostic value than traditional tumour markers in early-stage lung cancer and a supportive value for PNAIDS in the diagnosis of cancer based on lung nodules. The results of this study offer a new mode of screening for early-stage lung cancer using lung nodules.

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  • Artificial neural networks combined with serum protein signatures including beta-2-microglobulin, CEA, gastrin, CA125, NSE, sIL-6R, and three metal ions achieved an accuracy of approximately 85% during testing.

    “For example, during testing, the use of Artificial Neural Networks (ANNs) in combination with serum protein signatures (including β2-microglobulin, CEA, gastrin, CA125, NSE, sIL-6R, and three metal ions—Cu2+/Zn2+, Ca2+, and Mg2+) achieved an accuracy of approximately 85%.”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [21]

    The Results (Combination of different indicators for diagnosing lung nodules; page unavailable) report an AUC of 0.847 for a logistic-regression model combining CT-based PNAIDS with circulating genetically abnormal cell counts—not approximately 85% testing accuracy for an ANN using the stated proteins and metal ions. Table 2 lists CEA, CYFRA21-1 and NSE as the serum-marker combination; it does not report the claimed signature. (LLM, conf=0.88)

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    Experimental — accuracy not yet measured

  • Adding clinical data such as symptoms, risk factors, smoking exposure, and household environmental characteristics to ANN and serum protein signatures increased the prediction rate to 87.3%.

    “When clinical data—such as symptoms, risk factors, smoking exposure, and household environmental characteristics—were added, the prediction rate increased to 87.3% [21].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [21]

    The Results (Combination of different indicators for diagnosing lung nodules; Table 2) describe models combining CT-based PNAIDS, circulating genetically abnormal cell counts, and serum tumour markers; the best reported AUC is 0.847. The Methods (Data collection) record smoking history and other clinical data, but our copy does not report adding symptoms or household environmental characteristics to an ANN and serum-protein signature, or a prediction rate of 87.3% (searched those terms and the figure). (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Metadata

Authors
Yang, H., Chen, H., Zhang, G., Li, H., Ni, R., Yu, Y., Zhang, Y., Wu, Y., & Liu, H.
Affiliations
  • First Affiliated Hospital of Zhengzhou University (CN)
  • Zhengzhou University (CN)
Corresponding author
Liu, Hong
ORCID
Venue
BMC Cancer
Publisher
Springer Science and Business Media LLC
Published
2022-04-09
Volume
22
Issue
1
Pages
382-382
ISSN
1471-2407 (ISSN-L)
DOI
10.1186/s12885-022-09472-w
PMC
PMC8994303
OpenAlex
W4223423331
Cited by
13
References
0
Citations by year
2026: 2 · 2025: 5 · 2024: 2 · 2023: 4
FWCI
1.77
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Cancer (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
health commission of henan province (SB201901016)
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
pulmonary nodules, lung cancer screening, computer-aided diagnosis, serum tumor markers, CEA, CYFRA21-1, neuron-specific enolase, Mayo Clinic model, diagnostic accuracy, early-stage lung cancer, circulating tumor DNA, liquid biopsy
MeSH terms
Antigens, Neoplasm, Artificial Intelligence, Solitary Pulmonary Nodule, Humans, Lung Neoplasms, Tomography, X-Ray Computed, Biomarkers, Tumor, Keratin-19, Early Detection of Cancer, Multiple Pulmonary Nodules

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