Analysis of tumor markers in cytological fluid obtained from computed tomography–guided needle aspiration biopsies for the diagnosis of ground‐glass opacity pulmonary lesions

Kim, Ga Ram; Hur, Jin; Lee, Hye Jeong; Nam, Ji Eun; Kim, Young Jin; Hong, Yoo Jin; Shim, Hyo Sup; Kim, Hee Yeong; Lee, Ji Won; Choi, Byoung Wook

doi:10.1002/cncy.21244

Cited by 7 publications

(5 citation statements)

References 75 publications

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“…The SROC curve presented an overall summary of SEN and SPE, with an AUC of 0.97, also indicating a high overall accuracy. This result was similar to the article published in 2014, in which the SEN was in the range of 0.71–0.97 (pooled 0.92; 95% CI = 0.88–0.95), while SPE was in the range of 0.80–0.97 (pooled 0.94; 95% CI = 0.84–0.98) (16), and more related original studies that expanded the sample size have been included in our study (7,9–15).…”

Section: Discussionmentioning

confidence: 99%

“…CT-guided percutaneous lung biopsy is an effective diagnostic technique to make certain of the nature of pulmonary lesions (1). Previous studies have reported that the diagnostic accuracy of CT-guided puncture biopsy for GGNs is in the range of 78.6%-100.0%, which fluctuates widely (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). In addition, the sample size of each study varies, and the results of studies with small sample sizes were not convincing.…”

Section: Introductionmentioning

confidence: 99%

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The diagnostic value of CT-guided percutaneous puncture biopsy of pulmonary ground-glass nodules: a meta-analysis

Wang

Zhong

et al. 2022

Acta Radiol

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Background More and more pulmonary ground-glass nodules (GGNs) are screened with the extensive usage of low-dose computed tomography (CT). The need of CT-guided percutaneous puncture biopsy of GGN remains controversial. Purpose To explore the diagnostic accuracy of CT-guided percutaneous puncture biopsy of GGNs. Material and Methods We searched PubMed, EMBASE, the Cochrane Library, and CNKI. Included studies reported the puncture biopsy results of pulmonary GGNs, including the number of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) cases. After evaluating the studies, statistical analysis, and quality assessment, the pooled diagnostic sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was constructed and the area under the curve (AUC) was calculated. Subgroup analysis was performed according to whether spiral CT or fluoroscopy-guided CT was used in the study. Results This meta-analysis included 14 studies with a total of 759 patients (702 samples). The pooled SEN, SPE, and DOR of CT-guided puncture biopsy of pulmonary GGNs were 0.91 (95% confidence interval [CI] = 0.89–0.94), 0.99 (95% CI = 0.95–1.00), and 138.72 (95% CI = 57.98–331.89), respectively. The AUC was 0.97. Conclusion Our results indicated that CT-guided puncture biopsy of GGNs has high SEN, SPE, and DOR, which proved that CT-guided puncture biopsy was a good way to determine the pathological nature of GGN.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The diagnostic value of CT-guided percutaneous puncture biopsy of pulmonary ground-glass nodules: a meta-analysis

Wang

Zhong

et al. 2022

Acta Radiol

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“…Variations in their presence or levels [9,10] can indicate the presence of tumors, playing a crucial role in lung cancer screening, diagnosis, and treatment assessment. Nevertheless, the ability of tumor markers to detect early-stage lung cancer is not optimal, as indicated by the sensitivity rates reported in studies [11,12]. Additionally, no individual serum tumor marker is exclusively reliable for lung cancer detection [13,14].…”

Section: Introductionmentioning

confidence: 99%

Regression–Based Diagnostic Models for Early Lung Cancer Integrating Conventional Indicators with Tumor Markers

Zhou,

Ge,

Yang

et al. 2024

AJCEM

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The aim of this research was to develop a lung cancer diagnostic and predictive model that integrates traditional laboratory indicators with tumor markers. This model is intended to facilitate early screening and assist in the process of identifying or detecting lung cancer through a cost-effective, rapid, and convenient approach, ultimately enhancing the early detection rate of lung cancer. A retrospective study was conducted on 66 patients diagnosed with lung cancer and 159 patients with benign pulmonary conditions. Data including general clinical information, conventional laboratory test results, and tumor marker levels were collected. Data analysis was conducted using SPSS 26.0 (Statistical Product and Service Solutions 26.0). The lung cancer diagnosis and prediction model is created using a composite index established through binary logistic regression. The combined diagnostic prediction models, incorporating both traditional indicators and tumor markers, demonstrated a greater area under the curve (AUC) when compared to the diagnostic prediction model based solely on tumor markers and their combination testing. The values of cut-off point, AUC, accuracy, sensitivity, specificity, positive and negative detection rate and accuracy rate are 0.1805, 0.959, 86.67%, 0.955, 0.830, 95.45%, 83.02% and 89.33 respectively and it is shown that the combined diagnostic model display notable efficacy and clinical relevance in aiding the early diagnosis of lung cancer.

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“…However, the sensitivity of tumor markers in patients with early lung cancer is inferiority. 8 Moreover, no single serum tumor marker can be specifically used to detect lung cancer. 9,10 To improve the early diagnosis rate of lung cancer, it is a research hotspot to combine the conventional biological indicators and tumor markers in peripheral blood.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers

Luo

Yuan

Qin

et al. 2023

Exp Biol Med (Maywood)

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This study set out to establish a lung cancer diagnosis and prediction model uses conventional laboratory indicators combined with tumor markers, so as to help early screening and auxiliary diagnosis of lung cancer through a convenient, fast, and cheap way, and improve the early diagnosis rate of lung cancer. A total of 221 patients with lung cancer, 100 patients with benign pulmonary diseases, and 184 healthy subjects were retrospectively studied. General clinical data, the results of conventional laboratory indicators, and tumor markers were collected. Statistical Product and Service Solutions 26.0 was used for data analysis. The diagnosis and prediction model of lung cancer was established by artificial neural network – multilayer perceptron. After correlation and difference analysis, five comparison groups (lung cancer-benign lung disease group, lung cancer-health group, benign lung disease-health group, early-stage lung cancer-benign lung disease group, and early-stage lung cancer-health group) obtained 5, 28, 25, 16, and 25 valuable indicators for predicting lung cancer or benign lung disease, and then established five diagnostic prediction models, respectively. The area under the curve (AUC) of each combined diagnostic prediction model (0.848, 0.989, 0.949, 0.841, and 0.976) was higher than that of the diagnostic prediction model established only using tumor markers (0.799, 0.941, 0.830, 0.661, and 0.850), and the difference in the lung cancer-health group, the benign lung disease-health group, the early-stage lung cancer-benign lung disease group, and early-stage lung cancer-health group was statistically significant ( P < 0.05). The artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers have high performance and clinical significance in assisting the diagnosis of early lung cancer.

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Analysis of tumor markers in cytological fluid obtained from computed tomography–guided needle aspiration biopsies for the diagnosis of ground‐glass opacity pulmonary lesions

Cited by 7 publications

References 75 publications

The diagnostic value of CT-guided percutaneous puncture biopsy of pulmonary ground-glass nodules: a meta-analysis

The diagnostic value of CT-guided percutaneous puncture biopsy of pulmonary ground-glass nodules: a meta-analysis

Regression–Based Diagnostic Models for Early Lung Cancer Integrating Conventional Indicators with Tumor Markers

Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers

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