Application of circulating genetically abnormal cells in the diagnosis of early-stage lung cancer

Qiu, Xiaochang; Zhang, Haoran; Zhao, Yongheng; Zhao, Jing; Wan, Yongqi; Li, Dezhi; Yao, Zhou‐Hong; Dong, Lin

doi:10.1007/s00432-021-03648-w

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…Moreover, the diagnostic value of CACs in comparison with other traditional biomarkers has been confirmed using a cohort of patients with lung nodules, which is an independent validation to the work conducted by Ye et al [ 24 ]. In the present study, the highest diagnostic efficiency was achieved when a CAC counts of 3 was chosen as the cut-off value, this result is similar to the study conducted by Qiu [ 25 ] and Ye [ 18 ] et al Overall, CAC counts presented better diagnostic value than commonly used tumour markers (CEA, CYFRA21–1, and NSE), which agrees with the results of several studies reporting the advantages of CACs for the diagnosis of lung cancer [ 17 , 18 , 24 , 25 , 27 ], CACs have the potential to become a better novel diagnostic marker of lung cancer.…”

Section: Discussionsupporting

confidence: 88%

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

et al. 2022

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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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Section: Discussionsupporting

confidence: 88%

“…Therefore, the traditional mode of small lung nodule diagnosis should be investigated. Previous works as well as this work suggest that CACs are an ideal candidate marker, as such a test only requires the simple process of blood collection and its accuracy has been reported in early lung cancer patients [ 18 , 24 , 25 ].…”

Section: Discussionmentioning

confidence: 96%

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

et al. 2022

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“…The blood-based 4-color FISH method for the identification of CACs was possible to achieve undisputed detection results, which identified CACs by analyzing the number of fluorescence signals in the different channels according to the identification rules, as presented in Table 1. The method could accurately identify patients with lung cancer by enabling a reduction in the frequency of non-malignant nodules biopsied, which has been applied in clinical practice (32)(33)(34)(35)(36).…”

Section: Discussionmentioning

confidence: 99%

An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4

Xu¹,

Zhang²,

Fan³

et al. 2022

Quant Imaging Med Surg

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Background: Circulating tumor cells (CTCs) acting as "liquid biopsy" of cancer are cells that have been shed from the primary tumor, which cause the development of a secondary tumor in a distant organ site, leading to cancer metastasis. Recent research suggests that CTCs with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood samples, namely circulating genetically abnormal cells (CACs), could be used as a non-invasive decision tool to detect patients with benign pulmonary nodules. Such cells are identified by counting the fluorescence signals of fluorescence in situ hybridization (FISH). However, owing to the rarity of CACs in the blood, identification of CACs using this technique is time-consuming and is a drawback of this method.Methods: This study has proposed an efficient and automatic FISH-based CACs identification approach which is based on a combination of the high accuracy of You Only Look Once (YOLO)-V4 and the lightweight and rapidness of MobileNet-V3. The backbone of YOLO-V4 was replaced with MobileNet-V3 to improve the detection efficiency and prevent overfitting, and the architecture of YOLO-V4 was optimized by utilizing a new feature map with a larger scale to enable the enhanced detection ability for small targets.Results: We trained and tested the proposed model using a dataset containing more than 7,000 cells based on five-fold cross-validation. All the images in the dataset were 2,448×2,048 (pixels) in size. The number of cells in each image was >70. The accuracy of four-color fluorescence signals detection for our proposed model were all approximately 98%, and the mean average precision (mAP) were close to 100%. The final outcome of the developed method was the type of cells, i.e., normal cells, CACs, gaining cells or deletion cells. The method had a CACs identification accuracy of 93.86% (similar to an expert pathologist), and a detection speed that was about 500 times greater than that of a pathologist. Conclusions:The developed method could greatly increase the review accuracy, enhance the efficiency of reviewers, and reduce the review turnaround time during CACs identification.

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“…Individuals who are not prepared to conceive could be directed to regenerative endocrinology and infertility hospitals for further fertility alternatives such as storing embryos, oocytes, and other reproduction components [ 15 ]. There have been no randomized control trials specifically compared the impact of laparoscopy for harmless ovarian tumors in pregnant upon mother as well as fetal health or treatment adherence [ 16 ]. They used ROC curves as well as regression analysis to identify the optimum mixtures of 5 serological tests to cancer screening in diverse communities.…”

Section: Related Workmentioning

confidence: 99%

Clinical Analysis of 137 Cases of Ovarian Tumors in Pregnancy

Yin

Zhong

Wang

et al. 2022

Journal of Oncology

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Ovarian tumors do not really typically occur in association with pregnant; however, once they do, the treatment is critical. It is important to note that around 6% of ovarian tumors in pregnancies are cancerous. The problems induced by ovarian tumors in pregnancy particularly necessitate rapid medical intervention and are much more frequent than cancer. Medication choices and survival of ovary tumor patients could be influenced by varied diagnoses of ovarian masses. So, we present an upgraded logistic regression (ULR) approach in this paper. Initially, the collection of 137 patient datasets was employed in screening test to identify the ovarian tumor as benign-tumor and malignant-tumor by using contrast-enhanced ultrasonography (CEU) method. Then, the screening test images are preprocessed using wavelet transform (WT) approach. The preprocessed data are extracted by using local binary pattern (LBP) and laws’ texture energy (LTE) techniques. Finally, the clinical analysis of the ovarian tumor can be obtained by the proposed ULR approach. The performances were examined and compared with existing approaches to achieve the proposed approach with greatest correctness. The findings are depicted by utilizing the MATLAB tool.

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Application of circulating genetically abnormal cells in the diagnosis of early-stage lung cancer

Cited by 9 publications

References 36 publications

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

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

An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4

Clinical Analysis of 137 Cases of Ovarian Tumors in Pregnancy

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