Diagnosis of Non-Small Cell Lung Cancer via Liquid Biopsy Highlighting a <i>Fluorescence-in-situ-Hybridization</i> Circulating Tumor Cell Approach

Ye, Xin; Xiao, Yang; Carbone, Roberta; Barshack, Iris; Katz, Ruth L.

doi:10.5772/intechopen.97631

Cited by 3 publications

(3 citation statements)

References 114 publications

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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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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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“…Recently, the classification of LC, particularly the non-small cell variant, was discovered to be very important in targeted therapy, so an accurate subtype is critical, especially in small biopsy specimens [ 22 ]. The International Association for the Study of Lung Cancer (IASLC) has recommended the use of immune stains as a diagnostic aid, particularly in tumours that do not meet established morphologic criteria by hematoxylin and eosin (H&E) stain [ 23 ]. The established morphological criteria for ADC are glandular differentiation or mucin, whereas intercellular bridges and/or keratinization are the criteria for SCC [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Classifying Lung Carcinoma Using Immunohistochemical Markers on Limited Biopsy Material: A Two-Center Study

Hassan¹,

Alahmadi²,

Waqas³

et al. 2022

Cureus

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IntroductionAccurate classification of lung cancer into primary and metastatic carcinomas is critical for treatment approaches. Immunohistochemistry (IHC) has always been pivotal in unveiling the diverse cell differentiation lineages present in lung cancer by using specific biomarkers such as TTF1 and p63/p40, which closely reflect the relationship between genotype and phenotype.. MethodsA retrospective cross-sectional study was conducted to evaluate 57 Tru-Cut biopsies over two years, from 2020-2022. Tumour morphology was evaluated, and IHC for TTF-1, Napsin A, CK-7, P-63, P-40, and CD-56 was performed in two steps. ResultsOf the lung cancer cases, 58.5% were adenocarcinoma (ADC), 24.5% were squamous cell carcinoma (SCC), 9.4% were small cell carcinoma, and 7.5% were poorly differentiated carcinoma. TTF1 stain had sensitivity and specificity of 78.9% and 50% in 33 cases of ADC, respectively, while CK7 and Napsin A had 100% sensitivity. P63 stain had 77% sensitivity and 50% specificity in 15 cases of SCC, while P-40 had 100% sensitivity. The CD56 stain was 100% sensitive in five cases of small cell carcinoma. ConclusionIHC staining on small lung biopsies allows accurate sub-classification of poorly differentiated lung cancers; however, there is still significant variability. Surgical resection specimens can be further classified due to architectural features that biopsies lack. Morphological findings would be beneficial in the development of an algorithm for sub-classifying lung carcinoma using a variety of markers.

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“…This multiplex interphase FISH assay is composed of four DNA probes that are universally deleted in non-small cell lung cancer (NSCLC) and have been implicated in the pathogenesis of NSCLC ( 14 , 23 ). This assay has previously shown a high degree of accuracy in detecting cells containing chromosomal abnormalities at 10q22.3 and 3p22.1 and in the internal control genes CEP 10 and 3q29 ( 14 ) in several studies involving the detection of early lung cancer ( 24 ). Abnormal cells that were discovered by the 4-color FISH assay were identified as intact cells with a nucleus larger than a lymphocyte nucleus and polysomy of at least two probes per nucleus.…”

Section: Methodsmentioning

confidence: 99%

A Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy

Lin

Zheng

et al. 2022

Front. Oncol.

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Lung cancer is the leading cause of cancer-related deaths worldwide and in China. Screening for lung cancer by low dose computed tomography (LDCT) can reduce mortality but has resulted in a dramatic rise in the incidence of indeterminate pulmonary nodules, which presents a major diagnostic challenge for clinicians regarding their underlying pathology and can lead to overdiagnosis. To address the significant gap in evaluating pulmonary nodules, we conducted a prospective study to develop a prediction model for individuals at intermediate to high risk of developing lung cancer. Univariate and multivariate logistic analyses were applied to the training cohort (n = 560) to develop an early lung cancer prediction model. The results indicated that a model integrating clinical characteristics (age and smoking history), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the training cohort (sensitivity 89.53%, specificity 81.31%, area under the curve [AUC] = 0.880). In the independent validation cohort (n = 168), this model had an AUC of 0.895, which was greater than that of the Mayo Clinic Model (AUC = 0.772) and Veterans’ Affairs Model (AUC = 0.740). These results were significantly better for predicting the presence of cancer than radiological features and artificial intelligence risk scores alone. Applying this classifier prospectively may lead to improved early lung cancer diagnosis and early treatment for patients with malignant nodules while sparing patients with benign entities from unnecessary and potentially harmful surgery.Clinical Trial Registration NumberChiCTR1900026233, URL: http://www.chictr.org.cn/showproj.aspx?proj=43370.

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Diagnosis of Non-Small Cell Lung Cancer via Liquid Biopsy Highlighting a Fluorescence-in-situ-Hybridization Circulating Tumor Cell Approach

Cited by 3 publications

References 114 publications

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

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

Accuracy of Classifying Lung Carcinoma Using Immunohistochemical Markers on Limited Biopsy Material: A Two-Center Study

A Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy

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