scholarly journals Feasibility of circulating tumor cells (CTCs) combined with medical imaging examinations to screen for cancer and improve diagnosis in non-metastatic invasive breast cancer patients: a cohort study

2020 ◽  
Author(s):  
Xuan Shao ◽  
Xiao Yan Jin ◽  
Zhi Gang Chen ◽  
Zhi Gang Zhang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Medical Imaging ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Breast Imaging ◽  
Early Stage ◽  
False Negative ◽  
Breast Cancer Patients ◽  
Detection Rates ◽  
Gamma Imaging

Abstract Background Previous study has reported that circulating tumor cells (CTCs) could serve as a diagnostic biomarker in breast cancer (BC) screening. However, the differential efficacy of routine examination including ultrasound (US), mammogram (MG), magnetic resonance imaging (MR), and breast-specific gamma imaging (BSGI) and CTCs is unknown. This study aimed to combine CTCs with other BC screening imaging modalities to screen for cancer and enhance diagnostic potency in non-metastatic BC patients. Methods 102 treatment-naive non-metastatic BC patients were enrolled in this study between December 2017 and November 2018. All these patients received CTC detection and at least one medical imaging examination. Correlations of CTC enumeration with patients’ clinicopathological characteristics and medical imaging examinations were evaluated. Results CTC detection rates (average CTC counts) in stage I-III BC patients were 92.9% (2.1), 87.2% (2.4) and 100% (4.2), respectively. CTCs counts were positively associated with cancer stage (p = 0.0084) and tumor size (p = 0.0301). CTCs counts were more correlated with US than MR, and showed least correlation with MG. CTCs counts were not associated with molecular subtypes of BC nor breast-specific gamma imaging (BSGI) results, indicating that CTC enumeration cannot be used to reflect molecular signatures of BC. When both cut-off values for CTCs and Breast Imaging-Reporting and Data System (BI-RADS) were used, false negative rates (FNR) of CTCs, US, MG and MR in BC detection in this study were 22.5%, 15.8%, 30.6% and 12.8%, respectively. Combination of CTC with US, MG or MR decreased FNR of BC detection to 7.9%, 8.3% and 5.8%, respectively. False negatives are more common in early stage patients, and in patients with smaller tumors. Conclusion CTCs counts can be used as a diagnostic aid in BC screening and early diagnosis. CTCs counts were more relevant to US than MR or MG. Conjugation of CTCs counts would improve the diagnostic potency of medical imaging examinations for BC detection, especially for MG in Chinese women.

scholarly journals Feasibility of circulating tumor cells (CTCs) combined with medical imaging examinations to screen for cancer and improve diagnosis in non-metastatic invasive breast cancer patients: a cohort study

2020 ◽  
Author(s):  
Xuan Shao ◽  
xiao yan jin ◽  
zhi gang chen ◽  
zhi gang zhang ◽  
ke wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Medical Imaging ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Breast Imaging ◽  
Chinese Women ◽  
Clinicopathological Characteristics ◽  
Breast Cancer Patients ◽  
Detection Rates ◽  
Gamma Imaging

Abstract Background: Previous study has reported that circulating tumor cells (CTCs) could be served as a diagnostic biomarker in breast cancer (BC) screening. However, the differential efficacy of routine examination including ultrasound (US), mammogram (MG), magnetic resonance imaging (MR), and breast-specific gamma imaging (BSGI) and CTCs is unknown. This study aimed to compare CTCs with common used BC screening imaging modalities and to evaluate whether their combination would enhance the diagnostic potency in non-metastatic BC patients.Methods: 102 treatment-naive non-metastatic BC patients, 177 patients with breast benign diseases (BBD) and 64 healthy females, who had CTC detection and at least one of the following medical imaging examinations, US, MG or MR between December 2017 and November 2018, were enrolled in this study.Correlations of CTC enumeration with patients’ clinicopathological characteristics and medical imaging examinations were evaluated. Results: CTC detection rates (average CTC counts) in stage I-III BC patients were 92.9% (2.1), 87.2% (2.4) and 100% (4.2), respectively. CTCs counts were positively associated with cancer stage (p = 0.0084) and tumor size (p = 0.0301). CTC counts were more correlated with US than MR or MG. CTC counts were not associated with molecular subtypes of BC nor breast-specific gamma imaging (BSGI) results, indicating that CTC enumeration cannot be used to predict molecular signatures of BC. CTCs and medical imaging examinations would have the best diagnostic performance for BC when CTC cut-off was set to 2 and imaging Breast Imaging-Reporting and Data System (BI-RADS) was set to 4b. Combination of CTC with US, MG or MR increased the sensitivity for BC diagnosis, especially for MG. Sensitivity of MG increased from 0.694 to 0.917, even more than in conjugation with US (0.901). Conclusion: CTCs counts can be used as a diagnostic aid in BC screening and early diagnosis. CTCs counts were more relevant to US than MR or MG. Conjugation of CTCs counts would improve the diagnostic potency of medical imaging examinations for diagnosing BC, especially for MG in Chinese women.

scholarly journals Evaluation of Human Mammaglobin as a Biomarker of Circulating Tumor Cells in Breast Cancer and Clinical Verification

2020 ◽  
Author(s):  
Chuanguang Xiao ◽  
Xiaohong Wang ◽  
Shusheng Qiu ◽  
Wenyue Gu
Keyword(s):  
Breast Cancer ◽  
Differential Diagnosis ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Magnetic Particles ◽  
False Negative ◽  
Detection Accuracy ◽  
Breast Cancer Patients ◽  
Specificity And Sensitivity

Abstract Cytokeratin (CK) is the gold standard marker for the differential diagnosis of epithelial circulating tumor cells (CTC), but the low expression of CK can lead to false negative results.In this study, the specificity and sensitivity of human breast globin (hMAM) as a tumor marker of CTC in peripheral blood of breast cancer patients were analyzed and aim to improve the CTC detection accuracy in breast cancer, enrich the candidate markers for clinical differential diagnosis of breast cancer CTC. EpCAM antibody modified liposome magnetic particles (ELMP) were prepared, and then their physicochemical properties were characterized. ELMP has high physicochemical stability and can efficiently enrich the CTC of epithelial breast cancer. We found hMAM is more consistent with clinical and pathological diagnosis results. To be noted, CK19 combined with hMAM method efficiently distinguish the separated CTC from breast cancer patients, furthermore, the specificity and sensitivity of CTC detection get promoted.

scholarly journals The Mechanical Fingerprint of Circulating Tumor Cells (CTCs) in Breast Cancer Patients

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1119
Author(s):  
Ivonne Nel ◽  
Erik W. Morawetz ◽  
Dimitrij Tschodu ◽  
Josef A. Käs ◽  
Bahriye Aktas
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Hematopoietic Cells ◽  
Surrogate Marker ◽  
Clinical Samples ◽  
Breast Cancer Patients ◽  
Shape Restoration

Circulating tumor cells (CTCs) are a potential predictive surrogate marker for disease monitoring. Due to the sparse knowledge about their phenotype and its changes during cancer progression and treatment response, CTC isolation remains challenging. Here we focused on the mechanical characterization of circulating non-hematopoietic cells from breast cancer patients to evaluate its utility for CTC detection. For proof of premise, we used healthy peripheral blood mononuclear cells (PBMCs), human MDA-MB 231 breast cancer cells and human HL-60 leukemia cells to create a CTC model system. For translational experiments CD45 negative cells—possible CTCs—were isolated from blood samples of patients with mamma carcinoma. Cells were mechanically characterized in the optical stretcher (OS). Active and passive cell mechanical data were related with physiological descriptors by a random forest (RF) classifier to identify cell type specific properties. Cancer cells were well distinguishable from PBMC in cell line tests. Analysis of clinical samples revealed that in PBMC the elliptic deformation was significantly increased compared to non-hematopoietic cells. Interestingly, non-hematopoietic cells showed significantly higher shape restoration. Based on Kelvin–Voigt modeling, the RF algorithm revealed that elliptic deformation and shape restoration were crucial parameters and that the OS discriminated non-hematopoietic cells from PBMC with an accuracy of 0.69, a sensitivity of 0.74, and specificity of 0.63. The CD45 negative cell population in the blood of breast cancer patients is mechanically distinguishable from healthy PBMC. Together with cell morphology, the mechanical fingerprint might be an appropriate tool for marker-free CTC detection.

Diagnosing breast cancer tumors using stacked ensemble model

2021 ◽  
pp. 1-9
Author(s):  
Ahmet Haşim Yurttakal ◽  
Hasan Erbay ◽  
Türkan İkizceli ◽  
Seyhan Karaçavuş ◽  
Cenker Biçer
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Early Stage ◽  
False Negative ◽  
Gradient Boosting ◽  
Physical Sign ◽  
Ensemble Model ◽  
Learning Methods ◽  
Dce Mri

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

A novel approach using telomerase-specific replication-selective adenovirus for detection of circulating tumor cells in breast cancer patients

2011 ◽  
Vol 128 (3) ◽  
pp. 765-773 ◽  
Author(s):  
Seung Jin Kim ◽  
Akinori Masago ◽  
Yasuhiro Tamaki ◽  
Kenji Akazawa ◽  
Fumine Tsukamoto ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Breast Cancer Patients ◽  
Novel Approach
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