Photoacoustic Flow System for the Detection of Ovarian Circulating Tumor Cells Utilizing Copper Sulfide Nanoparticles

Lusk, Joel F.; Miranda, Christopher; Howell, Madeleine; Chrest, Matthew; Eshima, Jarrett; Smith, Barbara

doi:10.1021/acsbiomaterials.8b01217

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…CA-125 has limited accuracy and specificity in detecting early-stage ovarian cancer, and imaging methods such as CT, US, and MRI are commonly used for locating and detecting features of masses. However, the interpretation of images by expert radiographers can be time-consuming and prone to inter-observer variability [18].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women

Ziyambe,

Yahya,

Mushiri

et al. 2023

Diagnostics

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Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women

Ziyambe,

Yahya,

Mushiri

et al. 2023

Diagnostics

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“…Blood reflects the status of internal organs and tissues throughout the body and is therefore important in detecting, monitoring, and treating many diseases. Photoacoustic signals have been applied in many blood-related studies [5], such as the observation of size and shape of red blood cells [6][7][8][9], measurement of blood glucose concentration [10,11], early malaria diagnosis [12,13], diagnosis of bacteria in the blood [14], detection of circulating tumor cells [15,16], investigation of hemoglobin oxygen and carboxyhemoglobin saturation [17,18], bilirubin monitoring [19], and pharmaceuticals monitoring [20][21][22]. In particular, light scattering by blood cells can be effectively used to assess the general state of health and catch the early signs of multiple diseases [23].…”

Section: Introductionmentioning

confidence: 99%

Photoacoustic and absorption spectroscopy imaging analysis of human blood

et al. 2023

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Photoacoustic and absorption spectroscopy imaging are safe and non-invasive molecular quantification techniques, which do not utilize ionizing radiation and allow for repeated probing of samples without them being contaminated or damaged. Here we assessed the potential of these techniques for measuring biochemical parameters. We investigated the statistical association between 31 time and frequency domain features derived from photoacoustic and absorption spectroscopy signals and 19 biochemical blood parameters. We found that photoacoustic and absorption spectroscopy imaging features are significantly correlated with 14 and 17 individual biochemical parameters, respectively. Moreover, some of the biochemical blood parameters can be accurately predicted based on photoacoustic and absorption spectroscopy imaging features by polynomial regression. In particular, the levels of uric acid and albumin can be accurately explained by a combination of photoacoustic and absorption spectroscopy imaging features (adjusted R-squared > 0.75), while creatinine levels can be accurately explained by the features of the photoacoustic system (adjusted R-squared > 0.80). We identified a number of imaging features that inform on the biochemical blood parameters and can be potentially useful in clinical diagnosis. We also demonstrated that linear and non-linear combinations of photoacoustic and absorption spectroscopy imaging features can accurately predict some of the biochemical blood parameters. These results demonstrate that photoacoustic and absorption spectroscopy imaging systems show promise for future applications in clinical practice.

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“…Due to the rarity and heterogeneity of CTC, the detection technology has always been a lasting challenge in the CTC clinical application. Recently, the CTC detection system using copper sulfide nanoparticles through photoacoustic detection can specifically identify CTC in ovarian cancer (20). Besides, a new photoacoustic flow cytometry platform realized vivo detection of CTC within 20 seconds in the bloodstream of melanoma patients, which also provided new thoughts about dynamic and efficient CTC detection (21).…”

Section: Introductionmentioning

confidence: 99%

Detection of circulating tumor cells and evaluation of epithelial-mesenchymal transition patterns of circulating tumor cells in ovarian cancer

Jie¹,

Zhang²,

Du³

et al. 2022

Transl Cancer Res TCR

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Background: Circulating tumor cells (CTCs) have considered to be promising liquid biopsy in cancer due to the intact information of whole cells and the potential to reflect micrometastasis. However, CTCs research are extremely limited in ovarian cancer, probably due to their rarity. The predictive value of CTCs and circulating tumor microemboli (CTM) in metastasis remains to be elucidated in ovarian cancer. This study tried to identify CTCs/CTM in ovarian cancer with considerably positive rate. To preliminarily identify the invasive capacity of CTCs/CTM, the epithelial-mesenchymal transition (EMT) patterns of CTCs/CTM was evaluated. Moreover, for comprehensive understanding of invasiveness of disseminated cells in ovarian cancer, EMT pattern of exfoliated tumor cells in ascites were also confirmed in this study.Methods: Peripheral blood samples and ascites samples were collected from 22 ovarian cancer patients.The Microfiltration combined with morphological analysis was used to detect CTC single cells or cell clusters. Microfiltration combined with morphological analysis was applied in the CTC isolation and identification. EMT was evaluated by immunofluorescence via markers including vimentin and cytokeratin.Results: Microfiltration combined with morphological analysis was introduced to detect CTCs/CTM with a positivity rate of 40.9% in ovarian cancer patients. The number of CTC varied from 1 to 8, with CTM number from 4 to 30. CTCs/CTM of all samples have experienced EMT process. Vimentin was expressed in all CTC samples and all tumor cells in ascites, while cytokeratin was expressed in 44.4% (4/9) of CTC samples. There were no significant differences of the clinical parameters between the CTC-positive and CTC-negative patients.Conclusions: This study showed that both CTCs/CTM and detached tumor cells in ascites might have undergone complete or partial EMT in ovarian cancer. Moreover, microfiltration combined with cytomorphological analysis showed a considerable CTC detection rate.

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Photoacoustic Flow System for the Detection of Ovarian Circulating Tumor Cells Utilizing Copper Sulfide Nanoparticles

Cited by 12 publications

References 39 publications

A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women

A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women

Photoacoustic and absorption spectroscopy imaging analysis of human blood

Detection of circulating tumor cells and evaluation of epithelial-mesenchymal transition patterns of circulating tumor cells in ovarian cancer

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