Photoacoustics spectral analysis for in vivo detection of collagen contents in cancers

Li, Jiayan; Chen, Yingna; Zhi, Wenxiang; Qian, Cheng

doi:10.1109/ius54386.2022.9958255

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…They further quantified the Gleason score of prostate cancer based on the tissue microscopic architecture using PASA 45 . Our group combined the PASA with machine leaning to better mine the data information and achieved a high diagnostic accuracy of prostate cancer, 46 – 49 osteoporosis, 50 and breast cancer 51 , 52 . PASA has shown considerable potential in evaluating the endogenous chromophore in biological tissues for tumor diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors

et al. 2023

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.SignificanceCollagen and lipid are important components of tumor microenvironments (TME) and participates in tumor development and invasion. It has been reported that collagen and lipid can be used as a hallmark to diagnosis and differentiate tumors.AimWe aim to introduce photoacoustic spectral analysis (PASA) method that can provide both the content and structure distribution of endogenous chromophores in biological tissues to characterize the tumor-related features for identifying different types of tumors.ApproachEx vivo human tissues with suspected squamous cell carcinoma (SCC), suspected basal cell carcinoma (BCC), and normal tissue were used in this study. The relative lipid and collagen contents in the TME were assessed based on the PASA parameters and compared with histology. Support vector machine (SVM), one of the simplest machine learning tools, was applied for automatic skin cancer type detection.ResultsThe PASA results showed that the lipid and collagen levels of the tumors were significantly lower than those of the normal tissue, and there was a statistical difference between SCC and BCC (p < 0.05), consistent with the histopathological results. The SVM-based categorization achieved diagnostic accuracies of 91.7% (normal), 93.3% (SCC), and 91.7% (BCC).ConclusionsWe verified the potential use of collagen and lipid in the TME as biomarkers of tumor diversity and achieved accurate tumor classification based on the collagen and lipid content using PASA. The proposed method provides a new way to diagnose tumors.

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Section: Introductionmentioning

confidence: 99%

Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors

et al. 2023

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“…As a result of that, objects can be detected in several disconnected components and due to that it may result with high number of false positive detection. Some other methods from the literature are out of focus of this paper since the tasks and/or data acquisition methods are not comparable [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Collagen Bundles in Second Harmonic Generation Microscopy Images

KAYASANDIK

2023

Balkan Journal of Electrical and Computer Engineering

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Collagen is one of the most abundant proteins in the body. It is essential for the structure, functionality, and strength of the connective tissue such as skin, bone, tendon, and cornea. It is known that a change in the arrangement or morphology of these fibrillar structures relates to multiple dysfunctions including corneal diseases and various cancer types. Due to their critical roles in wide-range abnormalities, there is an increasing interest in the pattern analysis of collagen arrangements. In recent years, Second Harmonic Generation (SHG) microscopy is proven to be an efficient imaging modality for visualizing unstained collagen fibrils. There are plenty of studies in the literature on the analysis of collagen distribution in SHG images. However, the majority of these methods are limited to detecting simple, statistical and non-local properties such as pixel intensity and orientation variance. There is a need for a method to detect the local structural properties of collagen bundles. This paper is to introduce an automated method to detect collagen bundles in 3-dimensional SHG microscopy images. The origin of the proposed method is based on multiscale directional representation systems. The proposed method detects the collagen bundles by measuring the dominant orientation of local regions and an orientation-based connected component analysis. Through more local analysis and the detection of collagen bundles separately, the proposed method would lead to the extraction of more detailed structural information on collagen bundle distribution.

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Detection of collagen content evolution modulated by cancer-associated fibroblasts using photoacoustic spectral analysis

Li,

Bai,

Cao

et al. 2023

2023 IEEE International Ultrasonics Symposium (IUS)

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Photoacoustics spectral analysis for in vivo detection of collagen contents in cancers

Cited by 3 publications

References 23 publications

Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors

Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors

Automated Detection of Collagen Bundles in Second Harmonic Generation Microscopy Images

Detection of collagen content evolution modulated by cancer-associated fibroblasts using photoacoustic spectral analysis

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