Imaging of Tumor Boundary Based on Multielements and Molecular Fragments Heterogeneity in Lung Cancer

Yin, Pengkun; Li, Qiang; Duan, Yixiang; Lin, Qingyu

doi:10.1109/tim.2021.3102755

Cited by 12 publications

(8 citation statements)

References 43 publications

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“…In the previous work, we achieved 3D elemental imaging of mouse brain tissue with LIBS [13]. Yin et al delineated the human lung tumor boundary with LIBS, which is significantly displayed distinctly among healthy normal, inflammatory and cancer tissues [14]. Chen et al developed label-free LIBS method for high-throughput recognition of tumor cells [15].…”

Section: Introductionmentioning

confidence: 99%

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser‐induced breakdown spectroscopy

Wang

Huang

Shu

et al. 2023

Journal of Biophotonics

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The rapid and accurate diagnosis of cancer is an important topic in clinical medicine. In the present work, an innovative method based on laser‐induced breakdown spectroscopy (LIBS) combined with machine learning was developed to distinguish and classify different tumor cell lines. The LIBS spectra of cells were first acquired. Then the spectral pre‐processing was performed as well as detailed optimization to improve the classification accuracy. After that, the convolutional neural network (CNN), support vector machine (SVM), and K‐nearest neighbors were further compared for the optimized classification ability of tumor cells. Both the CNN algorithm and SVM algorithm have achieved impressive discrimination performances for tumor cells distinguishing, with an accuracy of 97.72%. The results show that the heterogeneity of elements in tumor cells plays an important role in distinguishing the cells. It also means that the LIBS technique can be used as a fast classification method for classifying tumor cells.

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Section: Introductionmentioning

confidence: 99%

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser‐induced breakdown spectroscopy

Wang

Huang

Shu

et al. 2023

Journal of Biophotonics

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“…The analytical instrumentation (Figure S1) comprises an Nd:YAG laser (λ= 1064 nm, 10 Hz, 30 mJ) ; a three-channel Czerny-Turner spectrometer (Avantes Technology, The Netherlands) of 0.15 nm resolution (175 nm to 814 nm); an XYZ stage (0.1 mm resolution); a control system and an optical path system as described in our previous work [13] .…”

Section: Methods Instrumentation For Libs-hsimentioning

confidence: 99%

Semi-Supervised Deep Learning Coupled with Laser-induced Breakdown Hyperspectral Imaging for the Margin Distance Determination of Non-small Cell Lung Cancer

Lin,

Chen,

Xie

et al. 2023

Preprint

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An optimal margin distance determination is crucial for successful resection surgeries targeting early-stage non-small cell lung cancer (NSCLC). Yet, there has been no clear evidence regarding optimal margin distance, which is tightly associated with the recurrence and mortality after the operation. In this study, we proposed an innovative approach, a laser-induced breakdown hyperspectral imaging (LIBS-HSI) technique, capable of capturing the multi-element distribution within tumour tissues to probe optimal margin distance. The imaging quality of LIBS-HSI was enhanced by hyperspectral image (HSI) denoising algorithms founded on sparse representation and low-rank constraint, complemented by the application of the Butterworth notch resist filter. A semi-supervised deep learning model was developed to achieve pathological imaging segmentation recognition of malignant areas. This technique revealed the margin distance after getting malignant areas prediction and gradient, based on pseudo-colour imaging of elements composition within NSCLC samples. It stands as a promising candidate in postoperative pathological reports, enabling doctors to evaluate surgical outcomes in the future effectively.

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“…International Journal of Intelligent Engineering and Systems, Vol. 17 The user-specified error threshold is reached or the predetermined numbers of repetitions have been reached before moving on to Step 2 [27]. The algorithm updates the weights immediately rather than waiting until all couples in the training set have used steps 2a and 2b [28][29].…”

Section: A Calculate the Actual Output Using Eq (7)mentioning

confidence: 99%

Mammography-based Computer-Aided Diagnostics for the Identification of Breast Cancer Based on Machine Learning

2024

IJIES

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Breast cancer is the most typical cancer overall and the one that affects women the most frequently. In 2022, there were 2.26 million or more brand-new cases of breast cancer in women. To identify the breast cancer in early stage, it's easy to cure. The apparent structure is examined using computer-aided diagnostic (CAD) technologies. The majority of existing diagnostic approaches rely on mammography mass features; however, the suggested strategy relies on MicroCalcification (MC) characteristics. In this research cancer cells are detected utilizing mammography images, pre-processing methods, segmentation, multi-layer perceptron with artificial algae algorithm and receiver operating characteristics (ROC) curve analysis. The national cancer institute (NCI) of provides mammogram scans. Normalization and median filtering were used as part of the pre-processing procedure. Segmentation is used to detect the location of MicroCalcification present cells using the local threshold approach. The MicroCalcification present cells and MicroCalcification missing cells are classified using the multi-layer perceptron (MPL) with artificial algae algorithm technique and also used to divide MicroCalcification-affected cells into the following categories: initial, very small, small, medium, high, and very high. ROC curve analysis was used to assess system performance. According to experimental findings, for the NCI, University of California Irvine (UCI), nathan kline institute (NKI), investigation of serial studies to predict your 1 (ISPY1), ISPY2, ISPY3, and ISPY4 datasets, the classification using the multi-layer perceptron with artificial algae method has the best accuracy of 96 percent when compared to random forest (RF), MPL with genetic algorithm (GA), hierarchical clustering random forest (HCRF), and convolutional neural network (CNN).

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Imaging of Tumor Boundary Based on Multielements and Molecular Fragments Heterogeneity in Lung Cancer

Cited by 12 publications

References 43 publications

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser‐induced breakdown spectroscopy

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser‐induced breakdown spectroscopy

Semi-Supervised Deep Learning Coupled with Laser-induced Breakdown Hyperspectral Imaging for the Margin Distance Determination of Non-small Cell Lung Cancer

Mammography-based Computer-Aided Diagnostics for the Identification of Breast Cancer Based on Machine Learning

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