Classification of hyperspectral images for detection of hepatic carcinoma cells based on spectral–spatial features of nucleus

Chen, Yao; Zhu, Siqi; Fu, Shenhe; Li, Zhen; Huang, Furong; Yin, Hao; Chen, Zhenqiang

doi:10.1142/s1793545820500029

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…There are only a few other studies which have investigated the identification of cancer cells [ 8 , 9 ] or cancer areas in HCC [ 14 ]. Most studies used hyperspectral imaging with or without DAPI, which does not allow for subsequent analyses of immune markers [ 8 , 9 , 14 , 15 ]. In addition, some studies have determined the accuracy of HCC differentiation according to the WHO tumour grade based on label-free approaches, but these studies did not identify the cancer areas [ 14 , 16 ].…”

Section: Discussionmentioning

confidence: 99%

A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel

Dievernich

Stegmaier

Achenbach

et al. 2023

Cells

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Liver cancer is one of the most frequently diagnosed and fatal cancers worldwide, with hepatocellular carcinoma (HCC) being the most common primary liver cancer. Hundreds of studies involving thousands of patients have now been analysed across different cancer types, including HCC, regarding the effects of immune infiltrates on the prognosis of cancer patients. However, for these analyses, an unambiguous delineation of the cancer area is paramount, which is difficult due to the strong heterogeneity and considerable inter-operator variability induced by qualitative visual assessment and manual assignment. Nowadays, however, multiplex analyses allow the simultaneous evaluation of multiple protein markers, which, in conjunction with recent machine learning approaches, may offer great potential for the objective, enhanced identification of cancer areas with further in situ analysis of prognostic immune parameters. In this study, we, therefore, used an exemplary five-marker multiplex immunofluorescence panel of commonly studied markers for prognosis (CD3 T, CD4 T helper, CD8 cytotoxic T, FoxP3 regulatory T, and PD-L1) and DAPI to assess which analytical approach is best suited to combine morphological and immunohistochemical data into a cancer score to identify the cancer area that best matches an independent pathologist’s assignment. For each cell, a total of 68 individual cell features were determined, which were used as input for 4 different approaches for computing a cancer score: a correlation-based selection of individual cell features, a MANOVA-based selection of features, a multilayer perceptron, and a convolutional neural network (a U-net). Accuracy was used to evaluate performance. With a mean accuracy of 75%, the U-net was best capable of identifying the cancer area. Although individual cell features showed a strong heterogeneity between patients, the spatial representations obtained with the computed cancer scores delineate HCC well from non-cancer liver tissues. Future analyses with larger sample sizes will help to improve the model and enable direct, in-depth investigations of prognostic parameters, ultimately enabling precision medicine.

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Section: Discussionmentioning

confidence: 99%

A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel

Dievernich

Stegmaier

Achenbach

et al. 2023

Cells

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“…6c) reveals great effects within a wavelength range of 400-600 nm. Since PC1 of both PCAs separates the two healthy tissue groups from the tumor cluster, the impact of this wavelength region could be ascribed to changes in the cell nuclei of the tissues [43]. Nuclei in tumorous tissues are often enlarged and vary in size and shape compared to healthy nuclei [44].…”

Section: Interpretation Of Whiskbroom and Pushbroom Pca-da Modelsmentioning

confidence: 99%

Comparison of Whiskbroom and Pushbroom darkfield elastic light scattering spectroscopic imaging for head and neck cancer identification in a mouse model

et al. 2021

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The early detection of head and neck cancer is a prolonged challenging task. It requires a precise and accurate identification of tissue alterations as well as a distinct discrimination of cancerous from healthy tissue areas. A novel approach for this purpose uses microspectroscopic techniques with special focus on hyperspectral imaging (HSI) methods. Our proof-of-principle study presents the implementation and application of darkfield elastic light scattering spectroscopy (DF ELSS) as a non-destructive, high-resolution, and fast imaging modality to distinguish lingual healthy from altered tissue regions in a mouse model. The main aspect of our study deals with the comparison of two varying HSI detection principles, which are a point-by-point and line scanning imaging, and whether one might be more appropriate in differentiating several tissue types. Statistical models are formed by deploying a principal component analysis (PCA) with the Bayesian discriminant analysis (DA) on the elastic light scattering (ELS) spectra. Overall accuracy, sensitivity, and precision values of 98% are achieved for both models whereas the overall specificity results in 99%. An additional classification of model-unknown ELS spectra is performed. The predictions are verified with histopathological evaluations of identical HE-stained tissue areas to prove the model’s capability of tissue distinction. In the context of our proof-of-principle study, we assess the Pushbroom PCA-DA model to be more suitable for tissue type differentiations and thus tissue classification. In addition to the HE-examination in head and neck cancer diagnosis, the usage of HSI-based statistical models might be conceivable in a daily clinical routine. Graphical abstract

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“…Using principal component analysis (PCA) to find the features for artificial neural networks (ANNs) and support vector machines (SVMs), different cancer types could be distinguished with an overall accuracy of 87.4% using an ANN solution whereas the SVM accuracy ranged from 73–88.9%. In 2019, Chen et al [ 9 ] used H&E to stain hepatic carcinoma tissues and obtained spectral–spatial data from their nuclei using hyperspectral microscopy. The transmission spectra of the nuclei were used to train an SVM model for cell classification.…”

Section: Introductionmentioning

confidence: 99%

Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning

Liu

et al. 2022

Biosensors

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Skin cancer, a common type of cancer, is generally divided into basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM). The incidence of skin cancer has continued to increase worldwide in recent years. Early detection can greatly reduce its morbidity and mortality. Hyperspectral microscopic imaging (HMI) technology can be used as a powerful tool for skin cancer diagnosis by reflecting the changes in the physical structure and microenvironment of the sample through the differences in the HMI data cube. Based on spectral data, this work studied the staging identification of SCC and the influence of the selected region of interest (ROI) on the staging results. In the SCC staging identification process, the optimal result corresponded to the standard normal variate transformation (SNV) for spectra preprocessing, the partial least squares (PLS) for dimensionality reduction, the hold-out method for dataset partition and the random forest (RF) model for staging identification, with the highest staging accuracy of 0.952 ± 0.014, and a kappa value of 0.928 ± 0.022. By comparing the staging results based on spectral characteristics from the nuclear compartments and peripheral regions, the spectral data of the nuclear compartments were found to contribute more to the accurate staging of SCC.

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Classification of hyperspectral images for detection of hepatic carcinoma cells based on spectral–spatial features of nucleus

Cited by 9 publications

References 22 publications

A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel

A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel

Comparison of Whiskbroom and Pushbroom darkfield elastic light scattering spectroscopic imaging for head and neck cancer identification in a mouse model

Staging of Skin Cancer Based on Hyperspectral Microscopic Imaging and Machine Learning

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