Using hyperspectral imaging automatic classification of gastric cancer grading with a shallow residual network

Identification of gastric cancer types based on hyperspectral imaging technology

Gastric cancer is becoming the second biggest cause of death from cancer. Treatment and prognosis of different types of gastric cancer vary greatly. However, the routine pathological examination is limited to the tissue level and is easily affected by subjective factors. In our study, we examined gastric mucosal samples from 50 normal tissue and 90 cancer tissues. Hyperspectral imaging technology was used to obtain spectral information. A two-classification model for normal tissue and cancer tissue identification and a four-classification model for cancer type identification are constructed based on the improved deep residual network (IDRN). The accuracy of the two-classification model and four-classification model are 0.947 and 0.965. Hyperspectral imaging technology was used to extract molecular information to realize real-time diagnosis and accurate typing. The results show that hyperspectral imaging technique has good effect on diagnosis and type differentiation of gastric cancer, which is expected to be used in auxiliary diagnosis and treatment.

Joint Diagnostic Method of Tumor Tissue Based on Hyperspectral Spectral-Spatial Transfer Features

In order to improve the clinical application of hyperspectral technology in the pathological diagnosis of tumor tissue, a joint diagnostic method based on spectral-spatial transfer features was established by simulating the actual clinical diagnosis process and combining micro-hyperspectral imaging with large-scale pathological data. In view of the limited sample volume of medical hyperspectral data, a multi-data transfer model pre-trained on conventional pathology datasets was applied to the classification task of micro-hyperspectral images, to explore the differences in spectral-spatial transfer features in the wavelength of 410-900 nm between tumor tissues and normal tissues. The experimental results show that the spectral-spatial transfer convolutional neural network (SST-CNN) achieved a classification accuracy of 95.46% for the gastric cancer dataset and 95.89% for the thyroid cancer dataset, thus outperforming models trained on single conventional digital pathology and single hyperspectral data. The joint diagnostic method established based on SST-CNN can complete the interpretation of a section of data in 3 min, thus providing a new technical solution for the rapid diagnosis of pathology. This study also explored problems involving the correlation between tumor tissues and typical spectral-spatial features, as well as the efficient transformation of conventional pathological and transfer spectral-spatial features, which solidified the theoretical research on hyperspectral pathological diagnosis.

Assembly of DNA triangular pyramid frustum for ultrasensitive quantification of exosomal miRNA

Early screening of biomarkers benefits therapy and prognosis of cancers. MiRNAs encapsulated in tumor-derived exosomes are emerging biomarkers for early diagnosis of cancers. Nevertheless, traditional methods suffer certain drawbacks, which hamper their wide applications. In this contribution, we have developed a convenient electrochemical approach for quantification of exosomal miRNA based on the assembly of DNA triangular pyramid frustum (TPF) and strand displacement amplification. Four single-stranded DNA helps the formation of primary DNA triangle with three thiols for gold electrode immobilization at the bottom and three amino groups on overhangs for the capture of silver nanoparticles. On the other hand, target miRNA induced strand displacement reaction produces abundant specific DNA strands, which help the DNA structural transition from triangle to TPF. Amino groups are thus hidden and the declined silver stripping current can be used for the evaluation of target miRNA concentration. This biosensor exhibits excellent analytical performances and successfully achieves analysis of exosomal miRNAs from cells and clinical serum samples.

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

SIGNIFICANCE

Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.

AIM

To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.

APPROACH

We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.

CONCLUSIONS

Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.