Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network.

Deep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.

Salivary Analysis Based on Surface Enhanced Raman Scattering Sensors Distinguishes Early and Advanced Gastric Cancer Patients from Healthy Persons.

Development of new methods to screen early gastric cancer patients has great clinical requirement. Ten amino acids in human saliva are identified as small metabolite biomarkers to distinguish early gastric cancer patients and advanced gastric cancer patients from healthy persons by using high performance liquid chromatography-mass spectrometry (HPLC-MS). Then, surface enhanced Raman scattering (SERS) sensors based on graphene oxide nanoscrolls wrapped with gold nanoparticles are developed to detect ten amino acids biomarkers in saliva. The distinctive graphene oxide nanoscrolls wrapped with gold nanoparticles are facilely prepared via ultrasonication without any organic stabilizer, and endow the SERS sensors with excellent uniformity, stability and SERS activity to adsorb and detect the biomarkers with 108 enhancement coefficient. The SERS sensors were confirmed to be feasible for distinguishing early gastric cancer patients and advanced gastric cancer patients from healthy persons by simulation samples and 220 clinical saliva samples with excellent performance (specificity >87.7% and sensitivity >80%). This non-invasive, cheap, fast and reliable salivary analysis method based on the SERS sensors provides a new strategy to screen out early gastric cancer patients and advanced gastric cancer patients from population, and owns clinical translational prospects.