Application of Artificial Neural Network in miRNA Biomarker Selection and Precise Diagnosis of Colorectal Cancer

Afshar, Saeid; Afshar, Sepideh; Warden, Emily; Manochehri, Hamed; Saidijam, Massoud

doi:10.29252/ibj.23.3.175

Cited by 32 publications

(21 citation statements)

References 36 publications

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“…The SVM classification model demonstrated 85% sensitivity and 90% specificity. Afshar et al [19] proposed an artificial neural network model, which accurately classified the sample data into cancerous and non-cancerous by screening four CRC-specific miRNAs retrieved from the Gene Expression Omnibus (GEO) database. In addition, Xuan et al [20] suggested using a dual CNN prediction model for discovering potential disease-related miRNAs.…”

Section: Artificial Intelligence Colorectal Cancer and Genomicsmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

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Section: Artificial Intelligence Colorectal Cancer and Genomicsmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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“…Recurrent neural network (RNN) has memory, parameter sharing, and Turing completeness, and it demonstrates superb capabilities in learning nonlinear characteristics of sequences. e combination of RNN and convolutional neural network (CNN) can extract image features frame by frame, thereby saving manpower and improving accuracy [8]. DC is used to solve the problem in image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Identification of Spinal Metastasis in Lung Cancer Using Spectral CT Images

Fan

Zhang

et al. 2021

Scientific Programming

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In this study, deep learning algorithm-based energy/spectral computed tomography (CT) for the spinal metastasis from lung cancer was used. A dilated convolutional U-Net model (DC-U-Net model) was first proposed, which was used to segment the energy/spectral CT image of patients with the spinal metastasis from lung cancer. Subsequently, energy/spectral CT images under different energy levels were collected for the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) comparison. It was found the learning rate of the model decreased exponentially as the number of training increased, with the lung contour segmented out of the image. Under 40–65 keV, the CT value of bone metastasis from lung cancer decreased with increasing energy, as with the average rank sum test result. The SNR and CNR values were the highest under 60 keV. The detection rate of the deep learning algorithm below 60 keV was 81.41%, and that of professional doctors was 77.56%. The detection rate of the deep learning algorithm below 140 keV was 66.03%, and that of professional doctors was 64.74%. In conclusion, the DC-U-Net model demonstrates better segmentation effects versus the convolutional neutral networ k (CNN), with the lung contour segmented. Further, a higher energy level leads to worse segmentation effects on the energy/spectral CT image.

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“…Colorectal cancer (CRC) is the fourth life-threatening cancer in the world, which is caused by the uncontrollable division and abnormal growth of the colorectal cells (1)(2)(3). Like all other types of cancer, surgical resection, chemotherapy, and radiotherapy are the main treatment options in clinical practice (4,5).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Chemical Composition of Hamadan Propolis as a Potential Anticancer Agent

Dastan

Mahmoudi

Saidijam

et al. 2021

Jentashapir J Cell Mol Biol

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: This study aimed to evaluate the chemical composition and anticancer activity of Hamadan propolis essential oil and methanolic extract on HCT116 cell line. The anticancer activity of propolis methanolic extract and essential oil was evaluated by using the MTT (3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide) test on the colorectal HCT116 cell line. The essential oil of Hamadan propolis was analyzed by gas chromatography-flame ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS). The total flavonoid and phenolic content of methanolic extract were assessed using the aluminum chloride colorimetric procedures and Folin-Ciocalteu (F-C) assay, respectively. The results of MTT assay confirmed the anticancer properties of Hamadan propolis essential oil and methanolic extract on the HCT116 cell line. Moreover, 30 compounds were characterized from Hamadan propolis essential oil, the main compounds of which included β-udesmol (25.7%), α-udesmol (20.4%), α-copaen-11-ol (13.7%), and γ-udesmol (10.1%). In addition, the total flavonoid content and total phenolic content of the methanolic extract were 14.12 mg/g and 32.01 mg/g dry weight, respectively. In summary, the most important constituents of Hamadan propolis essential oil were β-u desmol, α-eudesmol, α-copaen, and γ-u desmol. Despite the cytotoxic effects of Hamadan propolis methanolic extract and essential oil on HCT116, further evaluation of anticancer effects of the most important constituents of propolis methanolic extract and essential oil is recommended.

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Application of Artificial Neural Network in miRNA Biomarker Selection and Precise Diagnosis of Colorectal Cancer

Cited by 32 publications

References 36 publications

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

Deep Learning-Based Identification of Spinal Metastasis in Lung Cancer Using Spectral CT Images

Evaluation of Chemical Composition of Hamadan Propolis as a Potential Anticancer Agent

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