Cancer classification based on gene expression using neural networks

Hp, Hu; Zj, Niu; Yp, Bai; Xh, Tan

doi:10.4238/2015.december.21.33

Cited by 27 publications

(13 citation statements)

References 8 publications

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“…Overall, integrating AI algorithms with genetic testing has shown several promising results for CRC. Based on gene expression, Hu et al [8] performed a simulation experiment to classify 53 colon cancer patients with the Union for International Cancer Control (UICC) II into two groups: relapse and no relapse after surgery. The researchers compared the classification accuracy obtained by the S-Kohonen (91%), Back-propagation (BP, 66%), and SVM (70%) neural networks.…”

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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“…Employed patients were less likely to engage in sun protective behaviors, which may be due to the confounding variable of age, as those in the workforce tended to be younger, as well as more susceptible to societal pressures to maintain what is perceived as a "healthy" or "attractive" skin tan. Patients with other medical co-morbidities were less likely to change their behavior, perhaps due to focusing on other concerns that were perceived to be more pressing [5]. In light of these results, future skin cancer educational interventions may be more beneficial if targeted toward specific population subgroups of nonmelanoma skin cancer patients.…”

Section: IImentioning

confidence: 93%

An Approach for Skin Cancer Diagnosis using Retinex Enhancement Techniques

2017

IJRTER

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Abstract-In recent years there has been a dramatic increase in the prevalence of skin cancer worldwide. There has been a huge amount of research literature on skin cancer especially in the last 10-15 years. There is no doubt that this is in direct relation to the increases in prevalence of the disease on a worldwide basis. Australian and American research has led in the field and again it is probable that this has been due to the increase in rates of skin cancer in those countries. This paper provides a comprehensive review of the existing international literature in the area. The skin protects against heat, sunlight, injury, and infection. Skin also helps control body temperature and stores water and fat. Skin cancer is the most common type of cancer. It usually forms in skin that has been exposed to sunlight, but can occur anywhere on the body. Skin has several layers. Skin cancer begins in the epidermis, which is made up of squamous cells, basal cells, and melanocytes. There are several different types of skin cancer. Squamous cell and basal cell skin cancers are sometimes called melanoma skin cancers. Nonmelanoma skin cancer usually responds to treatment and rarely spreads to other parts of the body. Melanoma is more aggressive than most other types of skin cancer. If it isn't diagnosed early, it is likely to invade nearby tissues and spread to other parts of the body. The number of cases of melanoma is increasing each year. Keywords-: Skin cancer, diagnosed, self-examination, radiation, large proportion, Health System. I. INTRODUCTION Primary prevention and early detection continue to be of paramount importance in addressing the public health threat of skin cancer. The aim of this systematic review was to provide a comprehensive overview of the prevalence and correlates of skin cancer-related health behaviors in the general population. To achieve this aim, 91 studies published in international peer-reviewed journals over the past three decades were reviewed and synthesized. Reported estimates of sunscreen use varied considerably across studies, ranging from 7 to 90%. According to self-report, between 23 and 61% of individuals engage in skin self-examination at least once per year, and the documented prevalence of annual clinical skin examination ranges from 8 to 21%. Adherence to sun protection and screening recommendations is associated with a range of factors, including: female gender, sunsensitive phenotype and greater perceived risk of skin cancer, greater perceived benefits of sun protection or screening, and doctor recommendation for screening [1]. The literature suggests that a large proportion of the general population engage in suboptimal levels of sun protection, although there is substantial variability in findings. The strongest recommendation to emerge from this review is a call for the development and widespread use of standardized measurement scales in future research, in addition to more studies with a population-based, multivariate design. It is also recommended that specific targeted inter...

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“…Several neural networks, including S-Kohonen, backpropagation, and SVM, were compared for predicting the risk of relapse after surgery. The S-Kohonen neural network was found to be the most accurate [ 82 ]. Non-coding mi-RNA plays an important role in tumorigenesis and progression of cancer by interfering with various cell signaling pathways, including, WNT/beta-catenin, phosphoinositide-3-kinase (PI3 K)/protein kinase B (Akt), epidermal growth factor receptor (EGFR), NOTCH1, mechanistic target of rapamycin (mTOR), and TP53.…”

Section: Colonic Polyps and Colorectal Cancermentioning

confidence: 99%

Advancements in Oncology with Artificial Intelligence—A Review Article

Vobugari

Raja

Sethi

et al. 2022

Cancers

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Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.

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Cancer classification based on gene expression using neural networks

Cited by 27 publications

References 8 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

An Approach for Skin Cancer Diagnosis using Retinex Enhancement Techniques

Advancements in Oncology with Artificial Intelligence—A Review Article

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