Human papillomavirus infection, cervical cancer and the less explored role of trace elements

Sravani, Anne Boyina; Ghate, Vivek; Lewis, Shaila

doi:10.1007/s12011-022-03226-2

Cited by 26 publications

(12 citation statements)

References 237 publications

(210 reference statements)

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“…Effective prevention strategies for cervical cancer include the use of screening tests for primary and secondary prevention, human papillomavirus (HPV) vaccination, and early detection and treatment of cervical intraepithelial neoplasia. [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. The gold standard for the diagnosis of cervical cancer is still colposcopy-guided biopsy, which is then staged on the basis of the clinical examination and the outcome of imaging procedures [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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“…Recently, AI has been increasingly utilised to identify a wide range of illnesses [29], [30], [31], [32]. The use of AI in cervical cancer screening has improved diagnostic accuracy and helped alleviate the problem of an insufficient workforce [33], [34]. Most AI-supported pathology technologies are in development or observational studies.…”

Section: Literature Surveymentioning

confidence: 99%

“…Current stateof-the-art approaches that can illuminate the black box have been discussed in depth elsewhere. Intriguingly, the authors of that survey [33] come to the conclusion that there is no agreement on the precise definition of the term explainable since it has various criteria in different situations and for different stakeholders. However, when AI is integrated into the clinical workflow of pathology diagnostics, the degree to which emphasis should be placed on precise understanding of the mechanics of the technology as opposed to the functioning of the system as a whole in the context in which it is used remains open to debate.…”

Section: Future Workmentioning

confidence: 99%

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

Iqbal,

Qureshi,

Chishti

2024

Preprint

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Cervical cancer is the fourth highest cause of death among women, but in its early stages, it shows no symptoms. A lack of effective early diagnosis is the fundamental cause of the disease's prevalence and poses the greatest obstacle to researchers. Pap smears, human papillomavirus tests, colposcopy, and biopsies are all invasive procedures that necessitate the assistance of medical professionals; they are time-consuming and do not identify cancer until a later stage. Thus, the absence of early symptoms and lack of invasive diagnostics, highlight the need for utilizing machine learning and deep learning approaches to revolutionize cervical cancer detection. To address this, we propose GCFS-CC(Genetic Algorithm based Context-aware Feature Selection for Cervical Cancer), which is a framework to detect cervical cancer from Pap smear images of cervical cells and classify these using a deep learning method. In our work, we use a genetic algorithm which is a bio-inspired algorithm, to select the best features from the acquired images. For feature selection, we incorporate the context-aware entropy gain method. We evaluated our model on one data set which combined a total of 917 normal and cancerous images, divided into seven classes. Using the selected features we build a deep learning model to validate our proposed framework. The results showed that after feature selection, the performance metrics(accuracy, precision, and recall), all improved. These results pave the way for GCFS-CC to be a valuable tool for early cervical cancer detection. This, however, comes with certain challenges, availability of high-quality images and proper parameter tuning for the genetic algorithm required for feature selection. To successfully implement machine learning-based cervical cancer detection into clinical practice, it will be necessary to address these challenges. Furthermore, any ethical and privacy concerns associated with the use of machine learning algorithms in healthcare settings must be addressed.

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“…Protein p53 inilah yang bertanggung jawab atas proliferasi sel pada kanker serviks [34]. Interaksi yang paling stabil dilihat dari konformasi dengan energi paling rendah [39].…”

Section: Preparasi Proteinunclassified

Potensi Senyawa Hidrazon berbasis Turunan 2-Tiohidantoin dan Hidrazida sebagai Inhibitor Protein E6 pada Kanker Serviks secara In Silico

Alam

Abidiy

Gunawan

et al. 2022

Akta Kimia Indonesia

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Every year, the prevalence of cervical cancer in the world is increasing. Cervical cancer can occur as a result of Human Papillomavirus (HPV) infection. Currently, E6 oncoprotein inhibition targeting tends to be chosen because it is safer and better as a therapeutic agent of cervical cancer. In addition, E6 is also a crucial virus that induces cervical cancer through the inactivation of the p53 protein so that effective materials are needed to inhibit the performance of E6 oncogenes. Hydrazone is a Schiff base compound that has an azomethine group (-NHN=CR-). It is this azomethine group that plays a role in various bioactivity, including antivirals and anticancer. In this study, hydrazone was derived from the derivatives of 2-thihidantoin and hydrazide. The compound 2-thihidantoin belongs to the heterocyclic group that plays an important role in drug chemistry such as anticonvulsant, antibacterial, antidiabetic, antiviral and anticancer. Thus, this compound can be an alternative agent for cervical cancer treatment. Before the synthesis of such materials, it is necessary to carry out bioactivity analysis using computing. The study of the in-silico method includes the creation of structures using Avogadro, optimization of molecular geometry using Gaussian 16, as well as the results of ligand-protein interactions (RMSD, KI, Binding Affinity, ADME, Binding Pocket, and Drugability score) using AutoDock 4.2, PyMol, LigPlot, SwissADME, PLIP Tools, and Protein Plus. The results showed that hydrazone compounds derived from 2-thiohidantoin and hydrazide derivatives have the potential to inhibit the E6 protein and can be used as oral drugs..

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Human papillomavirus infection, cervical cancer and the less explored role of trace elements

Cited by 26 publications

References 237 publications

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

Potensi Senyawa Hidrazon berbasis Turunan 2-Tiohidantoin dan Hidrazida sebagai Inhibitor Protein E6 pada Kanker Serviks secara In Silico

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