Cervical Cancer Diagnosis: Insights into Biochemical Biomarkers and Imaging Techniques

Tavakoli, Fatemeh; Khatami, Sara Sadat; Momeni, Fatemeh; Azadbakht, Javid; Ghasemi, Faezeh

doi:10.2174/1386207323666200901101955

Cited by 7 publications

(5 citation statements)

References 182 publications

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“…The differences in systemic metabolic response profiles between the tumor and non-tumor groups were mainly in 12 metabolites such as formic acid, β-glucose, glycine, carnitine and very low density lipoprotein, which can be used as potential biomarkers to identify cervical cancer [ 68 ].…”

Section: Application Of Metabolomics In the Diagnosis And Treatment O...mentioning

confidence: 99%

Research progress of metabolomics in cervical cancer

Jia,

Zou,

Zou

2023

Eur J Med Res

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Introduction Cervical cancer threatens women's health seriously. In recent years, the incidence of cervical cancer is on the rise, and the age of onset tends to be younger. Prevention, early diagnosis and specific treatment have become the main means to change the prognosis of cervical cancer patients. Metabolomics research can directly reflect the changes of biochemical processes and microenvironment in the body, which can provide a comprehensive understanding of the changes of metabolites in the process of disease occurrence and development, and provide new ways for the prevention and diagnosis of diseases. Objectives The aim of this study is to review the metabolic changes in cervical cancer and the application of metabolomics in the diagnosis and treatment. Methods PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2022. Results With the emergence of metabolomics, metabolic regulation and cancer research are further becoming a focus of attention. By directly reflecting the changes in the microenvironment of the body, metabolomics research can provide a comprehensive understanding of the patterns of metabolites in the occurrence and development of diseases, thus providing new ideas for disease prevention and diagnosis. Conclusion With the continuous, in-depth research on metabolomics research technology, it will bring more benefits in the screening, diagnosis and treatment of cervical cancer with its advantages of holistic and dynamic nature.

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Section: Application Of Metabolomics In the Diagnosis And Treatment O...mentioning

confidence: 99%

Research progress of metabolomics in cervical cancer

Jia,

Zou,

Zou

2023

Eur J Med Res

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“…As HPV type 16 and HPV type 18 have a more aggressive behavior, and are related with more risk for progression to invasive cancer [9], it´s possible that HPV type determination on histologic lesions of HSIL may be a useful tool for risk stratification and treatment optimization. For clinical practice, there is the need to develop easy and cheaper techniques for HPV genotyping of histologic lesions of HSIL.…”

Section: Introductionmentioning

confidence: 99%

High Risk HPV Detection Using Anyplex™ Detection Kit in Paraffin-Embedded Tissue from Cervical Lesions

Almeida,

Caeiro,

Costa

et al. 2024

Preprint

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Background: Human papillomavirus (HPV) is diagnosed in most cervical cancers and is the fourth cause of cancer worldwide. Currently, fourteen HPV types are considered carcinogenic (HPV 16/18/31/33/35/39/45/51/52/56/58/59/66/68) with different oncogenicity. Cervical lesions might be due to HR (high risk)-HPV for what is important to identify new methods to genotype cervical lesions. The main goal was to identify, for the first time, HPV-HR in cervical lesions using Anyplex™ Detection kit in paraffin-embedded tissue. (2) Methods: It was performed a retrospective study in 45 women from Academic Hospital of Cova da Beira (CHUCB). DNA extraction was performed and through Anyplex™ II HPV-HR Detection kit HPV-HR were genotyped. (3) Results: HPV-HR were identified in 38 women; HPV-16 (55,26%), HPV-18/39/56/58/59 (5,26%), HPV-31 (21,05%), HPV-35 (7,89%), HPV-51/6 (2,63%) and HPV-52 (10,53%). (4) Conclusions: Our results reflect that Anyplex™ II HPV-HR Detection kit, designed for HR-HPV genotyping of cervical cancer screening, might be properly used to HR-HPV genotyping in histology. The proposed methodology allows an easy and cheaper technique that should be used as a future cervical risk stratification. It is a strong tool to be implemented in clinical practice in order to detect HR-HPV detection in cervical lesions, contributing to a more accurate diagnosis.

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“…The revision of the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging system acknowledges the central role of MRI in assessing the local extent of CC [4,5]. With the advantages of tumorto-normal tissue contrast, diffusion-weighted imaging (DWI) has been increasingly used to characterize CC [6]. The reduced field-of-view (rFOV) DWI, an MRI technique that enables minimizing the off-resonance induced artifacts by reducing the FOV in the phaseencoding direction, has been reported to considerably improve the conspicuity of CC [7,8].…”

Section: Introductionmentioning

confidence: 99%

Reduced field-of-view DWI‑derived clinical–radiomics model for the prediction of stage in cervical cancer

et al. 2023

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Background Pretreatment prediction of stage in patients with cervical cancer (CC) is vital for tailoring treatment strategy. This study aimed to explore the feasibility of a model combining reduced field-of-view (rFOV) diffusion-weighted imaging (DWI)-derived radiomics with clinical features in staging CC. Methods Patients with pathologically proven CC were enrolled in this retrospective study. The rFOV DWI with b values of 0 and 800 s/mm2 was acquired and the clinical characteristics of each patient were collected. Radiomics features were extracted from the apparent diffusion coefficient maps and key features were selected subsequently. A clinical–radiomics model combining radiomics with clinical features was constructed. The receiver operating characteristic curve was introduced to evaluate the predictive efficacy of the model, followed by comparisons with the MR-based subjective stage assessment (radiological model). Results Ninety-four patients were analyzed and divided into training (n = 61) and testing (n = 33) cohorts. In the training cohort, the area under the curve (AUC) of clinical–radiomics model (AUC = 0.877) for staging CC was similar to that of radiomics model (AUC = 0.867), but significantly higher than that of clinical model (AUC = 0.673). In the testing cohort, the clinical–radiomics model yielded the highest predictive performance (AUC = 0.887) of staging CC, even without a statistically significant difference when compared with the clinical model (AUC = 0.793), radiomics model (AUC = 0.846), or radiological model (AUC = 0.823). Conclusions The rFOV DWI-derived clinical–radiomics model has the potential for staging CC, thereby facilitating clinical decision-making.

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Cervical Cancer Diagnosis: Insights into Biochemical Biomarkers and Imaging Techniques

Cited by 7 publications

References 182 publications

Research progress of metabolomics in cervical cancer

Research progress of metabolomics in cervical cancer

High Risk HPV Detection Using Anyplex™ Detection Kit in Paraffin-Embedded Tissue from Cervical Lesions

Reduced field-of-view DWI‑derived clinical–radiomics model for the prediction of stage in cervical cancer

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