Cervical cancer situation in Malaysia: A systematic literature review

Mustafa, Wan Azani; Halim, Afiqah; Nasrudin, Mohd Wafi; Rahman, Khairul Shakir Ab

doi:10.32604/biocell.2022.016814

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…Cervical cancer (CC) is the most common gynecological malignancy in women and the leading cause of cancer death in some developing countries [ 1 ]. Despite improvements in screening and prevention, CC remains a significant cause of morbidity and mortality [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Intraoperative frozen section pathology of vaginal margin in radical hysterectomy on the prognosis and quality of life for patients with IB2–IIA2 cervical cancer: study protocol for a multicenter randomized controlled trial

Liu,

Xin,

Wang

et al. 2024

J Gynecol Oncol

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Background Several risk factors have been identified that compromise the treatment outcome in patients with early-to-mid-stage cervical cancer (CC) who are primarily treated with radical surgery. However, there is no report on the impact of intraoperative frozen pathology examination of vaginal margins on the prognosis of patients with CC. This study aimed to conduct a randomized controlled trial (RCT) to determine whether selective vaginal resection can reduce the incidence of operative complications and the risk of postoperative radiotherapy. The impact of the length of the vagina removed in radical hysterectomy (RH) on prognosis and quality of life (QoL) for IB2–IIA2 CC patients will be investigated. Methods A multicenter, non-inferiority, RCT at 7 institutions in China is designed to investigate the effect of intraoperative frozen pathology exam of vaginal margin in RH on the survival outcomes for patients with IB2–IIA2 CC. Eligible patients aged 18–70 years will be randomly assigned online by one-to-one random allocation to receive intraoperative frozen pathology exam of vaginal margin or not. If frozen pathology indicates positive margin, continue resection of 1 centimeter of vaginal tissue until negative margin is achieved. The primary end point is 2-year disease-free survival (DFS). Adverse events (AEs) caused by further vagina resection, 5-year DFS, 2-year overall survival (OS), 5-year OS and AEs caused by radiotherapy and QoL are secondary end points. A total of 310 patients will be enrolled from 7 tertiary hospitals in China within 3-year period and followed up for 5 years. Trial Registration Chinese Clinical Trial Registry Identifier: ChiCTR2000035668

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Section: Introductionmentioning

confidence: 99%

Intraoperative frozen section pathology of vaginal margin in radical hysterectomy on the prognosis and quality of life for patients with IB2–IIA2 cervical cancer: study protocol for a multicenter randomized controlled trial

Liu,

Xin,

Wang

et al. 2024

J Gynecol Oncol

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“…Early detection of cervical cancer is associated with more favorable outcomes, with treatments such as surgery or radiotherapy proving to be effective. Unfortunately, it is estimated that the mortality rate of women aged 30 to 60 due to cervical cancer has increased significantly [ 3 ]. Low- and middle-income countries account for the majority of newly diagnosed cases and fatalities.…”

Section: Introductionmentioning

confidence: 99%

Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer

Ramli,

Farizan,

Tamchek

et al. 2024

Cureus

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The diffusion-weighted imaging (DWI) technique is known for its capability to differentiate the diffusion of water molecules between cancerous and non-cancerous cervix tissues, which enhances the accuracy of detection. Despite the potential of DWI-MRI, its accuracy is limited by technical factors influencing in vivo data acquisition, thus impacting the quantification of radiomics features. This study aimed to measure the radiomics stability of manual and semi-automated segmentation on contrast limited adaptive histogram equalization (CLAHE)-enhanced DWI-MRI cervical images. Eighty diffusion-weighted MRI images were obtained from patients diagnosed with cervical cancer, and an active contour model was used to analyze the data. Radiomics analysis was conducted to extract the first statistical order, shape, and textural features with intraclass correlation coefficient (ICC) measurement. The results of the CLAHE segmentation approach showed a marked improvement when compared to the manual and semi-automated segmentation methods, with an ICC value of 0.990 ± 0.005 (p<0.05), compared to 0.864 ± 0.033 (p<0.05) and 0.554 ± 0.185 (p>0.05), respectively. The CLAHE segmentation displayed a higher level of robustness than the manual groups in terms of the features present in both categories. Thus, CLAHE segmentation is owing to its potential to generate radiomics features that are more durable and consistent.

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“…Cancer is a disease in which the body’s cells grow rapidly, generally termed after the part where it originates, even if it spreads to other parts of the body [ 3 , 4 , 5 ]. Cervical cancer denotes cancer that begins in the cervix [ 6 , 7 ]. In the year 2018, an estimation of more than 500,000 women worldwide were diagnosed with cervical cancer, resulting in approximately more than 300,000 women dying due to cancer.…”

Section: Introductionmentioning

confidence: 99%

Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

et al. 2022

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Cervical cancer, a common chronic disease, is one of the most prevalent and curable cancers among women. Pap smear images are a popular technique for screening cervical cancer. This study proposes a computer-aided diagnosis for cervical cancer utilising the novel Cervical Net deep learning (DL) structures and feature fusion with Shuffle Net structural features. Image acquisition and enhancement, feature extraction and selection, as well as classification are the main steps in our cervical cancer screening system. Automated features are extracted using pre-trained convolutional neural networks (CNN) fused with a novel Cervical Net structure in which 544 resultant features are obtained. To minimise dimensionality and select the most important features, principal component analysis (PCA) is used as well as canonical correlation analysis (CCA) to obtain the best discriminant features for five classes of Pap smear images. Here, five different machine learning (ML) algorithms are fed into these features. The proposed strategy achieved the best accuracy ever obtained using a support vector machine (SVM), in which in which fused features between Cervical Net and Shuffle Net is 99.1% for all classes.

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Cervical cancer situation in Malaysia: A systematic literature review

Cited by 12 publications

References 26 publications

Intraoperative frozen section pathology of vaginal margin in radical hysterectomy on the prognosis and quality of life for patients with IB2–IIA2 cervical cancer: study protocol for a multicenter randomized controlled trial

Intraoperative frozen section pathology of vaginal margin in radical hysterectomy on the prognosis and quality of life for patients with IB2–IIA2 cervical cancer: study protocol for a multicenter randomized controlled trial

Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer

Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion

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