AIMC Topic: Uterine Cervical Neoplasms

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Cervical cancer prediction using machine learning models based on routine blood analysis.

Scientific reports
Cervical cancer (CC) is the fourth most common cancer among women globally. The key to preventing and treating CC is early detection, diagnosis, and treatment. This study aimed to develop an interpretable model for predicting CC risk using routine bl...

Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data.

Scientific reports
Cervical cancer (CC) is a major cause of mortality in women, with stagnant survival rates, highlighting the need for improved prognostic models. This study aims to develop and compare machine learning models for predicting five-year cause-specific su...

A hybrid compound scaling hypergraph neural network for robust cervical cancer subtype classification using whole slide cytology images.

Scientific reports
Cervical cancer is a major cause of mortality among women, particularly in low-income countries with insufficient screening programs. Manual cytological examination is time-consuming, error-prone and subject to inter-observer variability. Automated a...

Patient acceptability of CITOBOT for cervical cancer screening: A mixed-method study.

PloS one
This study assessed the acceptability of CITOBOT, a device for early cervical cancer screening in a real-world pilot setting as part of a translational research project aimed at designing and clinically validating a portable, cost-effective device su...

MVT-Net: A novel cervical tumour segmentation using multi-view feature transfer learning.

PloS one
Cervical cancer is one of the most aggressive malignant tumours of the reproductive system, posing a significant global threat to women's health. Accurately segmenting cervical tumours in MR images remains a challenging task due to the complex charac...

Quantitative and automatic plan-of-the-day assessment to facilitate adaptive radiotherapy in cervical cancer.

Physics in medicine and biology
To facilitate implementation of plan-of-the-day (POTD) selection for treating locally advanced cervical cancer (LACC), we developed a POTD assessment tool for CBCT-guided radiotherapy (RT). A female pelvis segmentation model (U-Seg3) is combined with...

Split DNA Tetrahedron-Mediated Spatiotemporal-Hierarchy CRISPR Cascade Integrated with Au@Pt Nanolabels and Artificial Intelligence for a Cervical Cancer MicroRNA Bioassay.

ACS nano
The screening and monitoring of microRNAs as cancer molecular biomarkers is clinically significant, but traditional methods lack sufficient sensitivity, accuracy, and convenience. The CRISPR-colorimetric lateral flow assay (CLFA) integration offers a...

Challenges and Opportunities for Global Cervical Cancer Elimination: How Can We Build a Model for Other Cancers?

American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting
Cervical cancer remains a leading cause of cancer-related death among women globally, despite the availability of effective prevention through human papillomavirus (HPV) vaccination and HPV-based screening. This review explores the state-of-the-art t...

Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.

Physics in medicine and biology
High-dose-rate (HDR) brachytherapy is integral to the standard-of-care for locally advanced cervical cancer (LACC). Currently, selection of brachytherapy applicators relies on physician's clinical experience, which can lead to variability in treatmen...

Molecular detection of hrHPV-induced high-grade squamous intraepithelial lesions of the cervix through a targeted RNA next generation sequencing assay.

Molecular medicine (Cambridge, Mass.)
BACKGROUND: Cervical cancer screening programs are increasingly relying on sensitive molecular approaches as primary tests to detect high-risk human papillomaviruses (hrHPV), the causative agents of cervix cancer. Although hrHPV infection is a pre-re...