ArticleAmerican journal of cancer research2024
Developing a comprehensive molecular subgrouping model for cervical cancer using machine learning.
Article in American journal of cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Recent advances in applications of machine learning in cervical cancer research: a focus on prediction models.Obstetrics & gynecology science · 2025Article
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6 authors.
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Abstract
This study developed a molecular classification model for cervical cancer using machine learning, integrating prognosis related biomarkers with clinical features. Analyzing 281 specimens, 27 biomarkers were identified, associated with recurrence and treatment response. The model identified four molecular subgroups: group 1 (OALO) with Overexpression of ATP5H and LOw risk; group 2 (LASIM) with low expression of ATP5H and SCP, indicating InterMediate risk; group 3 (LASNIM) characterized by Low expression of ATP5H, SCP, and NANOG, also at InterMediate risk; and group 4 (LASONH), with Low expression of ATP5H, and SCP, Over expression of NANOG, indicating High risk, and potentially aggressive disease. This classification correlated with clinical outcomes such as tumor stage, lymph node metastasis, and response to treatment, demonstrating that combining molecular and clinical factors could significantly enhance the prediction of recurrence and aid in personalized treatment strategies for cervical cancer.
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