ArticleDiagnostics (Basel, Switzerland)2023
Mathematical Modelling of Cervical Precancerous Lesion Grade Risk Scores: Linear Regression Analysis of Cellular Protein Biomarkers and Human Papillomavirus
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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
8 citing papers in PubMed, 10 citations in OpenAlex.
- Diagnostic utility and tissue localization of HPV16 and HPV18 E7 immunohistochemistry in cervical lesion progression: a comparative study with PCR.Frontiers in oncology · 2026Article
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- Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach.Journal of inflammation research · 2025Article
- A Machine Learning Model Integrating Preoperative Blood-Based Indices for Early and Noninvasive Detection of Endometrial Cancer.Journal of inflammation research · 2025Article
- A Prospective Study on the Progression, Recurrence, and Regression of Cervical Lesions: Assessing Various Screening Approaches.Journal of clinical medicine · 2024Article
- Utility of four machine learning approaches for identifying ulcerative colitis and Crohn's disease.Heliyon · 2024Article
- Stochastic dynamics of human papillomavirus delineates cervical cancer progression.Journal of mathematical biology · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
The current practice of determining histologic grade with a single molecular biomarker can facilitate differential diagnosis but cannot predict the risk of lesion progression. Cancer is caused by complex mechanisms, and no single biomarker can both make accurate diagnoses and predict progression risk. Modelling using multiple biomarkers can be used to derive scores for risk prediction. Mathematical models (MMs) may be capable of making predictions from biomarker data. Therefore, this study aimed to develop MM-based scores for predicting the risk of precancerous cervical lesion progression and identifying precancerous lesions in patients in northern Thailand by evaluating the expression of multiple biomarkers. The MMs (Models 1-5) were developed in the test sample set based on patient age range (five categories) and biomarker levels (cortactin, p16
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Registered trials
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