Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

458
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
458

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Factors affecting the gut microbiota of rural residents under different physical activity levels: a cross-sectional study.

Frontiers in microbiology·2026
Same author

GPR161 contributes to macrophage glycolytic reprogramming via targeting C5aR1 in acute lung injury.

Cellular & molecular biology letters·2026
Same author

Dual-tuning of morphology and coordination in single-atom catalysts via organic linker engineering for singlet oxygen-dominated Fenton-like reactions.

Journal of hazardous materials·2026
Same author

Mechanistic insights into Cd resilience enhancement by molybdenum trioxide nanoparticles in Solanum nigrum L.: Distinct molecular regulation from Mo<sup>6+</sup> through multi-omics perspective.

Journal of environmental sciences (China)·2026
Same author

A telomere-to-telomere gap-free genome of the new cultivar 'Zhongtian No. 5', combined with pan-genome analysis, aids in exploration and genetic enhancement of red clover (<i>Trifolium pratense</i> L.).

Horticulture research·2026
Same author

Global, regional, and national burdens of contact dermatitis: A longitudinal analysis from the Global Burden of Disease Study, 1990∼2021.

Journal of the American Academy of Dermatology·2026

Related Experiment Video

Updated: Sep 17, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

201

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

Jie Su1, Hui Lu2, Ruihuan Zhang3,4

  • 1Medical neurobiology laboratory, Inner Mongolia Medical University, Huhhot, 010030, China.

Scientific Reports
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an interpretable model to predict cervical cancer (CC) risk using routine blood tests. Average platelet distribution width (PDW) emerged as the most significant predictor, enabling earlier detection and intervention for this common cancer.

Keywords:
Blood routineCervical cancerMachine learningShapley additive interpretation

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Related Experiment Videos

Last Updated: Sep 17, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

201
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Area of Science:

  • Oncology
  • Biomedical Data Science
  • Medical Diagnostics

Background:

  • Cervical cancer (CC) is a significant global health concern for women.
  • Early detection, diagnosis, and treatment are crucial for managing CC.
  • Routine blood tests offer a potential avenue for non-invasive CC risk assessment.

Purpose of the Study:

  • To develop an interpretable machine learning model for predicting CC risk.
  • To identify key routine blood parameters associated with CC occurrence.
  • To leverage explainability methods for understanding model predictions.

Main Methods:

  • Retrospective analysis of medical records from 2013-2023.
  • Inclusion of 2,503 CC patients and 3,794 controls.
  • Application of machine learning algorithms (LASSO, RF, XGBoost) with 15 selected blood features.
  • Utilized Shapley Additive Explanation (SHAP) for model interpretability.

Main Results:

  • The Extreme Gradient Boosting (XGBoost) model demonstrated superior predictive performance (AUC=0.964).
  • Key predictors identified include age, various blood cell counts (RBC, WBC, LYMPH%, BASO%, NEUT%), hemoglobin, and platelet indices (PDW, MPV, PCT).
  • Average platelet distribution width (PDW) was identified as the most influential predictor of CC risk.

Conclusions:

  • Interpretable models using routine blood data can effectively predict CC risk.
  • Platelet distribution width (PDW) is a critical biomarker for CC risk assessment.
  • This approach facilitates early CC detection and personalized risk stratification.