Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Diagnostic Prediction Models for Depression in Patients With Breast Cancer: A Systematic Review.

Xiaosong Yu^1,2, Lin Zhang¹, Qianqian Wan^2,3

¹Department of Nursing, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.

Journal of Clinical Nursing

|May 20, 2026

Summary

Related Concept Videos

Cancer Survival Analysis

Cancer Survival Analysis

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...

You might also read

Related Articles

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

Sort by

Same author

Correction: AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.

Journal of medical Internet research·2026

Same author

AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.

Journal of medical Internet research·2026

Same author

Biomarkers for intensive care unit-acquired weakness: a systematic review for prediction, diagnosis and prognosis.

Annals of intensive care·2025

Same author

Development of a predictive model for the risk of microalbuminuria: comparison of 2 machine learning algorithms.

Journal of diabetes and metabolic disorders·2024

Same author

Effectiveness and Feasibility of Digital Pulmonary Rehabilitation in Patients Undergoing Lung Cancer Surgery: Systematic Review and Meta-Analysis.

Journal of medical Internet research·2024

Same author

Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study.

JMIR medical informatics·2022

Diagnostic prediction models for depression in breast cancer patients show promise but have methodological weaknesses. Current models are not robust enough for routine nursing practice due to quality concerns.

Area of Science:

Oncology
Psychiatry
Health Services Research

Background:

Depression is a significant concern for patients with breast cancer.
Accurate diagnostic prediction models are needed to identify at-risk individuals.

Purpose of the Study:

To systematically review existing evidence on diagnostic prediction models for depression in breast cancer patients.
To assess the quality and applicability of these models.

Main Methods:

Systematic review of studies developing or validating depression prediction models in breast cancer patients.
Searched ten databases up to December 2025.
Used CHARMS for data extraction and PROBAST for risk of bias and applicability assessment.

Main Results:

Keywords:

breast cancer depression diagnostic prediction model systematic review

Related Experiment Videos

Eleven studies were included, with AUC values ranging from 0.784 to 0.890.
All studies had a high risk of bias, and seven had high concerns regarding applicability.
Common predictors included age, income, and family support, with substantial heterogeneity.

Conclusions:

Current diagnostic prediction models for depression in breast cancer patients are methodologically weak.
Limited external validation and calibration reporting hinder routine use in nursing.
Future research needs to standardize development, validation, and performance evaluation.