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Related Experiment Video

Updated: Apr 19, 2026

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia

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Published on: October 17, 2025

Dynamic Optimal Strategy for Monitoring Disease Recurrence.

Hong Li¹, Constantine Gatsonis²

¹Department of Preventive Medicine, Rush University Medical Center, Chicago, IL 60612, U.S.A.

Science China. Mathematics

|December 23, 2014

Summary

This summary is machine-generated.

This study introduces an optimal cancer recurrence surveillance strategy using patient-specific biomarker data. It dynamically adjusts monitoring intervals to improve early detection of disease recurrence in cancer survivors.

Keywords:

Cancer recurrence surveillance Keywords Biomarker trajectory Latent class model Optimal strategy Time-dependent hazard

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Area of Science:

Oncology
Biostatistics
Health Services Research

Background:

Cancer recurrence surveillance is crucial for survivors.
Current strategies may not fully leverage individual patient data.
Early detection of recurrence significantly impacts patient outcomes.

Purpose of the Study:

To design optimal cancer recurrence surveillance strategies.
To personalize monitoring based on individual biomarker trajectories and risk.
To improve early detection of disease recurrence.

Main Methods:

Latent class joint modeling of longitudinal biomarker and event processes.
Dynamic risk estimation and personalized monitoring interval adjustment.
Utility function optimization for biomarker assessment timing.

Main Results:

A novel joint model effectively captures patient heterogeneity and disease dynamics.
The proposed strategy dynamically modifies monitoring schedules based on updated risk.
Demonstrated application using simulated prostate cancer recurrence data.

Conclusions:

Personalized, dynamic surveillance strategies enhance early cancer recurrence detection.
The developed model and algorithm offer a framework for optimizing patient monitoring.
This approach holds promise for improving post-treatment care for cancer survivors.