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Updated: May 6, 2026

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Published on: November 2, 2014

Recent advances in the early detection of ovarian cancer.

Anahita Soleimani¹, Akbar Amirfiroozy², Mohammad M Pourseif³

¹Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran; Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran.

Clinica Chimica Acta; International Journal of Clinical Chemistry

|December 21, 2025

Summary

Early detection of ovarian cancer (OC) is crucial. Emerging liquid biopsy biomarkers and AI-driven models show promise for improving diagnostic accuracy, but require further validation for clinical use.

Keywords:

Artificial intelligence CA125 Circulating tumor DNA Early detection HE4 Liquid biopsy Multi-omics Ovarian Cancer

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Published on: March 3, 2023

Area of Science:

Gynecologic Oncology
Biomarker Discovery
Diagnostic Technologies

Background:

Ovarian cancer (OC) is a leading cause of cancer death, with most patients diagnosed at advanced stages due to non-specific symptoms.
Current screening methods like CA125 lack sufficient accuracy for early detection of OC.
Advanced stages often involve extensive peritoneal dissemination, complicating treatment and reducing survival rates.

Purpose of the Study:

To review emerging biomarkers and advanced diagnostic technologies for ovarian cancer detection.
To evaluate the potential of liquid biopsies and artificial intelligence (AI) in improving early diagnosis.
To highlight limitations and future directions for multimodal diagnostic strategies in OC.

Main Methods:

Comprehensive literature review of emerging biomarkers for ovarian cancer.
Evaluation of liquid biopsy analytes such as circulating tumor DNA and microRNAs.
Assessment of AI-driven models integrating multi-omic and radiomic data.

Main Results:

Liquid biopsies and multi-biomarker panels show potential for improved OC detection.
AI models integrating diverse data show promise but require further refinement.
Current diagnostic accuracy is stage-dependent, with limitations in early-stage detection.

Conclusions:

Liquid biopsies and AI-based approaches offer significant potential for earlier ovarian cancer detection.
Standardization of assays and large-scale prospective validation are critical for clinical implementation.
Multimodal diagnostic strategies are essential to reduce OC mortality through earlier diagnosis.