مقالات پذیرفته شده در نهمین کنگره بین المللی زیست پزشکی
Radiogenomics Biomarkers in Cervical Cancer: A Comprehensive Review of Prognostic Applications
Radiogenomics Biomarkers in Cervical Cancer: A Comprehensive Review of Prognostic Applications
Hossein Izi,1Iraj Abedi,2,*
1. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran 2. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Introduction: Cervical cancer remains a major global health challenge, particularly in low- and middle-income countries where access to preventive measures like vaccination and early diagnosis is often insufficient. Despite improvements in treatment approaches like concurrent chemotherapy leading to better clinical outcomes, there is still a critical unmet need for personalized prognostic evaluation. Existing prognostic models predominantly depend upon anatomical staging and histopathological criteria, which inadequately reflect the biological behavior and heterogeneity of the tumor. Radiogenomics, a translational discipline integrating advanced imaging features (radiomics) with molecular and genomic information, has emerged as a promising tool in precision oncology. In cervical cancer, radiogenomics biomarkers show potential for noninvasively predicting treatment response, risk of recurrence, and overall patient survival. This comprehensive review critically assesses the existing evidence surrounding radiogenomics biomarkers that possess prognostic significance in cervical cancer, emphasizing emerging trends, clinical ramifications, and prospective avenues for research.
Methods: This review employs a comprehensive and narrative methodology, utilizing peer-reviewed literature, expert consensus documents, and seminal studies published from January 2013 to March 2025. Relevant sources were identified through targeted inquiries into biomedical databases including PubMed, Embase, Scopus, and Web of Science, employing a combination of search terms such as “radiogenomics,” “cervical cancer,” “radiomics,” “biomarkers,” “prognosis,” and “genomic profiling.” Studies were selected based on their pertinence to prognostic biomarker identification, imaging-genomic correlations, and translational implications in cervical cancer. This study primarily examined imaging techniques such as MRI, diffusion-weighted imaging (DWI), and PET/CT scanning. Genomic characteristics were systematically classified by type (mutational, epigenetic, transcriptomic) and clinical relevance. Radiomics feature categories (shape, texture, intensity, heterogeneity) were evaluated concerning their associations with molecular markers and prognostic outcomes.
Results: Radiogenomics biomarkers have increasingly exhibited prognostic significance in cervical cancer, particularly concerning tumor aggressiveness, therapeutic responses, and survival predictions. Features derived from MRI, such as ADC entropy, irregularity in tumor shape, and intra-tumoral heterogeneity, have been correlated with unfavorable prognostic indicators, including hypoxia, lymphovascular invasion, and immune evasion. Radiomics derived from PET/CT imaging, specifically metrics such as SUVmax and metabolic heterogeneity, have demonstrated predictive capacity regarding recurrence and the risk of distant metastases. Gnomically, frequent alterations including PIK3CA and TP53 mutations, high-risk HPV integration patterns, and expression of immune-related genes (e.g., PD-L1, IFNG, CD8A) have been associated with distinct imaging phenotypes. Radiogenomics signatures that integrate these genomic markers with imaging-derived features have shown enhanced prognostic performance relative to either modality in isolation. Notably, models integrating machine learning algorithms—such as random forests and support vector machines—achieved area under the curve (AUC) values of more than 0.85 in predicting progression-free and overall survival in the validation cohorts. Despite these advances, clinical translation of radiogenomics biomarkers has been limited by challenges surrounding standardization, reproducibility, and external validation. Variability in the extraction of radiomics features, segmentation methodologies, and genomic assay platforms continues to impede comparability across studies. A limited number of investigations have assessed these biomarkers prospectively or across diverse ethnic and geographic populations. Additionally, the absence of harmonized data-sharing frameworks curtails the development of robust, generalizable models.
Conclusion: Radiogenomics biomarkers present a novel and potent framework for enhancing prognostic stratification in cervical cancer. By correlating radiological phenotypes with molecular genotypes, these tools facilitate a non-invasive, scalable, and biologically informed evaluation of tumor behavior. As artificial intelligence and multi-omics integration advance, radiogenomics models are poised to enhance precision in treatment selection, follow-up strategies, and adaptive therapy planning. Effective implementation of these biomarkers in routine clinical practice it is crucial to develop standardized protocols, conduct extensive validation studies, and ensure equitable access to radiogenomics technologies. This review underscores the pivotal role of radiogenomics in shaping the future of personalized cervical cancer care.