Authored by: Humza Rana
Innovative breakthroughs in biomarkers and algorithms are set to transform diagnosis and enhance patient results, profoundly altering the field of ovarian cancer treatment. Ovarian cancer (OC) stands as one of the most common malignancies and ranks as the fifth leading cause of cancer mortality among women globally. In 2020, around 314,000 new diagnoses were documented. Research indicates that OC was the fourth most widespread cancer among women in the UAE in 2019, with 100 reported instances. The UAE has transformed its healthcare system to manage OC effectively. Early detection plays a crucial role in enhancing patient outcomes and has the potential to save lives. More than 90% of people with stage I ovarian cancer can live longer than 5 years after diagnosis.
The existing situation
Women presenting with OC symptoms should receive a comprehensive pelvic examination, which should include a rectovaginal assessment. Nonetheless, the accuracy of physical examinations is somewhat limited. After the physical assessment, patients may need to undergo imaging studies, such as Transvaginal Ultrasound (TVUS), known for its reliability in identifying abnormalities in ovarian size and structure, but it is less dependable in distinguishing between benign and malignant tumors. To evaluate tumor spread, additional imaging techniques like chest and pelvic computed tomography (CT) scans, pelvic magnetic resonance imaging (MRI), and positron emission tomography (PET) scans may be pursued.
Ovarian cancer laboratory assessments
When ovarian cancer (OC) is suspected, a thorough blood count, blood chemistry analysis (which includes liver function tests and calcium levels), as well as serum cancer markers should be conducted.
- Cancer antigen (CA) 125 assessment: The levels of CA 125 in serum can be quantified using electrochemiluminescence immunoassay. Research indicates that most epithelial OC cases demonstrate elevated levels of CA 125. Although this assay is generally more reliable for postmenopausal women, it is not a definitive technique for detecting OC, given that CA 125 levels can be raised in various other physiological or non-cancerous conditions such as endometriosis, fibroids, and pelvic inflammatory disease. Consequently, researchers are investigating more effective screening methods for OC. Moreover, serum markers like human epididymis protein 4 are mainly employed to evaluate disease advancement and check for recurrences. While imaging studies and blood tests might imply the presence of OC in a woman, only surgery can provide a definitive diagnosis.
Surgical intervention
For numerous women, techniques such as minimally invasive laparoscopy and robotic surgery are employed to remove a tissue sample for cancer detection. A tissue biopsy remains the sole method that can conclusively verify an OC diagnosis.
Genetic analysis
Identifying a hereditary risk factor (BRCA mutation) for OC facilitates the development of tailored precision medicine for women diagnosed with cancer.
Innovative investigations in ovarian cancer
Worldwide, scientists are striving to establish precise and non-invasive techniques for OC detection. In pursuit of this goal, various serum tumor indicators, imaging methods, and risk assessment protocols have been emphasized for the prompt identification of OC. The emergence of AI technology has created a significant advancement in OC detection.
Clinical Relevance of Liquid Biopsies in Ovarian Cancer
With extensive research into liquid biopsies, a groundbreaking sampling method has emerged. This technique examines unique tumor elements released into the bloodstream, including circulating tumor DNA, circulating tumor cells, tumor-educated platelets, and exosomes. Mounting evidence suggests that liquid biopsy could enhance the clinical management of ovarian cancer by facilitating early detection. The primary benefits of liquid biopsy lie in its non-invasive nature and practicality, enabling repeated sampling and continuous observation of significant tumor changes over time.
A commercially available liquid biopsy test employs a cell-free DNA methylation technique, representing a promising new approach to detect early-stage high-grade serous ovarian carcinoma with 91% accuracy. This test identifies methylated DNA present in the blood at specific nucleic acid sites.
Malignancy Evaluation Through Gene Identification in Isolated Cells (MAGIC) Algorithm
A recent investigation has unveiled advanced technology capable of capturing stray ovarian cells via a simple blood test, successfully predicting cancer. The research identified nine gene transcripts and four biomarkers crucial for cancer detection. These findings were utilized to create an algorithm termed MAGIC. This algorithm achieved 95% sensitivity and 83% accuracy in identifying ovarian cancer.
Raman Detection Technique
This method is extensively utilized for identifying epithelial ovarian cysts (OCs) by measuring haptoglobin, a prognostic marker found in the fluid of ovarian cysts. This assay stands out as it triggers a biochemical reaction in the assay reagent in the presence of haptoglobin, leading to the formation of a product. The distinctive Raman signature produced lies within the wavenumber range of 1500–1700 cm⁻¹ and can be identified using the single peak Raman system, boasting a diagnostic performance of 100% sensitivity and 85% specificity.
Raman Detection Technique
This method is extensively utilized for identifying epithelial ovarian cysts (OCs) by measuring haptoglobin, a prognostic marker found in the fluid of ovarian cysts. This assay stands out as it triggers a biochemical reaction in the assay reagent in the presence of haptoglobin, leading to the formation of a product. The distinctive Raman signature produced lies within the wavenumber range of 1500–1700 cm⁻¹ and can be identified using the single peak Raman system, boasting a diagnostic performance of 100% sensitivity and 85% specificity.
Photoacoustic Imaging
Researchers have discovered a promising method to enhance diagnostic accuracy for potentially malignant ovarian lesions through photoacoustic imaging, a technique that integrates functional biomarkers. By using specific wavelengths of near-infrared light, the method distinguishes between oxygenated and deoxygenated hemoglobin in tissues. This approach has the potential to enable early and more accurate diagnosis of ovarian cancer, allowing some patients to avoid unnecessary surgeries. In turn, this could lower healthcare costs and reduce the risk of complications associated with surgical procedures.
Utilization of Artificial Intelligence
Artificial Intelligence (AI) holds considerable promise in improving the predictive assessment of ovarian cancer (OC) through pre-surgical evaluations. In one study, machine learning classifiers—such as Support Vector Machines, Random Forests, Naïve Bayes, Logistic Regression, and XGBoost—were employed, with XGBoost yielding the highest accuracy of 0.8. This research demonstrates the potential of AI to revolutionize OC prediction by enabling faster and more reliable assessments.
Challenges in Advancements for Ovarian Cancer Diagnosis
One of the primary challenges in medical research is the gap between laboratory breakthroughs and practical clinical applications. In ovarian cancer research, most studies on liquid biopsy techniques face limitations due to small sample sizes. Similarly, while Raman spectroscopy shows promise, it also requires validation using larger clinical groups for it to be deemed clinically effective.
Potential Avenues for Forthcoming Research in Ovarian Cancer Diagnostics
Future research is critical to establish and validate a commercially approved liquid biopsy system for high-grade serous ovarian carcinoma and further extend this technology for non-HGSOC epithelial ovarian cancer histotypes. Efforts should also focus on AI model validation to improve performance and ensure generalization across diverse patient cohorts. Moreover, increasing sample sizes through extensive cohort studies from various locations will further strengthen the diagnostic accuracy.
Challenges in Ovarian Cancer Diagnosis and Management
Ovarian cancer poses several challenges for both diagnosis and long-term patient management:
- Early Identification: Ovarian cancer often remains asymptomatic or displays vague signs until it reaches advanced stages. The lack of effective early detection methods remains a crucial obstacle, emphasizing the need for reliable screening techniques or blood assays.
- Genetic Intricacies: The genetic complexity of ovarian cancer complicates treatment strategies, highlighting the importance of precision medicine to target genetic variations more effectively. However, breakthroughs in genetic exploration are still needed.
- Treatment Resistance: Treatment resistance, particularly to chemotherapy, remains a common issue, making it a significant challenge to developing therapies that overcome resistance.
- Absence of Regular Screening Mechanism: Unlike breast cancer, ovarian cancer lacks a dependable regular screening process. Developing effective biomarkers for ovarian cancer screening is crucial to addressing this gap.
- Healthcare Access Inequities: Marginalized or rural women often experience delayed diagnoses and limited treatment options due to inequities in healthcare access.
- Post-Treatment Concerns: Many women experience lasting physical and emotional difficulties, including challenges related to fertility, and side effects from chemotherapy and surgery.
- Immunotherapy: While immunotherapy holds potential for ovarian cancer treatment, it remains in the early stages of development, and overcoming immune resistance remains a significant challenge.
- Prevention and Risk Factors: Understanding the correlation between genetic variations, reproductive history, and ovarian cancer risk is still under research.
Projections for Ovarian Cancer Cases by 2050
According to projections by Globcan in 2022, the number of women diagnosed with ovarian cancer is expected to rise by over 55% to 503,448 by 2050. The annual number of deaths from ovarian cancer is anticipated to increase to 350,956, nearly 70% more than in 2022.
Conclusion
Ovarian cancer remains the leading cause of mortality among women with gynecological cancers, with current diagnostic methods primarily relying on imaging studies, blood analyses, and biopsies. Promising techniques like liquid biopsy offer a potential alternative to traditional methods, enabling earlier detection and minimizing reliance on invasive tissue sampling. Artificial Intelligence also holds transformative potential for improving ovarian cancer diagnostics.
While ovarian cancer continues to pose a major health challenge, advancements in diagnostic tools and treatment options—along with increased awareness and early identification—are helping improve patient outcomes. Women, especially in the Arab world, should be proactive in seeking regular check-ups, participating in screening programs, and supporting regional awareness campaigns to lower the incidence of ovarian cancer.
Sources:
- Omnia Health Insights: Cutting-edge Technology in Ovarian Cancer Prediction
- WebMD: Advancements in Predicting Ovarian Cancer
- World Ovarian Cancer Coalition: Key Stats on Ovarian Cancer