Selina MY Chiu

Artificial intelligence in women’s cancers: innovation and challenges in clinical translation

CT and MRI in Advanced Ovarian Cancer: Advances in Imaging Techniques

Ovarian cancer (OC) remains one of the leading causes of gynecologic cancer-related mortality, with most patients presenting with disseminated disease, particularly within the peritoneal cavity. Standard treatment includes cytoreductive surgery, platinum-based chemotherapy, and targeted maintenance approaches depending on the patient's and tumor's genetic profile. Despite treatment advancements, approximately 25% of high-grade serous OC cases relapse within a year despite optimal primary treatment with complete tumor clearance at cytoreduction. Advances in contrast-enhanced CT (CE-CT) and MRI have revolutionized the evaluation and treatment planning of advanced OC. CT remains the gold standard for staging and assessing tumor extent, effectively identifying peritoneal, lymphatic, and distant metastases. However, it is less effective in detecting small-volume peritoneal dissemination. MRI, with superior soft-tissue contrast, complements CT by providing a detailed assessment of peritoneal disease, characterizing sonographically indeterminate adnexal masses. Diffusion-weighted imaging and gadolinium-enhanced MRI have improved the diagnostic sensitivity for peritoneal disease but are unable to predict treatment response, recurrence risk, and prognosis. Radiomics, which extracts quantitative tumor features from imaging data, holds promise for personalizing treatment and identifying patients at risk for early recurrence despite optimal therapy. The integration of CT, MRI, and radiomics could enhance surgical planning and improve long-term survival outcomes in patients with advanced OC.

Ovarian Cancer Screening: Recommendations and Future Prospects

Abstract Ovarian cancer remains a significant cause of mortality among women, largely due to challenges in early detection. Current screening strategies, including transvaginal ultrasound and CA125 testing, have limited sensitivity and specificity, particularly in asymptomatic women or those with early-stage disease. The European Society of Gynaecological Oncology, the European Society for Medical Oncology, the European Society of Pathology, and other health organizations currently do not recommend routine population-based screening for ovarian cancer due to the high rates of false-positives and the absence of a reliable early detection method. This review examines existing ovarian cancer screening guidelines and explores recent advances in diagnostic technologies including radiomics, artificial intelligence, point-of-care testing, and novel detection methods. Emerging technologies show promise with respect to improving ovarian cancer detection by enhancing sensitivity and specificity compared to traditional methods. Artificial intelligence and radiomics have potential for revolutionizing ovarian cancer screening by identifying subtle diagnostic patterns, while liquid biopsy-based approaches and cell-free DNA profiling enable tumor-specific biomarker detection. Minimally invasive methods, such as intrauterine lavage and salivary diagnostics, provide avenues for population-wide applicability. However, large-scale validation is required to establish these techniques as effective and reliable screening options.

Advancing personalised care in ovarian cancer using CT and MRI radiomics

Radiomics, utilising quantitative feature extraction from CT and MR imaging, offers significant potential in advancing the diagnosis and management of ovarian cancer. Through the analysis of high-dimensional imaging data, radiomics models may capture subtle phenotypic variations in tumour heterogeneity, texture, and shape that extend beyond the capabilities of traditional imaging interpretation. CT-based radiomics excels in evaluating the prognostic significance of peritoneal disease dissemination and treatment response, while MRI-based models provide enhanced soft tissue characterisation, particularly in assessing tumour microstructure, vascularity, and cellularity. Studies demonstrate that these models can improve diagnostic accuracy, predict therapeutic outcomes and assist in risk stratification. However, standardisation of imaging acquisition protocols, feature extraction techniques and validation across diverse patient cohorts remains a challenge for the incorporation of radiomics into routine clinical practice. Evidence strongly supports the incorporation of radiomic features with molecular, genomic and clinical data in developing high-performance integrated radiomics models, which can facilitate precision oncology in ovarian cancer.