Advancements in Ovarian Cancer Subtype Classification and Outlier Detection: A Comprehensive Survey of Machine Learning Approaches and Clinical Applications

Ovarian cancer is a very heterogeneous disease and, therefore, it is difficult to diagnose, which makes it necessary to clearly categorize the subtype in order to achieve further progress in patient care. The existing classification systems do not effectively use large amounts of molecular and clinical data, uncovering the best performance in the detection of cancer subtypes and in the detection of outliers. The presented literature review presents more than 20 articles published between 2000 and 2024 to review the latest methods of machine learning in the process of classifying subtypes of ovarian cancer and detecting outliers. The major shortcoming of current models is that they do not effectively combine multi-omics data, including genomic, transcriptomic and proteomic profiles, which are essential in improving the accuracy of the classification. Also, current methods of outlier detection often fail to focus on the clinical importance of uncommon subtypes of ovarian cancer, which may be deprived of significant information about the disease. This review will systematize the recent studies in three key directions: (1) development of sophisticated classification algorithms based on extensive molecular and clinical data, (2) development of new outlier detection algorithms to detect rare or unusual cases, and (3) the use and validation of machine learning models in clinical care. The two methodologies are considered based on the ability of the approach to generate clinically actionable information, and to improve individualized treatment plans specifically. Through the evaluation of these developments, this survey shows that the state-of-the-art machine learning solutions can help to fill existing gaps in the classification of subtypes of ovarian cancer and detecting outliers. It further pre-empts the work to come to incorporate computational models into practical clinical environments to eventual enhancements in the accuracy of diagnosis and improved patient outcomes by means of precision medicine.