Cancer Genetics and Epigenetics, 2025, Vol.13, No.2, 50-61 http://medscipublisher.com/index.php/cge 57 of patients' conditions and may even increase the overall survival time of patients, the treatment cost is too high, imposing an economic burden on both the medical system and patients. Cost-benefit analysis shows that these treatment methods have the best effect in the patient population screened by biomarkers, which also explains the importance of precisely selecting patients to maximize the therapeutic value (Cortez et al., 2017). Under different regions and insurance policies, the medical insurance reimbursement for individualized treatment varies greatly. Generally, it depends on whether the clinical evidence is sufficient and whether the relevant biomarker tests or treatment indications have been approved. With an increasing amount of data on the long-term efficacy and safety of individualized treatment, medical insurance payers and policymakers pay more attention to the actual treatment effect and the results of health economics analysis to formulate reimbursement policies to ensure that patients can receive treatment fairly and conveniently (Cortez et al., 2017). 7.3 Quality of life (QoL) and health economics research Quality of life (QoL) assessment is the core consideration of individualized treatment, as its goal is not only to prolong survival, but also to maintain or improve the well-being of patients (Cortez et al., 2017). Studies suggest that compared with traditional chemotherapy, targeted therapy and immunotherapy may bring about a better quality of life, especially when adverse reactions are effectively managed and the treatment meets individual needs (Guan and Lu, 2018). Patient-reported outcomes are increasingly being incorporated into clinical trials to comprehensively assess the impact of treatment on daily functions and symptom burden (Guy et al., 2022). Research in the field of ovarian cancer health economics is continuously advancing, and there is an increasing emphasis on integrating the data of patients' quality of life and the real situation in actual treatment into the cost-benefit analysis model. These research and analysis results can help rationally allocate medical resources, provide guidance for clinical treatment, and also contribute to promoting personalized treatment plans that are both helpful for patients' treatment and have medical value (Cortez et al., 2017; Guy et al., 2022). 8 Difficulties and Future Directions 8.1 Standardization issues of sample acquisition and testing Obtaining high-quality tumor samples for molecular and biomarker testing remains a major challenge in individualized treatment of advanced ovarian cancer. The differences in tumors, the scarcity of available tissues, and the trauma of biopsies may all affect the conduct of comprehensive genetic and immune analyses, which are important for guiding targeted drugs and immunotherapy (Morand et al., 2021). In addition, the lack of a unified method for sample collection, processing and analysis may lead to unstable test results and complicate the interpretation of biomarker data and its clinical application (Li and Li, 2024; Tavares et al., 2024). It is crucial to unify the testing methods. Only in this way can the test results obtained from different laboratories and hospitals be stable and reliable. If there is no universally recognized biomarker detection standard, such as the detection standard for homologous recombination deficiency (HRD) score or PD-L1 expression level, it is easy to have inconsistent standards when selecting patients, affecting the therapeutic effect (Morand et al., 2021; Tavares et al., 2024). To solve these problems, it is necessary for all parties to cooperate and formulate unified operation norms and quality control methods for molecular detection of ovarian cancer. 8.2 The potential of multi-omics and AI-assisted decision-making Integrating multiple analytical techniques such as genomics, transcriptomics, proteomics and metabolomics is conducive to a deeper understanding of the tumor characteristics of ovarian cancer and exploring therapeutic breakthroughs (Tavares et al., 2024). By integrating molecular information at different levels, doctors can more accurately distinguish patient types, predict treatment responses, and identify new therapeutic targets, laying the foundation for the development of more precise and efficient individualized treatments (Morand et al., 2021). Currently, artificial intelligence (AI) and machine learning technologies are increasingly applied in analyzing complex multi-omics data to assist in clinical decision-making. These tools can identify patterns and predictors that are not easily detectable by conventional methods, thereby improving the accuracy of patient stratification
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