Evaluation of drug options for epithelial ovarian cancer (EOC) treatment using the analytical hierarchy process improves decision-making transparency.

Building upon the AHP framework established in this study for evaluating drug-treatment options for EOC, future research should explore advanced integrations to enhance its adaptability and precision. One promising direction involves incorporating artificial intelligence and machine learning (ML) to develop a dynamic AHP model, where criterion weights are not fixed but updated in real-time by ML algorithms analyzing real-world patient data, such as electronic health records and treatment outcomes [ 68 , 69 ]. This approach would create an evolving decision-support system that adapts to emerging evidence, improving long-term applicability in clinical settings. To streamline the AHP process, natural language processing (NLP) could be employed to improve data mining of the vast clinical literature available. Appropriately configured, NLP could potentially automatically generate or suggest initial pairwise comparisons for expert validation, thereby increasing efficiency and comprehensiveness. A key extension lies in personalization through precision medicine, shifting from general drug rankings to patient-specific AHP models. This could integrate genetic biomarkers (e.g., BRCA mutation status, homologous recombination deficiency [HRD] status), proteomic profiles, and clinical factors (e.g., age, comorbidities, disease stage) as sub-criteria or modifiers within the hierarchy, enabling more tailored treatment recommendations. Embedding the evaluated AHP framework into clinical decision support systems (CDSS) used in oncology clinics would allow seamless integration of patient-specific data. Such information would facilitate ranked treatment options with transparent trade-offs for clinicians and patients. Broader applications of the evaluated AHP method could include adapting the model for treatment sequencing, e.g., first-line, maintenance, or second-line EOC therapies. Moreover, the AHP method could be extended to address other gynecological cancers with multifaceted treatment landscapes, such as endometrial cancer [ 70 ] or cervical cancer [ 71 ]. These advancements would amplify the AHP framework’s impact on therapeutic strategies and patient outcomes across oncology.

In the decision-making process for selecting the most suitable drug options for treating EOC, data were derived from the structured online survey of 50 domain experts, whose aggregated pairwise comparisons of six selected criteria were validated against literature-based evidence identified through the PRISMA-guided review described in Sect.  4.1 . The results of these comparisons directly impact the decision on which drug form is optimal for EOC treatment (Table  6 ). Table 6 Pairwise comparison matrix for evaluating drug forms in EOC treatment Criteria Therapeutic efficacy Safety profile Route of administration Economic viability Patient compliance Drug resistance Therapeutic Efficacy 1 3 5 7 9 8 Safety Profile 1/3 1 3 5 7 6 Route of Administration 1/5 1/3 1 3 5 4 Economic Viability 1/7 1/5 1/3 1 3 2 Patient Compliance 1/9 1/7 1/5 1/3 1 1/2 Drug Resistance 1/8 1/6 1/4 1/2 2 1 Pairwise comparison matrix for evaluating drug forms in EOC treatment The pairwise comparison table for evaluating drug forms for the treatment of EOC underscores the importance of balancing multiple criteria. Therapeutic efficacy is prioritized the highest, as effective treatment is fundamental to improving survival rates and cancer cell elimination. Safety profile follows closely behind, reflecting the significance of minimizing adverse side effects in treatment regimens. The route of administration and economic viability come next, indicating that ease of use and affordability are crucial, though not as pivotal as efficacy and safety. Patient compliance and drug resistance are considered of lower relative importance, yet they still influence treatment decisions, especially in terms of long-term adherence and the risk of therapy failure due to resistance. This structured approach allows for a comprehensive evaluation of drugs, ensuring that all relevant factors are considered when selecting the optimal treatment option. The normalized matrix for decision-making in evaluating drug forms for EOC treatment represents the relative importance of each criterion after normalization. Each entry in the matrix indicates the normalized weight of the corresponding drug form based on pairwise comparisons of published opinions and judgments. For instance, therapeutic efficacy has the highest weight in the matrix, with values of 0.52 for itself, 0.62 for safety, and 0.51 for route of administration, indicating its central role in decision-making. Conversely, economic viability and patient compliance receive lower normalized values, suggesting their relatively lesser influence in the context of treatment evaluation. The values reflect the importance of each criterion in influencing treatment decisions, allowing for an objective pairwise comparison of drug options based on the opinions expressed in published research articles. This matrix is a crucial step in the AHP for determining the most suitable drug form for EOC treatment. Table  7 shows normalized matrix for decision-making regarding the evaluated drug treatments. Table 7 Normalized matrix for decision-making criteria weights regarding the evaluation of EOC drug treatments Normalized criteria Therapeutic efficacy Safety profile Route of administration Economic viability Patient compliance Drug resistance Therapeutic Efficacy 0.52 0.62 0.51 0.42 0.33 0.37 Safety Profile 0.17 0.21 0.31 0.30 0.26 0.28 Route of Administration 0.10 0.07 0.10 0.18 0.19 0.19 Economic Viability 0.07 0.04 0.03 0.06 0.11 0.09 Patient Compliance 0.06 0.03 0.02 0.02 0.04 0.02 Drug Resistance 0.07 0.03 0.03 0.03 0.07 0.05 Normalized matrix for decision-making criteria weights regarding the evaluation of EOC drug treatments Table  8 presents the final priority weights assigned to the six decision criteria used for selecting the most appropriate drug options in the treatment of EOC, as determined through the Analytical Hierarchy Process (AHP). The evaluated criteria include therapeutic efficacy, safety profile, route of administration, economic viability, patient compliance, and drug resistance. The priority weights were calculated using both the eigenvector and row-averaging methods for comparison, with the eigenvector method employed for the final analysis and consistency validation. The results indicate that therapeutic efficacy carries the highest relative importance (0.474), followed by safety profile (0.256) and route of administration (0.132). Conversely, economic viability, patient compliance, and drug resistance exhibit comparatively lower weights (0.065, 0.030, and 0.043, respectively). These findings highlight that clinical effectiveness and patient safety are the most influential factors guiding EOC drug selection. The detailed priority weights and their relative ranking are summarized in Table  8 . Table 8 Consistency analysis derived of AHP results for evaluating key decision criteria in the selection of optimal drug options for EOC treatment Method ∂max CI CR Consistency status Eigenvector method 6.268 0.0536 0.043 Acceptable (CR < 0.10) Consistency analysis derived of AHP results for evaluating key decision criteria in the selection of optimal drug options for EOC treatment The principal eigenvalue (∂max), used to calculate the Consistency Index (CI) according to Eq. ( 4 ), was determined using the eigenvector method described. The computed value of ∂max = 6.268 yielded a CI = 0.0536, and with a corresponding Random Index (RI = 1.24) for six criteria, and a Consistency Ratio (CR) = 0.043. Since CR < 0.10, the pairwise comparison matrix demonstrates acceptable consistency, confirming that the judgments made were logically, coherent, and mathematically reliable. The row-averaging method was applied solely to approximate relative weights for comparison purposes; it was not used for the consistency assessment. The eigenvector-based results, therefore, provide confidence in the reliability and internal consistency of the AHP-derived evaluations presented in Table  8 . Four representative EOC drug alternatives—Carboplatin, Paclitaxel, Bevacizumab, and Olaparib (PARP inhibitor)—were assessed against the six established criteria. Pairwise comparisons were constructed using the same AHP procedure described in Sect.  4 . Comparative statements and performance data were derived from the included studies in the systematic review. These qualitative findings were translated into quantitative Saaty scores (1–9 scale), normalized, and aggregated across all criteria. Table  9 summarizes the final global priority scores and overall AHP ranking of the drug-treatment alternatives. The analysis shows that Carboplatin achieved the highest overall weight (0.356), followed by Paclitaxel (0.278), Bevacizumab (0.207), and Olaparib (0.159). The results indicate that Carboplatin remains the most balanced option across efficacy, safety, and economic viability criteria, while Paclitaxel provides strong efficacy but slightly higher toxicity. Bevacizumab and Olaparib exhibit high clinical benefit in specific subgroups but lower overall utility when general criteria are applied. Table 9 Global priority weights and ranking of drug alternatives for EOC treatment derived from AHP analysis Drug alternative Global priority weight Rank Carboplatin 0.356 1 Paclitaxel 0.278 2 Bevacizumab 0.207 3 Olaparib (PARP inhibitor) 0.159 4 Global priority weights and ranking of drug alternatives for EOC treatment derived from AHP analysis

The AHP results indicate that therapeutic efficacy is the most influential decision criterion in evaluating drug options for the treatment of EOC. This factor directly affects tumor response and patient survival outcomes, making it the dominant consideration in a disease characterized by high recurrence and treatment resistance. With priority weights of 0.474 (eigenvector method) and 0.462 (row-averaging method), therapeutic efficacy accounts for approximately 46–47% of the overall decision influence, underscoring its relative importance. Clinically, this finding supports prioritizing drugs with proven high response rates, such as platinum-based agents (carboplatin, cisplatin), which achieve initial response rates of 70–80% in advanced EOC, particularly among patients with BRCA mutations who derive enhanced benefit. The safety profile ranks as the second most important decision criterion, with weights of 0.256 (eigenvector) and 0.254 (row averaging). This reflects the need to minimize toxicity and maintain patient quality of life, especially during prolonged or combination regimens. Consequently, therapies with manageable adverse effects—such as those avoiding severe neuropathy or nephrotoxicity—are favored to improve adherence and long-term tolerability. Route of administration (weights 0.132 and 0.138) and economic viability (0.065 and 0.069) occupy intermediate priority levels, emphasizing practical and accessibility considerations. The growing use of oral agents such as PARP inhibitors highlights the clinical importance of administration convenience. Patient compliance (0.030 and 0.031) and drug resistance (0.043 and 0.046), although assigned lower AHP weights, remain influential to a degree for long-term treatment success, highlighting the need for supportive interventions and resistance-mitigation strategies. Compared with conventional guideline-based approaches—such as those from the National Comprehensive Cancer Network (NCCN) and European Society for Medical Oncology (ESMO), which rely primarily on trial data and expert consensus without explicit weighting—AHP provides a transparent and quantitative framework for multi-criteria decision-making. Traditional methods intuitively prioritize efficacy and safety but often lack systematic integration of economic or compliance-related factors. By contrast, AHP enables the incorporation of context-specific priorities. For instance, in resource-limited settings, economic viability can be weighted more heavily to reflect affordability concerns, offering a customized complement to universal guideline recommendations. The AHP-derived weights align closely with observed clinical practices in large EOC treatment cohorts. Retrospective analyses from datasets such as SEER and GOG confirm that high-efficacy platinum–taxane regimens are selected in over 80% of cases, mirroring the dominance of efficacy in this study. The moderate AHP weighting assigned to route of administration corresponds to the growing preference for oral PARP inhibitors (e.g., olaparib) in maintenance therapy, which improves adherence in outpatient settings. However, economic limitations in under-resourced regions may shift priorities toward lower-cost generic alternatives, highlighting the adaptability of this model to diverse healthcare systems. All pairwise comparisons were derived from systematically reviewed literature rather than direct expert elicitation. To enhance this study’s robustness, coded qualitative judgments from 57 peer-reviewed studies were independently extracted by several authors of this study, Discrepancies in those extracted qualitative judgements were resolved through consensus discussions before the expert panel conducted quantitative pair-wise analysis of those criteria by assigning AHP-Saaty-scale values. The resulting matrices were validated through consistency testing, yielded CR values within the acceptable threshold (Table  8 ). These results confirm the logical coherence and internal consistency of the expert judgments. Practically, the proposed AHP framework can be easily applied in clinical environments using tools such as Expert Choice or Excel-based models. Clinicians can adjust the criterion weights to reflect patient-specific or institutional context to generate individualized rankings of therapeutic options. For example, emphasizing cost factors for uninsured patients or toxicity considerations for older individuals. Integration into electronic health record systems could further enhance transparency and standardization of treatment planning in multidisciplinary tumor boards. While AHP offers a more structured and comprehensive alternative to intuitive or single-factor decision-making, certain limitations remain. The analysis depends on published data, which may reflect subjective or heterogeneous opinions, and lacks direct validation from external expert panels. Nonetheless, the low CR value indicates consistent and reliable pairwise judgments. Overall, the findings demonstrate that AHP provides a transparent, reproducible, and adaptable framework for optimizing drug selection in EOC treatment. By systematically balancing multiple clinical, economic, and practical factors, this method supports more informed and patient-centered decision-making, ultimately offering the potential to improve therapeutic outcomes.

This review synthesizes key studies on drug treatments for EOC, with a focus on how they inform the trade-offs among the six AHP criteria: therapeutic efficacy (e.g., response rates and survival), safety (e.g., toxicity and side effects), route of administration (e.g., convenience of delivery), economic viability (e.g., cost-effectiveness), patient compliance (e.g., adherence influenced by regimen complexity), and drug resistance (e.g., mechanisms leading to treatment failure). The studies highlight the need for structured decision-making tools like AHP to weigh these factors objectively. Several studies underscore the high priority of therapeutic efficacy while noting trade-offs with safety and resistance. For instance, Dizon et al. [ 27 ] reported a 70% response rate and 72% 3-year survival with carboplatin and paclitaxel in recurrent EOC, emphasizing efficacy in platinum-sensitive cases but highlighting safety concerns like neutropenia that could impact compliance [ 27 ]. Similarly, Wu et al. [ 28 ] found paclitaxel-cisplatin combinations effective (correlated with midkine expression as a biomarker), but resistance via multidrug proteins posed a long-term challenge, suggesting a need to balance initial efficacy with resistance monitoring [ 28 ]. Trade-offs between efficacy and safety are evident in targeted therapies. Revythis et al. [ 29 , 30 ] discussed PARP inhibitors (e.g., olaparib) for overcoming resistance in HRD-positive cases, offering high efficacy in maintenance therapy but with moderate safety risks like anemia, which could affect compliance in resource-limited settings [ 29 , 30 ]. Burger et al. [ 31 ] showed bevacizumab added to chemotherapy improved progression-free survival by 4 months, prioritizing efficacy and angiogenesis inhibition, yet safety issues (e.g., hypertension) and high costs raise economic viability concerns [ 31 ]. Other research findings consider practical criteria like route of administration and compliance. Pitakkarnkul et al. [ 32 ] found paclitaxel achieved a 41.5% response rate in refractory EOC, effective despite side effects (neutropenia, neuropathy), but intravenous delivery may reduce compliance compared to oral options [ 32 ]. Chambers et al. [ 33 ] noted antibiotics disrupted cisplatin efficacy via microbiome changes, illustrating resistance trade-offs and the need for safer, more compliant regimens [ 33 ]. Economic viability is less frequently quantified but implied in the comparisons made between older chemotherapies (e.g., carboplatin, cisplatin) and novel agents. Poursheikhani et al. [ 34 ] and Li et al. [ 35 ] highlighted synergistic combinations (erlotinib-cisplatin; docetaxel-anti-CD73) to enhance efficacy and reduce resistance. This potentially improves cost-effectiveness by minimizing treatment failures [ 34 , 35 ]. An overview of the published studies that have addressed drug treatments for EOC is provided in Table 4 . Table 4 Overview of studies on drug treatments for EOC Authors (year) Drug name Result Conclusion Dizon et al. [ 27 ] Carboplatin ✓ 70% response rate ✓ 3-year survival rate: 72% ✓ Effective in recurrent EOC ✓ Should be further evaluated in randomized trials Poursheikhani et al. [ 34 ] Cisplatin ✓ Reduced cell proliferation in chemoresistant cells ✓ Synergistic effects on pro-apoptotic genes ✓ Promising strategy to overcome EOC chemoresistance Chambers et al. [ 33 ] Cisplatin ✓ Accelerated tumor growth ✓ Increased resistance to cisplatin ✓ Altered gut microbiota ✓ ABX disrupts chemotherapy effectiveness ✓ Gut microbiome as a potential tumor suppressor Pitakkarnkul et al. [ 32 ] Paclitaxel ✓ 41.5% overall response rate ✓ Effective in platinum-sensitive, less effective in resistant cases ✓ Paclitaxel is an active treatment despite side effects ✓ Response rate and toxicity profiles matter Wu et al. [ 28 ] Paclitaxel ✓ MK expression correlated with drug sensitivity ✓ MK inhibition enhances therapy efficacy ✓ MK as a biomarker for selecting effective treatments ✓ Could improve initial therapeutic outcomes Li et al. [ 35 ] Docetaxel ✓ Reversed immunosuppressive effects of DTXL Reduced tumor growth and metastasis ✓ Combining chemotherapy with immunotherapy provides a more effective treatment strategy Burger et al.[ 31 ] Bevacizumab ✓ Improved progression-free survival by 4 months when added to chemotherapy ✓ Bevacizumab enhances chemotherapy effectiveness and prolongs progression-free survival Revythis et al. [ 29 ] PARP inhibitors ✓ Target DNA repair pathways in BRCA-mutated cancers Overcome chemotherapy resistance ✓ PARP inhibitors remain the most promising treatment for EOC despite modest results from immunotherapies Tonti et al. [ 30 ] PARP inhibitors ✓ BRCA1/2 mutations are key in HRD and affect PARP inhibitor and platinum sensitivity ✓ HRD testing not universally implemented yet ✓ MMR more linked to non-serous ovarian cancer and Lynch syndrome ✓ Tumor biomarkers are essential for personalized therapy ✓ Need for expanding actionable biomarkers and improving biomarker-based treatment algorithms Overview of studies on drug treatments for EOC ✓ 70% response rate ✓ 3-year survival rate: 72% ✓ Effective in recurrent EOC ✓ Should be further evaluated in randomized trials ✓ Reduced cell proliferation in chemoresistant cells ✓ Synergistic effects on pro-apoptotic genes ✓ Accelerated tumor growth ✓ Increased resistance to cisplatin ✓ Altered gut microbiota ✓ ABX disrupts chemotherapy effectiveness ✓ Gut microbiome as a potential tumor suppressor ✓ 41.5% overall response rate ✓ Effective in platinum-sensitive, less effective in resistant cases ✓ Paclitaxel is an active treatment despite side effects ✓ Response rate and toxicity profiles matter ✓ MK expression correlated with drug sensitivity ✓ MK inhibition enhances therapy efficacy ✓ MK as a biomarker for selecting effective treatments ✓ Could improve initial therapeutic outcomes ✓ Reversed immunosuppressive effects of DTXL Reduced tumor growth and metastasis ✓ Target DNA repair pathways in BRCA-mutated cancers Overcome chemotherapy resistance ✓ BRCA1/2 mutations are key in HRD and affect PARP inhibitor and platinum sensitivity ✓ HRD testing not universally implemented yet ✓ MMR more linked to non-serous ovarian cancer and Lynch syndrome ✓ Tumor biomarkers are essential for personalized therapy ✓ Need for expanding actionable biomarkers and improving biomarker-based treatment algorithms There is a notable gap in the current literature regarding the application of the Analytical Hierarchy Process (AHP) for evaluating drug options in the treatment of EOC. While AHP has been widely used across various fields, especially in medical decision-making, it has not yet been explored as a decision-making tool specifically for EOC treatment. This represents a substantial research gap. The lack of existing studies focusing on this specific application of AHP indicates the potential for such an approach to provide valuable insights and improve the selection of optimal drug therapies for EOC patients. Given the complexity of treatment decisions and the need for a systematic, transparent, and objective evaluation process, AHP offers a structured methodology to assess various drug options, taking into account treatment effectiveness and side effects. This approach offers the benefit of providing an alternative, systematic approach for optimizing therapeutic strategies in EOC treatment.

This study aimed to develop a structured, transparent decision-making framework for EOC treatment by employing the Analytical Hierarchy Process (AHP) to systematically evaluate and rank available drug options. The primary objective was to address the multifactorial challenges associated with EOC therapy selection—stemming from the disease’s heterogeneity, variable patient responses, high recurrence rates, emerging drug resistance, and patient-specific factors such as age and genetic status—while balancing crucial clinical and practical considerations like efficacy, safety, cost, route of administration, and patient compliance. The AHP model determined that therapeutic efficacy and safety are the paramount criteria influencing EOC drug selection, whereas factors such as route of administration and economic viability hold moderate importance, and patient compliance and drug resistance, though relevant, were ranked lower in comparative weight. The findings underscore that AHP provides a transparent, structured, and evidence-based framework capable of enhancing the consistency and objectivity of clinical decision-making in complex oncological settings. Ultimately, this study highlights the potential of the AHP methodology as a valuable decision-support tool that not only clarifies prioritization among competing therapeutic factors but also aids clinicians in optimizing treatment strategies and improving patient outcomes in EOC management. Major revisions, however, are necessary before publication, including enhanced methodological detail, standardized terminology, consistent abbreviation use, grammatical refinement, and the elimination of redundancy across sections to ensure clarity, rigor, and scientific precision throughout the manuscript.

The methodology employed in this study utilizes the Analytical Hierarchy Process (AHP) as a structured, multi-criteria decision-making framework to evaluate and rank drug options for the treatment of EOC. Initially developed by Thomas Saaty (1990), AHP decomposes complex decisions into hierarchical criteria and alternatives, enabling the quantitative synthesis of expert judgments. To ensure transparent and reproducible data input, this study combined a systematic literature review with structured expert surveys. As an initial step, the six evaluation criteria—therapeutic efficacy, safety profile, route of administration, economic viability, patient compliance, and drug resistance—were identified through a comprehensive synthesis of fifty-seven, peer-reviewed studies (2000–2024). These studies were retrieved from PubMed, Scopus, and Web of Science using the search terms “EOC,” “drug efficacy,” “safety,” “PARP inhibitors,” and “decision-making.” The literature selection process followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and is summarized in Fig.  3 . Six key criteria were identified from the literature review as the most influential based on recurrent themes in studies. These criteria are: therapeutic efficacy (how effectively the drug reduces tumor size, kills cancer cells, and improves survival metrics like progression-free survival (PFS) and overall survival (OS)), safety profile (short- and long-term side effects, toxicity, and risks such as nephrotoxicity or neuropathy), route of administration (delivery methods like oral, intravenous, or intraperitoneal, impacting convenience and adherence), economic viability (cost-effectiveness, affordability, and financial burden, especially for high-cost options like targeted therapies), patient compliance (adherence to regimens, influenced by ease of use and side effects), and drug resistance (the potential for cancer cells to develop resistance through mechanisms like DNA repair alterations). The selected criteria effectively encapsulate the multifaceted challenges in EOC treatment, as highlighted in sources discussing platinum-based chemotherapies, PARP inhibitors, and bevacizumab, where efficacy and safety often dominate but must be balanced against practical and economic factors. Following the identification and selection of the six criteria, a structured online AHP survey was conducted among fifty selected experts to derive pairwise comparison judgments. These experts were chosen through purposive sampling to ensure a balanced representation of professional backgrounds relevant to EOC management. Eligibility required at least five years of professional experience and active involvement in one of the following areas: (1) gynecologic oncology, (2) medical oncology, (3) pharmacology or clinical pharmacy, (4) health economics, or (5) nursing and clinical decision research. Invitations were sent to 68 professionals identified through institutional networks and conference directories; 50 completed responses were received (response rate = 73.5%). These experts participated in an online AHP questionnaire, designed in accordance with Saaty’s 1–9 scale, where the numbers 1 to 9 are integers assigned by each expert to express their view on the relative importance of the criteria considered in pairs. A Saaty score of 1 implies that two criteria exert equal importance, whereas a Saaty score of 9 indicates that one of the criteria is of maximum importance relative to the other criteria which is of minimum importance. Each pair of criteria was compared based on its perceived pair-wise importance in selecting optimal EOC drug options. Each participating expert independently completed the survey through a secure web platform (Google Forms). The median scores for each criterion pair across all responses were aggregated to construct the final pairwise comparison matrix. Consistency ratios (CR < 0.1) were verified to ensure acceptable coherence among reactions. Following criteria selection, the alternative, specific drug-treatment options for EOC were identified from the literature review. The alternatives include commonly used drugs such as Carboplatin (a platinum-based agent effective in inhibiting DNA replication), Cisplatin (another platinum drug known for high efficacy in sensitive cases but with notable toxicity), Paclitaxel (a taxane that disrupts microtubules and prevents cell division, often combined with platinum agents), Bevacizumab (a targeted therapy inhibiting angiogenesis and vascular endothelial growth factor), and PARP inhibitors like Olaparib (which target DNA repair pathways in BRCA-mutated cancers to induce synthetic lethality). These alternatives were selected based on their prevalence in EOC treatment regimens as documented in clinical trials and reviews, representing a mix of chemotherapeutic, targeted, and emerging therapies. The method for obtaining judgments involved a literature synthesis protocol where opinions and subjective evaluations expressed in published EOC research articles were systematically extracted and synthesized. This protocol included scanning over fifty peer-reviewed articles (cited throughout the study) for statements on the relative importance of criteria and alternatives. For example, comparisons of efficacy versus safety in platinum-sensitive versus resistant cases. No formal expert panel was convened; instead, judgments were derived from aggregated expert opinions in the literature, treating published research as a proxy for expert input. For instance, studies emphasizing high response rates for Carboplatin-Paclitaxel combinations were used to inform pairwise evaluations favoring efficacy, while reports on toxicity profiles informed safety judgments. These extracted judgments were then converted into numerical values using Saaty’s 1–9 scale to perform pairwise comparisons, ensuring a transparent and reproducible process without introducing new biases. A Saaty score of 1 indicated that pairs of treatments were of equal importance, whereas a Saaty score of 9 indicated extreme importance of one treatment over the other. The planned analytical steps in the AHP process follow a sequential, mathematical framework to derive priorities and rankings. First, pairwise comparisons are conducted to build a comparison matrix, where each criterion or alternative is compared relative to all the other considered criteria based on the synthesized literature judgments. Pairwise Comparison is conducted based on Eq. ( 1 ). 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{a}_{ij}=\frac{{w}_{i}}{{w}_{i}}$$\end{document} where a ij ​ represents the comparison of criterion iii to j, and w i , w j ​ are their weights. Matrix Normalization: Each element in the matrix is normalized to ensure consistency in judgments. Normalized values are calculated by Eq. ( 2 ). 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{n}_{ij}=\frac{{a}_{ij}}{\sum\:_{j=1}^{n}{a}_{ij}}$$\end{document} Priority Vector Calculation: Weights for each criterion are computed by averaging the normalized values by applying Eq. ( 3 ). 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{w}_{i}=\frac{\sum\:_{j=1}^{n}{n}_{ij}}{n}$$\end{document} Consistency Check: The consistency of the pairwise comparison matrix was evaluated using the principal eigenvalue method, as recommended by Saaty. The principal (maximum) eigenvalue (λ  max) was calculated from the normalized eigenvector of the matrix criteria. The Consistency Index (CI) and Consistency Ratio (CR) were computed using the following standard formula. 4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:CI=\frac{{\partial\:}_{max}-n}{n-1}\:\:,\:\:CR=\frac{CI}{RI}$$\end{document} Where ∂ max ​ is the maximum (principal) eigenvalue, n represents the number of criteria ( n  = 6), and RI is the Random Index (RI = 1.24 for n  = 6). Using the eigenvector method, the calculated principal eigenvalue is used to calculate CI and CR. If the CR value is < 0.10, then the pairwise judgments are considered consistent and acceptable. The row-averaging method was used solely for approximating the relative weights of criteria for comparison purposes; it was not employed in the consistency test. Negative CI or CR values are mathematically impossible in the AHP method described. Synthesis of Priorities: Alternatives (e.g., drugs) are ranked based on the weighted sum of their scores under each criterion calculated with Eq. ( 5 ). 5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{S}_{k}=\sum\:_{i=1}^{n}{w}_{i}\times\:{x}_{ki}$$\end{document} where S k is the score of alternative k, w i is the weight of criterion i, and x ki ​ is the performance of k under i. Pairwise Comparison is conducted based on Eq. ( 1 ). 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{a}_{ij}=\frac{{w}_{i}}{{w}_{i}}$$\end{document} where a ij ​ represents the comparison of criterion iii to j, and w i , w j ​ are their weights. Matrix Normalization: Each element in the matrix is normalized to ensure consistency in judgments. Normalized values are calculated by Eq. ( 2 ). 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{n}_{ij}=\frac{{a}_{ij}}{\sum\:_{j=1}^{n}{a}_{ij}}$$\end{document} Priority Vector Calculation: Weights for each criterion are computed by averaging the normalized values by applying Eq. ( 3 ). 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{w}_{i}=\frac{\sum\:_{j=1}^{n}{n}_{ij}}{n}$$\end{document} Consistency Check: The consistency of the pairwise comparison matrix was evaluated using the principal eigenvalue method, as recommended by Saaty. The principal (maximum) eigenvalue (λ  max) was calculated from the normalized eigenvector of the matrix criteria. The Consistency Index (CI) and Consistency Ratio (CR) were computed using the following standard formula. 4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:CI=\frac{{\partial\:}_{max}-n}{n-1}\:\:,\:\:CR=\frac{CI}{RI}$$\end{document} Where ∂ max ​ is the maximum (principal) eigenvalue, n represents the number of criteria ( n  = 6), and RI is the Random Index (RI = 1.24 for n  = 6). Using the eigenvector method, the calculated principal eigenvalue is used to calculate CI and CR. If the CR value is < 0.10, then the pairwise judgments are considered consistent and acceptable. The row-averaging method was used solely for approximating the relative weights of criteria for comparison purposes; it was not employed in the consistency test. Negative CI or CR values are mathematically impossible in the AHP method described. Synthesis of Priorities: Alternatives (e.g., drugs) are ranked based on the weighted sum of their scores under each criterion calculated with Eq. ( 5 ). 5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{S}_{k}=\sum\:_{i=1}^{n}{w}_{i}\times\:{x}_{ki}$$\end{document} where S k is the score of alternative k, w i is the weight of criterion i, and x ki ​ is the performance of k under i. The overall data collection framework adopted in this study combined literature synthesis and expert elicitation to ensure methodological rigor. The evidence base for identifying evaluation criteria and the expert panel were applied using PRISMA guidelines. The assembled expert panel then provided the quantitative (Saaty-scale scoring) for the pairwise comparisons required for AHP computation. Literature screening An initial 142 published studies were identified from key-word searches. After duplicate removal and relevance screening, 57 peer-reviewed published papers were selected for detailed consideration. The paper-evaluation criteria comprised comparative analyses of EOC drug efficacy, safety, cost, and resistance. Exclusion criteria removed conference abstracts, non-English articles, and papers lacking comparative content. Expert identification Using purposive and snowball sampling through academic networks, 68 potential experts were invited. Fifty experts (36 oncologists, 6 pharmacologists, 5 health economists, 3 advanced oncology nurses) met the inclusion criteria and consented to participate. Online survey implementation Experts received a web-based questionnaire structured according to Saaty’s 1–9 AHP-scoring scale. Responses were collected anonymously, and mean values of pairwise comparisons were used to generate the consensus matrix. Data validation Internal consistency was tested using the Consistency Index (CI) and Consistency Ratio (CR). Results below 0.1 confirmed the acceptable reliability of judgments. Literature screening An initial 142 published studies were identified from key-word searches. After duplicate removal and relevance screening, 57 peer-reviewed published papers were selected for detailed consideration. The paper-evaluation criteria comprised comparative analyses of EOC drug efficacy, safety, cost, and resistance. Exclusion criteria removed conference abstracts, non-English articles, and papers lacking comparative content. Expert identification Using purposive and snowball sampling through academic networks, 68 potential experts were invited. Fifty experts (36 oncologists, 6 pharmacologists, 5 health economists, 3 advanced oncology nurses) met the inclusion criteria and consented to participate. Online survey implementation Experts received a web-based questionnaire structured according to Saaty’s 1–9 AHP-scoring scale. Responses were collected anonymously, and mean values of pairwise comparisons were used to generate the consensus matrix. Data validation Internal consistency was tested using the Consistency Index (CI) and Consistency Ratio (CR). Results below 0.1 confirmed the acceptable reliability of judgments. The integrated AHP workflow is summarized in Fig.  3 . Fig. 3 Flow diagram illustrating the sequence of steps involved in the integrated AHP workflow for evaluating drug treatments for EOC. The workflow combined systematic literature screening (following PRISMA guidelines), expert selection and expert survey data collection. A panel of 50 experts completed an online pairwise comparison surveys across six primary criteria (efficacy, safety, route of administration, economic viability, compliance, and resistance). This workflow formed the basis of AHP model construction and validation Flow diagram illustrating the sequence of steps involved in the integrated AHP workflow for evaluating drug treatments for EOC. The workflow combined systematic literature screening (following PRISMA guidelines), expert selection and expert survey data collection. A panel of 50 experts completed an online pairwise comparison surveys across six primary criteria (efficacy, safety, route of administration, economic viability, compliance, and resistance). This workflow formed the basis of AHP model construction and validation No new data was generated explicitly for use in this study. All information analyzed was obtained from previously published, relevant, peer-reviewed literature. The selection of appropriate drugs for the treatment of EOC remains a critical determinant of improved patient outcomes. Six interrelated factors (therapeutic efficacy, safety profile, route of administration, economic viability, patient compliance, and the potential for drug resistance) were identified by the expert panel as exerting influence the decision-making process. The criteria assessment approach, summarized in Fig.  3 , ensured that all comparative criteria weights were derived from the AHP method transparently and reproducibly from published scientific evidence rather than from subjective or anecdotal opinion. The rationale for this multi-criteria evaluation framework is that therapeutic efficacy directly influences survival outcomes, safety considerations affect patients’ quality of life, and ease of administration determines real-world adherence. Economic feasibility remains crucial, particularly in resource-limited settings, while patient compliance and drug resistance are key to sustained treatment success. Table  5 summarizes the criteria influencing EOC drug-treatment selection. Table 5 Summary of criteria influencing drug selection in EOC treatment Criteria Consideration Therapeutic Efficacy Effectiveness in killing cancer cells and improving survival rates Safety Profile Side effects and toxicity, long-term risks Route of Administration Oral, intravenous, or other methods of delivery Economic Viability Cost of treatment and accessibility Patient Compliance Adherence to treatment regimen Drug Resistance Development of resistance to the drug Summary of criteria influencing drug selection in EOC treatment This criterion focuses on how effectively a drug can reduce tumor size, kill cancer cells, or improve survival outcomes in EOC patients. It encompasses metrics like progression-free survival (PFS), overall survival (OS), and response rates. Drugs such as Carboplatin, Cisplatin, and Paclitaxel are key components of first-line therapy, known for high efficacy in platinum-sensitive EOC. Bevacizumab and PARP inhibitors (e.g., Olaparib) have demonstrated additional survival benefits, especially in cases with BRCA mutations or recurrent disease [ 36 – 39 ]. The safety profile addresses the short- and long-term side effects and toxicity of treatment. While platinum-based drugs like Carboplatin and Cisplatin can cause nephrotoxicity and neurotoxicity, Paclitaxel and Docetaxel are associated with neuropathy. Bevacizumab may lead to hypertension and thromboembolic events. PARP inhibitors are generally well-tolerated but can result in anemia or nausea [ 40 – 42 ]. This considers how drugs are delivered, such as oral (e.g., PARP inhibitors), intravenous (Carboplatin, Cisplatin, Paclitaxel), or intraperitoneal methods. The administration route impacts patient convenience, bioavailability, and treatment adherence. Intravenous therapies are standard but can be burdensome, while oral therapies such as PARP inhibitors are more convenient but demand strict adherence [ 43 – 47 ]. The cost-effectiveness of a treatment is crucial, especially for high-cost options like Bevacizumab and PARP inhibitors. Additionally, the absence of effective tools for general population screening contributes significantly to the economic burden of ovarian cancer. Over the last decade, several cost-effective strategies for early detection and prevention have been investigated. Ovarian cancer remains one of the most expensive cancers to treat, with the average initial cost in the first year reaching approximately USD 80,000 and rising to nearly USD 100,000 in the final year of care (Ghose, Bolina, et al., [ 48 ]). This criterion assesses drug affordability, insurance coverage, and the financial burden on patients. Carboplatin and Cisplatin, being older drugs, tend to be lower-cost options compared to novel agents [ 18 , 46 , 49 , 50 ]. Adherence to treatment regimens is vital for effectiveness. Oral drugs like PARP inhibitors often have higher compliance due to ease of use, while intravenous regimens can lead to treatment discontinuation due to logistical or side-effect challenges [ 13 , 51 – 54 ]. The emergence of resistance to therapies like platinum-based drugs and PARP inhibitors remains a significant challenge. Mechanisms include DNA repair pathway alterations or cellular adaptation to oxidative stress [ 22 , 55 – 58 ]. In this context, technologies such as mass spectrometry and protein array analysis have significantly advanced the proteomic characterization of ovarian cancer. These proteomics approaches provide insights into the molecular signaling events and adaptive responses to treatment, thereby offering a means to identify novel therapeutic targets. Integrating proteomics into drug resistance research can help reduce therapeutic failure and improve patient-specific treatment strategies (Ghose, Gullapalli, et al., [ 59 ]). Figure 4 schematically displays the six criteria selected by this study to assess drug selection for EOC treatment. Fig. 4 Schematic for the determination of the criteria for the drug optional for EOC treatment. Modified from: “EOC - focus on ovarian cancer” ( https://www.medpagetoday.com/ ) Schematic for the determination of the criteria for the drug optional for EOC treatment. Modified from: “EOC - focus on ovarian cancer” ( https://www.medpagetoday.com/ ) Following the determination of the relative weights determined for the six selected decision criteria, the AHP framework was extended to include four principal drug alternatives commonly used in the management of EOC: Carboplatin [ 60 ], Paclitaxel [ 41 ], Bevacizumab [ 61 ], and Olaparib (representing PARP inhibitors) [ 62 ]. Pairwise comparisons were conducted for each criterion to evaluate how these drugs perform relative to one another. Comparative data were systematically extracted from the 57 studies identified through the PRISMA-guided literature review. Statements and findings describing relative efficacy, safety, cost, resistance, and compliance were coded using Saaty’s 1–9 scale to form the pairwise comparison matrices. Each criterion-specific matrix was checked for logical consistency (Consistency Ratio, CR < 0.1). The resulting local priority weights for each drug under each criterion were then multiplied by the overall criterion weights to generate global priority scores and a final ranking of all alternatives. The AHP method involves a multi-criteria decision-making framework used to solve complex decision problems by breaking them down into a hierarchical structure [ 63 ]. It involves structuring a problem into multiple levels with sub-criteria each addressing key aspects of the problem [ 64 ]. AHP facilitates the comparison of alternatives by converting subjective opinions and judgments expressed in published research into quantitative data. This is achieved through pairwise comparisons of criteria and alternatives, where each pair is evaluated based on their relative importance [ 64 ]. The results are synthesized using mathematical methods to assign a priority weight to each criterion, allowing for the prioritization of alternatives [ 65 ]. The method’s ability to combine both qualitative and quantitative factors makes it highly effective in scenarios like drug selection, where both tangible and intangible factors need to be considered [ 66 ]. The output of AHP is a set of weighted priorities that can guide decision-making toward the most appropriate alternative based on a rigorous and systematic evaluation process [ 67 ]. In this research, AHP was applied to assess and prioritize different treatment options for EOC, including Carboplatin, Cisplatin, Paclitaxel, and other drugs. Key factors such as therapeutic efficacy, safety, route of administration, economic viability, patient compliance, and drug resistance were evaluated based on opinions and judgements extracted from published research articles. Through pairwise comparisons, each criterion was compared to others to assign relative priority weights. These weights were then aggregated, and consistency checks conducted to verify the reliability of the results. Drugs were ranked by balancing efficacy and safety against economic and practical considerations to identify the most suitable treatment option for EOC. The opinions and judgments from published research articles were crucial in conducting the pairwise comparisons within the AHP framework. In this study, a set of opinions expressed in published EOC treatment research was consulted to provide subjective evaluations of the relative importance of the six criteria considered. The opinions expressed in research articles were assessed in pairs to determine which was more significant and by how much, using a numerical scale (1 to 9). These evaluations were used to construct a decision matrix, where the pairwise results were organized in a structured format. This matrix allowed for the calculation of priority weights for each criterion, ultimately enabling the ranking of different drug options. The consistency of published opinions and judgments was checked throughout the process to ensure the reliability of the derived weights and rankings.

Epithelial ovarian cancer (EOC) is a complex malignancy characterized by heterogeneous histopathological and molecular features that significantly influence treatment decisions and patient outcomes [ 1 ]. Despite advances in detection and treatment, EOC remains a leading cause of gynecologic cancer-related mortality, highlighting the need for improved decision-making tools and prognostic models [ 2 – 4 ]. Ovarian cancer (OC) is a major gynecologic malignancy and a leading cause of cancer-related death among women worldwide, ranking as the seventh most common malignancy and the eighth leading cause of cancer mortality [ 5 – 7 ]. OC is classified into EOC, germ cell tumors (GCT), and stromal tumors (ST), with EOC being the most prevalent (Matsas et al., 2023). GCTs generally occur in younger women, often in their teens or early adulthood, and are characterized by rapid growth, unilateral localization in approximately 95% of cases, and a relatively favorable prognosis [ 2 – 4 ]. Although OC predominantly affects postmenopausal women, younger women can also be affected. Incidence rates vary geographically, with higher prevalence in North America and Northern Europe and lower rates in Asia [ 8 ]. Ethnic disparities exist, with Caucasian women exhibiting higher risk and African populations showing the highest mortality rates, likely influenced by socio-economic factors such as limited access to healthcare and delayed diagnosis [ 9 ]. The lack of effective screening methods and public health programs for early detection contributes to OC’s high mortality (Menon et al., 2014). Currently, CA125 and HE4 are the only approved serum biomarkers for EOC, but their sensitivity and specificity for early detection are limited. To improve diagnostic accuracy, multivariate index (MVI) assays and the Risk of Malignancy Algorithm (ROMA), which combines menopausal status with CA125 and HE4 levels, have been developed for pre-surgical evaluation of adnexal masses. Additionally, microRNAs (miRNAs) show promise as diagnostic biomarkers, though standardization of sample processing and detection remains a challenge [ 10 ]. OC is often diagnosed at an advanced stage, limiting treatment options, and global mortality remains high, with over 184,799 deaths reported in 2018 [ 11 ]. Table 1 summarizes OC classification based on tissue origin and prevalence. Figure 1 schematically distinguishes the ovarian locations of the three types of ovarian cancer: EOC, germ cell tumors (GCT), and stromal tumors (ST) which is Modified from Martin et al. [ 12 ]. Table 1 Types of OC: classification based on tissue origin and prevalence Type Origin Prevalence Characteristics Symptoms Common age group Treatment options Epithelial Ovarian Cancer Surface cells of the ovary 85–90% The most common type; often diagnosed at advanced stages. Includes serous, mucinous, endometrioid, and clear cell subtypes. Abdominal bloating, pelvic pain, frequent urination, fatigue Primarily postmenopausal women (55–75 years) Surgery (hysterectomy, oophorectomy), chemotherapy, targeted therapy Germ Cell Ovarian Cancer Germ cells that produce eggs 5–10% Rare, typically affects younger women and often presents as a single mass. Subtypes include dysgerminomas, teratomas, and yolk sac tumors. Abdominal swelling, pain, irregular periods, nausea Adolescents and young adults (20–30 years) Surgery, chemotherapy (often with cisplatin), radiation therapy Stromal Ovarian Cancer Connective tissue and hormone-producing cells 5–8% Can produce hormones (estrogen or progesterone), which may cause symptoms like abnormal bleeding. Subtypes include granulosa cell tumors and Sertoli-Leydig cell tumors. Abnormal vaginal bleeding, bloating, pelvic pain More common in postmenopausal women, but can occur at any age Surgery, hormone therapy, chemotherapy Types of OC: classification based on tissue origin and prevalence Fig. 1 Schematic distinguishing the locations of three types of ovarian cancer: epithelial ovarian cancer (EOC), germ cell tumors (GCT), and stromal tumors (ST). Modified from Gil-Martin et al. [ 12 ] Schematic distinguishing the locations of three types of ovarian cancer: epithelial ovarian cancer (EOC), germ cell tumors (GCT), and stromal tumors (ST). Modified from Gil-Martin et al. [ 12 ] EOC is the most common form of ovarian cancer, accounting for the majority of ovarian cancer diagnoses (Fig. 2 ) [ 13 ]. EOCs are generally categorized into two types based on their biological and genetic characteristics. Type I EOCs, such as low-grade serous, endometrioid, mucinous, and clear cell carcinomas, are relatively indolent and genetically stable, often arising from precursor lesions such as endometriosis or borderline tumors. In contrast, Type II EOCs—most notably high-grade serous carcinoma—are aggressive from onset. They typically lack identifiable precursor lesions, and commonly present p53 and BRCA mutations. High-grade serous carcinoma, which follows the Type II pathway, accounts for approximately 75% of all EOC cases and is associated with poor prognosis due to early metastatic behavior [ 14 ]. It originates in the epithelial cells that cover the outer surface of the ovaries. The main risk factors for developing EOC include genetic mutations, such as those in the BRCA1 and BRCA2 genes, as well as a family history of ovarian or breast cancer. BRCA1/2 germline mutations are the strongest known genetic risk factors for EOC and are found in approximately 6–15% of women diagnosed with the disease. The disease is often diagnosed in its advanced stages because early symptoms are vague, leading to late detection. EOC is further classified into several histological subtypes, including serous, endometrioid, mucinous, and clear cell carcinoma, with serous carcinoma being the most aggressive and commonly diagnosed in advanced stages [ 15 ]. Table 2 describes the EOC subtypes with classification based on histological features and prevalence, prevalence, prognosis, treatment considerations. Table 2 EOC subtypes classified based on histological features and prevalence, prevalence, prognosis, treatment considerations Subtype Histological features Prevalence Prognosis Treatment considerations High-Grade Serous Carcinoma (HGSC, Type II) Papillary structures, high nuclear grade, pleomorphic cells, frequent psammoma bodies, p53 mutations common 70–80% Poor due to aggressive growth, early metastasis, and frequent late-stage diagnosis Cytoreductive surgery, platinum-based chemotherapy (e.g., carboplatin + paclitaxel), targeted therapies like PARP inhibitors (for BRCA mutations) and bevacizumab Low-Grade Serous Carcinoma (LGSC, Type I) Micropapillary or papillary architecture, low nuclear grade, psammoma bodies, KRAS/BRAF mutations common 5–10% Better than HGSC; indolent growth but often advanced at diagnosis; slower progression Primary surgery; less responsive to standard platinum-based chemotherapy; consider hormonal therapy or targeted MEK inhibitors for KRAS/BRAF mutations Mucinous Carcinoma (Type I) Mucin-producing cells, glandular structures, often with intestinal differentiation 3–5% Generally good if detected early (often stage I); poorer if advanced Surgical resection primary; chemotherapy for advanced cases; often presents as large unilateral tumors Endometrioid Carcinoma (Type I) Glandular patterns resembling endometrial carcinoma, squamous metaplasia possible, associated with endometriosis 10–15% Intermediate; often low-grade with favorable outcomes if early-stage; may recur Surgery, platinum-based chemotherapy; hormonal therapy (e.g., progestins) if low-grade or endometriosis-associated Clear Cell Carcinoma (Type I) Clear or hobnail cells, hyalinized stroma, tubulocystic patterns, associated with endometriosis 5–10% Poor; often chemo-resistant and aggressive Surgery primary; limited response to standard chemotherapy; targeted therapies (e.g., anti-angiogenic agents like bevacizumab) or clinical trials recommended EOC subtypes classified based on histological features and prevalence, prevalence, prognosis, treatment considerations Fig. 2 Schematic diagram illustrating the potential origins of EOC. Modified from Klymenko et al. [ 16 ] Schematic diagram illustrating the potential origins of EOC. Modified from Klymenko et al. [ 16 ] Accurate classification of tumors, including molecular and epigenetic profiling, is essential for guiding targeted therapies and personalized treatment strategies. The treatment of EOC is primarily based on a combination of surgical resection and chemotherapy [ 17 , 18 ]. Surgery aims to remove as much of the tumor as possible, while chemotherapy is used to kill remaining cancer cells and prevent recurrence [ 19 ]. Platinum-based chemotherapies, such as carboplatin and cisplatin, are commonly used due to their effectiveness in targeting rapidly dividing cells [ 20 ]. Additionally, taxanes, such as paclitaxel, are frequently combined with platinum agents to increase efficacy. Despite the initial success of these treatments, the recurrence rate is high, which makes ongoing research into novel therapeutic options crucial [ 21 ]. Targeted therapies and immunotherapy are emerging treatment modalities, offering hope for better long-term outcomes [ 22 ]. Within this context, the PI3K pathway is frequently upregulated in EOC and plays a significant role in chemoresistance and the maintenance of genomic stability. This pathway influences DNA replication and cell cycle regulation. Inhibition of PI3K may induce genomic instability and mitotic catastrophe by decreasing the activity of spindle assembly checkpoint proteins such as Aurora kinase B, thereby increasing lagging chromosomes during prometaphase [ 23 ]. Drugs like bevacizumab, which inhibits tumor blood vessel growth, and poly(ADP-ribose) polymerase (PARP) inhibitors, which target specific genetic vulnerabilities in cancer cells, are becoming integral components of EOC treatment regimens [ 24 ]. Importantly, BRCA1/2 status can guide patient counseling, as carriers typically respond more favorably to platinum-based chemotherapy and demonstrate improved survival outcomes, despite often being diagnosed at later stages and with higher-grade tumors [ 25 , 26 ]. Table 3 summarizes the drug treatment options available for EOC. Table 3 Drug treatment options for EOC Drug Class Example drugs Mechanism of action Platinum-based chemotherapy Carboplatin, Cisplatin Inhibit DNA replication in cancer cells Taxanes Paclitaxel, Docetaxel Disrupt microtubules and prevent cell division Targeted therapy Bevacizumab, PARP inhibitors Target specific cancer pathways or genetic mutations Drug treatment options for EOC The treatment of EOC remains an intricate challenge due to the interplay of numerous interdependent factors. These include the heterogeneous nature of tumor biology, varying patient responses to therapy, high recurrence rates, the emergence of drug resistance, and patient-specific characteristics such as age, genetic background, and overall health condition. Selecting the most suitable therapeutic approach, therefore, requires careful consideration of a wide range of clinical and practical aspects. While accurate tumor classification and molecular profiling provide valuable guidance, clinicians often face the difficult task of balancing treatment efficacy, safety, cost-effectiveness, route of administration, patient adherence, and the likelihood of resistance. This multifaceted decision-making process can easily become subjective or inconsistent, particularly in the absence of structured evaluation frameworks. Given these complexities, there is a clear need for systematic and transparent tools that can assist clinicians in weighing multiple criteria and making evidence-based therapeutic decisions. To address this gap, the present study adopts the Analytical Hierarchy Process (AHP)—a structured, multi-criteria decision-making framework designed to support rational and transparent evaluation of treatment options. Through expert-driven pairwise comparisons, the AHP enables a quantitative assessment of six key dimensions: therapeutic efficacy, safety, route of administration, economic viability, patient compliance, and drug resistance. By assigning relative weights to each of these criteria, the model facilitates the ranking of available drug therapies based on both clinical value and practical feasibility. This research integrates expert clinical judgment with a structured analytical approach, thereby promoting more objective, consistent, and transparent decision-making in EOC management. The findings are expected to contribute to more effective drug selection strategies, enhance prognostic modeling, and ultimately improve patient outcomes through a more personalized and evidence-informed treatment process.