Development and Validation of a KELIM-Based Prognostic Nomogram for Long-Term Survival in Epithelial Ovarian Cancer: A 10-Year Single-Center Study

Development and Validation of a KELIM-Based Prognostic Nomogram for Long-Term Survival in Epithelial Ovarian Cancer: A 10-Year Single-Center Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a KELIM-Based Prognostic Nomogram for Long-Term Survival in Epithelial Ovarian Cancer: A 10-Year Single-Center Study Zhongkang Li, Qianqian Liu, Guohong Qi, Yibin Liu, Zhiqiang Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7244619/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Epithelial ovarian cancer (EOC) remains the most lethal gynecologic malignancy worldwide, with late-stage diagnosis and poor long-term survival being major clinical challenges. This study aimed to identify key prognostic factors associated with long-term survival in patients with EOC and to develop a predictive nomogram for individualized risk assessment. A total of 100 patients diagnosed with EOC between April 2014 and October 2019 at the Second Hospital of Hebei Medical University were retrospectively enrolled. All patients underwent primary comprehensive staging or cytoreductive surgery followed by platinum-based chemotherapy. Clinical, surgical, and biochemical variables, including age, FIGO stage, histologic subtype, ascites volume, residual tumor size, preoperative CA125 levels, and the CA125 kinetic parameter KELIM, were collected and analyzed. Survival data were followed up through October 2024, with overall survival as the primary endpoint. Univariate and multivariate Cox regression identified FIGO stage, postoperative residual tumor size, and KELIM score as independent predictors of overall survival. A nomogram integrating these three factors was constructed and internally validated using bootstrapping. The model demonstrated excellent discrimination (C-index = 0.86) and calibration, outperforming FIGO stage alone. Time-dependent ROC analysis demonstrated that the nomogram achieved AUCs of 0.808, 0.945, and 0.950 for predicting 1-, 3-, and 5-year overall survival, respectively, outperforming FIGO stage alone (AUCs of 0.714, 0.824, and 0.889). These findings highlight that integrating the KELIM score with conventional staging and postoperative residual disease significantly improves prognostic accuracy in EOC and enables tailored therapeutic decision-making based on individual tumor response. Epithelial ovarian cancer KELIM score Prognostic Nomogram Long-term survival Retrospective study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy and represents a significant global health challenge( 1 ). In 2022, an estimated over 320,000 women were diagnosed with ovarian cancer, and more than 206,000 died from the disease worldwide, making it the eighth leading cause of cancer-related death in women( 2 ). The mortality-to-incidence ratio remains high, especially in low- and middle-income countries, where delayed diagnosis and limited access to specialized care contribute to poor outcomes. In China, both the incidence and mortality of EOC have steadily increased, reflecting demographic aging and insufficient early screening strategies( 3 ). EOC is a complex malignancy arising primarily from the ovarian surface epithelium or related tissues such as the fallopian tubes and peritoneum( 4 ). While its precise etiology remains incompletely understood, several reproductive, hormonal, and genetic factors have been implicated( 5 ). Increased lifetime ovulatory cycles, marked by early menarche, late menopause, nulliparity, or infertility, are associated with heightened EOC risk, likely due to repeated epithelial trauma and repair( 2 ). Hormonal influences, including elevated gonadotropins and estrogen levels during ovulation, may also stimulate abnormal cell proliferation in the ovarian epithelium( 6 ). Conversely, protective factors such as parity, oral contraceptive use, breastfeeding, and tubal ligation have been shown to reduce the risk by disrupting or suppressing ovulatory cycles( 2 ). Additionally, germline mutations in BRCA1 or BRCA2 genes confer a substantially increased risk, underscoring the role of genetic susceptibility in disease pathogenesis( 7 ). EOC is a heterogeneous disease encompassing multiple histological subtypes, including High-grade serous ovarian cancer (HGSOC), low-grade ser (LGSOC), endometrioid, mucinous, and clear cell carcinomas( 8 ). Among these, HGSC accounts for the majority of cases and is characterized by aggressive behavior and frequent late-stage diagnosis. Each histocyte differs in terms of molecular profile, response to therapy, and clinical outcome, necessitating subtype-specific research to improve prognostic and therapeutic strategies. One of the primary reasons for the poor prognosis of EOC is its insidious onset and lack of specific symptoms( 9 ). More than 70% of patients are diagnosed at an advanced stage (FIGO III–IV), when the disease has often spread throughout the peritoneal cavity( 10 ). Although significant advances have been made in surgical techniques and systemic therapies over the past decades, the 5-year overall survival (OS) rate for patients with advanced EOC remains disappointingly, typically under 40%( 11 ). Historically, prognostic evaluation in EOC has relied on a limited set of clinicopathological variables. Among these, FIGO stage, tumor histology, tumor grade, and the extent of residual disease after cytoreductive surgery are the most established( 12 ). Achieving complete macroscopic cytoreduction (no gross residual tumor) has been shown to significantly improve survival and remains a major goal of primary debulking surgery( 13 ). However, these parameters, while informative, are largely static and anatomical in nature. They do not reflect the biological behavior of tumors or their response to chemotherapy, which are both crucial determinants of clinical outcome in the era of individualized oncology. The serum biomarker CA125 has long been used in ovarian cancer for diagnosis, surveillance, and response evaluation( 14 ). Nevertheless, its prognostic value remains controversial. Preoperative CA125 levels vary greatly among patients and are influenced by non-malignant conditions such as endometriosis, menstruation, and peritoneal inflammation. Moreover, absolute CA125 levels, either before or after treatment, do not consistently correlate with survival outcomes ( 15 ). Thus, relying on CA125 as a static variable for risk stratification has limited clinical utility. To address these limitations, researchers have developed dynamic models based on CA125 kinetics during chemotherapy. Among these, the CA125 elimination rate constant K (commonly referred to as KELIM) has emerged as a robust and reproducible biomarker of tumor chemosensitivity. KELIM is calculated from serial CA125 measurements obtained during the first three cycles of chemotherapy, using a nonlinear mixed-effect model( 16 ). It reflects the rate at which tumor cells are eradicated under the effect of platinum-based agents, thereby providing insight into intrinsic tumor biology. Multiple prospective studies, including the GOG-0218( 17 ) and CALYPSO( 18 ) trials, have demonstrated the prognostic relevance of KELIM in patients with EOC. High KELIM scores are associated with better progression-free survival (PFS), overall survival, and increased sensitivity to platinum-based chemotherapy and maintenance therapy with bevacizumab or PARP inhibitors. Importantly, KELIM retains its predictive power independent of FIGO stage and residual tumor burden, suggesting it captures aspects of tumor behavior not accounted for by traditional staging systems( 18 ). In the era of precision medicine, there is growing interest in developing integrated prognostic models that combine multiple dimensions of patient and tumor characteristics. Nomograms, statistical tools that incorporate various weighted variables into a user-friendly scoring system, have been increasingly used to support individualized risk prediction and therapeutic decision-making( 19 ). Several nomograms for ovarian cancer have been proposed, incorporating factors such as stage, histology, tumor markers, and genomic features ( 20 ). However, few models to date have included dynamic treatment response indicators like KELIM, and even fewer have been validated in real-world cohorts from East Asian populations. In this study, we retrospectively analyzed clinical, surgical, and biochemical data from 100 patients with epithelial ovarian cancer treated at the Second Hospital of Hebei Medical University between April 2014 and October 2019. Our aim was to identify independent prognostic factors for overall survival and to construct a nomogram incorporating these variables, particularly the KELIM score, to improve survival prediction and clinical applicability. To our knowledge, this is one of the few studies in a Chinese population to integrate FIGO stage, residual tumor size, and KELIM into a single predictive model. By moving beyond static anatomical staging and incorporating dynamic measures of chemosensitivity, our study seeks to provide a more accurate and clinically relevant tool for prognostic assessment in EOC. In addition to its statistical validity, our model has practical value: the variables required, FIGO stage, surgical findings, and serial CA125 values, are readily available in most oncology centers. If validated in broader cohorts, such a model could assist clinicians in tailoring follow-up intensity, selecting patients for maintenance therapy, or considering clinical trial enrollment for those with predicted poor outcomes. Ultimately, improving survival in EOC requires not only therapeutic advances but also better tools to match treatment intensity with individual patient risk. Materials and Methods 2.1 Study Design and Patient Selection This was a retrospective observational cohort study conducted at the Second Hospital of Hebei Medical University, a tertiary referral center in northern China. The study aimed to evaluate prognostic factors for OS in patients with EOC and to develop an individualized prognostic model. The study period ranged from April 2014 to October 2019. Patients were included if they met all of the following criteria: (1) histopathologically confirmed diagnosis of epithelial ovarian cancer based on WHO classification; (2) receipt of initial treatment at our institution, including primary comprehensive staging surgery or cytoreductive debulking followed by platinum-based chemotherapy; (3) available preoperative and peri-chemotherapy serum CA125 measurements for KELIM modeling; (4) complete clinical, surgical, pathological, and follow-up data; and (5) age ≥18 years. Patients were excluded if they had: (1) borderline ovarian tumors or non-epithelial histologic types (e.g., germ cell or sex cord-stromal tumors); (2) recurrent disease at initial presentation; (3) incomplete baseline information or follow-up; or (4) previous treatment for malignancy prior to EOC diagnosis. A total of 100 patients met the inclusion criteria and were enrolled in the final analysis. Ethical approval was obtained from the institutional review board, and informed consent was waived due to the retrospective design. 2.2 Clinical and Pathological Data Collection Clinical and pathological data were retrospectively collected from the electronic medical records system of the Second Hospital of Hebei Medical University. A total of 100 patients with EOC who met the inclusion and exclusion criteria were identified. Clinical information included age at diagnosis, height, weight, body mass index (BMI, kg/m²), menstrual history, marital and reproductive history, menopausal status, and history of comorbidities such as hypertension and diabetes. Intraoperative data comprised the presence and volume of ascites, cytological findings of malignant cells in ascites, presence of omental metastasis, liver metastasis, and the status of residual tumor after surgery. Postoperative data included the time interval from surgery to initiation of chemotherapy and the number of chemotherapy cycles completed. Pathological information covered FIGO stage and histological subtype. Laboratory data consisted of postoperative CA125 levels and serial CA125 measurements obtained after each chemotherapy cycle during the first six cycles, which were used for KELIM calculation. Follow-up data were collected using a standardized patient follow-up interview form developed for this study ( Supplementary File 1 ) through telephone interviews or outpatient clinic visits, with death as the primary endpoint. The follow-up period was measured in months, starting from the date of hospital admission and ending at death or the last follow-up, with October 1, 2024, as the cutoff date. The median follow-up duration was 45.5 months (range, 11–116 months). OS was defined as the interval from surgery to death or last follow-up. 2.3 KELIM Score Calculation The KELIM was used as a dynamic biomarker of tumor chemosensitivity in ovarian cancer. KELIM was calculated using a validated nonlinear mixed-effects model based on longitudinal CA125 values obtained during the first three cycles of chemotherapy. The model was applied through the publicly available online tool (http://www.biomarker-kinetics.org/CA-125-neo), which generates an individualized KELIM value per patient. Following established methodology, patients were classified into two KELIM categories: high (K ≥ 1.0) and low (K < 1.0). A high KELIM score indicates favorable chemosensitivity and rapid CA125 decline, while a low score suggests a suboptimal response to chemotherapy. 2.4 Follow-Up and Outcome Definition Patients were followed at regular intervals (every 3 months for the first 2 years, every 6 months thereafter) through outpatient visits and telephone interviews. Clinical assessments included physical examination, imaging (CT or ultrasound), and CA125 measurement. The primary endpoint was OS, defined as the interval from the date of initial surgery to the date of death from any cause or the last confirmed follow-up. Follow-up was completed by October 1, 2024. Median follow-up duration and follow-up rate were calculated accordingly. 2.5 Ethical Approval This retrospective study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and relevant national regulations. Ethical approval was granted by the Research Ethics Committee of the Second Hospital of Hebei Medical University (Approval No. 2025-R271). Given the retrospective nature of the study and the use of anonymized clinical data, the requirement for written informed consent was waived by the ethics committee. All patient data were de-identified prior to analysis to ensure privacy and confidentiality. 2.6 Statistical Analysis All statistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA) and R version 4.4.0 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were assessed for normality using the Kolmogorov-Smirnov test. Normally distributed variables were expressed as mean ± standard deviation (SD), while skewed variables were reported as median (interquartile range). Categorical variables were described as frequencies and percentages. Comparisons between groups were made using the chi-square test or Fisher’s exact test for categorical variables, and Student’s t-test or Mann–Whitney U-test for continuous variables, as appropriate. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off points for continuous variables such as age and ascites volume in relation to OS. Survival curves were constructed using the Kaplan–Meier method and compared with the log-rank test. Prognostic variables were first screened by univariate Cox proportional hazards regression. Variables with a P-value <0.05 were entered into a multivariate Cox model to identify independent prognostic factors. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. 2.7 Nomogram Construction and Model Validation Based on the independent predictors identified in multivariate analysis, a prognostic nomogram was developed using the “rms” and “survival” packages in R. The nomogram was designed to predict 1-, 3-, and 5-year overall survival probabilities for individual patients. Internal validation was performed using bootstrap resampling (1,000 iterations). Model discrimination was evaluated by calculating the concordance index (C-index). Calibration curves were generated to assess agreement between predicted and observed survival at each time point. Time-dependent ROC analysis was used to compare the nomogram’s performance against traditional FIGO staging. Clinical utility was assessed via decision curve analysis (DCA), which estimates the net benefit of applying the model across a range of threshold probabilities. Results 3.1 Baseline Characteristics and ROC-Derived Cutoffs A total of 100 patients with pathologically confirmed EOC were included in this retrospective cohort. The median age at diagnosis was 59 years (range, 38–79 years), and 72.0% of patients were younger than 59 (Table 1). HGSOC represented the most common histological subtype (61.0%), with non-serous types including mucinous, endometrioid, clear cell, mixed, and undifferentiated carcinomas accounting for the remaining 39.0%. According to FIGO staging, the majority of patients (55.0%) were diagnosed at stage III, followed by stage I (35.0%), stage II (8.0%), and stage IV (2.0%) (Table 1). Postoperative residual tumor burden was ≤1 cm in 62.0% of patients, while 38.0% had residual lesions >1 cm (Table 1). Elevated preoperative serum CA125 (≥35 U/mL) was observed in 87.0% of cases. Ascites was present in most patients, with 55.0% having a volume ≥175 mL, and malignant cells detected in 62.0% of ascitic samples (Table 1). Omental metastases were present in 52.0%, while liver metastases were rare (2.0%). A total of 51.0% had a KELIM score ≥1, suggesting favorable early chemosensitivity, while 49.0% had a score <1 (Table 1). To identify optimal dichotomization thresholds for continuous variables, ROC curve analysis was performed using OS as the outcome. The cutoff for age was determined to be 58.5 years (AUC = 0.627, sensitivity = 39.0%, specificity = 84.6%) (Figure 1), and for ascites volume, 175 mL (AUC = 0.687, sensitivity = 66.1%, specificity = 64.1%) (Figure 2). These thresholds were used to classify variables in subsequent univariate and multivariate analyses. Statistical comparisons between survivors and non-survivors demonstrated significant differences in age, histologic subtype, FIGO stage, residual tumor size, KELIM score, CA125 level, ascites volume, cytologic positivity, and omental metastasis (all P 0.05). Detailed baseline characteristics are summarized in Table 1. 3.2 Association Between KELIM Score and Clinicopathological Features Patients were further stratified by KELIM. Fifty-one patients (51.0%) had a KELIM score ≥1, while 49 (49.0%) had KELIM <1. As shown in Table 2, low KELIM values were significantly associated with older age, HGSOC histology, advanced FIGO stage, presence of residual tumor ≥1 cm, malignant ascitic cytology, and omental metastasis ( P < 0.001 for all). The median KELIM score for patients with stage I disease was 1.60 (IQR: 1.20–1.90), compared with 0.79 (IQR: 0.56–0.97) in those with stage III. Additionally, patients with higher KELIM scores (≥1) were more likely to have undergone optimal cytoreduction, had negative ascitic cytology, and received ≥6 chemotherapy cycles. No significant association was observed between KELIM and BMI, ascites volume, liver metastasis, or chemotherapy initiation time ( P > 0.05). 3.3 Survival Outcomes and Prognostic Factor Analysis During the follow-up period, the cumulative 1-, 3-, and 5-year OS rates were 96.0%, 70.0%, and 35.0%, respectively, with a median OS of 45.5 months (range: 11–116 months). Univariate Cox proportional hazards regression identified several variables significantly associated with OS, including age ≥59 years (HR = 4.25, 95% CI: 2.49–7.24, P < 0.001), histological subtype (HGSOC: HR = 10.00, P < 0.001), FIGO stage (stage III vs. I: HR = 18.65; stage IV vs. I: HR = 92.46, P < 0.001), KELIM <1 (HR = 14.29, 95% CI: 7.14–25.00, P < 0.001), residual tumor ≥1 cm (HR = 11.11, P < 0.001), elevated CA125 (≥35 U/mL: HR = 3.88, P = 0.022), ascites volume ≥175 mL (HR = 2.23, P = 0.004), presence of malignant cells in ascites (HR = 3.33, P < 0.001), omental metastasis (HR = 7.69, P < 0.001), and liver metastasis (HR = 7.83, P = 0.048) (Table 3). By contrast, BMI, timing of initiation of postoperative chemotherapy, and total number of chemotherapy cycles were not significantly associated with OS ( P > 0.05 for all). Variables with P < 0.05 in univariate analysis were included in a multivariate Cox regression model using backward stepwise selection. The final model identified FIGO stage, KELIM score, and residual tumor size as independent prognostic factors for OS (Table 4). Compared with FIGO stage I, the hazard ratios for stages II, III, and IV were 9.45 ( P = 0.003), 8.06 ( P < 0.001), and 25.30 ( P < 0.001), respectively. A KELIM score ≥1 was associated with significantly lower risk of death (HR = 0.16, 95% CI: 0.06–0.39, P < 0.001), and patients with no or minimal residual disease (≤1 cm) had improved survival (HR = 0.39, 95% CI: 0.20–0.78, P = 0.008). 3.5 Kaplan–Meier Survival Analysis Kaplan–Meier survival analysis confirmed that FIGO stage, KELIM score, and postoperative residual tumor size were all significantly associated with overall survival in patients with epithelial ovarian cancer ( P < 0.001 for all; Figures 3–5). Patients with early-stage disease (FIGO I–II), higher KELIM scores (≥1), and minimal or no residual disease (≤1 cm) exhibited significantly better long-term survival than their counterparts. The survival curves for each subgroup demonstrated clear stratification, supporting the prognostic relevance of these three variables. The median OS was notably shorter among patients with KELIM 1 cm, or advanced-stage disease. 3.6 Nomogram Construction and Validation A prognostic nomogram was developed based on the three independent predictors identified in the multivariate Cox regression analysis: FIGO stage, KELIM score, and postoperative residual tumor size. Each variable was assigned a weighted score according to its regression coefficient, and the total score was used to estimate the probability of 1-, 3-, and 5-year overall survival (Figure 6). The graphical interface of the nomogram enables straightforward clinical application by summing points corresponding to each patient’s characteristics. Internal validation using bootstrapping (1000 resamples) demonstrated good calibration, with predicted survival probabilities closely matching observed outcomes across all time points (Figure 7). The concordance index (C-index) of the nomogram was 0.86 (95% CI: 0.83–0.89), indicating strong discriminative power. Time-dependent ROC analysis further confirmed its superior predictive performance, with AUCs of 0.808, 0.945, and 0.950 for 1-, 3-, and 5-year survival, respectively—substantially higher than those of FIGO stage alone (Figure 8 and 9). Decision curve analysis (DCA) demonstrated the net clinical benefit of the nomogram over a wide range of threshold probabilities, underscoring its practical utility in guiding individualized prognostic assessment (Figures 10–12). Discussion In this retrospective cohort study of patients with EOC at our center, we identified FIGO stage, postoperative residual tumor size, and the KELIM score as independent prognostic factors for OS. By integrating these variables into a nomogram, we constructed a robust and user-friendly prognostic model capable of predicting individualized 1-, 3-, and 5-year OS probabilities. Importantly, our findings highlight that dynamic biomarkers such as KELIM, reflecting early chemosensitivity, can markedly enhance prognostic accuracy over models relying solely on anatomical staging or static biomarkers. As in previous research, FIGO staging remains the cornerstone of prognostic assessment and therapeutic decision-making in EOC(21). Our analysis reaffirmed FIGO stage as the most powerful predictor of survival, with patients diagnosed at early stages (I–II) demonstrating significantly better outcomes than those with stage III–IV disease. These findings are consistent with established epidemiologic and clinical data(22). The sharp gradient in hazard ratios across FIGO stages, particularly the pronounced risk in stage IV, underscores the critical importance of early detection and accurate surgical staging. However, relying on FIGO stage alone is insufficient, as it does not account for substantial individual differences in treatment response among EOC patients, limiting its utility as a sole prognostic tool(23). Postoperative residual tumor size has also been established as a critical determinant of prognosis in EOC(24), and our data are consistent with this paradigm. Patients who achieved optimal cytoreduction (residual disease ≤1 cm) exhibited significantly prolonged survival, emphasizing that complete or near-complete removal of macroscopic disease is a key modifiable factor influencing patient outcomes. This likely reflects both tumor biology and the quality of surgical intervention(25). Maximal cytoreduction may also enhance chemotherapy efficacy by reducing the reservoir of resistant tumor clones, supporting the necessity of comprehensive surgical efforts in high-volume, experienced centers(26). Notably, our study emphasizes the clinical significance of the KELIM score as a dynamic, early marker of tumor chemosensitivity. Unlike static serum CA125 values, which may be confounded by tumor burden, peritoneal inflammation, or benign conditions, KELIM provides a dynamic, pharmacodynamic measure of tumor chemosensitivity(27). Our data demonstrate that a high KELIM score (≥1.0) is strongly predictive of prolonged survival, with patients in this group showing nearly an 84% reduction in mortality risk compared to those with low scores. Importantly, the prognostic value of KELIM remained significant even after adjusting for traditional clinical variables. These results are aligned with findings from international studies and randomized trials such as ICON-7(28) and CALYPSO(18), where KELIM consistently emerged as a robust predictor of progression-free and overall survival. Furthermore, emerging evidence suggests that KELIM is associated with the likelihood of benefit from maintenance therapies, including bevacizumab(29) and PARP inhibitors(30), thus providing a potential tool for individualized therapeutic stratification. Our analysis also elucidated the associations between KELIM score and clinicopathological characteristics. Higher KELIM scores were more commonly observed in patients with early-stage disease, non-serous histology, optimal debulking, and absence of malignant ascites, all markers of more favorable tumor biology. Conversely, lower KELIM values were seen in advanced-stage, high-grade serous subtypes, and patients with significant residual disease. These findings suggest that KELIM may serve as a surrogate not only for chemotherapy response but also for overall tumor aggressiveness. The nomogram we developed demonstrated excellent discriminative capacity (C-index 0.86) and calibration, as shown by its high AUCs for 1-, 3-, and 5-year survival (0.808, 0.945, and 0.950, respectively), all surpassing the performance of FIGO stage alone (C-index, 0.72; AUCs, 0.714, 0.824, and 0.889, for 1-, 3-, and 5-year respectively). Decision curve analysis further confirmed its net clinical benefit, supporting its potential value for real-world risk stratification and decision-making. As the nomogram utilizes routinely available clinical and laboratory data, it is readily applicable in daily practice without imposing additional costs or technical barriers. In the context of clinical application, our model offers several practical advantages. A noteworthy implication of our findings is the potential for KELIM to serve not only as a prognostic biomarker, but also as a real-time tool to guide dynamic adaptation of therapeutic strategies in EOC. Traditional prognostic models are often limited by static, baseline clinical or pathological variables, which cannot capture the evolving nature of tumor response during treatment. In contrast, KELIM provides an early, quantitative measure of individual tumor chemosensitivity within the initial cycles of chemotherapy. This opens the door to “response-guided therapy,” whereby real-time assessment of KELIM could inform early intensification, modification, or de-escalation of systemic treatment regimens. For example, patients demonstrating suboptimal KELIM kinetics could be candidates for alternative chemotherapy combinations, early addition of targeted agents, or enrollment in clinical trials(17). Meanwhile, for patients with high KELIM scores and optimal cytoreduction, de-escalation strategies (such as limiting the number of chemotherapy cycles) could be safely considered, particularly in elderly or frail populations. Incorporating KELIM-driven adaptive strategies into clinical protocols may further improve survival and optimize quality of life, representing a significant advance toward truly personalized medicine in EOC. Future prospective studies are warranted to validate the feasibility, safety, and long-term benefit of this approach in diverse patient populations. Several limitations should be acknowledged. The retrospective, single-center design and moderate sample size may introduce bias and limit the generalizability of our findings. Although the nomogram was internally validated using bootstrap resampling, external validation in larger and more diverse cohorts is warranted. In addition, our study did not include molecular biomarkers such as BRCA mutation or homologous recombination deficiency status, now recognized as important determinants of response, especially to PARP inhibitors. Nevertheless, in resource-limited settings where molecular testing is not universally available, KELIM provides valuable, actionable prognostic information. Conclusion In conclusion, our findings support the integration of FIGO stage, residual tumor status, and KELIM score into a comprehensive prognostic model for epithelial ovarian cancer. The resulting nomogram demonstrates strong predictive performance and may serve as a valuable clinical tool for individualized risk assessment and therapeutic decision-making. The incorporation of KELIM into standard prognostic evaluation reflects a shift toward dynamic, response-based oncology, with the potential to optimize outcomes in a disease characterized by significant biological heterogeneity and therapeutic challenges. Declarations Acknowledgements Not applicable. Funding This study was supported by the following funding sources: the Natural Science Foundation of Hebei Province, S&T Program of Hebei, Project No. H2023206356; the Natural Science Foundation of Hebei Province, S&T Program of Hebei, Project No. H2024206427; the Natural Science Foundation of Hebei Province, S&T Program of Hebei, Project No. H2024206433; the Hebei Provincial Central Government-Guided Local Science and Technology Development Fund Project (Science and Technology Innovation Base Project), S&T Program of Hebei, Project No. 236Z7756G; the 2024 Hebei Provincial Government-Sponsored Clinical Medicine Outstanding Talent Training Program, Project No. ZF2024040; and the 2025 Hebei Provincial Government-Sponsored Clinical Medicine Outstanding Talent Training Program, Project No. ZF2025102. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Author’s contributions Qianqian Liu, Zhongkang Li, Li Meng and Xianghua Huang conceived and designed the study. Qianqian Liu, Li Meng and Zhongkang Li performed data analysis and manuscript preparation. Qianqian Liu and Guohong Qi were responsible for figure preparation. Zhiqiang Zhang, Jinchai Zhao, Yanfang Du and Xianghua Huang discussed and revised the manuscript. Xianghua Huang and Li Meng reviewed and finalized the manuscript. All authors have read and approved the final manuscript. Meng Li and Xianghua Huang are the corresponding authors and confirm the authenticity of all the raw data. Ethics approval and consent to participate This study was approved by the Research Ethics Committee of the Second Hospital of Hebei Medical University (Approval No. 2025-R271). 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You B, Colomban O, Heywood M, Lee C, Davy M, Reed N, et al. The strong prognostic value of KELIM, a model-based parameter from CA 125 kinetics in ovarian cancer: data from CALYPSO trial (a GINECO-GCIG study). Gynecol Oncol. 2013;130(2):289–94. 10.1016/j.ygyno.2013.05.013 . Caulfield S, Menezes G, Marignol L, Poole C. Nomograms are key decision-making tools in prostate cancer radiation therapy. Urol Oncol. 2018;36(6):283–92. 10.1016/j.urolonc.2018.03.017 . Gao J, Wang S, Li F, Xu H, Li X, Yan L, et al. Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Young Patients with Epithelial Ovarian Cancer: Analysis Based on SEER Program. Adv Ther. 2022;39(1):257–85. 10.1007/s12325-021-01955-9 . Javadi S, Ganeshan DM, Qayyum A, Iyer RB, Bhosale P. Ovarian Cancer, the Revised FIGO Staging System, and the Role of Imaging. AJR Am J Roentgenol. 2016;206(6):1351–60. 10.2214/AJR.15.15199 . Paik ES, Lee YY, Lee EJ, Choi CH, Kim TJ, Lee JW, et al. 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Tables Table 1 Clinicopathological Data and Baseline Characteristics of Patients with Epithelial Ovarian Cancer Variable Total (n=100) Survived (n=40) Deceased (n=60) P value Age <0.001 <59 years 72 (72.0%) 38 (95.0%) 34 (56.7%) ≥59 years 28 (28.0%) 2 (5.0%) 26 (43.3%) BMI 0.683 <24 kg/m² 50 (50.0%) 21 (52.5%) 29 (48.3%) ≥24 kg/m² 50 (50.0%) 19 (47.5%) 31 (51.7%) Histological type <0.001 HGSOC 61 (61.0%) 10 (25.0%) 51 (85.0%) Others 39 (39.0%) 30 (75.0%) 9 (15.0%) FIGO stage <0.001 Stage I 35 (35.0%) 30 (75.0%) 5 (8.3%) Stage II 8 (8.0%) 4 (10.0%) 4 (6.7%) Stage III 55 (55.0%) 6 (15.0%) 49 (81.7%) Stage IV 2 (2.0%) 0 (0.0%) 2 (3.3%) KELIM score <0.001 <1 49 (49.0%) 5 (12.5%) 44 (73.3%) ≥1 51 (51.0%) 35 (87.5%) 16 (26.7%) Residual tumor 1 cm 38 (38.0%) 3 (8.6%) 35 (58.3%) ≤1 cm 62 (62.0%) 37 (92.5%) 25 (41.7%) Pre-op CA125 0.004 <35 U/mL 13 (13.0%) 10 (25.0%) 3 (5.0%) ≥35 U/mL 87 (87.0%) 30 (75.0%) 57 (95.0%) Ascites volume 0.004 <175 mL 45 (45.0%) 25 (62.5%) 20 (33.3%) ≥175 mL 55 (55.0%) 15 (37.5%) 40 (66.7%) Malignant cells in ascites 0.001 Absent 38 (38.0%) 23 (57.5%) 15 (25.0%) Present 62 (62.0%) 17 (42.5%) 45 (75.0%) Omental metastasis <0.001 Absent 48 (48.0%) 34 (85.0%) 14 (23.3%) Present 52 (52.0%) 6 (15.0%) 46 (76.7%) Liver metastasis 0.515 Absent 98 (98.0%) 40 (100.0%) 58 (96.7%) Present 2 (2.0%) 0 (0.0%) 2 (3.3%) Chemo start (days) 0.096 6 46 (46.0%) 14 (35.0%) 32 (52.3%) Table 2. Association Between KELIM Score and Clinicopathological Parameters in 100 Ovarian Cancer Patients Variable n KELIM Median (P25–P75) P value KELIM <1 (n=49) KELIM ≥1 (n=51) P value Age <59 72 1.20 (0.86–1.60) <0.001 27 45 <0.001 Age ≥59 28 0.73 (0.44–0.97) 22 6 BMI <24 kg/m² 50 1.10 (0.75–1.50) 0.915 23 27 0.548 BMI ≥24 kg/m² 50 0.99 (0.74–1.63) 26 24 HGSOC 61 0.82 (0.57–1.10) <0.001 45 16 <0.001 Non-HGSOC 39 1.50 (1.20–1.80) 4 35 FIGO I 35 1.60 (1.20–1.90) <0.001 3 32 <0.001 FIGO II 8 1.45 (1.10–1.73) 1 7 FIGO III 55 0.79 (0.56–0.97) 43 12 FIGO IV 2 0.48 (0.28– ) 2 0 Residual ≤1 cm 62 1.40 (1.10–1.70) <0.001 12 50 1 cm 38 0.70 (0.47–0.83) 37 1 CA125 <35 U/mL 13 1.50 (0.98–1.75) 0.012 4 9 0.266 CA125 ≥35 U/mL 87 0.97 (0.71–1.50) 45 42 Ascites <175 mL 45 1.10 (0.83–1.70) 0.052 19 26 0.220 Ascites ≥175 mL 55 0.91 (0.66–1.40) 30 25 No malignant cells in ascites 38 1.50 (1.08–1.73) <0.001 8 30 <0.001 Malignant cells in ascites 62 0.84 (0.57–1.30) 41 21 No omental metastasis 48 1.50 (1.10–1.80) <0.001 7 41 <0.001 Omental metastasis 52 0.78 (0.51–0.93) 42 10 No liver metastasis 98 1.10 (0.76–1.60) 0.307 47 51 0.457 Liver metastasis 2 2 0 Chemo start 6 46 0.92 (0.70–1.23) 28 18 Table 3. Univariate Cox Proportional Hazards Model for Overall Survival in Epithelial Ovarian Cancer Variable HR 95% CI P-value Age (years) <59 1.00 ≥59 4.25 2.49–7.24 <0.001 BMI (kg/m²) <24 1.00 ≥24 1.10 0.66–1.83 0.706 Histology High-grade serous carcinoma 1.00 Others 0.10 0.05–0.22 <0.001 FIGO Stage Stage I 1.00 Stage II 6.38 1.68–24.16 0.006 Stage III 18.65 7.25–47.97 <0.001 Stage IV 92.46 15.91–534.47 <0.001 KELIM score <1 1.00 ≥1 0.007 0.04–0.14 <0.001 Residual tumor (cm) ≥1 1.00 ≤1 0.009 0.05–0.16 <0.001 Preoperative CA125 (U/mL) <35 1.00 ≥35 3.88 1.21–12.42 0.022 Ascites volume (mL) <175 1.00 ≥175 2.23 1.30–3.82 0.004 Malignant cells in ascites Absent 1.00 Present 3.33 1.85–6.01 <0.001 Omental metastasis Absent 1.00 Present 7.69 4.14–14.32 <0.001 Liver metastasis Absent 1.00 Present 7.83 1.02–60.24 0.048 Chemotherapy initiation (days) 6 1.00 ≤6 0.60 0.36–1.00 0.052 Table 4. Multivariate Cox Proportional Hazards Model for Overall Survival in Epithelial Ovarian Cancer Variable HR 95% CI P-value FIGO Stage Stage I 1.00 Stage II 9.45 2.10–42.59 0.003 Stage III 8.06 2.74–23.67 <0.001 Stage IV 25.30 3.94–162.53 1 0.16 0.06–0.39 <0.001 Residual tumor (cm) ≥1 1.00 ≤1 0.39 0.20–0.78 0.008 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 04 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Editor invited by journal 11 Aug, 2025 Submission checks completed at journal 10 Aug, 2025 First submitted to journal 10 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7244619","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512450170,"identity":"b4cf2bfd-17de-4d38-b0d9-dd610295de7e","order_by":0,"name":"Zhongkang Li","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhongkang","middleName":"","lastName":"Li","suffix":""},{"id":512450171,"identity":"7abbdff5-c55e-4d11-882a-16068311f18d","order_by":1,"name":"Qianqian Liu","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Liu","suffix":""},{"id":512450172,"identity":"756c7f31-10dd-4fa8-8654-8a2374f5ba67","order_by":2,"name":"Guohong Qi","email":"","orcid":"","institution":"Tianjin Central Hospital of Gynecology Obstetrics","correspondingAuthor":false,"prefix":"","firstName":"Guohong","middleName":"","lastName":"Qi","suffix":""},{"id":512450174,"identity":"81f3b61a-a30a-4594-9995-b99a33114ba7","order_by":3,"name":"Yibin Liu","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yibin","middleName":"","lastName":"Liu","suffix":""},{"id":512450175,"identity":"1262474a-51d0-4ff7-9c0d-b3af50c627a6","order_by":4,"name":"Zhiqiang Zhang","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Zhang","suffix":""},{"id":512450176,"identity":"a07f26d4-3d64-45c4-a99a-872603ca6e24","order_by":5,"name":"Jinchai Zhao","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinchai","middleName":"","lastName":"Zhao","suffix":""},{"id":512450178,"identity":"bea86d7d-2a52-440b-85b6-32bc0d460e5f","order_by":6,"name":"Yanfang Du","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanfang","middleName":"","lastName":"Du","suffix":""},{"id":512450180,"identity":"70b4ca8d-4fb2-4432-ac5d-f3341e169fa5","order_by":7,"name":"Li Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYLCCBwYM/AzsjY0PPhCtJcGAQbKB53Cz4QzitTAAtUikt0lzEKNat7338IuEgjsSBjcfNkgzMNjJ6TYQ0GJ25lyaRYLBMwmD24kNxgUMycZmBwhpuZFjZpBgcLjODKgleQbDgcRtBLXcfwPWImF282DDYR6itNzgMX4A1nKDsbGZOC1ncswYQH6xP5PYzDjDgBi/HD9j/OHDnzsSku3Hn//4UGEnR1ALELBJMDDAlBkQVg4CzB8QWkbBKBgFo2AUYAEA7mZKwU02L/0AAAAASUVORK5CYII=","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Meng","suffix":""},{"id":512450182,"identity":"7c0a00f3-e5d3-4a26-8610-13cad84fa434","order_by":8,"name":"Xianghua Huang","email":"","orcid":"","institution":"The Second Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianghua","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-07-29 14:53:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7244619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7244619/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91187767,"identity":"a77ca8e2-9f44-4061-90e3-35ca57d76f26","added_by":"auto","created_at":"2025-09-12 14:21:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve for Age.\u003c/strong\u003e Receiver operating characteristic (ROC) curve for age in predicting overall survival in patients with epithelial ovarian cancer (EOC). The optimal cutoff value was determined using the Youden index.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/8a2bd46c56101fac7e0a4cf8.png"},{"id":91187764,"identity":"151b6372-a988-48c8-989e-e66e6e8de1ac","added_by":"auto","created_at":"2025-09-12 14:21:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":259733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve for Ascites Volume. \u003c/strong\u003eROC curve for ascites volume in predicting overall survival in EOC patients. A cutoff value of 175 mL was selected based on maximum sensitivity and specificity.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/c050569a8bc499549025f8c2.png"},{"id":91189472,"identity":"fafaaec3-d876-4c2b-9a83-d0014cac2bd0","added_by":"auto","created_at":"2025-09-12 14:29:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":528311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier Survival by FIGO Stage. \u003c/strong\u003eKaplan–Meier survival curves stratified by FIGO stage (I–IV) in EOC patients. Advanced FIGO stages were significantly associated with poorer overall survival (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/80a6bbf486954edb168f0c2d.png"},{"id":91187766,"identity":"48ac9c19-e724-43e7-b2f1-1906156f45e8","added_by":"auto","created_at":"2025-09-12 14:21:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":563693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier Survival by KELIM Score. \u003c/strong\u003eKaplan–Meier survival curves comparing patients with KELIM score \u0026lt;1 versus ≥1. Higher KELIM scores were associated with significantly longer overall survival (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/86575deb246f75188ee208aa.png"},{"id":91189473,"identity":"94489313-99ee-444d-9d04-6bc84e93ec1d","added_by":"auto","created_at":"2025-09-12 14:29:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":505431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier Survival by Residual Disease\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan–Meier survival analysis based on postoperative residual disease size (\u0026gt;1 cm vs. ≤1 cm). Optimal cytoreduction (≤1 cm) was significantly associated with improved survival outcomes (P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/a9628b3437a130f1615ac00a.png"},{"id":91189476,"identity":"b70a6f95-f2f4-4e01-bfea-fb89fae441b7","added_by":"auto","created_at":"2025-09-12 14:29:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":377673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for Overall Survival Prediction. \u003c/strong\u003eNomogram predicting 1-, 3-, and 5-year overall survival in EOC patients based on FIGO stage, KELIM score, and postoperative residual disease. Points assigned to each variable can be summed to estimate survival probability at each time point.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/95ed79c20cb16526fe2181ec.png"},{"id":91193453,"identity":"32ac007d-390e-4fde-bf23-c267337b338b","added_by":"auto","created_at":"2025-09-12 14:45:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":354862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-dependent ROC Curves for FIGO Stage. \u003c/strong\u003eTime-dependent ROC curves for FIGO stage in predicting 1-, 3-, and 5-year survival in EOC patients. AUCs for 1-, 3-, and 5-year survival were 0.714, 0.824, and 0.889, respectively.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/2288e74c699e5a94447af2ba.png"},{"id":91190819,"identity":"5fb3f8c9-243f-4de2-8677-0e59eaa0d19a","added_by":"auto","created_at":"2025-09-12 14:37:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":483223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-dependent ROC Curves for Nomogram. \u003c/strong\u003eTime-dependent ROC curves for the nomogram model in predicting 1-, 3-, and 5-year survival. The model showed strong discriminative power with AUCs of 0.808, 0.945, and 0.950, respectively.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/cbac6d87ddb5fa4167d791ab.png"},{"id":91187777,"identity":"194e1ace-40d1-4b30-a6b4-ee1cde9f5a55","added_by":"auto","created_at":"2025-09-12 14:21:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":304179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration Plot of the Nomogram Model. \u003c/strong\u003eCalibration plot of the nomogram model for 1-, 3-, and 5-year survival predictions. The predicted survival probabilities closely matched actual survival outcomes, demonstrating good calibration.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/0a6db8cbd60f356a8f3fce1c.png"},{"id":91189481,"identity":"4d6a8189-7650-4485-8dfd-8216f22320e6","added_by":"auto","created_at":"2025-09-12 14:29:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":277160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCA Curve for 1-Year Survival. \u003c/strong\u003eDecision curve analysis (DCA) for the nomogram in predicting 1-year survival. The model showed a net clinical benefit across a range of threshold probabilities.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/e19dc77e33c9575874d83f00.png"},{"id":91189485,"identity":"8125f4cd-0149-4070-8318-d6403734896e","added_by":"auto","created_at":"2025-09-12 14:29:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":344898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCA Curve for 3-Year Survival. \u003c/strong\u003eDCA curve evaluating the clinical utility of the nomogram for predicting 3-year survival in EOC patients.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/e103cd75da3126ede76918c1.png"},{"id":91189483,"identity":"73f33b26-acf6-4040-a877-74c1c6414304","added_by":"auto","created_at":"2025-09-12 14:29:08","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":398397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCA Curve for 5-Year Survival. \u003c/strong\u003eDCA curve assessing the decision value of the nomogram for predicting 5-year survival in EOC patients. The model demonstrated favorable net benefit over treat-all or treat-none strategies.\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/0706af47f2b8ff1a10d23ce1.png"},{"id":91194494,"identity":"c43b312d-c199-4d2e-8b10-92ce05f9deb9","added_by":"auto","created_at":"2025-09-12 14:53:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6315122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7244619/v1/1e1da405-b206-4495-9a7b-23391e2a3d98.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a KELIM-Based Prognostic Nomogram for Long-Term Survival in Epithelial Ovarian Cancer: A 10-Year Single-Center Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEpithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy and represents a significant global health challenge(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2022, an estimated over 320,000 women were diagnosed with ovarian cancer, and more than 206,000 died from the disease worldwide, making it the eighth leading cause of cancer-related death in women(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The mortality-to-incidence ratio remains high, especially in low- and middle-income countries, where delayed diagnosis and limited access to specialized care contribute to poor outcomes. In China, both the incidence and mortality of EOC have steadily increased, reflecting demographic aging and insufficient early screening strategies(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEOC is a complex malignancy arising primarily from the ovarian surface epithelium or related tissues such as the fallopian tubes and peritoneum(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). While its precise etiology remains incompletely understood, several reproductive, hormonal, and genetic factors have been implicated(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Increased lifetime ovulatory cycles, marked by early menarche, late menopause, nulliparity, or infertility, are associated with heightened EOC risk, likely due to repeated epithelial trauma and repair(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Hormonal influences, including elevated gonadotropins and estrogen levels during ovulation, may also stimulate abnormal cell proliferation in the ovarian epithelium(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Conversely, protective factors such as parity, oral contraceptive use, breastfeeding, and tubal ligation have been shown to reduce the risk by disrupting or suppressing ovulatory cycles(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Additionally, germline mutations in BRCA1 or BRCA2 genes confer a substantially increased risk, underscoring the role of genetic susceptibility in disease pathogenesis(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEOC is a heterogeneous disease encompassing multiple histological subtypes, including High-grade serous ovarian cancer (HGSOC), low-grade ser (LGSOC), endometrioid, mucinous, and clear cell carcinomas(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Among these, HGSC accounts for the majority of cases and is characterized by aggressive behavior and frequent late-stage diagnosis. Each histocyte differs in terms of molecular profile, response to therapy, and clinical outcome, necessitating subtype-specific research to improve prognostic and therapeutic strategies.\u003c/p\u003e\u003cp\u003eOne of the primary reasons for the poor prognosis of EOC is its insidious onset and lack of specific symptoms(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). More than 70% of patients are diagnosed at an advanced stage (FIGO III\u0026ndash;IV), when the disease has often spread throughout the peritoneal cavity(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Although significant advances have been made in surgical techniques and systemic therapies over the past decades, the 5-year overall survival (OS) rate for patients with advanced EOC remains disappointingly, typically under 40%(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHistorically, prognostic evaluation in EOC has relied on a limited set of clinicopathological variables. Among these, FIGO stage, tumor histology, tumor grade, and the extent of residual disease after cytoreductive surgery are the most established(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Achieving complete macroscopic cytoreduction (no gross residual tumor) has been shown to significantly improve survival and remains a major goal of primary debulking surgery(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, these parameters, while informative, are largely static and anatomical in nature. They do not reflect the biological behavior of tumors or their response to chemotherapy, which are both crucial determinants of clinical outcome in the era of individualized oncology.\u003c/p\u003e\u003cp\u003eThe serum biomarker CA125 has long been used in ovarian cancer for diagnosis, surveillance, and response evaluation(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Nevertheless, its prognostic value remains controversial. Preoperative CA125 levels vary greatly among patients and are influenced by non-malignant conditions such as endometriosis, menstruation, and peritoneal inflammation. Moreover, absolute CA125 levels, either before or after treatment, do not consistently correlate with survival outcomes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Thus, relying on CA125 as a static variable for risk stratification has limited clinical utility. To address these limitations, researchers have developed dynamic models based on CA125 kinetics during chemotherapy. Among these, the CA125 elimination rate constant K (commonly referred to as KELIM) has emerged as a robust and reproducible biomarker of tumor chemosensitivity. KELIM is calculated from serial CA125 measurements obtained during the first three cycles of chemotherapy, using a nonlinear mixed-effect model(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It reflects the rate at which tumor cells are eradicated under the effect of platinum-based agents, thereby providing insight into intrinsic tumor biology. Multiple prospective studies, including the GOG-0218(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and CALYPSO(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) trials, have demonstrated the prognostic relevance of KELIM in patients with EOC. High KELIM scores are associated with better progression-free survival (PFS), overall survival, and increased sensitivity to platinum-based chemotherapy and maintenance therapy with bevacizumab or PARP inhibitors. Importantly, KELIM retains its predictive power independent of FIGO stage and residual tumor burden, suggesting it captures aspects of tumor behavior not accounted for by traditional staging systems(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In the era of precision medicine, there is growing interest in developing integrated prognostic models that combine multiple dimensions of patient and tumor characteristics. Nomograms, statistical tools that incorporate various weighted variables into a user-friendly scoring system, have been increasingly used to support individualized risk prediction and therapeutic decision-making(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Several nomograms for ovarian cancer have been proposed, incorporating factors such as stage, histology, tumor markers, and genomic features (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, few models to date have included dynamic treatment response indicators like KELIM, and even fewer have been validated in real-world cohorts from East Asian populations.\u003c/p\u003e\u003cp\u003eIn this study, we retrospectively analyzed clinical, surgical, and biochemical data from 100 patients with epithelial ovarian cancer treated at the Second Hospital of Hebei Medical University between April 2014 and October 2019. Our aim was to identify independent prognostic factors for overall survival and to construct a nomogram incorporating these variables, particularly the KELIM score, to improve survival prediction and clinical applicability. To our knowledge, this is one of the few studies in a Chinese population to integrate FIGO stage, residual tumor size, and KELIM into a single predictive model. By moving beyond static anatomical staging and incorporating dynamic measures of chemosensitivity, our study seeks to provide a more accurate and clinically relevant tool for prognostic assessment in EOC. In addition to its statistical validity, our model has practical value: the variables required, FIGO stage, surgical findings, and serial CA125 values, are readily available in most oncology centers. If validated in broader cohorts, such a model could assist clinicians in tailoring follow-up intensity, selecting patients for maintenance therapy, or considering clinical trial enrollment for those with predicted poor outcomes. Ultimately, improving survival in EOC requires not only therapeutic advances but also better tools to match treatment intensity with individual patient risk.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Patient Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective observational cohort study conducted at the Second Hospital of Hebei Medical University, a tertiary referral center in northern China. The study aimed to evaluate prognostic factors for OS in patients with EOC and to develop an individualized prognostic model. The study period ranged from April 2014 to October 2019. Patients were included if they met all of the following criteria: (1) histopathologically confirmed diagnosis of epithelial ovarian cancer based on WHO classification; (2) receipt of initial treatment at our institution, including primary comprehensive staging surgery or cytoreductive debulking followed by platinum-based chemotherapy; (3) available preoperative and peri-chemotherapy serum CA125 measurements for KELIM modeling; (4) complete clinical, surgical, pathological, and follow-up data; and (5) age ≥18 years. Patients were excluded if they had: (1) borderline ovarian tumors or non-epithelial histologic types (e.g., germ cell or sex cord-stromal tumors); (2) recurrent disease at initial presentation; (3) incomplete baseline information or follow-up; or (4) previous treatment for malignancy prior to EOC diagnosis. A total of 100 patients met the inclusion criteria and were enrolled in the final analysis. Ethical approval was obtained from the institutional review board, and informed consent was waived due to the retrospective design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Clinical and Pathological Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical and pathological data were retrospectively collected from the electronic medical records system of the Second Hospital of Hebei Medical University. A total of 100 patients with EOC who met the inclusion and exclusion criteria were identified. Clinical information included age at diagnosis, height, weight, body mass index (BMI, kg/m²), menstrual history, marital and reproductive history, menopausal status, and history of comorbidities such as hypertension and diabetes. Intraoperative data comprised the presence and volume of ascites, cytological findings of malignant cells in ascites, presence of omental metastasis, liver metastasis, and the status of residual tumor after surgery. Postoperative data included the time interval from surgery to initiation of chemotherapy and the number of chemotherapy cycles completed. Pathological information covered FIGO stage and histological subtype. Laboratory data consisted of postoperative CA125 levels and serial CA125 measurements obtained after each chemotherapy cycle during the first six cycles, which were used for KELIM calculation. Follow-up data were collected using a standardized patient follow-up interview form developed for this study (\u003cstrong\u003eSupplementary File 1\u003c/strong\u003e) through telephone interviews or outpatient clinic visits, with death as the primary endpoint. The follow-up period was measured in months, starting from the date of hospital admission and ending at death or the last follow-up, with October 1, 2024, as the cutoff date. The median follow-up duration was 45.5 months (range, 11–116 months). OS was defined as the interval from surgery to death or last follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 KELIM Score Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe KELIM was used as a dynamic biomarker of tumor chemosensitivity in ovarian cancer. KELIM was calculated using a validated nonlinear mixed-effects model based on longitudinal CA125 values obtained during the first three cycles of chemotherapy. The model was applied through the publicly available online tool (http://www.biomarker-kinetics.org/CA-125-neo), which generates an individualized KELIM value per patient. Following established methodology, patients were classified into two KELIM categories: high (K ≥ 1.0) and low (K \u0026lt; 1.0). A high KELIM score indicates favorable chemosensitivity and rapid CA125 decline, while a low score suggests a suboptimal response to chemotherapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Follow-Up and Outcome Definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were followed at regular intervals (every 3 months for the first 2 years, every 6 months thereafter) through outpatient visits and telephone interviews. Clinical assessments included physical examination, imaging (CT or ultrasound), and CA125 measurement. The primary endpoint was OS, defined as the interval from the date of initial surgery to the date of death from any cause or the last confirmed follow-up. Follow-up was completed by October 1, 2024. Median follow-up duration and follow-up rate were calculated accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and relevant national regulations. Ethical approval was granted by the Research Ethics Committee of the Second Hospital of Hebei Medical University (Approval No. 2025-R271). Given the retrospective nature of the study and the use of anonymized clinical data, the requirement for written informed consent was waived by the ethics committee. All patient data were de-identified prior to analysis to ensure privacy and confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA) and R version 4.4.0 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were assessed for normality using the Kolmogorov-Smirnov test. Normally distributed variables were expressed as mean ± standard deviation (SD), while skewed variables were reported as median (interquartile range). Categorical variables were described as frequencies and percentages. Comparisons between groups were made using the chi-square test or Fisher’s exact test for categorical variables, and Student’s t-test or Mann–Whitney U-test for continuous variables, as appropriate.\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off points for continuous variables such as age and ascites volume in relation to OS. Survival curves were constructed using the Kaplan–Meier method and compared with the log-rank test. Prognostic variables were first screened by univariate Cox proportional hazards regression. Variables with a P-value \u0026lt;0.05 were entered into a multivariate Cox model to identify independent prognostic factors. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Nomogram Construction and Model Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the independent predictors identified in multivariate analysis, a prognostic nomogram was developed using the “rms” and “survival” packages in R. The nomogram was designed to predict 1-, 3-, and 5-year overall survival probabilities for individual patients. Internal validation was performed using bootstrap resampling (1,000 iterations). Model discrimination was evaluated by calculating the concordance index (C-index). Calibration curves were generated to assess agreement between predicted and observed survival at each time point. Time-dependent ROC analysis was used to compare the nomogram’s performance against traditional FIGO staging. Clinical utility was assessed via decision curve analysis (DCA), which estimates the net benefit of applying the model across a range of threshold probabilities.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics and ROC-Derived Cutoffs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 100 patients with pathologically confirmed EOC were included in this retrospective cohort. The median age at diagnosis was 59 years (range, 38–79 years), and 72.0% of patients were younger than 59 (Table 1). HGSOC\u0026nbsp;represented the most common histological subtype (61.0%), with non-serous types including mucinous, endometrioid, clear cell, mixed, and undifferentiated carcinomas accounting for the remaining 39.0%.\u003c/p\u003e\n\u003cp\u003eAccording to FIGO staging, the majority of patients (55.0%) were diagnosed at stage III, followed by stage I (35.0%), stage II (8.0%), and stage IV (2.0%) (Table 1). Postoperative residual tumor burden was ≤1 cm in 62.0% of patients, while 38.0% had residual lesions \u0026gt;1 cm (Table 1). Elevated preoperative serum CA125 (≥35 U/mL) was observed in 87.0% of cases. Ascites was present in most patients, with 55.0% having a volume ≥175 mL, and malignant cells detected in 62.0% of ascitic samples (Table 1). Omental metastases were present in 52.0%, while liver metastases were rare (2.0%). A total of 51.0% had a KELIM score ≥1, suggesting favorable early chemosensitivity, while 49.0% had a score \u0026lt;1 (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify optimal dichotomization thresholds for continuous variables, ROC curve analysis was performed using OS as the outcome. The cutoff for age was determined to be 58.5 years (AUC = 0.627, sensitivity = 39.0%, specificity = 84.6%) (Figure 1), and for ascites volume, 175 mL (AUC = 0.687, sensitivity = 66.1%, specificity = 64.1%) (Figure 2). These thresholds were used to classify variables in subsequent univariate and multivariate analyses.\u003c/p\u003e\n\u003cp\u003eStatistical comparisons between survivors and non-survivors demonstrated significant differences in age, histologic subtype, FIGO stage, residual tumor size, KELIM score, CA125 level, ascites volume, cytologic positivity, and omental metastasis (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). In contrast, BMI, presence of liver metastasis, timing of chemotherapy initiation, and number of chemotherapy cycles showed no statistically significant association with OS (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Detailed baseline characteristics are summarized in\u0026nbsp;Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Association Between KELIM Score and Clinicopathological Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were further stratified by KELIM. Fifty-one patients (51.0%) had a KELIM score ≥1, while 49 (49.0%) had KELIM \u0026lt;1. As shown in Table 2, low KELIM values were significantly associated with older age, HGSOC histology, advanced FIGO stage, presence of residual tumor ≥1 cm, malignant ascitic cytology, and omental metastasis (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 for all). The median KELIM score for patients with stage I disease was 1.60 (IQR: 1.20–1.90), compared with 0.79 (IQR: 0.56–0.97) in those with stage III. Additionally, patients with higher KELIM scores (≥1) were more likely to have undergone optimal cytoreduction, had negative ascitic cytology, and received ≥6 chemotherapy cycles. No significant association was observed between KELIM and BMI, ascites volume, liver metastasis, or chemotherapy initiation time (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Survival Outcomes and Prognostic Factor Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the follow-up period, the cumulative 1-, 3-, and 5-year OS rates were 96.0%, 70.0%, and 35.0%, respectively, with a median OS of 45.5 months (range: 11–116 months). Univariate Cox proportional hazards regression identified several variables significantly associated with OS, including age ≥59 years (HR = 4.25, 95% CI: 2.49–7.24,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001), histological subtype (HGSOC: HR = 10.00, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), FIGO stage (stage III vs. I: HR = 18.65; stage IV vs. I: HR = 92.46,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), KELIM \u0026lt;1 (HR = 14.29, 95% CI: 7.14–25.00, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), residual tumor ≥1 cm (HR = 11.11,\u003cem\u003e\u0026nbsp;P\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), elevated CA125 (≥35 U/mL: HR = 3.88, \u003cem\u003eP\u003c/em\u003e = 0.022), ascites volume ≥175 mL (HR = 2.23, \u003cem\u003eP\u003c/em\u003e = 0.004), presence of malignant cells in ascites (HR = 3.33, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), omental metastasis (HR = 7.69, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), and liver metastasis (HR = 7.83, \u003cem\u003eP\u003c/em\u003e = 0.048) (Table 3). By contrast, BMI, timing of initiation of postoperative chemotherapy, and total number of chemotherapy cycles were not significantly associated with OS (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05 for all).\u003c/p\u003e\n\u003cp\u003eVariables with \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 in univariate analysis were included in a multivariate Cox regression model using backward stepwise selection. The final model identified FIGO stage, KELIM score, and residual tumor size as independent prognostic factors for OS (Table 4). Compared with FIGO stage I, the hazard ratios for stages II, III, and IV were 9.45 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.003), 8.06 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and 25.30 (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), respectively. A KELIM score ≥1 was associated with significantly lower risk of death (HR = 0.16, 95% CI: 0.06–0.39, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and patients with no or minimal residual disease (≤1 cm) had improved survival (HR = 0.39, 95% CI: 0.20–0.78, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.008).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Kaplan–Meier Survival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan–Meier survival analysis confirmed that FIGO stage, KELIM score, and postoperative residual tumor size were all significantly associated with overall survival in patients with epithelial ovarian cancer (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 for all; Figures 3–5). Patients with early-stage disease (FIGO I–II), higher KELIM scores (≥1), and minimal or no residual disease (≤1 cm) exhibited significantly better long-term survival than their counterparts. The survival curves for each subgroup demonstrated clear stratification, supporting the prognostic relevance of these three variables. The median OS was notably shorter among patients with KELIM \u0026lt;1, residual tumor \u0026gt;1 cm, or advanced-stage disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Nomogram Construction and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA prognostic nomogram was developed based on the three independent predictors identified in the multivariate Cox regression analysis: FIGO stage, KELIM score, and postoperative residual tumor size. Each variable was assigned a weighted score according to its regression coefficient, and the total score was used to estimate the probability of 1-, 3-, and 5-year overall survival (Figure 6). The graphical interface of the nomogram enables straightforward clinical application by summing points corresponding to each patient’s characteristics.\u003c/p\u003e\n\u003cp\u003eInternal validation using bootstrapping (1000 resamples) demonstrated good calibration, with predicted survival probabilities closely matching observed outcomes across all time points (Figure 7). The concordance index (C-index) of the nomogram was 0.86 (95% CI: 0.83–0.89), indicating strong discriminative power. Time-dependent ROC analysis further confirmed its superior predictive performance, with AUCs of 0.808, 0.945, and 0.950 for 1-, 3-, and 5-year survival, respectively—substantially higher than those of FIGO stage alone (Figure 8 and 9). Decision curve analysis (DCA) demonstrated the net clinical benefit of the nomogram over a wide range of threshold probabilities, underscoring its practical utility in guiding individualized prognostic assessment (Figures 10–12).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study of patients with EOC at our center, we identified FIGO stage, postoperative residual tumor size, and the KELIM score as independent prognostic factors for OS. By integrating these variables into a nomogram, we constructed a robust and user-friendly prognostic model capable of predicting individualized 1-, 3-, and 5-year OS probabilities. Importantly, our findings highlight that dynamic biomarkers such as KELIM, reflecting early chemosensitivity, can markedly enhance prognostic accuracy over models relying solely on anatomical staging or static biomarkers.\u003c/p\u003e\n\u003cp\u003eAs in previous research, FIGO staging remains the cornerstone of prognostic assessment and therapeutic decision-making in EOC(21). Our analysis reaffirmed FIGO stage as the most powerful predictor of survival, with patients diagnosed at early stages (I–II) demonstrating significantly better outcomes than those with stage III–IV disease. These findings are consistent with established epidemiologic and clinical data(22). The sharp gradient in hazard ratios across FIGO stages, particularly the pronounced risk in stage IV, underscores the critical importance of early detection and accurate surgical staging. However, relying on FIGO stage alone is insufficient, as it does not account for substantial individual differences in treatment response among EOC patients, limiting its utility as a sole prognostic tool(23).\u003c/p\u003e\n\u003cp\u003ePostoperative residual tumor size has also been established as a critical determinant of prognosis in EOC(24), and our data are consistent with this paradigm. Patients who achieved optimal cytoreduction (residual disease ≤1 cm) exhibited significantly prolonged survival, emphasizing that complete or near-complete removal of macroscopic disease is a key modifiable factor influencing patient outcomes. This likely reflects both tumor biology and the quality of surgical intervention(25). Maximal cytoreduction may also enhance chemotherapy efficacy by reducing the reservoir of resistant tumor clones, supporting the necessity of comprehensive surgical efforts in high-volume, experienced centers(26).\u003c/p\u003e\n\u003cp\u003eNotably, our study emphasizes the clinical significance of the KELIM score as a dynamic, early marker of tumor chemosensitivity. Unlike static serum CA125 values, which may be confounded by tumor burden, peritoneal inflammation, or benign conditions, KELIM provides a dynamic, pharmacodynamic measure of tumor chemosensitivity(27).\u0026nbsp;Our data demonstrate that a high KELIM score (≥1.0) is strongly predictive of prolonged survival, with patients in this group showing nearly an 84% reduction in mortality risk compared to those with low scores. Importantly, the prognostic value of KELIM remained significant even after adjusting for traditional clinical variables. These results are aligned with findings from international studies and randomized trials such as ICON-7(28)\u0026nbsp;and CALYPSO(18), where KELIM consistently emerged as a robust predictor of progression-free and overall survival. Furthermore, emerging evidence suggests that KELIM is associated with the likelihood of benefit from maintenance therapies, including bevacizumab(29)\u0026nbsp;and PARP inhibitors(30), thus providing a potential tool for individualized therapeutic stratification.\u003c/p\u003e\n\u003cp\u003eOur analysis also elucidated the associations between KELIM score and clinicopathological characteristics. Higher KELIM scores were more commonly observed in patients with early-stage disease, non-serous histology, optimal debulking, and absence of malignant ascites, all markers of more favorable tumor biology. Conversely, lower KELIM values were seen in advanced-stage, high-grade serous subtypes, and patients with significant residual disease. These findings suggest that KELIM may serve as a surrogate not only for chemotherapy response but also for overall tumor aggressiveness.\u003c/p\u003e\n\u003cp\u003eThe nomogram we developed demonstrated excellent discriminative capacity (C-index 0.86) and calibration, as shown by its high AUCs for 1-, 3-, and 5-year survival (0.808, 0.945, and 0.950, respectively), all surpassing the performance of FIGO stage alone (C-index, 0.72; AUCs, 0.714, 0.824, and 0.889, for 1-, 3-, and 5-year respectively). Decision curve analysis further confirmed its net clinical benefit, supporting its potential value for real-world risk stratification and decision-making. As the nomogram utilizes routinely available clinical and laboratory data, it is readily applicable in daily practice without imposing additional costs or technical barriers.\u003c/p\u003e\n\u003cp\u003eIn the context of clinical application, our model offers several practical advantages. A noteworthy implication of our findings is the potential for KELIM to serve not only as a prognostic biomarker, but also as a real-time tool to guide dynamic adaptation of therapeutic strategies in EOC. Traditional prognostic models are often limited by static, baseline clinical or pathological variables, which cannot capture the evolving nature of tumor response during treatment. In contrast, KELIM provides an early, quantitative measure of individual tumor chemosensitivity within the initial cycles of chemotherapy. This opens the door to “response-guided therapy,” whereby real-time assessment of KELIM could inform early intensification, modification, or de-escalation of systemic treatment regimens.\u0026nbsp;For example, patients demonstrating suboptimal KELIM kinetics could be candidates for alternative chemotherapy combinations, early addition of targeted agents, or enrollment in clinical trials(17). Meanwhile, for patients with high KELIM scores and optimal cytoreduction, de-escalation strategies (such as limiting the number of chemotherapy cycles) could be safely considered, particularly in elderly or frail populations. Incorporating KELIM-driven adaptive strategies into clinical protocols may further improve survival and optimize quality of life, representing a significant advance toward truly personalized medicine in EOC. Future prospective studies are warranted to validate the feasibility, safety, and long-term benefit of this approach in diverse patient populations.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. The retrospective, single-center design and moderate sample size may introduce bias and limit the generalizability of our findings. Although the nomogram was internally validated using bootstrap resampling, external validation in larger and more diverse cohorts is warranted. In addition, our study did not include molecular biomarkers such as BRCA mutation or homologous recombination deficiency status, now recognized as important determinants of response, especially to PARP inhibitors. Nevertheless, in resource-limited settings where molecular testing is not universally available, KELIM provides valuable, actionable prognostic information.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings support the integration of FIGO stage, residual tumor status, and KELIM score into a comprehensive prognostic model for epithelial ovarian cancer. The resulting nomogram demonstrates strong predictive performance and may serve as a valuable clinical tool for individualized risk assessment and therapeutic decision-making. The incorporation of KELIM into standard prognostic evaluation reflects a shift toward dynamic, response-based oncology, with the potential to optimize outcomes in a disease characterized by significant biological heterogeneity and therapeutic challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the following funding sources: the Natural Science Foundation of Hebei Province, S\u0026amp;T Program of Hebei, Project No. H2023206356; the Natural Science Foundation of Hebei Province, S\u0026amp;T Program of Hebei, Project No. H2024206427; the Natural Science Foundation of Hebei Province, S\u0026amp;T Program of Hebei, Project No. H2024206433; the Hebei Provincial Central Government-Guided Local Science and Technology Development Fund Project (Science and Technology Innovation Base Project), S\u0026amp;T Program of Hebei, Project No. 236Z7756G; the 2024 Hebei Provincial Government-Sponsored Clinical Medicine Outstanding Talent Training Program, Project No. ZF2024040; and the 2025 Hebei Provincial Government-Sponsored Clinical Medicine Outstanding Talent Training Program, Project No. ZF2025102.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQianqian Liu, Zhongkang Li, Li Meng and Xianghua Huang conceived and designed the study. Qianqian Liu, Li Meng and Zhongkang Li performed data analysis and manuscript preparation. Qianqian Liu and Guohong Qi were responsible for figure preparation. Zhiqiang Zhang, Jinchai Zhao, Yanfang Du and Xianghua Huang discussed and revised the manuscript. Xianghua Huang and Li Meng reviewed and finalized the manuscript. All authors have read and approved the final manuscript. Meng Li and Xianghua Huang are the corresponding authors and confirm the authenticity of all the raw data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of the Second Hospital of Hebei Medical University (Approval No. 2025-R271). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and the Declaration of Helsinki. The requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWebb PM, Jordan SJ. Global epidemiology of epithelial ovarian cancer. Nat Rev Clin Oncol. 2024;21(5):389\u0026ndash;400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41571-024-00881-3\u003c/span\u003e\u003cspan address=\"10.1038/s41571-024-00881-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu Z, Taylor S, Modugno F. 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Cancers (Basel). 2024;16(13). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers16132339\u003c/span\u003e\u003cspan address=\"10.3390/cancers16132339\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Clinicopathological Data and Baseline Characteristics of Patients with Epithelial Ovarian Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTotal (n=100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eSurvived (n=40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDeceased (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e72 (72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e38 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e34 (56.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ge;59 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e28 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e26 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;24 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e50 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e21 (52.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e29 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ge;24 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e50 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e19 (47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e31 (51.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eHGSOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e61 (61.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e10 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e51 (85.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e39 (39.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e30 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e9 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eFIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e35 (35.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e30 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e5 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e8 (8.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e55 (55.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e6 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e49 (81.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eKELIM score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e49 (49.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e5 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e44 (73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e51 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e35 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e16 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eResidual tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026gt;1 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e38 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e3 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e35 (58.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026le;1 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e62 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e37 (92.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e25 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePre-op CA125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;35 U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e13 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e10 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e3 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ge;35 U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e87 (87.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e30 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e57 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAscites volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;175 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e45 (45.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e25 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e20 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ge;175 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e55 (55.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e15 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMalignant cells in ascites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e38 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e23 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e15 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e62 (62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e17 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e45 (75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eOmental metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e48 (48.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e34 (85.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e14 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e52 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e6 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e46 (76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eLiver metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e98 (98.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e58 (96.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eChemo start (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026lt;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e20 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e20 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026ge;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e60 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e20 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eNo. of chemo cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026le;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e54 (54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e26 (65.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e28 (46.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026gt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e46 (46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e14 (35.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e32 (52.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Association Between KELIM Score and Clinicopathological Parameters in 100 Ovarian Cancer Patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eKELIM Median (P25\u0026ndash;P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKELIM \u0026lt;1 (n=49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eKELIM \u0026ge;1 (n=51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAge \u0026lt;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.20 (0.86\u0026ndash;1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAge \u0026ge;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.73 (0.44\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBMI \u0026lt;24 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.10 (0.75\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBMI \u0026ge;24 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.99 (0.74\u0026ndash;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eHGSOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.82 (0.57\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNon-HGSOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.50 (1.20\u0026ndash;1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eFIGO I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.60 (1.20\u0026ndash;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eFIGO II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.45 (1.10\u0026ndash;1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eFIGO III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.79 (0.56\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eFIGO IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.48 (0.28\u0026ndash; )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eResidual \u0026le;1 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.40 (1.10\u0026ndash;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eResidual \u0026gt;1 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.70 (0.47\u0026ndash;0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCA125 \u0026lt;35 U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.50 (0.98\u0026ndash;1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCA125 \u0026ge;35 U/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.97 (0.71\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAscites \u0026lt;175 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.10 (0.83\u0026ndash;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAscites \u0026ge;175 mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.91 (0.66\u0026ndash;1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNo malignant cells in ascites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.50 (1.08\u0026ndash;1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMalignant cells in ascites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.84 (0.57\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNo omental metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.50 (1.10\u0026ndash;1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eOmental metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.78 (0.51\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNo liver metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.10 (0.76\u0026ndash;1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eLiver metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChemo start \u0026lt;11 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.15 (0.78\u0026ndash;1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChemo start \u0026ge;11 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.98 (0.74\u0026ndash;1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChemo cycles \u0026le;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.25 (0.81\u0026ndash;1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eChemo cycles \u0026gt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.92 (0.70\u0026ndash;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Univariate Cox Proportional Hazards Model for Overall Survival in Epithelial Ovarian Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ge;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.49\u0026ndash;7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ge;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.66\u0026ndash;1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHigh-grade serous carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.05\u0026ndash;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.68\u0026ndash;24.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e18.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7.25\u0026ndash;47.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e92.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e15.91\u0026ndash;534.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eKELIM score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.04\u0026ndash;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eResidual tumor (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026le;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.05\u0026ndash;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePreoperative CA125 (U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.21\u0026ndash;12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAscites volume (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ge;175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.30\u0026ndash;3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMalignant cells in ascites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.85\u0026ndash;6.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOmental metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4.14\u0026ndash;14.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLiver metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.02\u0026ndash;60.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eChemotherapy initiation (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ge;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.87\u0026ndash;2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eChemotherapy cycles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026gt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026le;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.36\u0026ndash;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Multivariate Cox Proportional Hazards Model for Overall Survival in Epithelial Ovarian Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eFIGO Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e9.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.10\u0026ndash;42.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e8.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.74\u0026ndash;23.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e25.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.94\u0026ndash;162.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eKELIM score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026le;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026gt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.06\u0026ndash;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eResidual tumor (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026le;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.20\u0026ndash;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Epithelial ovarian cancer, KELIM score, Prognostic Nomogram, Long-term survival, Retrospective study","lastPublishedDoi":"10.21203/rs.3.rs-7244619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7244619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpithelial ovarian cancer (EOC) remains the most lethal gynecologic malignancy worldwide, with late-stage diagnosis and poor long-term survival being major clinical challenges. This study aimed to identify key prognostic factors associated with long-term survival in patients with EOC and to develop a predictive nomogram for individualized risk assessment. A total of 100 patients diagnosed with EOC between April 2014 and October 2019 at the Second Hospital of Hebei Medical University were retrospectively enrolled. All patients underwent primary comprehensive staging or cytoreductive surgery followed by platinum-based chemotherapy. Clinical, surgical, and biochemical variables, including age, FIGO stage, histologic subtype, ascites volume, residual tumor size, preoperative CA125 levels, and the CA125 kinetic parameter KELIM, were collected and analyzed. Survival data were followed up through October 2024, with overall survival as the primary endpoint. Univariate and multivariate Cox regression identified FIGO stage, postoperative residual tumor size, and KELIM score as independent predictors of overall survival. A nomogram integrating these three factors was constructed and internally validated using bootstrapping. The model demonstrated excellent discrimination (C-index\u0026thinsp;=\u0026thinsp;0.86) and calibration, outperforming FIGO stage alone. Time-dependent ROC analysis demonstrated that the nomogram achieved AUCs of 0.808, 0.945, and 0.950 for predicting 1-, 3-, and 5-year overall survival, respectively, outperforming FIGO stage alone (AUCs of 0.714, 0.824, and 0.889). These findings highlight that integrating the KELIM score with conventional staging and postoperative residual disease significantly improves prognostic accuracy in EOC and enables tailored therapeutic decision-making based on individual tumor response.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a KELIM-Based Prognostic Nomogram for Long-Term Survival in Epithelial Ovarian Cancer: A 10-Year Single-Center Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 14:21:03","doi":"10.21203/rs.3.rs-7244619/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-09-04T10:01:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T08:17:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-11T13:52:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-10T21:51:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-08-10T21:48:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4dc19b40-423f-449f-8a66-8d32bd279c0d","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-12T14:21:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-12 14:21:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7244619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7244619","identity":"rs-7244619","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}