Diagnostic value of CA125, HE4, systemic immune‑inflammatory index (SII), fibrinogen-to-albumin ratio(FAR), and prognostic nutritional index(PNI) in the Preoperative Investigation of ovarian Masses

Diagnostic value of CA125, HE4, systemic immune‑inflammatory index (SII), fibrinogen-to-albumin ratio(FAR), and prognostic nutritional index(PNI) in the Preoperative Investigation of ovarian Masses | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diagnostic value of CA125, HE4, systemic immune‑inflammatory index (SII), fibrinogen-to-albumin ratio(FAR), and prognostic nutritional index(PNI) in the Preoperative Investigation of ovarian Masses Liyun Song, Jie Qi, Jing Zhao, Suning Bai, Qi Wu, Ren Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3003534/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The aim of this study was to ascertain the diagnostic accuracy of Carbohydrate Antigen 125(CA125), Human Epididymis Protein 4(HE4), systemic immune-inflammation index (SII), fibrinogen-to-albumin ratio (FAR), prognostic nutritional index (PNI), and their combinations for ovarian cancer (OC) in order to discover an optimal combined diagnostic index for early diagnosis of OC. A thorough investigation was conducted to ascertain the correlation between these markers and the pathological characteristics of OC, thereby furnishing a foundation for the early identification and treatment of this disorder. Methods 170 patients with documented OC and benign ovarian tumors (BOTs) treated at Hebei General Hospital between January 2019 and December 2022 were included in this retrospective study. The formula for serum inflammation related markers was: FAR = fibrinogen(g/L)/ albumin(g/L); PNI = albumin (g/L) + 5 × lymphocyte counts (109/L); SII = platelet count (109/L) × neutrophil count (109/L)/ lymphocyte count (109/L). Data analysis was conducted with IBM SPSS statistics version V26.0 software, MedCalc Statistical Software version 19.4.0 software, and R Environment for Statistical Computing software (R Foundation for Statistical Computing). Results The isolated CA125 tested showed the best application value to differentiate BOTs from OC when the defined variables were compared separately. The combination of CA125, HE4, FAR, SII, and PNI displayed a greater area under the ROC curve (AUC) than any one of them or other combinations of the five variables. Compared to CA125 alone, the combination of CA125, HE4, FAR, SII, and PNI showed a slight gain in sensitivity (83.91%), negative predictive value (NPV) (83.91%), accuracy (85.88%), and a decrease in negative likelihood ratio (LR) (0.180%). Higher preoperative CA125, HE4, SII, and FAR levels and lower PNI levels predicted a higher probability of advanced OC progression and lymph node metastasis. FAR had a better application value than other inflammation-related markers (PNI and SII). Conclusions The study suggested that preoperative serum SII, PNI, and FAR might potentially be clinically valuable markers in patients with OC. FAR had a better application value than other inflammation-related markers (PNI and SII). As we delve deeper into the inflammatory mechanisms associated with tumors, we may discover more effective combinations of tumor and inflammatory biomarkers. ovarian cancer Carbohydrate Antigen 125 Human Epididymis Protein 4 systemic immune-inflammation index fibrinogen-to-albumin ratio prognostic nutritional index diagnosis Figures Figure 1 Figure 2 Figure 3 1. Introduction The fifth-leading cause of cancer-related death in women is ovarian cancer (OC), which is the most prevalent malignant tumor in females [ 1 ]. In recent years, OC has been on the rise [ 2 ]. The prognosis for OC is quite poor with a survival rate of just 30% [ 3 ]. As the early manifestations of OC are rather hidden, the disease may have developed to a middle or advanced stage at diagnosis, resulting in missed optimal treatment timing and increased mortality [ 4 – 6 ]. The 5-year survival rate of OC can be as high as 90% when detected early and treated with standard surgery and adjuvant therapy [ 7 ]. It is imperative to differentiate between malignant ovarian tumors and benign ovarian tumors (BOTs). Efforts to identify more dependable biomarkers for the early detection of OC have been made, and serum biomarkers are a practical, cost-effective, and non-invasive approach for predicting malignancy. The Carbohydrate Antigen 125(CA125) is expressed in over 80% of OC patients, and can be detected in serum, thus enabling the differentiation of malignant ovarian tumors from normal ovarian tissue [ 8 ]. However, this marker has a low sensitivity in the early stages of OC [ 9 ]. In addition, it has a high false-positive rate in benign gynecological conditions such as acute pelvic inflammation, adenomyosis, uterine myoma, and endometriosis [ 10 ]. Additional biomarkers, such as Human Epididymis Protein 4 (HE4), have been developed to enhance the specificity of ovarian carcinomas' specificity [ 11 ]. This biomarker is reported to be overexpressed in OC tissues [ 12 ]. In addition to colorectal cancer and gastrointestinal malignancies, HE4 is a non-specific tumor marker expressed to varying degrees in cervical, endometrial, ovarian, and nonepithelial tumors [ 13 ]. It is strongly associated with tumor invasion, migration, and recurrence. The two most effective markers currently available, CA125 and HE4, are insufficient for detecting early-stage OC [ 14 – 16 ]. A great deal of effort is being put forth to discover additional biomarkers that, either alone or in combination with CA125 and HE4, could enhance the sensitivity and specificity of detecting OC in a more timely, treatable manner. The behavior of OC has also been better understood recently by the scientific community, in which the body's inflammatory and immune response plays a crucial role [ 17 ]. The inflammatory response is significantly impacted by the immunological and nutritional state of the body, and the presence and metastatic spread of tumor cells are closely linked to inflammation. Malnutrition has been reported to make patients more susceptible to infection and promote tumor recurrence through suppression of tumor immunity [ 18 , 19 ]. Therefore, a growing number of studies have concentrated on how nutrition and inflammation interact in cancer patients. Studies have shown that peripheral blood neutrophils, lymphocytes, platelets, albumin, globulin, and fibrinogen play an essential role in the inflammatory microenvironment of cancer [ 20 ]. In recent times, the preoperative systemic immune-inflammation index (SII), prognostic nutritional index (PNI), and fibrinogen-to-albumin ratio (FAR) have been identified as significant indicators of the diagnostic utility and prognosis of prostate cancer, lung cancer, and gastrointestinal tumors [ 21 – 23 ]. However, the role of these inflammation-related indicators in the diagnosis of ovarian cancer has rarely been reported. To the best of our knowledge, no paper has been published up to now that examines the clinical utility of CA125 combined with HE4, SII, PNI, and FAR in predicting OC in the preoperative setting. The aim of this study was to ascertain the diagnostic accuracy of CA125, HE4, SII, FAR, PNI, and their combinations for OC in order to discover an optimal combined diagnostic index for early diagnosis of OC. A thorough investigation was conducted to ascertain the correlation between these markers and the pathological characteristics of OC, thereby furnishing a foundation for the early identification and treatment of this disorder. 2. Materials and methods Inclusion and exclusion criteria This retrospective study included 170 patients with documented OC and BOTs who were treated at Hebei General Hospital between January 2019 and December 2022 and were divided into two groups, based on postoperative pathological results reviewed by two senior pathologists, of 87 OC and 83 BOTs. Patients with OC were not given chemotherapy or radiation therapy prior to the surgery. The International Federation of Gynecology and Obstetrics (FIGO) stage was utilized to ascertain the clinical stage of OC. All enrolled OC patients underwent a comprehensive staging surgery, comprising of a total hysterectomy, adnexectomy, complete pelvic/para-aortic lymphadenectomy, and peritoneal cytology. Infectious conditions, autoimmune diseases, severe liver or kidney damage, thrombotic diseases, other benign or malignant tumors, pregnancy, and preoperative complications of blood diseases were all disqualified from the study. Clinical and laboratory data collection The data analyzed consisted of clinical and laboratory factors such as age, pathological type, FIGO staging, degree of tissue differentiation, presence or absence of lymph node metastasis, albumin, fibrinogen, neutrophil count, platelet count, lymphocyte count, CA125, and HE4. Prior to surgery, albumin, fibrinogen, neutrophil count, platelet count, lymphocyte count, and serum tumor biomarkers were all tested and recorded within a week. Using a COBAS E602 analyzer (Roche, Switzerland) and the chemiluminescent reagent kit supplied by Roche, preoperative CA125 and HE4 concentrations were measured. Two senior pathologists reviewed pathological examinations, and the Ethics Committee of Hebei General Hospital approved the collection of patients' clinical and laboratory data, following the Declaration of Helsinki. The ethical committee's conclusion that informed consent was not necessary meant that the need for written informed consent was no longer necessary. Inflammation-Related Markers The formula for serum inflammation-related markers was: FAR = fibrinogen(g/L)/ albumin(g/L); PNI = albumin (g/L) + 5 × lymphocyte counts (10 9 /L); SII = platelet count (10 9 /L) × neutrophil count (10 9 /L)/ lymphocyte count (10 9 /L). Statistical analysis Data were analyzed using IBM SPSS statistics version V26.0 software, MedCalc Statistical Software version 19.4.0 software, and R Environment for Statistical Computing software (R Foundation for Statistical Computing). Statistical significance was set at P < 0.05, and P values were calculated as two-sided. The Shapiro-Wilk test was then employed to assess the normality of the variables' distributions. The data are presented as means ± SD for continuous variables with normal distribution and as the median and interquartile range for continuous variables without normal distribution. The T-test or Mann-Whitney U test was employed to assess the differences in variables among groups, and then the Kruskal-Wallis test was employed for multiple comparisons. The area under the operating characteristic curve (ROC), 95% confidence intervals, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR), negative LR, and accuracy for the defined variables were calculated to test the diagnostic performance for the prediction of OC by receiver ROC analysis. The Youden index of the ROC curve was then used to determine the optimal cut-off value of the parameters. We employed Spearman's rank correlation test and logistic regression analysis to assess the associations between the pertinent parameters. Subsequently, decision curve analysis (DCA) was conducted to determine which single parameters and parameter combinations provided the most clinical utility in distinguishing BOTs from OC. For this visual analysis, we used software explicitly designed for DCA. All DCA calculations were performed as described by Vickers and Elkin [ 24 ]. 3. Results CA125, HE4, SII, PNI, and FAR showed significant differences among the benign ovarian tumor group and OC group In this study, 170 patients with ovarian tumors were enrolled, with the primary laboratory parameters of all participants being briefly outlined in Table 1 . The overall malignancy prevalence of our cohort was 51.18%, with the OC group having a mean age of 52.22 ± 11.26 years (range, 21–76 years) and the control group having a mean age of 51.71 ± 12.20 years (range, 26–78 years). The two groups had no significant difference when compared by age (t = -0.282, P = 0.778). Table 1 reveals a significant difference between patients with BOTs and OC in terms of absolute neutrophil count, absolute lymphocyte count, blood platelet count, albumin, fibrinogen, SII, PNI, FAR, CA125, and HE4 ( P = 0.001, P = 0.001, P = 0.001, P = 0.001, P < 0.001, P < 0.001, P < 0.001, P < 0.001, P < 0.001, P < 0.001, respectively). The median CA125, HE4, SII, and FAR values were found to be significantly higher in the OC group [ 884.86 (859.31), 0.087 (0.049), 228.50 (777.87), 180.20 (308.80)] compared to those in the BOT group [567.82 (354.31), 0.062 (0.017), 16.43 (13.85), 45.90 (17.22)]. Conversely, the median PNI value was found to be significantly lower in the OC group [ 46.40 (8.10)] compared to it in the BOT group [ 51.20 (5.10)]. Table 1 Comparison of defined variables between ovarian cancer and benign ovarian tumor Variables Ovarian cancer, median (IQR) Benign tumor, median (IQR) Reference level Z-value P-value Number 87 83 N (10 9 /L) 4.20 (1.97) 3.45 (2.02) 1.8–6.3 -3.361 0.001 L (10 9 /L) 1.41 (0.68) 1.72 (0.68) 1.1–3.2 -3.378 0.001 PLT (10 9 /L) 311.00 (125.00) 258.00 (92.00) 125–350 -3.248 0.001 Alb(g/L) 39.70 (5.97) 42.60 (4.60) 40–55 -3.451 0.001 Fib(g/L) 3.59 (1.54) 2.73 (0.85) 2–4 -6.50 < 0.001 SII 884.86 (859.31) 567.82 (354.31) / -5.379 < 0.001 PNI 46.40 (8.10) 51.20 (5.10) / -4.322 < 0.001 FAR 0.087 (0.049) 0.062 (0.017) / -6.586 < 0.001 CA125 (U/ml) 228.50 (777.87) 16.43 (13.85) 0–35 -8.780 < 0.001 HE4 (pmol/L) 180.20 (308.80) 45.90 (17.22) premenopause < 70 postmenopause < 140 -8.080 < 0.001 N absolute neutrophil count, L absolute lymphocyte count, PLT blood platelet count, Alb albumin, Fib fibrinogen, FAR fibrinogen(g/L)/ albumin(g/L), PNI albumin (g/L) + 5 × lymphocyte counts (10 9 /L), SII platelet count (10 9 /L) × neutrophil count (10 9 /L)/ lymphocyte count (10 9 /L), CA125 cancer antigen 125, HE4 human epididymis protein 4, IQR interquartile range Correlation between CA125, HE4, SII, PNI, FAR, and clinic‑pathological characteristics of OC patients Table 2 displays the histopathology and characteristics of the OC-enrolled patients, with differentiation grades and cancer stages for OC also specified. A comparison of CA125, HE4, SII, PNI, FAR, and clinical characteristics among the BOT group and OC group is presented in Table 2 . Compared with the early stage OC (Stage I-II) group, the values of CA125 [107.09(267.10)vs. 618.40༈1086.20༉, (Stage I-II) vs. (Stage III-IV), P <0.001], HE4 [82.80༈153.37༉vs. 299.00༈816.95༉, (Stage I-II) vs. (Stage III-IV), P <0.001], SII [685.64༈511.86༉vs. 1252.37༈857.18༉, (Stage I-II) vs. (Stage III-IV), P <0.001], and FAR [0.08༈0.04༉vs. 0.11༈0.06༉, (Stage I-II) vs. (Stage III-IV), P = 0.001] in the advanced OC (Stage III-IV) group were significantly higher, while the value of PNI [49.90༈8.77༉vs. 45.05༈8.08༉, (Stage I-II) vs. (Stage III-IV), P = 0.001] in the advanced OC (Stage III-IV) group was significantly lower. Moreover, compared with the non-lymph node metastasis OC group, the values of CA125 [161.90༈345.05༉vs. 714.50༈1074.40༉, ( non-lymph node metastasis ) vs. (lymph node metastasis), P <0.001], HE4 [84.65༈165.88༉vs. 331.00༈859.80༉, ( non-lymph node metastasis ) vs. (lymph node metastasis), P <0.001], SII [711.42༈552.86༉vs. 1332.02༈999.87༉, ( non-lymph node metastasis ) vs. (lymph node metastasis), P <0.001], and FAR [0.08༈0.03༉vs. 0.12༈0.08༉,( non-lymph node metastasis ) vs. (lymph node metastasis), P = 0.001] in the lymph node metastasis OC group were significantly higher, while the value of PNI [49.45༈7.45༉vs. 44.45༈5.20༉, ( Lymph nodes negative ) vs. (Lymph nodes positive), P = 0.002] was significantly lower. These results suggested that higher preoperative CA125, HE4, SII, and FAR levels and lower PNI levels predict a higher probability of advanced OC progression and lymph node metastasis. Table 2 Relationship between laboratory variables and clinic-pathological characteristics of OC patients Variables N (%) CA125(U/ml), median (IQR) HE4 (pmol/L), median (IQR) SII, median (IQR) PNI, median (IQR) FAR, median (IQR) Age ≤ 50 30(34.48%) 329.90(878.63) 174.30(317.61) 795.75(797.02) 49.13(6.85) 0.08(0.04) > 50 57(65.52%) 215.80(683.83) 180.20(329.31) 947.76(938.70) 45.85(9.42) 0.09(0.06) Z-value -0.040 -0.621 -0.406 -1.072 -0.380 P -value 0.968 0.535 0.685 0.284 0.704 FIGO staging I-II 42(48.28%) 107.09(267.10) 82.80(153.37) 685.64(511.86) 49.90(8.77) 0.08(0.04) III-IV 45(51.72%) 618.40(1086.20) 299.00(816.95) 1252.37(857.18) 45.05(8.08) 0.11(0.06) Z-value -4.672 -4.196 -3.610 -3.462 -3.381 P -value <0.001 <0.001 <0.001 0.001 0.001 Histological grade G1 29(33.33%) 121.15(490.74) 66.99(151.25) 829.65(779.16) 46.23(12.94) 0.09(0.07) G2-G3 58(66.67%) 307.00(784.60) 206.90(339.60) 947.76(931.14) 46.90(6.52) 0.09(0.05) Z-value -1.972 -3.134 -1.054 -0.232 -0.790 P -value 0.049 0.002 0.292 0.817 0.429 Pathological type Serous 68(78.15%) 294.40(777.60) 204.65(363.93) 962.18(928.09) 46.08(7.18) 0.09(0.05) Mucinous 8(9.20%) 32.21(177.89) 52.43(39.66) 704.91(729.60) 54.00(14.78) 0.08(0.06) Clearcell 5(5.75%) 29.28(342.21) 39.10(82.82) 676.47(652.84) 49.70(16.15) 0.09(0.08) others 6(6.90%) 675.20(2182.28) 138.35(674.45) 743.06(3499.84) 47.29(18.48) 0.08(0.07) H(K) 12.300 18.219 4.734 3.530 0.972 P -value 0.006 <0.001 0.192 0.317 0.808 Lymph nodes Negative 56(64.37%) 161.90(345.05) 84.65(165.88) 711.42(552.86) 49.45(7.45) 0.08(0.03) Positive 31(35.63%) 714.50(1074.40) 331.00(859.80) 1332.02(999.87) 44.45(5.20) 0.12(0.08) Z-value -4.431 -4.768 -3.873 -3.036 -3.341 P -value <0.001 <0.001 <0.001 0.002 0.001 CA125 cancer antigen 125, HE4 human epididymis protein 4, SII platelet count (10 9 /L) × neutrophil count (10 9 /L)/ lymphocyte count (10 9 /L), PNI albumin (g/L) + 5 × lymphocyte counts (10 9 /L), FAR fibrinogen(g/L)/ albumin(g/L), IQR interquartile range Others: including immature teratoma (one case), granulosa cell tumor (one case), endometrioid carcinoma (two cases), carcinosarcoma (two case) Next, we investigated differences in the variables with respect to ages, histological grades, and pathological types. Table 2 shows no statistically significant differences in the variables among different ages ( P = 0.968, P = 0.535, P = 0.685, P = 0.284, P = 0.704, respectively). However, CA125 and HE4 showed significant differences for categorical variables such as histological grades ( P = 0.049, P = 0.002, respectively) and pathological type ( P = 0.006, P < 0.001, respectively). No statistically significant differences were found in SII, PNI, and FAR between histological grades ( P = 0.292, P = 0.817, P = 0.429, respectively) and pathological types ( P = 0.192, P = 0.317, P = 0.808, respectively). Efficiency of single CA125, HE4, SII, PNI, FAR, and different combinations of the variables in the diagnosis of OC According to the association of single CA125, HE4, SII, PNI, and FAR with OC, ROC curves were made and used to determine the optimal cut-off value and the corresponding sensitivity and specificity (Fig. 1 ). The results are shown in Table 3 . The optimum cut-off value was chosen to maximize the Youden index (sensitivity + specificity − 1). The appropriate cut-off value of CA125 (AUC = 0.890, P <0.001), HE4 (AUC = 0.859, P <0.001), FAR (AUC = 0.793, P <0.001), SII (AUC = 0.739, P <0.001), and PNI(AUC = 0.692, P <0.001) for differentiating BOTs and OC were 79.89, 65.16, 0.084, 945.206, and 46.9, respectively; with the corresponding sensitivity of 73.6%, 72.4%, 58.6%, 47.1%, and 52.9%, respectively; specificity of 97.6%, 92.8%, 91.6%, 92.8%, and 89.2%, respectively; PPV of 95.52%, 90%, 88.14%, 85.42%, and 82.14%, respectively; NPV of 77.67%, 76%, 68.47%, 62.30%, and 62.39%, respectively; positive LR of 30.667, 10.056, 6.976, 6.542, and 4.898, respectively; negative LR of 0.270, 0.297, 0.452, 0.570, and 0.528, respectively; accuracy of 84.71, 81.76, 75.29, 68.82, and 70.0 respectively. The FAR tested showed the highest sensitivity (58.6%), PPV (88.14%), NPV (68.47%), positive LR (6.976), accuracy (75.29), and lowest negative LR (0.452) to differentiate BOTs from OC In the three inflammatory-nutritional indices (FAR, PNI, and SII). Table 3 also provides the relevant cut-off value, sensitivity, specificity, PPV, NPV, positive LR, negative LR, and accuracy of the various combinations of the defined variables for differentiating BOTs from OC. Overall, the CA125 tested, when compared separately, displayed the highest sensitivity (73.6%), specificity (97.6%), PPV (95.52%), NPV (77.67%), positive LR (30.667), accuracy (84.71%), and lowest negative LR (0.270) when it was used to differentiate BOTs from OC. The AUC of the combination of five variables was higher than any single one. The AUC of the combination of five variables was higher than any single one. In comparison to CA125 alone, the combination of the five variables showed a slight increase in sensitivity (83.91%), NPV (83.91%), accuracy (85.88%), and a decrease in negative LR (0.180%) as seen in Table 3 . The DCA for single CA125, HE4, and CA125 combined with HE4, as well as the combination of CA125, HE4, FAR, SII, and PNI, are shown in Fig. 3 .This graphic analysis showed that the combination of CA125, HE4, FAR, SII, and PNI presented a higher clinical utility than isolated CA125 or HE4 or the combination of CA125 and HE4. When we associated CA125, HE4, FAR, SII, and PNI, we observed an enhancement of this clinical value, which was higher than the combination of CA125 and HE4 clinical value in the range of 15–75% risks thresholds. Table 3 Cut-off value and diagnostic value of CA125, HE4, SII, PNI, FAR, and different combinations of the variables in the diagnosis of OC Variables AUC Cut-off 95%CI P -value Sensitivity (%) Specificity (%) PPV (%) NPV (%) Positive LR Negative LR Accuracy(%) CA125 0.89 79.89 0.833 to 0.933 <0.001 73.6 97.6 95.52 77.67 30.667 0.270 84.71 HE4 0.859 65.16 0.797 to 0.908 <0.001 72.4 92.8 90 76 10.056 0.297 81.76 FAR 0.793 0.084 0.724 to 0.851 <0.001 58.6 91.6 88.14 68.47 6.976 0.452 75.29 SII 0.739 945.206 0.666 to 0.803 <0.001 47.1 92.8 85.42 62.30 6.542 0.570 68.82 PNI 0.692 46.9 0.617 to 0.761 <0.001 52.9 89.2 82.14 62.39 4.898 0.528 70.0 CA125 + HE4 0.893 0.338 0.836 to 0.935 <0.001 81.6 90.36 88.75 82.22 8.465 0.204 85.29 CA125 + HE4 + SII 0.902 0.400 0.847 to 0.942 <0.001 79.31 91.57 90.91 81.72 9.408 0.226 85.88 CA125 + HE4 + PNI 0.891 0.414 0.834 to 0.934 <0.001 78.16 92.77 90.67 80.00 10.811 0.235 84.71 CA125 + HE4 + FAR 0.906 0.531 0.852 to 0.946 <0.001 78.16 96.39 94.44 80.61 21.651 0.227 86.47 CA125 + HE4 + SII + PNI + FAR 0.910 0.350 0.857 to 0.949 <0.001 83.91 89.16 87.95 83.91 7.74 0.180 85.88 CA125 cancer antigen 125, HE4 human epididymis protein 4, SII platelet count (10 9 /L) × neutrophil count (10 9 /L)/ lymphocyte count (10 9 /L), PNI albumin (g/L) + 5 × lymphocyte counts (10 9 /L), FAR fibrinogen(g/L)/ albumin(g/L), AUC area under the curve, CI confidence interval, PPV positive predictive value, NPV negative predictive value, LR likelihood ratio Overall, compared to the early-stage OC (Stage I-II) group, CA125, HE4, SII, and FAR values in the advanced OC (Stage III-IV) group were significantly higher, while PNI was significantly lower. The isolated CA125 tested showed the best application value to differentiate BOTs from OC when the defined variables were compared separately. The combination of five variables displayed greater AUC than any one of them. Compared to CA125 alone, the combination of CA125, HE4, FAR, SII, and PNI showed a slight gain in sensitivity (83.91%), NPV (83.91%), accuracy (85.88%), and a decrease in negative LR (0.180%). The five variables combined yielded a more advantageous clinical outcome than either CA125 alone, HE4 alone, or CA125 and HE4 combined. Higher preoperative CA125, HE4, SII, and FAR levels and lower PNI levels indicate a greater likelihood of advanced OC progression and lymph node metastasis. FAR had a better application value than other inflammation-related markers (PNI and SII). 4. Discussion The incidence of OC, the most deadly of gynecological malignancies and a major contributor to cancer-related fatalities in women globally [ 25 ], has been on the rise in recent years [ 2 ]. Less than 30% of patients survive since there are no early signs of OC and no early screening or diagnosis [ 26 ]. Given the low prognosis of this cancer, it is necessary to improve the survival rates of patients by using methods to accurately predict the risk factors which affect the severity of cancer and the early diagnosis. CA125 was first described in the early 1980s [ 27 ]. In cases of OC, the serum level of CA125 may be higher. However, in stage I, only 23–50% of cases demonstrate sensitivity to this measurement; it is not particularly sensitive during the early stages of the disease [ 11 ]. The specificity of CA125 for identifying OC was 78% (95%CI 76–80) in a meta-analysis by Ferraro et al [ 28 ]. The AUC for CA125 in the study by Dikmen et al.[ 29 ] was relatively low (0.78), indicating that it was probably not the ideal marker for diagnosing OC. In an effort to better detect OC in the early stages, new biological markers such as HE4[ 11 ] have been studied. Reports suggest that HE4 is overexpressed in ovarian tumors, particularly in endometrioid OC [ 12 ]. Moreover, it appears that HE4 is not as highly expressed in clear-cell ovarian carcinomas as in other epithelial ovarian cancers (EOCs) [ 30 ]. According to Yanaranop et al. [ 31 ], HE4 had an 86% specificity, and the AUC was superior to CA125 alone, with values of 0.893 and 0.865, respectively [ 32 ]. A recent Italian multicenter study suggested that HE4 may have at least partially different roles in EOC diagnosis than CA125 [ 33 ]. Moreover, HE4 was found to be more effective than CA125 in ruling EOC patients in both the disease group and the early stages of tumors [ 33 ]. The CA125 test, however, performed better than HE4 in our study in terms of sensitivity (73.6%), specificity (97.6%), PPV (95.52%), NPV (77.67%), positive LR (30.667), accuracy (84.71%), and lower negative LR (0.270). The immune response and systemic inflammatory processes have been revealed to be essential in the initiation and progression of various tumors [ 17 ]. The immune system and inflammatory response are linked to various stages of carcinogenesis, such as initiation, invasion, promotion, and metastasis [ 34 ]. Studies have shown that malnutrition can heighten the likelihood of postoperative complications, increase the vulnerability of patients to infection, and even encourage tumor recurrence by suppressing tumor immunity [ 18 , 19 , 35 ]. The immune and nutritional state of the body is a critical element of the inflammatory reaction. Malnutrition associated with cancer typically results from the activation of systemic inflammation brought on by the progression of the disease, which impairs immunity and decreases survival [ 19 , 36 , 37 ]. In addition, patients with advanced OC often suffer from malnutrition due to peritoneal dissemination caused by intestinal obstruction [ 38 ]. The role of inflammation and nutrition in cancer patients has been the focus of a growing number of studies. Inflammation is a major factor in the tumor microenvironment [ 39 ]. Many inflammatory cells and cytokines in the tumor microenvironment can have an effect on the growth, development, and metastasis of cancer. Platelets, neutrophils, and lymphocytes, which can aggregate in vessels and release factors such as vascular endothelial growth factor, TGF-beta, platelet-derived growth factor, and more, can affect the biological behavior of cancer cells [ 40 – 44 ]. Thrombopoietin and inflammatory mediators released by cancer cells may stimulate platelet growth and, in turn, stimulate tumor growth. Neutrophils, by releasing VEGF and matrix metalloproteinase, can foster angiogenesis, tumor growth, and metastasis [ 39 ]. Lymphocytes are responsible for the immune defense against tumor cells by releasing tumor necrotic factor (TNF), interferon-γ, and other cytokines. When their levels are low, these cytokines may create a favorable tumor microenvironment for cancer cells to proliferate, progress, and spread. At the same time, the body's immune system may be weakened by a reduction in the number of lymphocytes, and cancer cells are more likely to immune escape, leading to a poor prognosis for cancer patients. Ostroumov et al. reported that CD4 and CD8 T lymphocytes can mediate the growth of cancer cells [ 45 ]. SII, which takes into account peripheral blood counts of platelets, neutrophils, and lymphocytes [SII = (P*N)/L], can represent different inflammatory and immune pathways in the body and has greater stability [ 46 ]. It has been used in the diagnosis and treatment of a range of malignant tumors and is associated with the prognosis of patients, indicating the immunological and inflammatory status of patients with malignant tumors [ 47 – 49 ]. The marker, with its greater predictive power than either the neutrophil-to-lymphocyte ratio (NLR) or the platelet-to-lymphocyte ratio (PLR) [ 50 – 52 ], has been linked to a decrease in both overall survival (OS) and disease-free survival (DFS) rates in those who have colon cancer, OC, or hepatocarcinoma. SII has been evaluated as a relevant prognostic factor for OC, but few studies have focused on its role in predicting malignancy preoperatively. Albumin serves as a significant indicator of acute-phase proteins and systemic chronic inflammation [ 53 ]. Its usage is widespread in reflecting the overall nutrition status of the body and it is deemed a promising prognostic factor for various malignancies [ 54 , 55 ]. When inflammation occurs, proinflammatory cytokines like interleukin-1 (IL-1), interleukin-6 (IL-6), and TNF-α can suppress the liver's production of albumin [ 56 , 57 ]. A low serum albumin level may indicate that the host is experiencing malnutrition, which can negatively impact their overall health. This state of malnourishment can weaken the body's defense mechanisms, including cellular immunity, humoral immunity, and phagocytic function [ 58 ]. According to a meta-analysis, preoperative serum albumin levels can be used as an independent prognostic indicator for overall survival (OS) in patients with EOC [ 59 ]. It has been well demonstrated that low serum albumin concentrations are linked to poor survival in EOC. PNI, as well as SII, is known as a marker that reflects systemic inflammatory status. PNI, calculated by the serum albumin concentration and the peripheral blood lymphocyte count, could reflect both the nutritional and immunological statuses of the host and has been validated as an indicator for predicting short- and long-term prognosis [ 60 ]. Recently, it was discovered that low PNI was linked to a poor prognosis for HGSOC and cervical cancer [ 61 , 62 ]. Currently, the prognostic value of pretreatment PNI has been verified in several tumors, such as pancreatic cancer [ 63 ], liver cancer [ 64 ], and colorectal cancer [ 65 ]. There is mounting evidence suggesting that preoperative PNI could serve as an indicator of the prognosis for patients with OC [ 60 , 66 , 67 ]. According to Miao et al., PNI is an independent prognostic indicator for OS and progression-free survival (PFS) in patients with OC [ 68 ]. However, it is rarely reported as an indicator of the diagnosis of OC. Plasma fibrinogen is another acute-phase protein, produced by proinflammatory cytokines and characterized by elevated levels during systemic inflammation [ 69 ]. It is considered as a key factor in the regulation of inflammation and cancer development by mediating the proliferation and migration of tumor cells, as well as the initiation of angiogenesis [ 70 ]. Additionally, it may be a crucial component of cellular adhesion, proliferation, and migration throughout the processes of angiogenesis and tumor cell growth as a molecular bridge [ 71 ]. Moreover, fibrinogen has the ability to hinder the natural killer cells from attacking tumor cells or cytotoxic agents that target tumor cells, thus giving rise to the growth of tumor cells and enabling them to evade immune surveillance. This mechanism can potentially act as a promotion of tumor cell growth [ 72 ]. Various studies have consistently revealed that an elevated level of fibrinogen prior to treatment is linked to an unfavorable prognosis in various cancers [ 73 , 74 ]. A study conducted by Perisanidis et al. in 2015 demonstrated that fibrinogen could serve as an independent prognostic biomarker in cancer patients. It was also found to contribute to tumor growth and metastasis potentially [ 75 , 76 ]. The fibrinogen-albumin ratio index (FAR), which takes both fibrinogen and albumin into account, has recently been discovered as a prognostic factor for various malignancies [ 77 – 82 ]. One potential approach to improve the accuracy of assessing inflammation and nutritional status in patients is to combine fibrinogen and albumin into FAR. Both elevated serum fibrinogen and decreased serum albumin have been widely recognized as effective biomarkers for detecting elevated systemic inflammation [ 75 , 83 ]. According to research conducted by Xie H et al. a high FAR could indicate more aggressive tumor biological characteristics as well as progressive systemic inflammation [ 84 ]. In addition, FAR could amplify the sensitivity of inflammation and nutrition status in EOC patients, and it was found to be more effective than using single fibrinogen or albumin in predicting the prognosis of EOC. Moreover, based on the ROC curve analysis, FAR was found to have superior utility compared to other inflammation-related markers such as NLR, PLR, and PNI. Notably, although both FAR and PNI incorporated albumin, FAR had a more pronounced impact on prognosis, possibly due to its greater emphasis on fibrinogen. However, we have yet to find any studies on the use of FAR in diagnosing OC, and the role of FAR in OC remains relatively unexplored. In this study, the FAR tested showed the highest sensitivity (58.6%), PPV (88.14%), NPV (68.47%), positive LR (6.976), accuracy (75.29), and lowest negative LR (0.452) to differentiate BOTs from OC in the three inflammatory-nutritional indices (FAR, PNI, SII). The present study included 170 patients with documented OC and BOT. It demonstrated that preoperative CA125 combined with HE4, SII, PNI, and FAR made up for single-use shortcomings, ensuring high sensitivity, NPV, and accuracy. To the best of our knowledge, no paper regarding the clinical value of CA125 combined with HE4, SII, PNI, and FAR in the prediction of OC in the preoperative setting has been published until now. Our results suggest that preoperative serum SII, PNI, and FAR might be clinically valuable markers in patients with OC. Of the three inflammatory-nutritional indices tested (FAR, PNI, SII), the FAR showed the highest sensitivity (58.6%), PPV (88.14%), NPV (68.47%), positive LR (6.976), accuracy (75.29) and lowest negative LR (0.452) for differentiating between BOTs and OC. The combination of CA125, HE4, SII, PNI, and FAR had better application values than other combinations of the five variables. Compared with the early-stage OC (Stage I-II) group, CA125, HE4, SII, and FAR values in the advanced OC (Stage III-IV) group were significantly higher. In contrast, the value of PNI in the advanced OC (Stage III-IV) group was significantly lower. Based on the results of previous studies and the current study, it is thought that FAR and SII increase, and PNI decrease as cancer progresses. Therefore, increases in FAR and SII and decreases in PNI are associated with advanced ovarian cancer and may be associated with poor prognosis. FAR had better application value than other inflammation-related markers (PNI and SII). In the present study, DCA showed that the combination of CA125, HE4, FAR, SII, and PNI presented a higher net benefit than CA125 alone or HE4 alone, or CA125 combined with HE4. The inflammation-related markers changes are not cancer-specific findings. The increase or decrease in these values does not indicate absolute ovarian cancer risk. However, the inflammation-related markers with a differential count are a common and inexpensive preoperative test. Therefore, even if it is not a confirmatory test for OC, it has clinical utility as a potential auxiliary tool for differential diagnosis before surgery. Our study showed that adding inflammatory biomarkers to CA125 and HE4 can achieve satisfactory efficiency in distinguishing BOTs from OC. Preoperative biomarkers provide some assistance, but the management process often relies on imaging examinations once abnormal serum biomarker results are detected. As we delve deeper into the inflammatory mechanisms associated with tumors, we may discover more effective combinations of tumor and inflammatory biomarkers. It's important to note that this study was conducted retrospectively and the inclusion of a relatively small number of patients from a single center. However, the rigorous inclusion and exclusion criteria and the high statistical significance achieved for the diagnostic traits tested in our series provide strong evidence for the reliability and reproducibility of our findings. 5. Conclusions The study suggested that preoperative serum SII, PNI, and FAR might potentially be clinically valuable markers in patients with OC. The combination of CA125, HE4, SII, PNI, and FAR had better application values than other combinations of the five variables. It was superior to applying CA125, HE4, SII, PNI, and FAR alone in predicting OC in the preoperative setting. Higher preoperative CA125, HE4, SII, and FAR levels and lower PNI levels predicted a higher probability of advanced OC progression and lymph node metastasis. FAR had better application value than other inflammation-related markers (PNI and SII). As we delve deeper into the inflammatory mechanisms associated with tumors, we may discover more effective combinations of tumor and inflammatory biomarkers. The main limitation of our study is that it is a retrospective analysis with a small sample of patients from a single center and the same region. Thus, a broader multi-center study to validate results and attain higher statistical power would be interesting. Abbreviations CA125 Carbohydrate Antigen 125 HE4 Human Epididymis Protein 4 SII systemic immune-inflammation index FAR fibrinogen-to-albumin ratio PNI prognostic nutritional index OC ovarian cancer BOT benign ovarian tumor ROC operating characteristic curve AUC greater area under the ROC curve NPV negative predictive value LR likelihood ratio PPV positive predictive value DCA decision curve analysis EOC epithelial ovarian cancer NLR neutrophil-to-lymphocyte ratio PLR platelet-to-lymphocyte ratio TNF tumor necrotic factor OS overall survival IL-1 like interleukin-1 IL-6 interleukin-6 PFS progression-free survival DFS disease-free survival Declarations Ethics approval and consent to participate This study was conducted according to the Declaration of Helsinki and was approved by the ETHICS Committee. Research Ethics Review No.2023075. Written informed consent was obtained from all individual participants included in the study. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grants from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions LYS, JQ, JZ, and SNB designed the research; LYS, QW, and RX collected the data and prepared the manuscript; LYS, JQ, JZ, SNB, QW, and RX wrote the manuscript; All authors have read and approved the final manuscript. Acknowledgments We would like to thank all doctors, nurses, patients, and their family members for their kindness to support our study. References Menon U, Karpinskyj C, Gentry-Maharaj A. Ovarian Cancer Prevention and Screening. Obstet Gynecol. 2018 May;131(5):909-927. doi: 10.1097/AOG.0000000000002580. PMID: 29630008. Ge L, Liu G, Hu K, et al. 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The preoperative prognostic nutritional index is a predictive and prognostic factor of high-grade serous ovarian cancer. BMC Cancer. 2018 Sep 10;18(1):883. doi: 10.1186/s12885-018-4732-8. PMID: 30200903; PMCID: PMC6131794. Zhu Y, Zhou S, Liu Y, Zhai L, Sun X. Prognostic value of systemic inflammatory markers in ovarian Cancer: a PRISMA-compliant meta-analysis and systematic review. BMC Cancer. 2018 Apr 18;18(1):443. doi: 10.1186/s12885-018-4318-5. PMID: 29669528; PMCID: PMC5907305. Haraga J, Nakamura K, Omichi C, et al. Pretreatment prognostic nutritional index is a significant predictor of prognosis in patients with cervical cancer treated with concurrent chemoradiotherapy. Mol Clin Oncol. 2016 Nov;5(5):567-574. doi: 10.3892/mco.2016.1028. Epub 2016 Sep 21. PMID: 27900086; PMCID: PMC5103867. Li S, Tian G, Chen Z, Zhuang Y, Li G. Prognostic Role of the Prognostic Nutritional Index in Pancreatic Cancer: A Meta-analysis. Nutr Cancer. 2019;71(2):207-213. doi: 10.1080/01635581.2018.1559930. Epub 2019 Jan 20. PMID: 30663390. Man Z, Pang Q, Zhou L, et al. Prognostic significance of preoperative prognostic nutritional index in hepatocellular carcinoma: a meta-analysis. HPB (Oxford). 2018 Oct;20(10):888-895. doi: 10.1016/j.hpb.2018.03.019. Epub 2018 May 28. PMID: 29853431. Sun G, Li Y, Peng Y, et al. Impact of the preoperative prognostic nutritional index on postoperative and survival outcomes in colorectal cancer patients who underwent primary tumor resection: a systematic review and meta-analysis. Int J Colorectal Dis. 2019 Apr;34(4):681-689. doi: 10.1007/s00384-019-03241-1. Epub 2019 Jan 24. PMID: 30680451. Komura N, Mabuchi S, Yokoi E, et al. Prognostic significance of the pretreatment prognostic nutritional index in patients with epithelial ovarian cancer. Oncotarget. 2019 Jun 4;10(38):3605-3613. doi: 10.18632/oncotarget.26914. PMID: 31217896; PMCID: PMC6557203. Zhang W, Ye B, Liang W, Ren Y. Preoperative prognostic nutritional index is a powerful predictor of prognosis in patients with stage III ovarian cancer. Sci Rep. 2017 Aug 25;7(1):9548. doi: 10.1038/s41598-017-10328-8. Erratum in: Sci Rep. 2018 Jun 22;8(1):9736. PMID: 28842710; PMCID: PMC5573316. Miao Y, Li S, Yan Q, Li B, Feng Y. Prognostic Significance of Preoperative Prognostic Nutritional Index in Epithelial Ovarian Cancer Patients Treated with Platinum-Based Chemotherapy. Oncol Res Treat. 2016;39(11):712-719. doi: 10.1159/000452263. Epub 2016 Oct 19. PMID: 27855385. Ghanim B, Hoda MA, Klikovits T, et al. Circulating fibrinogen is a prognostic and predictive biomarker in malignant pleural mesothelioma. Br J Cancer. 2014 Feb 18;110(4):984-90. doi: 10.1038/bjc.2013.815. Epub 2014 Jan 16. PMID: 24434429; PMCID: PMC3929892. Li W, Tang Y, Song Y, et al. Prognostic Role of Pretreatment Plasma D-Dimer in Patients with Solid Tumors: a Systematic Review and Meta-Analysis. Cell Physiol Biochem. 2018;45(4):1663-1676. doi: 10.1159/000487734. Epub 2018 Feb 22. PMID: 29490291. Simpson-Haidaris PJ, Rybarczyk B. Tumors and fibrinogen. The role of fibrinogen as an extracellular matrix protein. Ann N Y Acad Sci. 2001;936:406-25. PMID: 11460495. Li Y, Yang JN, Cheng SS, Wang Y. Prognostic significance of FA score based on plasma fibrinogen and serum albumin in patients with epithelial ovarian cancer. Cancer Manag Res. 2019 Aug 14;11:7697-7705. doi: 10.2147/CMAR.S211524. PMID: 31616185; PMCID: PMC6698597. Qi Q, Geng Y, Sun M, Chen H, Wang P, Chen Z. Hyperfibrinogen Is Associated With the Systemic Inflammatory Response and Predicts Poor Prognosis in Advanced Pancreatic Cancer. Pancreas. 2015 Aug;44(6):977-82. doi: 10.1097/MPA.0000000000000353. PMID: 25931258. Sheng L, Luo M, Sun X, Lin N, Mao W, Su D. Serum fibrinogen is an independent prognostic factor in operable nonsmall cell lung cancer. Int J Cancer. 2013 Dec 1;133(11):2720-5. doi: 10.1002/ijc.28284. Epub 2013 Jun 14. PMID: 23716344. Perisanidis C, Psyrri A, Cohen EE, et al. Prognostic role of pretreatment plasma fibrinogen in patients with solid tumors: A systematic review and meta-analysis. Cancer Treat Rev. 2015 Dec;41(10):960-70. doi: 10.1016/j.ctrv.2015.10.002. PMID: 26604093.doi:10.1016/j.ctrv.2015.10.002 Lv GY, Yu Y, An L, Sun XD, Sun DW. Preoperative plasma fibrinogen is associated with poor prognosis in esophageal carcinoma: a meta-analysis. Clin Transl Oncol. 2018 Jul;20(7):853-861. doi: 10.1007/s12094-017-1794-z. Epub 2017 Nov 13. PMID: 29134563. Tan Z, Zhang M, Han Q, et al. A novel blood tool of cancer prognosis in esophageal squamous cell carcinoma: the Fibrinogen/Albumin Ratio. J Cancer. 2017 Apr 8;8(6):1025-1029. doi: 10.7150/jca.16491. PMID: 28529615; PMCID: PMC5436255. Yu W, Ye Z, Fang X, Jiang X, Jiang Y. Preoperative albumin-to-fibrinogen ratio predicts chemotherapy resistance and prognosis in patients with advanced epithelial ovarian cancer. J Ovarian Res. 2019 Sep 18;12(1):88. doi: 10.1186/s13048-019-0563-8. PMID: 31533857; PMCID: PMC6751810. Xu Q, Yan Y, Gu S, et al. A Novel Inflammation-Based Prognostic Score: The Fibrinogen/Albumin Ratio Predicts Prognoses of Patients after Curative Resection for Hepatocellular Carcinoma. J Immunol Res. 2018 May 22;2018:4925498. doi: 10.1155/2018/4925498. PMID: 30027102; PMCID: PMC6031154. Hwang KT, Chung JK, Roh EY, et al. Prognostic Influence of Preoperative Fibrinogen to Albumin Ratio for Breast Cancer. J Breast Cancer. 2017 Sep;20(3):254-263. doi: 10.4048/jbc.2017.20.3.254. Epub 2017 Sep 22. PMID: 28970851; PMCID: PMC5620440. Wang Y, Chen W, Hu C, et al. Albumin and Fibrinogen Combined Prognostic Grade Predicts Prognosis of Patients with Prostate Cancer. J Cancer. 2017 Oct 23;8(19):3992-4001. doi: 10.7150/jca.21061. PMID: 29187874; PMCID: PMC5706001. Miura K, Hamanaka K, Koizumi T, et al. Clinical significance of preoperative serum albumin level for prognosis in surgically resected patients with non-small cell lung cancer: Comparative study of normal lung, emphysema, and pulmonary fibrosis. Lung Cancer. 2017 Sep;111:88-95. doi: 10.1016/j.lungcan.2017.07.003. Epub 2017 Jul 17. PMID: 28838406. Gupta D, Lis CG. Pretreatment serum albumin as a predictor of cancer survival: a systematic review of the epidemiological literature. Nutr J. 2010 Dec 22;9:69. doi: 10.1186/1475-2891-9-69. PMID: 21176210; PMCID: PMC3019132. Xie H, Yuan G, Liu M, et al. Pretreatment Albumin-to-Fibrinogen Ratio is a Promising Biomarker for Predicting Postoperative Clinical Outcomes in Patients with Colorectal Cancer. Nutr Cancer. 2022;74(8):2896-2909. doi: 10.1080/01635581.2022.2042572. Epub 2022 Feb 23. PMID: 35193433. Additional Declarations No competing interests reported. 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Also discoverable on Platform About In Review Editorial Policies 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-3003534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":205811476,"identity":"ffd5c701-869a-493a-9a34-adff55d4cad9","order_by":0,"name":"Liyun Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3PsWvCQBTH8RcOLsuVrO+w2H/hyUGi/82FrMGlS4aiJ0Ini2uG0r8hk9DtQsEuoXM2a127lA4tVEX/AEns5nCf+X3h9wAc5wIRCICvPY6Dm5dys82wG5yTeDnva5nzhETVV9KckTDBM025CPHqPouLtiTyH8p3IXBIHUMon9ArgK0/6oZkMHtLCBFvo2urqbdAFgFXKm0aVqchEqH3bLTV8QL5wAjeaUxWn9Gv1sc9Nja2fERBti2pRQjWYlzUiTeZGMT2pEqVPF4qOVsyBkskOW375bXqfe/MqBv4858/uBuN5/50vWlKTmD/O3ccx3FOOAAN50yzuS+hEAAAAABJRU5ErkJggg==","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Liyun","middleName":"","lastName":"Song","suffix":""},{"id":205811478,"identity":"36ac8621-d977-4568-823b-85a057a65f56","order_by":1,"name":"Jie Qi","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Qi","suffix":""},{"id":205811480,"identity":"ff5c8aa2-aed5-4d8e-b272-dc18cf4cebc7","order_by":2,"name":"Jing Zhao","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhao","suffix":""},{"id":205811482,"identity":"13b8d7f3-3f3f-4c8a-b3b8-197e0ff4115e","order_by":3,"name":"Suning Bai","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Suning","middleName":"","lastName":"Bai","suffix":""},{"id":205811483,"identity":"7ac03796-b01a-4668-9181-aeff9493be4e","order_by":4,"name":"Qi Wu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Wu","suffix":""},{"id":205811484,"identity":"949e1f3b-0269-4342-9b16-0f081087c407","order_by":5,"name":"Ren Xu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ren","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2023-05-31 07:29:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3003534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3003534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":37924041,"identity":"d3701460-7e8e-4a35-a1cb-d90b7228ce42","added_by":"auto","created_at":"2023-06-02 15:49:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55948,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of CA125, HE4, SII, PNI, and FAR in the diagnosis of OC\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3003534/v1/55e8957955e3bd1b4e4b767b.png"},{"id":37924567,"identity":"d4af7769-14bb-4abe-a3b6-ff8b92d8fd2e","added_by":"auto","created_at":"2023-06-02 15:57:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56276,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of different combinations of variables in the diagnosis of OC\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3003534/v1/6119b8a28fb8c4ec97104929.png"},{"id":37924043,"identity":"315421a2-22a7-464c-b44c-e98f11e0f24e","added_by":"auto","created_at":"2023-06-02 15:49:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86525,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve showing the net benefit of CA125, HE4, CA125+HE4, and\u003c/p\u003e\n\u003cp\u003eCA125+HE4+SII+PNI+FAR in women at risk of developing ovarian cancer.\u003c/p\u003e\n\u003cp\u003eCA125 dosage, positive if ≥79.89 U/mL, negative if \u0026lt;79.89 U/mL; HE4, HE4 dosage, positive if ≥65.16 pmol/L, negative if \u0026lt; 65.16 pmol/L; FAR, fibrinogen-to-albumin ratio, positive if ≥0.084, negative if \u0026lt;0.084; PNI, prognostic nutritional index, positive if ≥46.9, negative if \u0026lt;46.9; SII, systemic immune-inflammation index, positive if ≥945.206, negative if \u0026lt;945.206;CA125.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3003534/v1/c151afb50e4a8f3d49bece8d.png"},{"id":39266486,"identity":"dedada0d-00a4-45cb-9c01-73be2cf932a9","added_by":"auto","created_at":"2023-06-29 01:29:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":634939,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3003534/v1/6b9b5246-9eb2-4f80-92c1-e6165a663fee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic value of CA125, HE4, systemic immune‑inflammatory index (SII), fibrinogen-to-albumin ratio(FAR), and prognostic nutritional index(PNI) in the Preoperative Investigation of ovarian Masses","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe fifth-leading cause of cancer-related death in women is ovarian cancer (OC), which is the most prevalent malignant tumor in females [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent years, OC has been on the rise [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The prognosis for OC is quite poor with a survival rate of just 30% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As the early manifestations of OC are rather hidden, the disease may have developed to a middle or advanced stage at diagnosis, resulting in missed optimal treatment timing and increased mortality [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The 5-year survival rate of OC can be as high as 90% when detected early and treated with standard surgery and adjuvant therapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is imperative to differentiate between malignant ovarian tumors and benign ovarian tumors (BOTs).\u003c/p\u003e \u003cp\u003eEfforts to identify more dependable biomarkers for the early detection of OC have been made, and serum biomarkers are a practical, cost-effective, and non-invasive approach for predicting malignancy. The Carbohydrate Antigen 125(CA125) is expressed in over 80% of OC patients, and can be detected in serum, thus enabling the differentiation of malignant ovarian tumors from normal ovarian tissue [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, this marker has a low sensitivity in the early stages of OC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, it has a high false-positive rate in benign gynecological conditions such as acute pelvic inflammation, adenomyosis, uterine myoma, and endometriosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additional biomarkers, such as Human Epididymis Protein 4 (HE4), have been developed to enhance the specificity of ovarian carcinomas' specificity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This biomarker is reported to be overexpressed in OC tissues [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition to colorectal cancer and gastrointestinal malignancies, HE4 is a non-specific tumor marker expressed to varying degrees in cervical, endometrial, ovarian, and nonepithelial tumors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is strongly associated with tumor invasion, migration, and recurrence. The two most effective markers currently available, CA125 and HE4, are insufficient for detecting early-stage OC [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A great deal of effort is being put forth to discover additional biomarkers that, either alone or in combination with CA125 and HE4, could enhance the sensitivity and specificity of detecting OC in a more timely, treatable manner.\u003c/p\u003e \u003cp\u003eThe behavior of OC has also been better understood recently by the scientific community, in which the body's inflammatory and immune response plays a crucial role [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The inflammatory response is significantly impacted by the immunological and nutritional state of the body, and the presence and metastatic spread of tumor cells are closely linked to inflammation. Malnutrition has been reported to make patients more susceptible to infection and promote tumor recurrence through suppression of tumor immunity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, a growing number of studies have concentrated on how nutrition and inflammation interact in cancer patients. Studies have shown that peripheral blood neutrophils, lymphocytes, platelets, albumin, globulin, and fibrinogen play an essential role in the inflammatory microenvironment of cancer [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In recent times, the preoperative systemic immune-inflammation index (SII), prognostic nutritional index (PNI), and fibrinogen-to-albumin ratio (FAR) have been identified as significant indicators of the diagnostic utility and prognosis of prostate cancer, lung cancer, and gastrointestinal tumors [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the role of these inflammation-related indicators in the diagnosis of ovarian cancer has rarely been reported. To the best of our knowledge, no paper has been published up to now that examines the clinical utility of CA125 combined with HE4, SII, PNI, and FAR in predicting OC in the preoperative setting. The aim of this study was to ascertain the diagnostic accuracy of CA125, HE4, SII, FAR, PNI, and their combinations for OC in order to discover an optimal combined diagnostic index for early diagnosis of OC. A thorough investigation was conducted to ascertain the correlation between these markers and the pathological characteristics of OC, thereby furnishing a foundation for the early identification and treatment of this disorder.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e \u003cb\u003eInclusion and exclusion criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis retrospective study included 170 patients with documented OC and BOTs who were treated at Hebei General Hospital between January 2019 and December 2022 and were divided into two groups, based on postoperative pathological results reviewed by two senior pathologists, of 87 OC and 83 BOTs. Patients with OC were not given chemotherapy or radiation therapy prior to the surgery. The International Federation of Gynecology and Obstetrics (FIGO) stage was utilized to ascertain the clinical stage of OC. All enrolled OC patients underwent a comprehensive staging surgery, comprising of a total hysterectomy, adnexectomy, complete pelvic/para-aortic lymphadenectomy, and peritoneal cytology. Infectious conditions, autoimmune diseases, severe liver or kidney damage, thrombotic diseases, other benign or malignant tumors, pregnancy, and preoperative complications of blood diseases were all disqualified from the study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical and laboratory data collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe data analyzed consisted of clinical and laboratory factors such as age, pathological type, FIGO staging, degree of tissue differentiation, presence or absence of lymph node metastasis, albumin, fibrinogen, neutrophil count, platelet count, lymphocyte count, CA125, and HE4. Prior to surgery, albumin, fibrinogen, neutrophil count, platelet count, lymphocyte count, and serum tumor biomarkers were all tested and recorded within a week. Using a COBAS E602 analyzer (Roche, Switzerland) and the chemiluminescent reagent kit supplied by Roche, preoperative CA125 and HE4 concentrations were measured. Two senior pathologists reviewed pathological examinations, and the Ethics Committee of Hebei General Hospital approved the collection of patients' clinical and laboratory data, following the Declaration of Helsinki. The ethical committee's conclusion that informed consent was not necessary meant that the need for written informed consent was no longer necessary.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInflammation-Related Markers\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe formula for serum inflammation-related markers was: FAR\u0026thinsp;=\u0026thinsp;fibrinogen(g/L)/ albumin(g/L); PNI\u0026thinsp;=\u0026thinsp;albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte counts (10\u003csup\u003e9\u003c/sup\u003e/L); SII\u0026thinsp;=\u0026thinsp;platelet count (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)/ lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData were analyzed using IBM SPSS statistics version V26.0 software, MedCalc Statistical Software version 19.4.0 software, and R Environment for Statistical Computing software (R Foundation for Statistical Computing). Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and \u003cem\u003eP\u003c/em\u003e values were calculated as two-sided. The Shapiro-Wilk test was then employed to assess the normality of the variables' distributions. The data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables with normal distribution and as the median and interquartile range for continuous variables without normal distribution. The T-test or Mann-Whitney U test was employed to assess the differences in variables among groups, and then the Kruskal-Wallis test was employed for multiple comparisons. The area under the operating characteristic curve (ROC), 95% confidence intervals, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR), negative LR, and accuracy for the defined variables were calculated to test the diagnostic performance for the prediction of OC by receiver ROC analysis. The Youden index of the ROC curve was then used to determine the optimal cut-off value of the parameters. We employed Spearman's rank correlation test and logistic regression analysis to assess the associations between the pertinent parameters.\u003c/p\u003e \u003cp\u003eSubsequently, decision curve analysis (DCA) was conducted to determine which single parameters and parameter combinations provided the most clinical utility in distinguishing BOTs from OC. For this visual analysis, we used software explicitly designed for DCA. All DCA calculations were performed as described by Vickers and Elkin [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003eCA125, HE4, SII, PNI, and FAR showed significant differences among the benign ovarian tumor group and OC group\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, 170 patients with ovarian tumors were enrolled, with the primary laboratory parameters of all participants being briefly outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The overall malignancy prevalence of our cohort was 51.18%, with the OC group having a mean age of 52.22\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26 years (range, 21\u0026ndash;76 years) and the control group having a mean age of 51.71\u0026thinsp;\u0026plusmn;\u0026thinsp;12.20 years (range, 26\u0026ndash;78 years). The two groups had no significant difference when compared by age (t = -0.282, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.778). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reveals a significant difference between patients with BOTs and OC in terms of absolute neutrophil count, absolute lymphocyte count, blood platelet count, albumin, fibrinogen, SII, PNI, FAR, CA125, and HE4 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). The median CA125, HE4, SII, and FAR values were found to be significantly higher in the OC group [ 884.86 (859.31), 0.087 (0.049), 228.50 (777.87), 180.20 (308.80)] compared to those in the BOT group [567.82 (354.31), 0.062 (0.017), 16.43 (13.85), 45.90 (17.22)]. Conversely, the median PNI value was found to be significantly lower in the OC group [ 46.40 (8.10)] compared to it in the BOT group [ 51.20 (5.10)].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of defined variables between ovarian cancer and benign ovarian tumor\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvarian cancer, median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign tumor, median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.20 (1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.45 (2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u0026ndash;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026ndash;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311.00 (125.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258.00 (92.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125\u0026ndash;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.70 (5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.60 (4.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFib(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59 (1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e884.86 (859.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e567.82 (354.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.40 (8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.20 (5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.087 (0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062 (0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125 (U/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228.50 (777.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.43 (13.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHE4 (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180.20 (308.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.90 (17.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003epremenopause\u0026thinsp;\u0026lt;\u0026thinsp;70\u003c/p\u003e \u003cp\u003epostmenopause\u0026thinsp;\u0026lt;\u0026thinsp;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eN absolute neutrophil count, L absolute lymphocyte count, PLT blood platelet count, Alb albumin, Fib fibrinogen, FAR fibrinogen(g/L)/ albumin(g/L), PNI albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte counts (10\u003csup\u003e9\u003c/sup\u003e/L), SII platelet count (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)/ lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L), CA125 cancer antigen 125, HE4 human epididymis protein 4, IQR interquartile range\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between CA125, HE4, SII, PNI, FAR, and clinic‑pathological characteristics of OC patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the histopathology and characteristics of the OC-enrolled patients, with differentiation grades and cancer stages for OC also specified. A comparison of CA125, HE4, SII, PNI, FAR, and clinical characteristics among the BOT group and OC group is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared with the early stage OC (Stage I-II) group, the values of CA125 [107.09(267.10)vs. 618.40༈1086.20༉, (Stage I-II) vs. (Stage III-IV), \u003cem\u003eP\u003c/em\u003e\u003c0.001], HE4 [82.80༈153.37༉vs. 299.00༈816.95༉, (Stage I-II) vs. (Stage III-IV), \u003cem\u003eP\u003c/em\u003e\u003c0.001], SII [685.64༈511.86༉vs. 1252.37༈857.18༉, (Stage I-II) vs. (Stage III-IV), \u003cem\u003eP\u003c/em\u003e\u003c0.001], and FAR [0.08༈0.04༉vs. 0.11༈0.06༉, (Stage I-II) vs. (Stage III-IV), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001] in the advanced OC (Stage III-IV) group were significantly higher, while the value of PNI [49.90༈8.77༉vs. 45.05༈8.08༉, (Stage I-II) vs. (Stage III-IV), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001] in the advanced OC (Stage III-IV) group was significantly lower. Moreover, compared with the non-lymph node metastasis OC group, the values of CA125 [161.90༈345.05༉vs. 714.50༈1074.40༉, ( non-lymph node metastasis ) vs. (lymph node metastasis), \u003cem\u003eP\u003c/em\u003e\u003c0.001], HE4 [84.65༈165.88༉vs. 331.00༈859.80༉, ( non-lymph node metastasis ) vs. (lymph node metastasis), \u003cem\u003eP\u003c/em\u003e\u003c0.001], SII [711.42༈552.86༉vs. 1332.02༈999.87༉, ( non-lymph node metastasis ) vs. (lymph node metastasis), \u003cem\u003eP\u003c/em\u003e\u003c0.001], and FAR [0.08༈0.03༉vs. 0.12༈0.08༉,( non-lymph node metastasis ) vs. (lymph node metastasis), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001] in the lymph node metastasis OC group were significantly higher, while the value of PNI [49.45༈7.45༉vs. 44.45༈5.20༉, ( Lymph nodes negative ) vs. (Lymph nodes positive), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002] was significantly lower. These results suggested that higher preoperative CA125, HE4, SII, and FAR levels and lower PNI levels predict a higher probability of advanced OC progression and lymph node metastasis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between laboratory variables and clinic-pathological characteristics of OC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCA125(U/ml), median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHE4 (pmol/L), median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSII, median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePNI, median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFAR, median (IQR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30(34.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e329.90(878.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e174.30(317.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e795.75(797.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.13(6.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08(0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57(65.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215.80(683.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180.20(329.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e947.76(938.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.85(9.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIGO staging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42(48.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107.09(267.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.80(153.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e685.64(511.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.90(8.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08(0.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45(51.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e618.40(1086.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299.00(816.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1252.37(857.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.05(8.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29(33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121.15(490.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.99(151.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e829.65(779.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.23(12.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09(0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2-G3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58(66.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e307.00(784.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e206.90(339.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e947.76(931.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.90(6.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09(0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68(78.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294.40(777.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204.65(363.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e962.18(928.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.08(7.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09(0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucinous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(9.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.21(177.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.43(39.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e704.91(729.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.00(14.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08(0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClearcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5(5.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.28(342.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.10(82.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e676.47(652.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.70(16.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09(0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6(6.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e675.20(2182.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138.35(674.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e743.06(3499.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.29(18.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08(0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH(K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56(64.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161.90(345.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.65(165.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e711.42(552.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.45(7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08(0.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31(35.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e714.50(1074.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331.00(859.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1332.02(999.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.45(5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12(0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCA125 cancer antigen 125, HE4 human epididymis protein 4, SII platelet count (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)/ lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L), PNI albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte counts (10\u003csup\u003e9\u003c/sup\u003e/L), FAR fibrinogen(g/L)/ albumin(g/L), IQR interquartile range\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOthers: including immature teratoma (one case), granulosa cell tumor (one case), endometrioid carcinoma (two cases), carcinosarcoma (two case)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, we investigated differences in the variables with respect to ages, histological grades, and pathological types. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows no statistically significant differences in the variables among different ages (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.968, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.535, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.685, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.284, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.704, respectively). However, CA125 and HE4 showed significant differences for categorical variables such as histological grades (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, respectively) and pathological type (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). No statistically significant differences were found in SII, PNI, and FAR between histological grades (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.292, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.817, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.429, respectively) and pathological types (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.192, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.317, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.808, respectively).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEfficiency of single CA125, HE4, SII, PNI, FAR, and different combinations of the variables in the diagnosis of OC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccording to the association of single CA125, HE4, SII, PNI, and FAR with OC, ROC curves were made and used to determine the optimal cut-off value and the corresponding sensitivity and specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The optimum cut-off value was chosen to maximize the Youden index (sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026minus;\u0026thinsp;1). The appropriate cut-off value of CA125 (AUC\u0026thinsp;=\u0026thinsp;0.890, \u003cem\u003eP\u003c/em\u003e\u003c0.001), HE4 (AUC\u0026thinsp;=\u0026thinsp;0.859, \u003cem\u003eP\u003c/em\u003e\u003c0.001), FAR (AUC\u0026thinsp;=\u0026thinsp;0.793, \u003cem\u003eP\u003c/em\u003e\u003c0.001), SII (AUC\u0026thinsp;=\u0026thinsp;0.739, \u003cem\u003eP\u003c/em\u003e\u003c0.001), and PNI(AUC\u0026thinsp;=\u0026thinsp;0.692, \u003cem\u003eP\u003c/em\u003e\u003c0.001) for differentiating BOTs and OC were 79.89, 65.16, 0.084, 945.206, and 46.9, respectively; with the corresponding sensitivity of 73.6%, 72.4%, 58.6%, 47.1%, and 52.9%, respectively; specificity of 97.6%, 92.8%, 91.6%, 92.8%, and 89.2%, respectively; PPV of 95.52%, 90%, 88.14%, 85.42%, and 82.14%, respectively; NPV of 77.67%, 76%, 68.47%, 62.30%, and 62.39%, respectively; positive LR of 30.667, 10.056, 6.976, 6.542, and 4.898, respectively; negative LR of 0.270, 0.297, 0.452, 0.570, and 0.528, respectively; accuracy of 84.71, 81.76, 75.29, 68.82, and 70.0 respectively. The FAR tested showed the highest sensitivity (58.6%), PPV (88.14%), NPV (68.47%), positive LR (6.976), accuracy (75.29), and lowest negative LR (0.452) to differentiate BOTs from OC In the three inflammatory-nutritional indices (FAR, PNI, and SII). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e also provides the relevant cut-off value, sensitivity, specificity, PPV, NPV, positive LR, negative LR, and accuracy of the various combinations of the defined variables for differentiating BOTs from OC. Overall, the CA125 tested, when compared separately, displayed the highest sensitivity (73.6%), specificity (97.6%), PPV (95.52%), NPV (77.67%), positive LR (30.667), accuracy (84.71%), and lowest negative LR (0.270) when it was used to differentiate BOTs from OC. The AUC of the combination of five variables was higher than any single one. The AUC of the combination of five variables was higher than any single one. In comparison to CA125 alone, the combination of the five variables showed a slight increase in sensitivity (83.91%), NPV (83.91%), accuracy (85.88%), and a decrease in negative LR (0.180%) as seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The DCA for single CA125, HE4, and CA125 combined with HE4, as well as the combination of CA125, HE4, FAR, SII, and PNI, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.This graphic analysis showed that the combination of CA125, HE4, FAR, SII, and PNI presented a higher clinical utility than isolated CA125 or HE4 or the combination of CA125 and HE4. When we associated CA125, HE4, FAR, SII, and PNI, we observed an enhancement of this clinical value, which was higher than the combination of CA125 and HE4 clinical value in the range of 15\u0026ndash;75% risks thresholds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCut-off value and diagnostic value of CA125, HE4, SII, PNI, FAR, and different combinations of the variables in the diagnosis of OC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePositive LR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNegative LR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAccuracy(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.833 to 0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e73.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e97.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e30.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e84.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.797 to 0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e81.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.724 to 0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e75.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e945.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.666 to 0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e68.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.617 to 0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u0026thinsp;+\u0026thinsp;HE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.836 to 0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e82.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e85.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u0026thinsp;+\u0026thinsp;HE4\u0026thinsp;+\u0026thinsp;SII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.847 to 0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e85.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u0026thinsp;+\u0026thinsp;HE4\u0026thinsp;+\u0026thinsp;PNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.834 to 0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e84.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u0026thinsp;+\u0026thinsp;HE4\u0026thinsp;+\u0026thinsp;FAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.852 to 0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e21.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e86.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125\u0026thinsp;+\u0026thinsp;HE4\u0026thinsp;+\u0026thinsp;SII\u0026thinsp;+\u0026thinsp;PNI\u0026thinsp;+\u0026thinsp;FAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.857 to 0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e83.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e87.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e83.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e85.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eCA125 cancer antigen 125, HE4 human epididymis protein 4, SII platelet count (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; neutrophil count (10\u003csup\u003e9\u003c/sup\u003e/L)/ lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L), PNI albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte counts (10\u003csup\u003e9\u003c/sup\u003e/L), FAR fibrinogen(g/L)/ albumin(g/L), AUC area under the curve, CI confidence interval, PPV positive predictive value, NPV negative predictive value, LR likelihood ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, compared to the early-stage OC (Stage I-II) group, CA125, HE4, SII, and FAR values in the advanced OC (Stage III-IV) group were significantly higher, while PNI was significantly lower. The isolated CA125 tested showed the best application value to differentiate BOTs from OC when the defined variables were compared separately. The combination of five variables displayed greater AUC than any one of them. Compared to CA125 alone, the combination of CA125, HE4, FAR, SII, and PNI showed a slight gain in sensitivity (83.91%), NPV (83.91%), accuracy (85.88%), and a decrease in negative LR (0.180%). The five variables combined yielded a more advantageous clinical outcome than either CA125 alone, HE4 alone, or CA125 and HE4 combined. Higher preoperative CA125, HE4, SII, and FAR levels and lower PNI levels indicate a greater likelihood of advanced OC progression and lymph node metastasis. FAR had a better application value than other inflammation-related markers (PNI and SII).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe incidence of OC, the most deadly of gynecological malignancies and a major contributor to cancer-related fatalities in women globally [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], has been on the rise in recent years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Less than 30% of patients survive since there are no early signs of OC and no early screening or diagnosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Given the low prognosis of this cancer, it is necessary to improve the survival rates of patients by using methods to accurately predict the risk factors which affect the severity of cancer and the early diagnosis.\u003c/p\u003e \u003cp\u003eCA125 was first described in the early 1980s [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In cases of OC, the serum level of CA125 may be higher. However, in stage I, only 23\u0026ndash;50% of cases demonstrate sensitivity to this measurement; it is not particularly sensitive during the early stages of the disease [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The specificity of CA125 for identifying OC was 78% (95%CI 76\u0026ndash;80) in a meta-analysis by Ferraro et al [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The AUC for CA125 in the study by Dikmen et al.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was relatively low (0.78), indicating that it was probably not the ideal marker for diagnosing OC.\u003c/p\u003e \u003cp\u003eIn an effort to better detect OC in the early stages, new biological markers such as HE4[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] have been studied. Reports suggest that HE4 is overexpressed in ovarian tumors, particularly in endometrioid OC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, it appears that HE4 is not as highly expressed in clear-cell ovarian carcinomas as in other epithelial ovarian cancers (EOCs) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. According to Yanaranop et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], HE4 had an 86% specificity, and the AUC was superior to CA125 alone, with values of 0.893 and 0.865, respectively [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A recent Italian multicenter study suggested that HE4 may have at least partially different roles in EOC diagnosis than CA125 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, HE4 was found to be more effective than CA125 in ruling EOC patients in both the disease group and the early stages of tumors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The CA125 test, however, performed better than HE4 in our study in terms of sensitivity (73.6%), specificity (97.6%), PPV (95.52%), NPV (77.67%), positive LR (30.667), accuracy (84.71%), and lower negative LR (0.270).\u003c/p\u003e \u003cp\u003eThe immune response and systemic inflammatory processes have been revealed to be essential in the initiation and progression of various tumors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The immune system and inflammatory response are linked to various stages of carcinogenesis, such as initiation, invasion, promotion, and metastasis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Studies have shown that malnutrition can heighten the likelihood of postoperative complications, increase the vulnerability of patients to infection, and even encourage tumor recurrence by suppressing tumor immunity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The immune and nutritional state of the body is a critical element of the inflammatory reaction. Malnutrition associated with cancer typically results from the activation of systemic inflammation brought on by the progression of the disease, which impairs immunity and decreases survival [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, patients with advanced OC often suffer from malnutrition due to peritoneal dissemination caused by intestinal obstruction [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The role of inflammation and nutrition in cancer patients has been the focus of a growing number of studies. Inflammation is a major factor in the tumor microenvironment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Many inflammatory cells and cytokines in the tumor microenvironment can have an effect on the growth, development, and metastasis of cancer.\u003c/p\u003e \u003cp\u003ePlatelets, neutrophils, and lymphocytes, which can aggregate in vessels and release factors such as vascular endothelial growth factor, TGF-beta, platelet-derived growth factor, and more, can affect the biological behavior of cancer cells [\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Thrombopoietin and inflammatory mediators released by cancer cells may stimulate platelet growth and, in turn, stimulate tumor growth. Neutrophils, by releasing VEGF and matrix metalloproteinase, can foster angiogenesis, tumor growth, and metastasis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Lymphocytes are responsible for the immune defense against tumor cells by releasing tumor necrotic factor (TNF), interferon-γ, and other cytokines. When their levels are low, these cytokines may create a favorable tumor microenvironment for cancer cells to proliferate, progress, and spread. At the same time, the body's immune system may be weakened by a reduction in the number of lymphocytes, and cancer cells are more likely to immune escape, leading to a poor prognosis for cancer patients. Ostroumov et al. reported that CD4 and CD8 T lymphocytes can mediate the growth of cancer cells [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSII, which takes into account peripheral blood counts of platelets, neutrophils, and lymphocytes [SII = (P*N)/L], can represent different inflammatory and immune pathways in the body and has greater stability [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. It has been used in the diagnosis and treatment of a range of malignant tumors and is associated with the prognosis of patients, indicating the immunological and inflammatory status of patients with malignant tumors [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The marker, with its greater predictive power than either the neutrophil-to-lymphocyte ratio (NLR) or the platelet-to-lymphocyte ratio (PLR) [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], has been linked to a decrease in both overall survival (OS) and disease-free survival (DFS) rates in those who have colon cancer, OC, or hepatocarcinoma. SII has been evaluated as a relevant prognostic factor for OC, but few studies have focused on its role in predicting malignancy preoperatively.\u003c/p\u003e \u003cp\u003eAlbumin serves as a significant indicator of acute-phase proteins and systemic chronic inflammation [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Its usage is widespread in reflecting the overall nutrition status of the body and it is deemed a promising prognostic factor for various malignancies [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. When inflammation occurs, proinflammatory cytokines like interleukin-1 (IL-1), interleukin-6 (IL-6), and TNF-α can suppress the liver's production of albumin [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. A low serum albumin level may indicate that the host is experiencing malnutrition, which can negatively impact their overall health. This state of malnourishment can weaken the body's defense mechanisms, including cellular immunity, humoral immunity, and phagocytic function [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. According to a meta-analysis, preoperative serum albumin levels can be used as an independent prognostic indicator for overall survival (OS) in patients with EOC [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. It has been well demonstrated that low serum albumin concentrations are linked to poor survival in EOC.\u003c/p\u003e \u003cp\u003ePNI, as well as SII, is known as a marker that reflects systemic inflammatory status. PNI, calculated by the serum albumin concentration and the peripheral blood lymphocyte count, could reflect both the nutritional and immunological statuses of the host and has been validated as an indicator for predicting short- and long-term prognosis [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Recently, it was discovered that low PNI was linked to a poor prognosis for HGSOC and cervical cancer [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Currently, the prognostic value of pretreatment PNI has been verified in several tumors, such as pancreatic cancer [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], liver cancer [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], and colorectal cancer [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. There is mounting evidence suggesting that preoperative PNI could serve as an indicator of the prognosis for patients with OC [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. According to Miao et al., PNI is an independent prognostic indicator for OS and progression-free survival (PFS) in patients with OC [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. However, it is rarely reported as an indicator of the diagnosis of OC.\u003c/p\u003e \u003cp\u003ePlasma fibrinogen is another acute-phase protein, produced by proinflammatory cytokines and characterized by elevated levels during systemic inflammation [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. It is considered as a key factor in the regulation of inflammation and cancer development by mediating the proliferation and migration of tumor cells, as well as the initiation of angiogenesis [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Additionally, it may be a crucial component of cellular adhesion, proliferation, and migration throughout the processes of angiogenesis and tumor cell growth as a molecular bridge [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Moreover, fibrinogen has the ability to hinder the natural killer cells from attacking tumor cells or cytotoxic agents that target tumor cells, thus giving rise to the growth of tumor cells and enabling them to evade immune surveillance. This mechanism can potentially act as a promotion of tumor cell growth [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Various studies have consistently revealed that an elevated level of fibrinogen prior to treatment is linked to an unfavorable prognosis in various cancers [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. A study conducted by Perisanidis et al. in 2015 demonstrated that fibrinogen could serve as an independent prognostic biomarker in cancer patients. It was also found to contribute to tumor growth and metastasis potentially [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe fibrinogen-albumin ratio index (FAR), which takes both fibrinogen and albumin into account, has recently been discovered as a prognostic factor for various malignancies [\u003cspan additionalcitationids=\"CR78 CR79 CR80 CR81\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. One potential approach to improve the accuracy of assessing inflammation and nutritional status in patients is to combine fibrinogen and albumin into FAR. Both elevated serum fibrinogen and decreased serum albumin have been widely recognized as effective biomarkers for detecting elevated systemic inflammation [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. According to research conducted by Xie H et al. a high FAR could indicate more aggressive tumor biological characteristics as well as progressive systemic inflammation [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. In addition, FAR could amplify the sensitivity of inflammation and nutrition status in EOC patients, and it was found to be more effective than using single fibrinogen or albumin in predicting the prognosis of EOC.\u003c/p\u003e \u003cp\u003eMoreover, based on the ROC curve analysis, FAR was found to have superior utility compared to other inflammation-related markers such as NLR, PLR, and PNI. Notably, although both FAR and PNI incorporated albumin, FAR had a more pronounced impact on prognosis, possibly due to its greater emphasis on fibrinogen. However, we have yet to find any studies on the use of FAR in diagnosing OC, and the role of FAR in OC remains relatively unexplored. In this study, the FAR tested showed the highest sensitivity (58.6%), PPV (88.14%), NPV (68.47%), positive LR (6.976), accuracy (75.29), and lowest negative LR (0.452) to differentiate BOTs from OC in the three inflammatory-nutritional indices (FAR, PNI, SII).\u003c/p\u003e \u003cp\u003eThe present study included 170 patients with documented OC and BOT. It demonstrated that preoperative CA125 combined with HE4, SII, PNI, and FAR made up for single-use shortcomings, ensuring high sensitivity, NPV, and accuracy. To the best of our knowledge, no paper regarding the clinical value of CA125 combined with HE4, SII, PNI, and FAR in the prediction of OC in the preoperative setting has been published until now. Our results suggest that preoperative serum SII, PNI, and FAR might be clinically valuable markers in patients with OC. Of the three inflammatory-nutritional indices tested (FAR, PNI, SII), the FAR showed the highest sensitivity (58.6%), PPV (88.14%), NPV (68.47%), positive LR (6.976), accuracy (75.29) and lowest negative LR (0.452) for differentiating between BOTs and OC. The combination of CA125, HE4, SII, PNI, and FAR had better application values than other combinations of the five variables. Compared with the early-stage OC (Stage I-II) group, CA125, HE4, SII, and FAR values in the advanced OC (Stage III-IV) group were significantly higher. In contrast, the value of PNI in the advanced OC (Stage III-IV) group was significantly lower. Based on the results of previous studies and the current study, it is thought that FAR and SII increase, and PNI decrease as cancer progresses. Therefore, increases in FAR and SII and decreases in PNI are associated with advanced ovarian cancer and may be associated with poor prognosis. FAR had better application value than other inflammation-related markers (PNI and SII). In the present study, DCA showed that the combination of CA125, HE4, FAR, SII, and PNI presented a higher net benefit than CA125 alone or HE4 alone, or CA125 combined with HE4. The inflammation-related markers changes are not cancer-specific findings. The increase or decrease in these values does not indicate absolute ovarian cancer risk. However, the inflammation-related markers with a differential count are a common and inexpensive preoperative test. Therefore, even if it is not a confirmatory test for OC, it has clinical utility as a potential auxiliary tool for differential diagnosis before surgery. Our study showed that adding inflammatory biomarkers to CA125 and HE4 can achieve satisfactory efficiency in distinguishing BOTs from OC. Preoperative biomarkers provide some assistance, but the management process often relies on imaging examinations once abnormal serum biomarker results are detected. As we delve deeper into the inflammatory mechanisms associated with tumors, we may discover more effective combinations of tumor and inflammatory biomarkers.\u003c/p\u003e \u003cp\u003eIt's important to note that this study was conducted retrospectively and the inclusion of a relatively small number of patients from a single center. However, the rigorous inclusion and exclusion criteria and the high statistical significance achieved for the diagnostic traits tested in our series provide strong evidence for the reliability and reproducibility of our findings.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe study suggested that preoperative serum SII, PNI, and FAR might potentially be clinically valuable markers in patients with OC. The combination of CA125, HE4, SII, PNI, and FAR had better application values than other combinations of the five variables. It was superior to applying CA125, HE4, SII, PNI, and FAR alone in predicting OC in the preoperative setting. Higher preoperative CA125, HE4, SII, and FAR levels and lower PNI levels predicted a higher probability of advanced OC progression and lymph node metastasis. FAR had better application value than other inflammation-related markers (PNI and SII). As we delve deeper into the inflammatory mechanisms associated with tumors, we may discover more effective combinations of tumor and inflammatory biomarkers. The main limitation of our study is that it is a retrospective analysis with a small sample of patients from a single center and the same region. Thus, a broader multi-center study to validate results and attain higher statistical power would be interesting.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCA125\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCarbohydrate Antigen 125\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHE4\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Epididymis Protein 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSII\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystemic immune-inflammation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFAR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efibrinogen-to-albumin ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprognostic nutritional index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eovarian cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBOT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebenign ovarian tumor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoperating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egreater area under the ROC curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNPV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enegative predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elikelihood ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePPV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEOC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eepithelial ovarian cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePLR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eplatelet-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTNF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etumor necrotic factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIL-1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elike interleukin-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIL-6\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterleukin-6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePFS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogression-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDFS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edisease-free survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the Declaration of Helsinki and was approved by the\u0026nbsp;ETHICS Committee. Research Ethics Review No.2023075. Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grants from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLYS, JQ, JZ, and SNB designed the research; LYS, QW, and RX collected the data and prepared the manuscript; LYS, JQ, JZ, SNB, QW, and RX wrote the manuscript; All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all doctors, nurses, patients, and their family members for their kindness to support our study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMenon U, Karpinskyj C, Gentry-Maharaj A. 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PMID: 35193433.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ovarian cancer, Carbohydrate Antigen 125, Human Epididymis Protein 4, systemic immune-inflammation index, fibrinogen-to-albumin ratio, prognostic nutritional index, diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-3003534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3003534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe aim of this study was to ascertain the diagnostic accuracy of Carbohydrate Antigen 125(CA125), Human Epididymis Protein 4(HE4), systemic immune-inflammation index (SII), fibrinogen-to-albumin ratio (FAR), prognostic nutritional index (PNI), and their combinations for ovarian cancer (OC) in order to discover an optimal combined diagnostic index for early diagnosis of OC. A thorough investigation was conducted to ascertain the correlation between these markers and the pathological characteristics of OC, thereby furnishing a foundation for the early identification and treatment of this disorder.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e170 patients with documented OC and benign ovarian tumors (BOTs) treated at Hebei General Hospital between January 2019 and December 2022 were included in this retrospective study. The formula for serum inflammation related markers was: FAR\u0026thinsp;=\u0026thinsp;fibrinogen(g/L)/ albumin(g/L); PNI\u0026thinsp;=\u0026thinsp;albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte counts (109/L); SII\u0026thinsp;=\u0026thinsp;platelet count (109/L) \u0026times; neutrophil count (109/L)/ lymphocyte count (109/L). Data analysis was conducted with IBM SPSS statistics version V26.0 software, MedCalc Statistical Software version 19.4.0 software, and R Environment for Statistical Computing software (R Foundation for Statistical Computing).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe isolated CA125 tested showed the best application value to differentiate BOTs from OC when the defined variables were compared separately. The combination of CA125, HE4, FAR, SII, and PNI displayed a greater area under the ROC curve (AUC) than any one of them or other combinations of the five variables. Compared to CA125 alone, the combination of CA125, HE4, FAR, SII, and PNI showed a slight gain in sensitivity (83.91%), negative predictive value (NPV) (83.91%), accuracy (85.88%), and a decrease in negative likelihood ratio (LR) (0.180%). Higher preoperative CA125, HE4, SII, and FAR levels and lower PNI levels predicted a higher probability of advanced OC progression and lymph node metastasis. FAR had a better application value than other inflammation-related markers (PNI and SII).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study suggested that preoperative serum SII, PNI, and FAR might potentially be clinically valuable markers in patients with OC. FAR had a better application value than other inflammation-related markers (PNI and SII). As we delve deeper into the inflammatory mechanisms associated with tumors, we may discover more effective combinations of tumor and inflammatory biomarkers.\u003c/p\u003e","manuscriptTitle":"Diagnostic value of CA125, HE4, systemic immune‑inflammatory index (SII), fibrinogen-to-albumin ratio(FAR), and prognostic nutritional index(PNI) in the Preoperative Investigation of ovarian Masses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-02 15:49:17","doi":"10.21203/rs.3.rs-3003534/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"418da421-858b-433d-8beb-47c2844289c4","owner":[],"postedDate":"June 2nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-29T01:29:27+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-02 15:49:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3003534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3003534","identity":"rs-3003534","version":["v1"]},"buildId":"re_ckhLnmML6MCF96OHNJ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}