Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma

Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma Qingling Song, Ye Li, Tingfan Wu, Wenjun Hu, Yijun Liu, Ailian Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4476893/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 Objectives: To investigate the feasibility of using the iodine concentration (IC) parameter and extracellular volume (ECV) fraction derived from dual-energy CT for distinguishing between type Ⅰ and type Ⅱ epithelial ovarian carcinoma (EOC). Methods: This study retrospectively included 140 patients with EOC preoperatively underwent dual-energy CT scans. Patients were grouped as type Ⅰ and type Ⅱ EOC according to postoperatively pathologic results. Normalized IC (NIC, %) values from arterial-phase (AP), venous-phase (VP) and delay-phase (DP) were measured by two observers. ECV fraction (%) was calculated by DP-NIC and hematocrit. Intra-observer correlation coefficient (ICC) was used to assess the agreement between measurements made by two observers. The differences of imaging parameters between the two groups were compared. Logistic regression was used to select independent predictive factors and establish combined parameter. Receiver operating characteristic curve was used to analyze performance of all parameters. Results: The ICCs for all parameters exceeded 0.75 . All parameters in type Ⅱ EOC were all significantly higher than those in type Ⅰ EOC (all P < 0.05). DP-NIC exhibited the highest Area under the curve (AUC) of 0.828, along with 88.51% sensitivity and 62.26% specificity. DP-NIC was identified as the independent factor. The sensitivity and specificity of ECV fraction were 83.91% and 67.92%, respectively. The combined parameter consisting of AP-NIC, VP-NIC, DP-NIC, and ECV fraction yielded an AUC of 0.848, with sensitivity of 82.76% and specificity of 75.47%. The AUC of the combined parameter was significantly higher than that of VP-NIC ( P = 0.042). Conclusion: It is valuable for dual-energy CT IC-based parameters and ECV fraction in preoperatively identifying type Ⅰ and type Ⅱ EOC. Critical relevance statement Dual-energy CT-normalized iodine concentration and extracellular volume fraction achieved satisfactory discriminative efficacy, distinguishing between type Ⅰ and type Ⅱ epithelial ovarian carcinoma. Dual-energy CT Iodine concentration Extracellular volume fraction histological types epithelial ovarian carcinoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Points • Iodine concentration showed value in differentiating type Ⅰ from type Ⅱ EOC. • ECV fraction is feasible in differentiating type Ⅰ from type Ⅱ EOC. • Combining triple-phase enhanced NICs and ECV fraction improved the identification performance. Introduction Ovarian cancer (OC) is ranked eighth in terms of global female cancer incidence and mortality. In 2020, approximately 314 thousand cases in worldwide and 207 thousand patients died of OC, which is the most fatal gynecological malignancy threatening the health and life of patients [ 1 , 2 ]. About 90% of OCs are epithelial ovarian cancers (EOC) [ 2 ]. EOC were classified into type Ⅰ and type Ⅱ with the dualistic model according to different pathogenesis information [ 3 , 4 ]. Type Ⅰ tumors include low-grade serous carcinoma (LGSC), mucinous carcinoma, endometrioid carcinoma, clear cell carcinoma, and malignant Brenner tumor. Type Ⅱ tumors consist of high-grade serous carcinoma (HGSOC), carcinosarcoma, and undifferentiated carcinoma. Type Ⅰ tumors usually have a relatively mild biological behavior and show a better prognosis [ 5 ]. Type Ⅱ tumors, with their increased aggressiveness, result in a higher risk of recurrence and poorer prognosis for patients [ 6 ], which account for 90% of deaths from ovarian cancer [ 7 ]. Type Ⅰ tumors are candidate to some cytotoxic therapies while type Ⅱ tumors are sensitive to the platinum-based chemotherapy. Therefore, preoperatively identifying the subtype of EOC facilitates the development of individual therapeutic plans and the evaluation of the prognosis. Currently, invasive techniques are primary methods to identify the EOC histologic subtypes. However, diagnostic accuracy of examination techniques such as cytology and biopsy is limited in differentiating the EOC subtype preoperatively [ 8 ]. Moreover, the invasive examinations might cause some side effects such as bleeding, infection. Fine needle aspiration should be avoided in the diagnosis of early ovarian cancer in order to prevent tumor rupture from spreading in the abdominal cavity [ 9 ]. Therefore, an effective non-invasive examination will be an important supplementary approach to distinguish type Ⅰ from type Ⅱ tumors. CT of the abdomen and pelvis is the first line imaging modality for staging, selecting treatment options and assessing disease response in ovarian cancer [ 10 ]. Routine CECT/MRI provided additional information for distinguishing between type Ⅰ and type Ⅱ tumors, but its sensitivity was not high. The study of Liu et al demonstrated that combining tumor morphology and enhancement degree from CECT/MRI imaging achieved a sensitivity of 61.36% [ 11 ]. Other studies indicated that MRI functional parameters had the sensitivity 76.0%-85.0% in identifying EOC subtypes [ 12 , 13 ]. Notably, despite high AUC 0.823–0.970 for CECT/MRI radiomics analysis in EOC subtype evaluation [ 14 – 18 ], satisfying results in addressing methodological quality challenges remain elusive [ 19 ]. The 18F-FDG PET/CT parameter standard uptake value (SUV) max showed sensitivity 77.8%, specificity 69.2% in differentiating subtypes of EOC [ 20 ], but the high cost and high radiation dose limit its clinical application. Dual-energy CT can capture X-rays at different energy levels, providing more information about tissue composition and density through several quantitative parameters. Previous studies have reported the value of dual-energy CT in assessing the biological behavior of various cancers, including gastric, renal, and lung cancer [ 21 – 23 ]. The iodine concentration (IC) from material decomposition image is one of the important parameters with extensive clinical applications. [ 24 ]. The iodine is the main component of contrast medium, IC based parameters can reflect the tumor vascular growth and permeability. P53 mutation and WT1 positive are common immunohistochemical expression patterns of HGSOC [ 25 , 26 ], which are closely facilitate tumor neovascularization [ 27 , 28 ]. Thus, IC based parameters may be a promise method to discriminate EOC subtype. The extracellular volume (ECV) fraction, which can be calculated by post-contrast CT/MRI, reflects the intravascular and extravascular extracellular space. ECV fraction relates to the factors that effects the extracellular space such as extracellular matrix [ 29 ], the status of tumor vascularity [ 30 ]. Previous studies showed equilibrium contrast-enhanced based ECV fraction have value in different tumor diagnosis, evaluation of pathologic characteristics and prediction of prognosis [ 31 – 33 ]. Whereas, CECT-based ECV fraction requires unenhanced images before administrating contrast agent, which may lead to the misregistration. Dual-energy CT-based ECV fraction does not need the enhanced images, which can avoid the misregistration. Dual-energy CT-derived ECV fraction and has been applied in differentiating and evaluating the biological behavior of thoracic and abdominal tumors [ 34 – 36 ]. Exploring the association of iodine concentration and ECV fraction with the subtype of EOC is necessary. Therefore, the present study aimed to investigate the feasibility of dual-energy CT iodine concentration, ECV fraction and the combination of iodine concentration and ECV fraction in preoperatively differentiating type Ⅰ from type Ⅱ epithelial ovarian carcinoma. Materials and methods Patients This retrospective study was approved by our hospital ethics review board and the need for informed consent was waived. Patients who underwent surgical staging and/or debulking surgery for ovarian tumor between March 2012 and December 2022 were included in this study. The inclusion criteria were as follows: ①, Patients who underwent preoperative abdominal and pelvic contrast-enhanced dual-energy CT scan; ②, no chemotherapy or radiotherapy before CT examination. The exclusion criteria were as follows: ①, patients with concomitant malignancies; ②, solid tumor part diameter < 5 mm being not enough for placing regions of interest (ROIs); ③, poor image quality to affect the measurement of lesion; ④, pathological record of histologic subtype was incomplete; ⑤, postoperative pathology confirmed that patients had other pathological subtypes of ovarian tumor (including benign or borderline epithelial ovarian tumor, other non-epithelial ovarian malignancies).The patient selection flow chart was shown in Fig. 1 . Clinical characteristics comprised age, menstrual status, The International Federation of Gynecology and Obstetrics (FIGO) stage, serum tumor markers included carbohydrate antigen125 (CA125), Human Epididymis Protein 4 (HE4) and hematocrit. CT protocol The triple-phase contrast-enhanced scans were performed using a using the dual-energy CT imaging mode on a GE Discovery CT 750 HD system (GE Healthcare, Milwaukee, WI, USA). The scan area was from the pubic symphysis to the diaphragm, covering abdomen and pelvis. The dual-energy CT scan parameters were as follows: rapid switching between 80 and 140 kVp tube voltages; tube current, 375 mA; 0.6 seconds rotation time; slice thickness/gap, 5/5 mm. A dose of 0.8ཞ1.0 ml/kg of ionic media (iohexol,350 mg iodine/ml; Lubei medicine, Beijing, China) was administered to patients via median cubital vein at the injection rate of 3ཞ5ml/s using high-pressure syringe. Followed by a bolus injection of 20 ml of saline given at the same flow rate. The arterial phase (AP), venous phase (VP), and delayed phase (DP) contrast-enhanced scan were acquired at 28s, 60s, and 120s after the contrast agent injection. Image post-processing and interpretation The 70 keV monoenergetic images and decomposition images of iodine and water-based materials were reconstructed from the triple-phase enhanced CT. Layer thickness and spacing of reconstructed images were 1.25 mm. All reconstructed images were analyzed using Gemstone Spectral Imaging (GSI) Viewer software on an Advanced Workstation 4.6 (GE Healthcare, USA). All imaging parameters were measured by two observers (Q.L.S. and Y.L., with 6 and 12 years of experience in pelvic CT, respectively), blinded to the clinical characteristics and postoperative pathological results. A region of interest (ROI) was manually placed on monochromatic image at 70 keV then was copied on corresponding iodine maps to obtain triple-phase enhanced iodine concentrations (IC, mg/ml), Fig. 2 . ROI was delineated as circular or oval at the largest section of the tumor solid part with area approximate 10 ~ 100mm². Necrotic and cystic part showed unenhanced area were excluded from ROI. About 2–3 mm space was saved between ROI and tumor margin to avoid partial volume effect and obvious vessels. At the same section with that of tumor ROI, a circular ROI was placed in the ipsilateral external iliac aorta to obtain IC Aorta . The normalized IC (NIC, %) was calculated with IC Lesion ∕ IC Aorta . Where IC Lesion and IC Aorta were ICs (mg/ml) during the triple-phase enhanced for the EOC lesion and the external iliac aorta, respectively. Then ECV fraction was calculated using the following formula: ECV fraction (%) = (1 − hematocrit) ×(IC Lesion ∕ IC Aorta )× 100. Where IC Lesion and IC Aorta were from delay phase. Statistical analysis Statistical analyses were performed using SPSS 21.0 software (IBM, Armonk, NY, USA). Intra-observer correlation coefficient (ICC) was used to test the intra-observer agreement between two times measurements of triple-phase enhanced NIC and ECV fraction. ICC > 0.75 means the agreement of imaging parameters was good [ 37 ].The Kolmogorov-Smirnov test was employed to test whether continuous variables were normally distributed. Student t-test and Whitney U test were used to compare the difference of clinical and imaging parameters between type Ⅰ and type Ⅱ groups. The parameters with statistical significance in univariate test were followed included in binary logistic regression analysis, the forward stepwise selection was employed to select the independent predictive factors. Logistic analysis also was used to established the combined parameter with NIC-based parameter and ECV fraction. Receiver operating characteristic (ROC) curves were plotted to analyze the performance of each univariate and the combined parameter in differentiating type Ⅰ from type Ⅱ EOC. The largest Youden index was used to determine optimal cut-off value, and the corresponding AUC, sensitivity and specificity were calculated. Delong's test was used to compare the difference in AUC between the independent factor, combined parameter and other single parameters. Spearman correlation analysis was used to analyze the correlation between clinical and imaging parameters. A P value < 0.05 was considered statistically significant. Results Patients One hundred and forty consecutive patients were included in this study. According to postoperative pathological results, 53 patients were included in the type Ⅰ group (9 patients with LGSC, 23 patients with clear cell carcinoma, 12 patients with endometrioid carcinoma, 9 patients with mucinous carcinoma), 87 patients were included in the type Ⅱ group (83 patients with HGSOC, 4 patients with carcinosarcoma). The patient clinical information was listed in Table 1 . The FIGO stage in type Ⅰ group was earlier than type Ⅱ group. The menstruation status was significant different between type Ⅰ and type Ⅱ groups. The CA125 and HE4 of patients in type Ⅱ group were significantly larger than those in type Ⅰ group (all P < 0.001). Table 1 Patient characteristics (n = 140) Clinical factors Type I (n = 53) Type II (n = 87) P Age (years) 51.26 ± 10.97 60.93 ± 8.94 < 0.001 Menstruation < 0.001 Premenopausal 42 19 Menopause 11 68 FIGO stage < 0.001 I ~ II 25 16 III ~ IV 28 71 CA125 (U/ml) 116.40 (34.13, 334.90) 632.50 (125.70, 1882.00) < 0.001 HE4 (pmol/L) 110.90 (54.91, 186.75) 340.10 (135.60, 625.30) < 0.001 Hematocrit (%) 36.16 ± 4.40 37.21 ± 3.84 0.141 FIGO = International Federation of Gynecology and Obstetrics; CA125 = carbohydrate antigen125; HE4 = Human Epididymis Protein 4 Agreement between two observers The interobserver agreement of triple-phase NIC (AP-NIC, VP-NIC, DP-NIC) and the ECV fraction measurement were good (all ICC > 0.75), the details were shown in Table S1 . Difference of all parameters between type Ⅰ and type Ⅱ groups Table 2 listed the difference of triple-phase NIC and ECV fraction between the two groups. AP-NIC, VP-NIC, DP-NIC and ECV fraction in the type Ⅰ group were all significantly lower than those in type Ⅱ group (6% vs. 15%, 24% vs. 51%, 36% vs. 70%, 21% vs. 43%, respectively, all P < 0.001) (Fig. 3 a-b). Table 2 Comparison of DECT IC and ECV fraction between type I and type II groups Parameters Type I (n = 53) Type II (n = 87) P AP-NIC (%) 6 (2, 10) 15 (10, 21) < 0.001 VP-NIC (%) 24 (10, 38) 51 (32, 63) < 0.001 DP-NIC (%) 36 (16, 51) 70 (48, 95) < 0.001 ECV fraction (%) 21 (10, 34) 43 (31, 59) < 0.001 AP = arterial phase; VP = venous phase; DP = delayed phase; NIC = normalized iodine concentration; ECV = extracellular volume Logistic analysis and combined parameter AP-NIC, VP-NIC and DP-NIC and ECV fraction were included in subsequent univariate logistic analysis, indicating all imaging parameters had statistical significance. Then these variables were incorporated into multivariate binary logistic regression analysis, DP-NIC was the only independent predictive factor for distinguishing EOC subtypes (Table 3 ). The DP-NIC was combined with AP-NIC, VP-NIC and ECV fraction to establish the combined parameter. Table 3 Univariate and multivariate logistic regression analysis Parameter Univariable Multivariable Odds ratio 95% CI p Odds ratio 95% CI p AP-NIC 1.192 1.116–1.272 < 0.001 VP-NIC 1.062 1.039–1.087 < 0.001 DP-NIC 1.051 1.032–1.071 < 0.001 23.229 2.759-195.598 0.004 ECV fraction 1.078 1.048–1.109 < 0.001 AP = arterial phase; VP = venous phase; DP = delayed phase; NIC = normalized iodine concentration; ECV = extracellular volume ROC analysis of all imaging parameters ROC analysis showed that the threshold of DP-NIC was 40%, with the AUC 0.828, sensitivity 88.51% and specificity 62.26%. The AUC and sensitivity of ECV fraction were 0.819 and 83.91%, respectively, which were slightly lower than those of DP-NIC ( P > 0.05). The AUC of combined parameter was 0.848, with sensitivity 82.76% and specificity 75.47%. The details were shown in Table 4 . Table 4 Diagnostic performances of significant imaging parameters for predicting type Ⅰ and type Ⅱ epithelial ovarian carcinoma AUC 95% CI Threshold value Sensitivity Specificity AP-NIC (%) 0.813 0.737–0.890 10 74.71% 81.13% VP-NIC (%) 0.804 0.727–0.880 40 70.11% 81.13% DP-NIC (%) 0.828 0.758–0.899 40 88.51% 62.26% ECV fraction (%) 0.819 0.747–0.891 28 83.91% 67.92% Combined parameter* 0.848 0.778–0.903 82.76% 75.47% AP = arterial phase; VP = venous phase; DP = delayed phase; NIC = normalized iodine concentration; ECV = extracellular volume * Combination parameter comprised APNIC, VPNIC, DPNIC and ECV fraction Delong's test revealed a significantly higher AUC for the combined parameter compared to VP-NIC ( P = 0.042). However, there was no significant difference in AUC between the combined parameter and other corresponding single parameters (all P > 0.05) ( Table S2 and Fig. 4 ). Analysis of correlation Spearman’s correlation coefficient analysis was conducted to examine the correlation between clinical parameters included age, FIGO stage, menopausal, CA125, HE4 and imaging parameters which were AP-NIC, VP-NIC and DP-NIC and ECV fraction (Fig. 3 ). There was no evidence of a correlation between menopausal and AP-NIC ( P > 0.05). Other clinical parameters had significant positive correlations with imaging parameters ( P < 0.05). We observed a strong degree of correlation between FIGO stage and imaging parameters, there was a moderate degree of correlation between CA125 and imaging parameters (Fig. 5 ). Discussion This study primarily investigated the feasibility of dual-energy CT iodine concentrations and dual-energy CT-derived ECV fraction in preoperatively differentiating type Ⅰ from type Ⅱ EOC. The results showed that triple-phase enhanced NIC, ECV fraction from dual-energy CT were able to discriminate the subtype of EOC, the above parameters were significantly higher in type Ⅱ EOC. DP-NIC was the only independent factor in differentiating EOC subtype. Combining AP-NIC, VP-NIC, DP-NIC and ECV fraction improved the predictive performance. Type Ⅰ and type Ⅱ tumors have different sites of origin, different biomarkers and molecular features [ 7 , 38 ]. Difference for biological behavior of the EOC subtype is closely related to the choice of treatment plans. For instance, type Ⅱ tumors are more likely to have homologous recombination repair and have a better response to the platinum-based chemotherapy [ 7 ], while, the primary treatment for type Ⅰ EOC is cytoreduction. Identification of subtype of EOC facilitate gynecologists develop specialized therapeutic strategy for different patients. Moreover, type Ⅰ tumors are frequently characterized by mutations affecting the RAS/MAPK pathway, medicine targets mitogen-activated protein kinase (MAPK) or KRAS genes or some cytotoxic chemotherapies may be more suitable for type Ⅰ EOC [ 39 , 40 ]. For patients who need to receive neoadjuvant chemotherapy (NACT), it is crucial to choose a sensitive chemotherapeutic or target medicine. Thus, the premise of achieving an optimal surgery timing, obtain better surgery outcomes and prognosis is to correctly discriminate the subtype of EOC [ 41 ]. Differences in certain clinical parameters were observed between type Ⅰ and type Ⅱ tumors. Type I EOC tend to occur in younger patients clinically, with a lower proportion of postmenopausal patients compared to type II EOC [ 42 ]. The age and menstrual status distribution of patients in our study were in line with the above results. Type Ⅱ EOC grow rapidly and are highly aggressive, which present in advanced stage in over 75% of cases [ 43 ]. Our study also revealed that type Ⅱ EOC had advanced FIGO stages than type Ⅰ EOC. CA125 and HE4 serve as common tumor markers in the diagnosis, treatment evaluation and prognosis prediction of ovarian cancer. Our study found significantly higher CA125 and HE4 values in type Ⅱ EOC than those in type Ⅰ EOC, which confirmed the previous results as well [ 44 ]. The dual-energy CT is widely used in the evaluation of malignancies. Dual-energy CT derived iodine concentration was highly consistent with the actual IC [ 45 ]. The iodine concentration reflects the vascular angiogenesis, which is associated with the blood supply to the tumor. A recently study indicated that the iodine concentration change is able to assess response to treatment in patients with HGSOC [ 44 ], that study investigated the value of iodine concentration in the evaluation of EOC. Previous studies showed that NICs can minimize the effects of individual variability and had good performance [ 21 , 46 ], NIC can provide stable measurements by correcting factors such as injection rate and dose. The results of this study showed that triple-phase enhanced NIC of type Ⅱ EOC were significantly higher than those of type Ⅰ EOC, which suggested that type Ⅱ EOC has more blood perfusion than those of type Ⅰ EOC. The growth of malignant tumor needs the support of angiogenesis. HGSOC is a highly aggressive histological subtype of EOC, has aggressive neovascularization, high expression of tumor micro vessel density (MVD) and vascular endothelial growth factor (VEGF) [ 47 ]. The efficacy of anti-VEGF drug, bevacizumab in the treatment of patients with HGSOC also demonstrates this [ 48 , 49 ]. While type Ⅰ tumors is an indolent fashion, derive from precursor lesions, may have a longer process to transform to malignancies [ 50 , 51 ]. The tumoral neovascularization in type Ⅰ tumors might be less than that of type Ⅱ tumors, which leading to less iodine concentration. In our study, the AUCs of DP-NIC were slightly higher than those of from AP-NIC, VP-NIC and ECV fraction. Li Q et al studied the usefulness of IC for evaluating lung cancer angiogenesis, and showed that VP outperformed DP in reflecting the microcirculation of the tumor [ 52 ], which was not in consist with our study. Neovascularization is disordered and tortuous, the structure is incomplete with a lower pericyte coverage index, these characteristics of neovascularization determined the high permeability and the difference perfusion modality from healthy vessels [ 53 , 54 ]. Li Q et al. found that AP mainly reflected the functional capillary density of tumor, VP more reflects dysfunctional blood vessels, DP represented contrast agent retained in the interstitial space. Whereas, their enhanced time for DP was 90s, the enhanced time for DP was 120s in our study, which was more able to characterize the filling of contrast agent in interstitial space and had better AUC. The longer delayed-phase enhanced time might improve the AUC and sensitivity of DP-NIC. Moreover, the DP-NIC in this study was the independent predictive factors for differentiating type Ⅰ EOC from type Ⅱ EOC, which also demonstrated the value for DP-NIC in identifying EOC subtype. ECV fraction has been regarded as a robust quantitative parameter, independent of several technical confounders and physiological variants [ 55 ]. The value of ECV fraction was proved in the evaluation of liver fibrosis and cardiotoxicity [ 56 , 57 ], it recently has been used to diagnose malignancies and predict the prognosis after chemotherapy [ 35 , 58 ]. Among these studies, dual-energy CT showed an advantage. Compare to single-energy CT, dual-energy CT reduced the radiation exposure generated from pre- and post-contrast scans, and also minimized the misregistration. The ECV fraction of type Ⅱ EOC in this study was significantly higher than that of type Ⅰ EOC. The ECV fraction was calculated by the delayed phase parameters, reflected the difference of tumor interstitial space between type Ⅰ and type Ⅱ EOC. During longer delayed-phase, more blood leak into extravascular space from the neo vessels and enlarge the extravascular extracellular space. Moreover, high invasiveness of type Ⅱ EOC may cause interstitial edema or inflammatory infiltration. As the result of above reason, ECV fraction of type Ⅱ EOC was higher. ECV fraction might be promising parameter to characterize the tumor interstitial status. AP-NIC, VP-NIC and ECV fraction were not independent factors, however, they reflect tumor microcirculation and tumor interstitium from different functional aspects, so they were combined with DP-NIC to establish the combined parameter. The result showed the performance of combined parameter improved, especially significantly higher than VP-NIC. Our study suggested the feasibility of using iodine quantitative parameters and ECV fraction at the delayed phase to distinguish between type Ⅰ and type Ⅱ EOCs. Combining NIC and ECV fraction provides more valuable information. There are several limitations in our study should be noted. First, this was a retrospective study, a prospective study should perform to further verify the efficacy of above parameters. Second, the results of this study were obtained from one cohort patients, cross validation and external validation need to be developed in the future study. Thirdly, this study did not analyze tumor morphological features, combining quantitative parameters with morphological features in future studies may benefit the evaluation process. Conclusion Iodine concentrations and extracellular volume fraction derived from dual-energy CT are useful parameters to non-invasively differentiate type Ⅰ from type Ⅱ epithelial ovarian carcinoma, DP-NIC is able to independently differentiate type Ⅰ from type Ⅱ EOC, combining with triple-phase enhanced NIC and ECV fraction shows greater value. Dual-energy CT derived Iodine concentration parameters and ECV fraction are easily obtained method to assist gynecologist to preoperatively identify the subtype of EOC. Abbreviations EOC epithelial ovarian carcinoma HGSOC high-grade serous carcinoma FIGO International Federation of Gynecology and Obstetrics CECT Contrast-enhanced CT ROI region of interest IC iodine concentration ECV extracellular volume NIC normalized iodine concentration AP arterial-phased VP venous-phased DP delay-phased ICC intraobserver correlation coefficient ROC receiver operating characteristic AUC areas under the curves Declarations Funding No funding Availability of data and materials Datasets and material analyzed are available on reasonable request from the corresponding author. Conflict of interest Disclosure the authors have no conflicts of interest with respect to this work. Ethical approval The studies involving human participants were reviewed and approved by Institutional Review Board, First Affiliated Hospital of Dalian Medical University. Written informed consent for participation of this retrospective study was waived. Consent for publication All authors contributed to the writing of the manuscript and consented to publication. Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Cancer J Clin. 2021;71(3):209–49. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. Cancer J Clin. 2022;72(1):7–33. Karnezis AN, Cho KR, Gilks CB, Pearce CL, Huntsman DG. The disparate origins of ovarian cancers: pathogenesis and prevention strategies. Nat Rev Cancer. 2017;17(1):65–74. Kurman RJ, Shih Ie M. Pathogenesis of ovarian cancer: lessons from morphology and molecular biology and their clinical implications. Int J Gynecol pathology: official J Int Soc Gynecol Pathologists. 2008;27(2):151–60. McCluggage WG. Morphological subtypes of ovarian carcinoma: a review with emphasis on new developments and pathogenesis. Pathology. 2011;43(5):420–32. Cree IA, White VA, Indave BI, Lokuhetty D. Revising the WHO classification: female genital tract tumours. Histopathology. 2020;76(1):151–6. Kurman RJ, Shih Ie M. The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. Am J Pathol. 2016;186(4):733–47. Hoang LN, Zachara S, Soma A, Köbel M, Lee CH, McAlpine JN, Huntsman D, Thomson T, van Niekerk D, Singh N, et al. Diagnosis of Ovarian Carcinoma Histotype Based on Limited Sampling: A Prospective Study Comparing Cytology, Frozen Section, and Core Biopsies to Full Pathologic Examination. Int J Gynecol pathology: official J Int Soc Gynecol Pathologists. 2015;34(6):517–27. NCCN Clinical Practice Guidelines in Oncology. Ovarian CancerIncluding Fallopian Tube Cancerand Primary Peritoneal Cancer. (Version 1.2023). . Sahdev A. CT in ovarian cancer staging: how to review and report with emphasis on abdominal and pelvic disease for surgical planning. Cancer imaging: official publication Int Cancer Imaging Soc. 2016;16(1):19. Liu D, Zhang L, Indima N, Peng K, Li Q, Hua T, Tang G. CT and MRI findings of type I and type II epithelial ovarian cancer. Eur J Radiol. 2017;90:225–33. Zhou Q, Jin J, Wang Y, Wang Y, Wang T, Zhang H, Liu Y. Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker. Am J translational Res. 2023;15(3):1862–70. Song H, Bak S, Kim I, Woo JY, Cho EJ, Choi YJ, Rha SE, Oh SA, Youn SY, Lee SJ. An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer. Journal of clinical medicine 2021, 11(1). Wang M, Perucho JAU, Hu Y, Choi MH, Han L, Wong EMF, Ho G, Zhang X, Ip P, Lee EYP. Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma. JAMA Netw Open. 2022;5(12):e2245141. Li J, Li X, Ma J, Wang F, Cui S, Ye Z. Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers. Eur Radiol. 2023;33(7):5193–204. Jian J, Li Y, Pickhardt PJ, Xia W, He Z, Zhang R, Zhao S, Zhao X, Cai S, Zhang J, et al. MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers. Eur Radiol. 2021;31(1):403–10. Xu Y, Luo HJ, Ren J, Guo LM, Niu J, Song X. Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype. Front Oncol. 2022;12:978123. Qian L, Ren J, Liu A, Gao Y, Hao F, Zhao L, Wu H, Niu G. MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes. Eur Radiol. 2020;30(11):5815–25. Huang ML, Ren J, Jin ZY, Liu XY, He YL, Li Y, Xue HD. A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights into imaging. 2023;14(1):117. González García B, García Vicente AM, Jiménez Londoño GA, Pena Pardo FJ, Bellón Guardia ME, Talavera Rubio MP, Palomar Muñoz A, Gómez Herrero P. Soriano Castrejón Á M: (18)F-FDG PET/CT as predictor of tumour biology and prognosis in epithelial ovarian carcinoma. Revista Esp de Med nuclear e imagen Mol. 2017;36(4):233–40. Li J, Xu S, Wang Y, Fang M, Ma F, Xu C, Li H. Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study. Eur Radiol. 2023;33(7):5172–83. Yang F, Dong J, Wang X, Fu X, Zhang T. Non-small cell lung cancer: Spectral computed tomography quantitative parameters for preoperative diagnosis of metastatic lymph nodes. Eur J Radiol. 2017;89:129–35. Zhang X, Zhang G, Xu L, Bai X, Lu X, Yu S, Sun H, Jin Z. Utilisation of virtual non-contrast images and virtual mono-energetic images acquired from dual-layer spectral CT for renal cell carcinoma: image quality and radiation dose. Insights into imaging. 2022;13(1):12. Muenzel D, Lo GC, Yu HS, Parakh A, Patino M, Kambadakone A, Rummeny EJ, Sahani DV. Material density iodine images in dual-energy CT: Detection and characterization of hypervascular liver lesions compared to magnetic resonance imaging. Eur J Radiol. 2017;95:300–6. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609–15. Lisio MA, Fu L, Goyeneche A, Gao ZH, Telleria C. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int J Mol Sci 2019, 20(4). Wagner KD, Cherfils-Vicini J, Hosen N, Hohenstein P, Gilson E, Hastie ND, Michiels JF, Wagner N. The Wilms' tumour suppressor Wt1 is a major regulator of tumour angiogenesis and progression. Nat Commun. 2014;5:5852. Pal S, Datta K, Mukhopadhyay D. Central role of p53 on regulation of vascular permeability factor/vascular endothelial growth factor (VPF/VEGF) expression in mammary carcinoma. Cancer Res. 2001;61(18):6952–7. Diao KY, Yang ZG, Xu HY, Liu X, Zhang Q, Shi K, Jiang L, Xie LJ, Wen LY, Guo YK. Histologic validation of myocardial fibrosis measured by T1 mapping: a systematic review and meta-analysis. J Cardiovasc Magn resonance: official J Soc Cardiovasc Magn Reson. 2016;18(1):92. Luo Y, Liu L, Liu D, Shen H, Wang X, Fan C, Zeng Z, Zhang J, Tan Y, Zhang X, et al. Extracellular volume fraction determined by equilibrium contrast-enhanced CT for the prediction of the pathological complete response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Eur Radiol. 2023;33(6):4042–51. Takumi K, Nagano H, Oose A, Gohara M, Kamimura K, Nakajo M, Harada-Takeda A, Ueda K, Tabata K, Yoshiura T. Extracellular volume fraction derived from equilibrium contrast-enhanced CT as a diagnostic parameter in anterior mediastinal tumors. Eur J Radiol. 2023;165:110891. Li Q, Bao J, Zhang Y, Dou Y, Liu A, Liu M, Wu H, Wu J, Zhao L, Yang Z, et al. Predictive value of CT-based extracellular volume fraction in the preoperative pathologic grading of rectal adenocarcinoma: A preliminary study. Eur J Radiol. 2023;163:110811. Fukukura Y, Kumagae Y, Higashi R, Hakamada H, Takumi K, Maemura K, Higashi M, Kamimura K, Nakajo M, Yoshiura T. Extracellular volume fraction determined by equilibrium contrast-enhanced multidetector computed tomography as a prognostic factor in unresectable pancreatic adenocarcinoma treated with chemotherapy. Eur Radiol. 2019;29(1):353–61. Peng Y, Tang G, Sun M, Yu S, Cheng Y, Wang Y, Deng W, Li Y, Guan J. Feasibility of spectral CT-derived extracellular volume fraction for differentiating aldosterone-producing from nonfunctioning adrenal nodules. European radiology 2023. Takumi K, Nagano H, Myogasako T, Nakano T, Fukukura Y, Ueda K, Tabata K, Tanimoto A, Yoshiura T. Feasibility of iodine concentration and extracellular volume fraction measurement derived from the equilibrium phase dual-energy CT for differentiating thymic epithelial tumors. Japanese J Radiol. 2023;41(1):45–53. Fujita N, Ushijima Y, Itoyama M, Okamoto D, Ishimatsu K, Wada N, Takao S, Murayama R, Fujimori N, Nakata K, et al. Extracellular volume fraction determined by dual-layer spectral detector CT: Possible role in predicting the efficacy of preoperative neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma. Eur J Radiol. 2023;162:110756. Benchoufi M, Matzner-Lober E, Molinari N, Jannot AS, Soyer P. Interobserver agreement issues in radiology. Diagn Interv Imaging. 2020;101(10):639–41. Prat J, D'Angelo E, Espinosa I. Ovarian carcinomas: at least five different diseases with distinct histological features and molecular genetics. Hum Pathol. 2018;80:11–27. Colic E, Patel PU, Kent OA. Aberrant MAPK Signaling Offers Therapeutic Potential for Treatment of Ovarian Carcinoma. OncoTargets therapy. 2022;15:1331–46. Nara K, Taguchi A, Yamamoto T, Hara K, Tojima Y, Honjoh H, Nishijima A, Eguchi S, Miyamoto Y, Sone K, et al. Heterogeneous effects of cytotoxic chemotherapies for platinum-resistant ovarian cancer. Int J Clin Oncol. 2023;28(9):1207–17. Marsh LA, Kim TH, Zhang M, Kubalanza K, Treece CL, Chase D, Memarzadeh S, Salani R, Karlan B, Rao J, et al. Pathologic response to neoadjuvant chemotherapy in ovarian cancer and its association with outcome: A surrogate marker of survival. Gynecol Oncol. 2023;177:173–9. Alcázar JL, Utrilla-Layna J, Mínguez J, Jurado M. Clinical and ultrasound features of type I and type II epithelial ovarian cancer. Int J Gynecol cancer: official J Int Gynecol Cancer Soc. 2013;23(4):680–4. Kurman RJ, Shih Ie M. Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer–shifting the paradigm. Hum Pathol. 2011;42(7):918–31. Kristjansdottir B, Levan K, Partheen K, Sundfeldt K. Diagnostic performance of the biomarkers HE4 and CA125 in type I and type II epithelial ovarian cancer. Gynecol Oncol. 2013;131(1):52–8. Tang L, Li ZY, Li ZW, Zhang XP, Li YL, Li XT, Wang ZL, Ji JF, Sun YS. Evaluating the response of gastric carcinomas to neoadjuvant chemotherapy using iodine concentration on spectral CT: a comparison with pathological regression. Clin Radiol. 2015;70(11):1198–204. Li R, Li J, Wang X, Liang P, Gao J. Detection of gastric cancer and its histological type based on iodine concentration in spectral CT. Cancer imaging: official publication Int Cancer Imaging Soc. 2018;18(1):42. Ruscito I, Cacsire Castillo-Tong D, Vergote I, Ignat I, Stanske M, Vanderstichele A, Glajzer J, Kulbe H, Trillsch F, Mustea A, et al. Characterisation of tumour microvessel density during progression of high-grade serous ovarian cancer: clinico-pathological impact (an OCTIPS Consortium study). Br J Cancer. 2018;119(3):330–8. Andrikopoulou A, Liontos M, Skafida E, Koutsoukos K, Apostolidou K, Kaparelou M, Rouvalis A, Bletsa G, Dimopoulos MA, Zagouri F. Pembrolizumab in combination with bevacizumab and oral cyclophosphamide in heavily pre-treated platinum-resistant ovarian cancer. Int J Gynecol cancer: official J Int Gynecol Cancer Soc. 2023;33(4):571–6. Pignata S, Lorusso D, Joly F, Gallo C, Colombo N, Sessa C, Bamias A, Salutari V, Selle F, Frezzini S, et al. Carboplatin-based doublet plus bevacizumab beyond progression versus carboplatin-based doublet alone in patients with platinum-sensitive ovarian cancer: a randomised, phase 3 trial. Lancet Oncol. 2021;22(2):267–76. Kurman RJ, Shih Ie M. The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory. Am J Surg Pathol. 2010;34(3):433–43. Dey P, Nakayama K, Razia S, Ishikawa M, Ishibashi T, Yamashita H, Kanno K, Sato S, Kiyono T, Kyo S. Development of Low-Grade Serous Ovarian Carcinoma from Benign Ovarian Serous Cystadenoma Cells. Cancers 2022, 14(6). Li Q, Li X, Li XY, Huo JW, Lv FJ, Luo TY. Spectral CT in Lung Cancer: Usefulness of Iodine Concentration for Evaluation of Tumor Angiogenesis and Prognosis. AJR Am J Roentgenol. 2020;215(3):595–602. George ML, Dzik-Jurasz AS, Padhani AR, Brown G, Tait DM, Eccles SA, Swift RI. Non-invasive methods of assessing angiogenesis and their value in predicting response to treatment in colorectal cancer. Br J Surg. 2001;88(12):1628–36. Knopp MV, Weiss E, Sinn HP, Mattern J, Junkermann H, Radeleff J, Magener A, Brix G, Delorme S, Zuna I, et al. Pathophysiologic basis of contrast enhancement in breast tumors. J Magn Reson imaging: JMRI. 1999;10(3):260–6. Kim PK, Hong YJ, Sakuma H, Chawla A, Park JK, Park CH, Hong D, Han K, Lee JY, Hur J, et al. Myocardial Extracellular Volume Fraction and Change in Hematocrit Level: MR Evaluation by Using T1 Mapping in an Experimental Model of Anemia. Radiology. 2018;288(1):93–8. Tago K, Tsukada J, Sudo N, Shibutani K, Okada M, Abe H, Ibukuro K, Higaki T, Takayama T. Comparison between CT volumetry and extracellular volume fraction using liver dynamic CT for the predictive ability of liver fibrosis in patients with hepatocellular carcinoma. Eur Radiol. 2022;32(11):7555–65. Monti CB, Zanardo M, Bosetti T, Alì M, De Benedictis E, Luporini A, Secchi F, Sardanelli F. Assessment of myocardial extracellular volume on body computed tomography in breast cancer patients treated with anthracyclines. Quant imaging Med Surg. 2020;10(5):934–44. Fukukura Y, Kumagae Y, Higashi R, Hakamada H, Nakajo M, Maemura K, Arima S, Yoshiura T. Extracellular volume fraction determined by equilibrium contrast-enhanced dual-energy CT as a prognostic factor in patients with stage IV pancreatic ductal adenocarcinoma. Eur Radiol. 2020;30(3):1679–89. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team 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-4476893","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314029637,"identity":"140d8942-906d-4e8a-b531-6e3e7e0045e4","order_by":0,"name":"Qingling Song","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qingling","middleName":"","lastName":"Song","suffix":""},{"id":314029638,"identity":"79a9f568-c3e3-4a78-8696-af20a21c8b2e","order_by":1,"name":"Ye Li","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Li","suffix":""},{"id":314029639,"identity":"d4b6123c-521d-4d84-a7ab-39d7c4b035bb","order_by":2,"name":"Tingfan Wu","email":"","orcid":"","institution":"United Imaging Research Institute of Innovative Medical Equipment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tingfan","middleName":"","lastName":"Wu","suffix":""},{"id":314029641,"identity":"7f978638-3381-4ee7-b6ad-422916e26593","order_by":3,"name":"Wenjun Hu","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Hu","suffix":""},{"id":314029643,"identity":"30e2f729-4102-4973-8f7c-4b6ada7928c8","order_by":4,"name":"Yijun Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Liu","suffix":""},{"id":314029645,"identity":"20e812b1-6a45-4091-9adb-16dcf3b24cba","order_by":5,"name":"Ailian Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACPmYgkVAgwcDA3mAAEmBsIKSFDazFAKiF5wCxWsAkSLFEArFa2HkMPzwwsJA3l3y8dTMPg43shgPMzx7gdxiPMdB8CcOds9PKbvMwpBlvOMBmbkBAiwFIC+OG2zlmQC2HEzcc4GGTIGTLD6AW+w03z4C0/CdKixnIlsQNN3hAWg4Qo4WtzAKoJXnDmbSym3MMko1nHmYzw6uFn//w5ps/KupsNxw/vO3Gmwo72b7jzc/wakEDoKBiJkH9KBgFo2AUjALsAAC0BUDVQ+4pmgAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ailian","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-25 13:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4476893/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4476893/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59050280,"identity":"b8acefec-e4e0-478d-9e68-71f5e114a9f8","added_by":"auto","created_at":"2024-06-25 19:39:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":317078,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study population. LG, low grade; HG, high grade\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4476893/v1/f7a2e4def2d9b16f9c2b8fcc.png"},{"id":59048965,"identity":"150a4cbb-cf88-408d-bb0b-3f2de39f30b1","added_by":"auto","created_at":"2024-06-25 19:31:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1580964,"visible":true,"origin":"","legend":"\u003cp\u003eA 69-year-old woman with high grade serous ovarian carcinoma. The monochromatic image at 70 keV in the arterial phase of the largest slice of the solid part of the tumor (a), the ROI is manually placed in the solid part, which shows as the area of uniform density, adjacent unenhanced cystic areas are avoided, the area of ROI is about 72.8mm²; (b-d) were corresponding ROIs of arterial phase, venous phase and delay phase image from iodine (water) maps, respectively. The AP-IC, VP-IC and DP-IC are 15.30 mg/ml, 14.82 mg/ml and 16.35 mg/ml; AP-NIC, VP-NIC and DP-NIC are 18.93%, 50.51% and 107.26%, respectively. ROI, region of interest; IC, iodine concentration; NIC, normalized iodine concentration; ECV, extracellular volume\u003c/p\u003e","description":"","filename":"Figure2ad.png","url":"https://assets-eu.researchsquare.com/files/rs-4476893/v1/7c7b6fa60c485b77378c2603.png"},{"id":59048968,"identity":"a5a48368-493e-4229-86b7-44367dbb3863","added_by":"auto","created_at":"2024-06-25 19:31:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147141,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The boxplot shows the difference of NIC from Arterial phase, venous phase, delayed phase between the type Ⅰ and type Ⅱ EOC groups; (b) the boxplot showing the difference of ECV fraction between the two groups. NIC, normalized iodine concentration; EOC, epithelial ovarian cancer; ECV, extracellular volume\u003c/p\u003e","description":"","filename":"Figure3ab.png","url":"https://assets-eu.researchsquare.com/files/rs-4476893/v1/6698390c3d7dd35011f6e1ab.png"},{"id":59048964,"identity":"a9ac18e1-cd7a-47d6-bb33-8bd50d22ce3a","added_by":"auto","created_at":"2024-06-25 19:31:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47552,"visible":true,"origin":"","legend":"\u003cp\u003eROC of AP-NIC, VP-NIC, DP-NIC and ECV fraction and combined parameter, the AUC were 0.813, 0.804, 0.828, 0.819, 0.848, respectively; the combined parameter has the highest AUC. NIC, normalized iodine concentration; ECV, extracellular volume\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4476893/v1/b51e89816040a3ccaf2217d5.png"},{"id":59048966,"identity":"efe29547-37e8-4e95-8180-f18d3a0199e6","added_by":"auto","created_at":"2024-06-25 19:31:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":311822,"visible":true,"origin":"","legend":"\u003cp\u003eParameter correlation coefficient plot. FIGO, International Federation of Gynecology and Obstetrics; NIC, normalized iodine concentration; ECV, extracellular volume\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4476893/v1/27d9ada924299bec9425ad09.png"},{"id":62346930,"identity":"42cb4bde-c283-43e7-ae5a-2d0e4ece60d7","added_by":"auto","created_at":"2024-08-13 07:29:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3457549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4476893/v1/e280087b-86b2-4b10-a89a-374147725ab8.pdf"},{"id":59050279,"identity":"96db78d5-9521-4c61-b0e0-a61553c880bc","added_by":"auto","created_at":"2024-06-25 19:39:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17812,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4476893/v1/4a1206f72df309875cd2803b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u0026bull; Iodine concentration showed value in differentiating type Ⅰ from type Ⅱ EOC.\u003c/p\u003e\n\u003cp\u003e\u0026bull; ECV fraction is feasible in differentiating type Ⅰ from type Ⅱ EOC.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Combining triple-phase enhanced NICs and ECV fraction improved the identification performance.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OC) is ranked eighth in terms of global female cancer incidence and mortality. In 2020, approximately 314 thousand cases in worldwide and 207 thousand patients died of OC, which is the most fatal gynecological malignancy threatening the health and life of patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. About 90% of OCs are epithelial ovarian cancers (EOC) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. EOC were classified into type Ⅰ and type Ⅱ with the dualistic model according to different pathogenesis information [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Type Ⅰ tumors include low-grade serous carcinoma (LGSC), mucinous carcinoma, endometrioid carcinoma, clear cell carcinoma, and malignant Brenner tumor. Type Ⅱ tumors consist of high-grade serous carcinoma (HGSOC), carcinosarcoma, and undifferentiated carcinoma. Type Ⅰ tumors usually have a relatively mild biological behavior and show a better prognosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Type Ⅱ tumors, with their increased aggressiveness, result in a higher risk of recurrence and poorer prognosis for patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which account for 90% of deaths from ovarian cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Type Ⅰ tumors are candidate to some cytotoxic therapies while type Ⅱ tumors are sensitive to the platinum-based chemotherapy. Therefore, preoperatively identifying the subtype of EOC facilitates the development of individual therapeutic plans and the evaluation of the prognosis.\u003c/p\u003e \u003cp\u003eCurrently, invasive techniques are primary methods to identify the EOC histologic subtypes. However, diagnostic accuracy of examination techniques such as cytology and biopsy is limited in differentiating the EOC subtype preoperatively [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, the invasive examinations might cause some side effects such as bleeding, infection. Fine needle aspiration should be avoided in the diagnosis of early ovarian cancer in order to prevent tumor rupture from spreading in the abdominal cavity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, an effective non-invasive examination will be an important supplementary approach to distinguish type Ⅰ from type Ⅱ tumors.\u003c/p\u003e \u003cp\u003eCT of the abdomen and pelvis is the first line imaging modality for staging, selecting treatment options and assessing disease response in ovarian cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Routine CECT/MRI provided additional information for distinguishing between type Ⅰ and type Ⅱ tumors, but its sensitivity was not high. The study of Liu et al demonstrated that combining tumor morphology and enhancement degree from CECT/MRI imaging achieved a sensitivity of 61.36% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Other studies indicated that MRI functional parameters had the sensitivity 76.0%-85.0% in identifying EOC subtypes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Notably, despite high AUC 0.823\u0026ndash;0.970 for CECT/MRI radiomics analysis in EOC subtype evaluation [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], satisfying results in addressing methodological quality challenges remain elusive [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The 18F-FDG PET/CT parameter standard uptake value (SUV) max showed sensitivity 77.8%, specificity 69.2% in differentiating subtypes of EOC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], but the high cost and high radiation dose limit its clinical application.\u003c/p\u003e \u003cp\u003eDual-energy CT can capture X-rays at different energy levels, providing more information about tissue composition and density through several quantitative parameters. Previous studies have reported the value of dual-energy CT in assessing the biological behavior of various cancers, including gastric, renal, and lung cancer [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The iodine concentration (IC) from material decomposition image is one of the important parameters with extensive clinical applications. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The iodine is the main component of contrast medium, IC based parameters can reflect the tumor vascular growth and permeability. P53 mutation and WT1 positive are common immunohistochemical expression patterns of HGSOC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which are closely facilitate tumor neovascularization [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Thus, IC based parameters may be a promise method to discriminate EOC subtype.\u003c/p\u003e \u003cp\u003eThe extracellular volume (ECV) fraction, which can be calculated by post-contrast CT/MRI, reflects the intravascular and extravascular extracellular space. ECV fraction relates to the factors that effects the extracellular space such as extracellular matrix [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], the status of tumor vascularity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Previous studies showed equilibrium contrast-enhanced based ECV fraction have value in different tumor diagnosis, evaluation of pathologic characteristics and prediction of prognosis [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Whereas, CECT-based ECV fraction requires unenhanced images before administrating contrast agent, which may lead to the misregistration. Dual-energy CT-based ECV fraction does not need the enhanced images, which can avoid the misregistration. Dual-energy CT-derived ECV fraction and has been applied in differentiating and evaluating the biological behavior of thoracic and abdominal tumors [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Exploring the association of iodine concentration and ECV fraction with the subtype of EOC is necessary.\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to investigate the feasibility of dual-energy CT iodine concentration, ECV fraction and the combination of iodine concentration and ECV fraction in preoperatively differentiating type Ⅰ from type Ⅱ epithelial ovarian carcinoma.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by our hospital ethics review board and the need for informed consent was waived. Patients who underwent surgical staging and/or debulking surgery for ovarian tumor between March 2012 and December 2022 were included in this study. The inclusion criteria were as follows: ①, Patients who underwent preoperative abdominal and pelvic contrast-enhanced dual-energy CT scan; ②, no chemotherapy or radiotherapy before CT examination. The exclusion criteria were as follows: ①, patients with concomitant malignancies; ②, solid tumor part diameter\u0026thinsp;\u0026lt;\u0026thinsp;5 mm being not enough for placing regions of interest (ROIs); ③, poor image quality to affect the measurement of lesion; ④, pathological record of histologic subtype was incomplete; ⑤, postoperative pathology confirmed that patients had other pathological subtypes of ovarian tumor (including benign or borderline epithelial ovarian tumor, other non-epithelial ovarian malignancies).The patient selection flow chart was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eClinical characteristics comprised age, menstrual status, The International Federation of Gynecology and Obstetrics (FIGO) stage, serum tumor markers included carbohydrate antigen125 (CA125), Human Epididymis Protein 4 (HE4) and hematocrit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCT protocol\u003c/h2\u003e \u003cp\u003eThe triple-phase contrast-enhanced scans were performed using a using the dual-energy CT imaging mode on a GE Discovery CT 750 HD system (GE Healthcare, Milwaukee, WI, USA). The scan area was from the pubic symphysis to the diaphragm, covering abdomen and pelvis. The dual-energy CT scan parameters were as follows: rapid switching between 80 and 140 kVp tube voltages; tube current, 375 mA; 0.6 seconds rotation time; slice thickness/gap, 5/5 mm. A dose of 0.8ཞ1.0 ml/kg of ionic media (iohexol,350 mg iodine/ml; Lubei medicine, Beijing, China) was administered to patients via median cubital vein at the injection rate of 3ཞ5ml/s using high-pressure syringe. Followed by a bolus injection of 20 ml of saline given at the same flow rate. The arterial phase (AP), venous phase (VP), and delayed phase (DP) contrast-enhanced scan were acquired at 28s, 60s, and 120s after the contrast agent injection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImage post-processing and interpretation\u003c/h2\u003e \u003cp\u003eThe 70 keV monoenergetic images and decomposition images of iodine and water-based materials were reconstructed from the triple-phase enhanced CT. Layer thickness and spacing of reconstructed images were 1.25 mm. All reconstructed images were analyzed using Gemstone Spectral Imaging (GSI) Viewer software on an Advanced Workstation 4.6 (GE Healthcare, USA). All imaging parameters were measured by two observers (Q.L.S. and Y.L., with 6 and 12 years of experience in pelvic CT, respectively), blinded to the clinical characteristics and postoperative pathological results. A region of interest (ROI) was manually placed on monochromatic image at 70 keV then was copied on corresponding iodine maps to obtain triple-phase enhanced iodine concentrations (IC, mg/ml), Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. ROI was delineated as circular or oval at the largest section of the tumor solid part with area approximate 10\u0026thinsp;~\u0026thinsp;100mm\u0026sup2;. Necrotic and cystic part showed unenhanced area were excluded from ROI. About 2\u0026ndash;3 mm space was saved between ROI and tumor margin to avoid partial volume effect and obvious vessels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the same section with that of tumor ROI, a circular ROI was placed in the ipsilateral external iliac aorta to obtain IC\u003csub\u003eAorta\u003c/sub\u003e. The normalized IC (NIC, %) was calculated with IC\u003csub\u003eLesion\u003c/sub\u003e ∕ IC\u003csub\u003eAorta\u003c/sub\u003e. Where IC\u003csub\u003eLesion\u003c/sub\u003e and IC\u003csub\u003eAorta\u003c/sub\u003e were ICs (mg/ml) during the triple-phase enhanced for the EOC lesion and the external iliac aorta, respectively.\u003c/p\u003e \u003cp\u003eThen ECV fraction was calculated using the following formula:\u003c/p\u003e \u003cp\u003eECV fraction (%) = (1\u0026thinsp;\u0026minus;\u0026thinsp;hematocrit) \u0026times;(IC\u003csub\u003eLesion\u003c/sub\u003e ∕ IC\u003csub\u003eAorta\u003c/sub\u003e)\u0026times; 100.\u003c/p\u003e \u003cp\u003eWhere IC\u003csub\u003eLesion\u003c/sub\u003e and IC\u003csub\u003eAorta\u003c/sub\u003e were from delay phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS 21.0 software (IBM, Armonk, NY, USA). Intra-observer correlation coefficient (ICC) was used to test the intra-observer agreement between two times measurements of triple-phase enhanced NIC and ECV fraction. ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.75 means the agreement of imaging parameters was good [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].The Kolmogorov-Smirnov test was employed to test whether continuous variables were normally distributed. Student t-test and Whitney U test were used to compare the difference of clinical and imaging parameters between type Ⅰ and type Ⅱ groups. The parameters with statistical significance in univariate test were followed included in binary logistic regression analysis, the forward stepwise selection was employed to select the independent predictive factors. Logistic analysis also was used to established the combined parameter with NIC-based parameter and ECV fraction. Receiver operating characteristic (ROC) curves were plotted to analyze the performance of each univariate and the combined parameter in differentiating type Ⅰ from type Ⅱ EOC. The largest Youden index was used to determine optimal cut-off value, and the corresponding AUC, sensitivity and specificity were calculated. Delong's test was used to compare the difference in AUC between the independent factor, combined parameter and other single parameters. Spearman correlation analysis was used to analyze the correlation between clinical and imaging parameters. A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003ePatients\u003c/h2\u003e\n \u003cp\u003eOne hundred and forty consecutive patients were included in this study. According to postoperative pathological results, 53 patients were included in the type Ⅰ group (9 patients with LGSC, 23 patients with clear cell carcinoma, 12 patients with endometrioid carcinoma, 9 patients with mucinous carcinoma), 87 patients were included in the type Ⅱ group (83 patients with HGSOC, 4 patients with carcinosarcoma).\u003c/p\u003e\n \u003cp\u003eThe patient clinical information was listed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The FIGO stage in type Ⅰ group was earlier than type Ⅱ group. The menstruation status was significant different between type Ⅰ and type Ⅱ groups. The CA125 and HE4 of patients in type Ⅱ group were significantly larger than those in type Ⅰ group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient characteristics (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType I (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType II (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.26\u0026thinsp;\u0026plusmn;\u0026thinsp;10.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMenstruation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePremenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMenopause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u0026thinsp;~\u0026thinsp;II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u0026thinsp;~\u0026thinsp;IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA125 (U/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.40 (34.13, 334.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e632.50 (125.70, 1882.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHE4 (pmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.90 (54.91, 186.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340.10 (135.60, 625.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematocrit (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eFIGO\u0026thinsp;=\u0026thinsp;International Federation of Gynecology and Obstetrics; CA125\u0026thinsp;=\u0026thinsp;carbohydrate antigen125; HE4\u0026thinsp;=\u0026thinsp;Human Epididymis Protein 4\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eAgreement between two observers\u003c/h2\u003e\n \u003cp\u003eThe interobserver agreement of triple-phase NIC (AP-NIC, VP-NIC, DP-NIC) and the ECV fraction measurement were good (all ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.75), the details were shown in \u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDifference of all parameters between type Ⅰ and type Ⅱ groups\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e listed the difference of triple-phase NIC and ECV fraction between the two groups. AP-NIC, VP-NIC, DP-NIC and ECV fraction in the type Ⅰ group were all significantly lower than those in type Ⅱ group (6% vs. 15%, 24% vs. 51%, 36% vs. 70%, 21% vs. 43%, respectively, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-b).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of DECT IC and ECV fraction between type I and type II groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType I (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType II (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAP-NIC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (2, 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (10, 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVP-NIC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (10, 38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (32, 63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDP-NIC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (16, 51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (48, 95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECV fraction (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (10, 34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (31, 59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAP\u0026thinsp;=\u0026thinsp;arterial phase; VP\u0026thinsp;=\u0026thinsp;venous phase; DP\u0026thinsp;=\u0026thinsp;delayed phase; NIC\u0026thinsp;=\u0026thinsp;normalized iodine concentration; ECV\u0026thinsp;=\u0026thinsp;extracellular volume\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eLogistic analysis and combined parameter\u003c/h2\u003e\n \u003cp\u003eAP-NIC, VP-NIC and DP-NIC and ECV fraction were included in subsequent univariate logistic analysis, indicating all imaging parameters had statistical significance. Then these variables were incorporated into multivariate binary logistic regression analysis, DP-NIC was the only independent predictive factor for distinguishing EOC subtypes (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe DP-NIC was combined with AP-NIC, VP-NIC and ECV fraction to establish the combined parameter.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate logistic regression analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariable\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAP-NIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.116\u0026ndash;1.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVP-NIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.039\u0026ndash;1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDP-NIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.032\u0026ndash;1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.759-195.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECV fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.048\u0026ndash;1.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAP\u0026thinsp;=\u0026thinsp;arterial phase; VP\u0026thinsp;=\u0026thinsp;venous phase; DP\u0026thinsp;=\u0026thinsp;delayed phase; NIC\u0026thinsp;=\u0026thinsp;normalized iodine concentration; ECV\u0026thinsp;=\u0026thinsp;extracellular volume\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eROC analysis of all imaging parameters\u003c/h2\u003e\n \u003cp\u003eROC analysis showed that the threshold of DP-NIC was 40%, with the AUC 0.828, sensitivity 88.51% and specificity 62.26%. The AUC and sensitivity of ECV fraction were 0.819 and 83.91%, respectively, which were slightly lower than those of DP-NIC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The AUC of combined parameter was 0.848, with sensitivity 82.76% and specificity 75.47%. The details were shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiagnostic performances of significant imaging parameters for predicting type Ⅰ and type Ⅱ epithelial ovarian carcinoma\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThreshold value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAP-NIC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.737\u0026ndash;0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVP-NIC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u0026ndash;0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDP-NIC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.758\u0026ndash;0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECV fraction (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.747\u0026ndash;0.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCombined parameter*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778\u0026ndash;0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eAP\u0026thinsp;=\u0026thinsp;arterial phase; VP\u0026thinsp;=\u0026thinsp;venous phase; DP\u0026thinsp;=\u0026thinsp;delayed phase; NIC\u0026thinsp;=\u0026thinsp;normalized iodine concentration; ECV\u0026thinsp;=\u0026thinsp;extracellular volume\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003e*\u003c/strong\u003e Combination parameter comprised APNIC, VPNIC, DPNIC and ECV fraction\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eDelong\u0026apos;s test revealed a significantly higher AUC for the combined parameter compared to VP-NIC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). However, there was no significant difference in AUC between the combined parameter and other corresponding single parameters (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (\u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e and\u003c/strong\u003e Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of correlation\u003c/h2\u003e\n \u003cp\u003eSpearman\u0026rsquo;s correlation coefficient analysis was conducted to examine the correlation between clinical parameters included age, FIGO stage, menopausal, CA125, HE4 and imaging parameters which were AP-NIC, VP-NIC and DP-NIC and ECV fraction (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). There was no evidence of a correlation between menopausal and AP-NIC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Other clinical parameters had significant positive correlations with imaging parameters (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We observed a strong degree of correlation between FIGO stage and imaging parameters, there was a moderate degree of correlation between CA125 and imaging parameters (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study primarily investigated the feasibility of dual-energy CT iodine concentrations and dual-energy CT-derived ECV fraction in preoperatively differentiating type Ⅰ from type Ⅱ EOC. The results showed that triple-phase enhanced NIC, ECV fraction from dual-energy CT were able to discriminate the subtype of EOC, the above parameters were significantly higher in type Ⅱ EOC. DP-NIC was the only independent factor in differentiating EOC subtype. Combining AP-NIC, VP-NIC, DP-NIC and ECV fraction improved the predictive performance.\u003c/p\u003e \u003cp\u003eType Ⅰ and type Ⅱ tumors have different sites of origin, different biomarkers and molecular features [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Difference for biological behavior of the EOC subtype is closely related to the choice of treatment plans. For instance, type Ⅱ tumors are more likely to have homologous recombination repair and have a better response to the platinum-based chemotherapy [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while, the primary treatment for type Ⅰ EOC is cytoreduction. Identification of subtype of EOC facilitate gynecologists develop specialized therapeutic strategy for different patients. Moreover, type Ⅰ tumors are frequently characterized by mutations affecting the RAS/MAPK pathway, medicine targets mitogen-activated protein kinase (MAPK) or KRAS genes or some cytotoxic chemotherapies may be more suitable for type Ⅰ EOC [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For patients who need to receive neoadjuvant chemotherapy (NACT), it is crucial to choose a sensitive chemotherapeutic or target medicine. Thus, the premise of achieving an optimal surgery timing, obtain better surgery outcomes and prognosis is to correctly discriminate the subtype of EOC [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDifferences in certain clinical parameters were observed between type Ⅰ and type Ⅱ tumors. Type I EOC tend to occur in younger patients clinically, with a lower proportion of postmenopausal patients compared to type II EOC [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The age and menstrual status distribution of patients in our study were in line with the above results. Type Ⅱ EOC grow rapidly and are highly aggressive, which present in advanced stage in over 75% of cases [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our study also revealed that type Ⅱ EOC had advanced FIGO stages than type Ⅰ EOC. CA125 and HE4 serve as common tumor markers in the diagnosis, treatment evaluation and prognosis prediction of ovarian cancer. Our study found significantly higher CA125 and HE4 values in type Ⅱ EOC than those in type Ⅰ EOC, which confirmed the previous results as well [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe dual-energy CT is widely used in the evaluation of malignancies. Dual-energy CT derived iodine concentration was highly consistent with the actual IC [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The iodine concentration reflects the vascular angiogenesis, which is associated with the blood supply to the tumor. A recently study indicated that the iodine concentration change is able to assess response to treatment in patients with HGSOC [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], that study investigated the value of iodine concentration in the evaluation of EOC. Previous studies showed that NICs can minimize the effects of individual variability and had good performance [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], NIC can provide stable measurements by correcting factors such as injection rate and dose. The results of this study showed that triple-phase enhanced NIC of type Ⅱ EOC were significantly higher than those of type Ⅰ EOC, which suggested that type Ⅱ EOC has more blood perfusion than those of type Ⅰ EOC. The growth of malignant tumor needs the support of angiogenesis. HGSOC is a highly aggressive histological subtype of EOC, has aggressive neovascularization, high expression of tumor micro vessel density (MVD) and vascular endothelial growth factor (VEGF) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The efficacy of anti-VEGF drug, bevacizumab in the treatment of patients with HGSOC also demonstrates this [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. While type Ⅰ tumors is an indolent fashion, derive from precursor lesions, may have a longer process to transform to malignancies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The tumoral neovascularization in type Ⅰ tumors might be less than that of type Ⅱ tumors, which leading to less iodine concentration.\u003c/p\u003e \u003cp\u003eIn our study, the AUCs of DP-NIC were slightly higher than those of from AP-NIC, VP-NIC and ECV fraction. Li Q et al studied the usefulness of IC for evaluating lung cancer angiogenesis, and showed that VP outperformed DP in reflecting the microcirculation of the tumor [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], which was not in consist with our study. Neovascularization is disordered and tortuous, the structure is incomplete with a lower pericyte coverage index, these characteristics of neovascularization determined the high permeability and the difference perfusion modality from healthy vessels [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Li Q et al. found that AP mainly reflected the functional capillary density of tumor, VP more reflects dysfunctional blood vessels, DP represented contrast agent retained in the interstitial space. Whereas, their enhanced time for DP was 90s, the enhanced time for DP was 120s in our study, which was more able to characterize the filling of contrast agent in interstitial space and had better AUC. The longer delayed-phase enhanced time might improve the AUC and sensitivity of DP-NIC. Moreover, the DP-NIC in this study was the independent predictive factors for differentiating type Ⅰ EOC from type Ⅱ EOC, which also demonstrated the value for DP-NIC in identifying EOC subtype.\u003c/p\u003e \u003cp\u003eECV fraction has been regarded as a robust quantitative parameter, independent of several technical confounders and physiological variants [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The value of ECV fraction was proved in the evaluation of liver fibrosis and cardiotoxicity [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], it recently has been used to diagnose malignancies and predict the prognosis after chemotherapy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Among these studies, dual-energy CT showed an advantage. Compare to single-energy CT, dual-energy CT reduced the radiation exposure generated from pre- and post-contrast scans, and also minimized the misregistration. The ECV fraction of type Ⅱ EOC in this study was significantly higher than that of type Ⅰ EOC. The ECV fraction was calculated by the delayed phase parameters, reflected the difference of tumor interstitial space between type Ⅰ and type Ⅱ EOC. During longer delayed-phase, more blood leak into extravascular space from the neo vessels and enlarge the extravascular extracellular space. Moreover, high invasiveness of type Ⅱ EOC may cause interstitial edema or inflammatory infiltration. As the result of above reason, ECV fraction of type Ⅱ EOC was higher. ECV fraction might be promising parameter to characterize the tumor interstitial status. AP-NIC, VP-NIC and ECV fraction were not independent factors, however, they reflect tumor microcirculation and tumor interstitium from different functional aspects, so they were combined with DP-NIC to establish the combined parameter. The result showed the performance of combined parameter improved, especially significantly higher than VP-NIC. Our study suggested the feasibility of using iodine quantitative parameters and ECV fraction at the delayed phase to distinguish between type Ⅰ and type Ⅱ EOCs. Combining NIC and ECV fraction provides more valuable information.\u003c/p\u003e \u003cp\u003eThere are several limitations in our study should be noted. First, this was a retrospective study, a prospective study should perform to further verify the efficacy of above parameters. Second, the results of this study were obtained from one cohort patients, cross validation and external validation need to be developed in the future study. Thirdly, this study did not analyze tumor morphological features, combining quantitative parameters with morphological features in future studies may benefit the evaluation process.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIodine concentrations and extracellular volume fraction derived from dual-energy CT are useful parameters to non-invasively differentiate type Ⅰ from type Ⅱ epithelial ovarian carcinoma, DP-NIC is able to independently differentiate type Ⅰ from type Ⅱ EOC, combining with triple-phase enhanced NIC and ECV fraction shows greater value. Dual-energy CT derived Iodine concentration parameters and ECV fraction are easily obtained method to assist gynecologist to preoperatively identify the subtype of EOC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eepithelial ovarian carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGSOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-grade serous carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFIGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Federation of Gynecology and Obstetrics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCECT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContrast-enhanced CT\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eregion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eiodine concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eextracellular volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enormalized iodine concentration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earterial-phased\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evenous-phased\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edelay-phased\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintraobserver correlation coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eareas under the curves\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets and material analyzed are available on reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDisclosure the authors have no conflicts of interest with respect to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Institutional Review Board, First Affiliated Hospital of Dalian Medical University. Written informed consent for participation of this retrospective study was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the writing of the manuscript and consented to publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. Cancer J Clin. 2022;72(1):7\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarnezis AN, Cho KR, Gilks CB, Pearce CL, Huntsman DG. The disparate origins of ovarian cancers: pathogenesis and prevention strategies. Nat Rev Cancer. 2017;17(1):65\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurman RJ, Shih Ie M. Pathogenesis of ovarian cancer: lessons from morphology and molecular biology and their clinical implications. Int J Gynecol pathology: official J Int Soc Gynecol Pathologists. 2008;27(2):151\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCluggage WG. Morphological subtypes of ovarian carcinoma: a review with emphasis on new developments and pathogenesis. Pathology. 2011;43(5):420\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCree IA, White VA, Indave BI, Lokuhetty D. Revising the WHO classification: female genital tract tumours. Histopathology. 2020;76(1):151\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurman RJ, Shih Ie M. The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. Am J Pathol. 2016;186(4):733\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoang LN, Zachara S, Soma A, K\u0026ouml;bel M, Lee CH, McAlpine JN, Huntsman D, Thomson T, van Niekerk D, Singh N, et al. Diagnosis of Ovarian Carcinoma Histotype Based on Limited Sampling: A Prospective Study Comparing Cytology, Frozen Section, and Core Biopsies to Full Pathologic Examination. Int J Gynecol pathology: official J Int Soc Gynecol Pathologists. 2015;34(6):517\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNCCN Clinical Practice Guidelines in Oncology. Ovarian CancerIncluding Fallopian Tube Cancerand Primary Peritoneal Cancer. (Version 1.2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.nccn.org/patients\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahdev A. CT in ovarian cancer staging: how to review and report with emphasis on abdominal and pelvic disease for surgical planning. Cancer imaging: official publication Int Cancer Imaging Soc. 2016;16(1):19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu D, Zhang L, Indima N, Peng K, Li Q, Hua T, Tang G. CT and MRI findings of type I and type II epithelial ovarian cancer. Eur J Radiol. 2017;90:225\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Q, Jin J, Wang Y, Wang Y, Wang T, Zhang H, Liu Y. Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker. Am J translational Res. 2023;15(3):1862\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong H, Bak S, Kim I, Woo JY, Cho EJ, Choi YJ, Rha SE, Oh SA, Youn SY, Lee SJ. An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer. \u003cem\u003eJournal of clinical medicine\u003c/em\u003e 2021, 11(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Perucho JAU, Hu Y, Choi MH, Han L, Wong EMF, Ho G, Zhang X, Ip P, Lee EYP. Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma. JAMA Netw Open. 2022;5(12):e2245141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Li X, Ma J, Wang F, Cui S, Ye Z. Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers. Eur Radiol. 2023;33(7):5193\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJian J, Li Y, Pickhardt PJ, Xia W, He Z, Zhang R, Zhao S, Zhao X, Cai S, Zhang J, et al. MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers. Eur Radiol. 2021;31(1):403\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Luo HJ, Ren J, Guo LM, Niu J, Song X. Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype. Front Oncol. 2022;12:978123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian L, Ren J, Liu A, Gao Y, Hao F, Zhao L, Wu H, Niu G. MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes. Eur Radiol. 2020;30(11):5815\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang ML, Ren J, Jin ZY, Liu XY, He YL, Li Y, Xue HD. A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights into imaging. 2023;14(1):117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez Garc\u0026iacute;a B, Garc\u0026iacute;a Vicente AM, Jim\u0026eacute;nez Londo\u0026ntilde;o GA, Pena Pardo FJ, Bell\u0026oacute;n Guardia ME, Talavera Rubio MP, Palomar Mu\u0026ntilde;oz A, G\u0026oacute;mez Herrero P. Soriano Castrej\u0026oacute;n \u0026Aacute; M: (18)F-FDG PET/CT as predictor of tumour biology and prognosis in epithelial ovarian carcinoma. Revista Esp de Med nuclear e imagen Mol. 2017;36(4):233\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Xu S, Wang Y, Fang M, Ma F, Xu C, Li H. Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study. Eur Radiol. 2023;33(7):5172\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang F, Dong J, Wang X, Fu X, Zhang T. Non-small cell lung cancer: Spectral computed tomography quantitative parameters for preoperative diagnosis of metastatic lymph nodes. Eur J Radiol. 2017;89:129\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Zhang G, Xu L, Bai X, Lu X, Yu S, Sun H, Jin Z. Utilisation of virtual non-contrast images and virtual mono-energetic images acquired from dual-layer spectral CT for renal cell carcinoma: image quality and radiation dose. Insights into imaging. 2022;13(1):12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuenzel D, Lo GC, Yu HS, Parakh A, Patino M, Kambadakone A, Rummeny EJ, Sahani DV. Material density iodine images in dual-energy CT: Detection and characterization of hypervascular liver lesions compared to magnetic resonance imaging. Eur J Radiol. 2017;95:300\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIntegrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLisio MA, Fu L, Goyeneche A, Gao ZH, Telleria C. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int J Mol Sci 2019, 20(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagner KD, Cherfils-Vicini J, Hosen N, Hohenstein P, Gilson E, Hastie ND, Michiels JF, Wagner N. The Wilms' tumour suppressor Wt1 is a major regulator of tumour angiogenesis and progression. Nat Commun. 2014;5:5852.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal S, Datta K, Mukhopadhyay D. Central role of p53 on regulation of vascular permeability factor/vascular endothelial growth factor (VPF/VEGF) expression in mammary carcinoma. Cancer Res. 2001;61(18):6952\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiao KY, Yang ZG, Xu HY, Liu X, Zhang Q, Shi K, Jiang L, Xie LJ, Wen LY, Guo YK. Histologic validation of myocardial fibrosis measured by T1 mapping: a systematic review and meta-analysis. J Cardiovasc Magn resonance: official J Soc Cardiovasc Magn Reson. 2016;18(1):92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Y, Liu L, Liu D, Shen H, Wang X, Fan C, Zeng Z, Zhang J, Tan Y, Zhang X, et al. Extracellular volume fraction determined by equilibrium contrast-enhanced CT for the prediction of the pathological complete response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Eur Radiol. 2023;33(6):4042\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakumi K, Nagano H, Oose A, Gohara M, Kamimura K, Nakajo M, Harada-Takeda A, Ueda K, Tabata K, Yoshiura T. Extracellular volume fraction derived from equilibrium contrast-enhanced CT as a diagnostic parameter in anterior mediastinal tumors. Eur J Radiol. 2023;165:110891.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Bao J, Zhang Y, Dou Y, Liu A, Liu M, Wu H, Wu J, Zhao L, Yang Z, et al. Predictive value of CT-based extracellular volume fraction in the preoperative pathologic grading of rectal adenocarcinoma: A preliminary study. Eur J Radiol. 2023;163:110811.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukukura Y, Kumagae Y, Higashi R, Hakamada H, Takumi K, Maemura K, Higashi M, Kamimura K, Nakajo M, Yoshiura T. Extracellular volume fraction determined by equilibrium contrast-enhanced multidetector computed tomography as a prognostic factor in unresectable pancreatic adenocarcinoma treated with chemotherapy. Eur Radiol. 2019;29(1):353\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng Y, Tang G, Sun M, Yu S, Cheng Y, Wang Y, Deng W, Li Y, Guan J. Feasibility of spectral CT-derived extracellular volume fraction for differentiating aldosterone-producing from nonfunctioning adrenal nodules. \u003cem\u003eEuropean radiology\u003c/em\u003e 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakumi K, Nagano H, Myogasako T, Nakano T, Fukukura Y, Ueda K, Tabata K, Tanimoto A, Yoshiura T. Feasibility of iodine concentration and extracellular volume fraction measurement derived from the equilibrium phase dual-energy CT for differentiating thymic epithelial tumors. Japanese J Radiol. 2023;41(1):45\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujita N, Ushijima Y, Itoyama M, Okamoto D, Ishimatsu K, Wada N, Takao S, Murayama R, Fujimori N, Nakata K, et al. Extracellular volume fraction determined by dual-layer spectral detector CT: Possible role in predicting the efficacy of preoperative neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma. Eur J Radiol. 2023;162:110756.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenchoufi M, Matzner-Lober E, Molinari N, Jannot AS, Soyer P. Interobserver agreement issues in radiology. Diagn Interv Imaging. 2020;101(10):639\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrat J, D'Angelo E, Espinosa I. Ovarian carcinomas: at least five different diseases with distinct histological features and molecular genetics. Hum Pathol. 2018;80:11\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColic E, Patel PU, Kent OA. Aberrant MAPK Signaling Offers Therapeutic Potential for Treatment of Ovarian Carcinoma. OncoTargets therapy. 2022;15:1331\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNara K, Taguchi A, Yamamoto T, Hara K, Tojima Y, Honjoh H, Nishijima A, Eguchi S, Miyamoto Y, Sone K, et al. Heterogeneous effects of cytotoxic chemotherapies for platinum-resistant ovarian cancer. Int J Clin Oncol. 2023;28(9):1207\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarsh LA, Kim TH, Zhang M, Kubalanza K, Treece CL, Chase D, Memarzadeh S, Salani R, Karlan B, Rao J, et al. Pathologic response to neoadjuvant chemotherapy in ovarian cancer and its association with outcome: A surrogate marker of survival. Gynecol Oncol. 2023;177:173\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlc\u0026aacute;zar JL, Utrilla-Layna J, M\u0026iacute;nguez J, Jurado M. Clinical and ultrasound features of type I and type II epithelial ovarian cancer. Int J Gynecol cancer: official J Int Gynecol Cancer Soc. 2013;23(4):680\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurman RJ, Shih Ie M. Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer\u0026ndash;shifting the paradigm. Hum Pathol. 2011;42(7):918\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKristjansdottir B, Levan K, Partheen K, Sundfeldt K. Diagnostic performance of the biomarkers HE4 and CA125 in type I and type II epithelial ovarian cancer. Gynecol Oncol. 2013;131(1):52\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang L, Li ZY, Li ZW, Zhang XP, Li YL, Li XT, Wang ZL, Ji JF, Sun YS. Evaluating the response of gastric carcinomas to neoadjuvant chemotherapy using iodine concentration on spectral CT: a comparison with pathological regression. Clin Radiol. 2015;70(11):1198\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Li J, Wang X, Liang P, Gao J. Detection of gastric cancer and its histological type based on iodine concentration in spectral CT. Cancer imaging: official publication Int Cancer Imaging Soc. 2018;18(1):42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuscito I, Cacsire Castillo-Tong D, Vergote I, Ignat I, Stanske M, Vanderstichele A, Glajzer J, Kulbe H, Trillsch F, Mustea A, et al. Characterisation of tumour microvessel density during progression of high-grade serous ovarian cancer: clinico-pathological impact (an OCTIPS Consortium study). Br J Cancer. 2018;119(3):330\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrikopoulou A, Liontos M, Skafida E, Koutsoukos K, Apostolidou K, Kaparelou M, Rouvalis A, Bletsa G, Dimopoulos MA, Zagouri F. Pembrolizumab in combination with bevacizumab and oral cyclophosphamide in heavily pre-treated platinum-resistant ovarian cancer. Int J Gynecol cancer: official J Int Gynecol Cancer Soc. 2023;33(4):571\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePignata S, Lorusso D, Joly F, Gallo C, Colombo N, Sessa C, Bamias A, Salutari V, Selle F, Frezzini S, et al. Carboplatin-based doublet plus bevacizumab beyond progression versus carboplatin-based doublet alone in patients with platinum-sensitive ovarian cancer: a randomised, phase 3 trial. Lancet Oncol. 2021;22(2):267\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurman RJ, Shih Ie M. The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory. Am J Surg Pathol. 2010;34(3):433\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDey P, Nakayama K, Razia S, Ishikawa M, Ishibashi T, Yamashita H, Kanno K, Sato S, Kiyono T, Kyo S. Development of Low-Grade Serous Ovarian Carcinoma from Benign Ovarian Serous Cystadenoma Cells. Cancers 2022, 14(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Li X, Li XY, Huo JW, Lv FJ, Luo TY. Spectral CT in Lung Cancer: Usefulness of Iodine Concentration for Evaluation of Tumor Angiogenesis and Prognosis. AJR Am J Roentgenol. 2020;215(3):595\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge ML, Dzik-Jurasz AS, Padhani AR, Brown G, Tait DM, Eccles SA, Swift RI. Non-invasive methods of assessing angiogenesis and their value in predicting response to treatment in colorectal cancer. Br J Surg. 2001;88(12):1628\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnopp MV, Weiss E, Sinn HP, Mattern J, Junkermann H, Radeleff J, Magener A, Brix G, Delorme S, Zuna I, et al. Pathophysiologic basis of contrast enhancement in breast tumors. J Magn Reson imaging: JMRI. 1999;10(3):260\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim PK, Hong YJ, Sakuma H, Chawla A, Park JK, Park CH, Hong D, Han K, Lee JY, Hur J, et al. Myocardial Extracellular Volume Fraction and Change in Hematocrit Level: MR Evaluation by Using T1 Mapping in an Experimental Model of Anemia. Radiology. 2018;288(1):93\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTago K, Tsukada J, Sudo N, Shibutani K, Okada M, Abe H, Ibukuro K, Higaki T, Takayama T. Comparison between CT volumetry and extracellular volume fraction using liver dynamic CT for the predictive ability of liver fibrosis in patients with hepatocellular carcinoma. Eur Radiol. 2022;32(11):7555\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonti CB, Zanardo M, Bosetti T, Al\u0026igrave; M, De Benedictis E, Luporini A, Secchi F, Sardanelli F. Assessment of myocardial extracellular volume on body computed tomography in breast cancer patients treated with anthracyclines. Quant imaging Med Surg. 2020;10(5):934\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukukura Y, Kumagae Y, Higashi R, Hakamada H, Nakajo M, Maemura K, Arima S, Yoshiura T. Extracellular volume fraction determined by equilibrium contrast-enhanced dual-energy CT as a prognostic factor in patients with stage IV pancreatic ductal adenocarcinoma. Eur Radiol. 2020;30(3):1679\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\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":"Dual-energy CT, Iodine concentration, Extracellular volume fraction, histological types, epithelial ovarian carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-4476893/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4476893/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives: \u003c/strong\u003eTo investigate the feasibility of using the iodine concentration (IC) parameter and extracellular volume (ECV) fraction derived from dual-energy CT for distinguishing between type Ⅰ and type Ⅱ epithelial ovarian carcinoma (EOC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study retrospectively included 140 patients with EOC preoperatively underwent dual-energy CT scans. Patients were grouped as type Ⅰ and type Ⅱ EOC according to postoperatively pathologic results. Normalized IC (NIC, %) values from arterial-phase (AP), venous-phase (VP) and delay-phase (DP) were measured by two observers. ECV fraction (%) was calculated by DP-NIC and hematocrit. Intra-observer correlation coefficient (ICC) was used to assess the agreement between measurements made by two observers. The differences of imaging parameters between the two groups were compared. Logistic regression was used to select independent predictive factors and establish combined parameter. Receiver operating characteristic curve was used to analyze performance of all parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eICCs for all parameters exceeded 0.75\u003cstrong\u003e. \u003c/strong\u003eAll parameters in type Ⅱ EOC were all significantly higher than those in type Ⅰ EOC (all \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). DP-NIC exhibited the highest Area under the curve (AUC) of 0.828, along with 88.51% sensitivity and 62.26% specificity. DP-NIC was identified as the independent factor. The sensitivity and specificity of ECV fraction were 83.91% and 67.92%, respectively. The combined parameter consisting of AP-NIC, VP-NIC, DP-NIC, and ECV fraction yielded an AUC of 0.848, with sensitivity of 82.76% and specificity of 75.47%. The AUC of the combined parameter was significantly higher than that of VP-NIC (\u003cem\u003eP\u003c/em\u003e = 0.042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIt is valuable for dual-energy CT IC-based parameters and ECV fraction in preoperatively identifying type Ⅰ and type Ⅱ EOC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCritical relevance statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDual-energy CT-normalized iodine concentration and extracellular volume fraction achieved satisfactory discriminative efficacy, distinguishing between type Ⅰ and type Ⅱ epithelial ovarian carcinoma.\u003c/p\u003e","manuscriptTitle":"Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-25 19:31:32","doi":"10.21203/rs.3.rs-4476893/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":"1b39762f-1f3a-4cd2-a3bc-2439208e479b","owner":[],"postedDate":"June 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T07:21:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-25 19:31:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4476893","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4476893","identity":"rs-4476893","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}