18FDG-PET/CT versus Contrast Enhanced CT in detection of mucinous ovarian cancer recurrence: comparative study

18FDG-PET/CT versus Contrast Enhanced CT in detection of mucinous ovarian cancer recurrence: comparative study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 18FDG-PET/CT versus Contrast Enhanced CT in detection of mucinous ovarian cancer recurrence: comparative study Ismail Ali, Ibrahim Nasr, shaimaa farouk, Mai Elahmadawy, Omnia Talaat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3961163/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 assess the added value of 18 FDG-PET/CT in detection of mucinous ovarian cancer (MOC) recurrence and its effect on patient management compared to contrast enhanced computerized tomography (CECT). Methods: All patients underwent 18 F-FDG PET/CT and CECT for detection of MOC recurrence. PET/CT and CT were interpreted separately and the significance of difference between them was evaluated. Results: The study included 59 patients, out of them 18 and 29 patients were proven to have local and distant recurrence respectively. PET/CT demonstrated greater sensitivity (SN) , positive predictive value (PPV), negative predictive value (NPV) and accuracy, but the same specificity (SP) in recurrence detection (97.9%, 90.2%, 87.5%, 89.8%, and 58.3%, vs. 85.1%, 88.9%, 50%, 79.7%, and 58.3%, respectively) and showed significantly higher sensitivity for detection of omento-peritoneal and LNs metastases (mets) (36 and 27 versus 22 and 18, p- 0.0001 and 0.004, respectively). Both modalities were comparable in identifying distant organ mets (p >0.05). PET/CT changed patient management in 25.4% of patients, Conclusion: 18 FDG-PET/CT showed higher SN and accuracy than CECT in MOC recurrence detection, mainly the omento-peritoneal and nodal deposits, which allow better guidance for proper therapy planning. Mucinous ovarian cancer 18FDG PET/CT CECT and recurrence Figures Figure 1 Figure 2 Background There are about 22,000 newly diagnosed cases of OC every year and it is the most common reason of cancer-related deaths amongst women [1]. Mucinous ovarian carcinoma is a unique and uncommon kind of OC [2,3]. It was previously believed that MOC accounted for a higher percentage of the diagnosed OC (≥ 10%) [4]. Presently MOC is considered as a rare form of OC as true primary MOC accounts for roughly 5% of OC cases [2,5]. Even with a good initial response, around 80% of patients eventually relapse and need further treatment [6].Clinical examination, assessment of the serum tumor marker (CA-125), and morphological imaging methods such computed tomography (CT), magnetic resonance imaging (MRI), and ultrasonography (US) are typically included in follow-up programs. These techniques do have certain drawbacks. The limits of the use of CA-125 are known as increased CA-125 levels cannot be used to distinguish between localized and diffuse tumor recurrence, nor normal CA-125 values can be used to rule out the existence of disease [7]. Furthermore, the diagnosis of tumor recurrence may be difficult to achieve with traditional imaging methods dependent on anatomical variations, such as the discovery of a new aberrant lesion or change in the size of an existing lesion. Furthermore, CT and MRI imaging cannot identify mets of normal-sized LNs and they are not very useful in accurately distinguishing a recurrence from a post-surgical change; neither immediately following treatment nor later on [8]. A solution to these issues has been suggested: FDG with PET. It has been shown to be extremely sensitive in identifying OC recurrence, particularly in individuals exhibiting an inexplicable rise in the level of tumor marker. It provides the advantages of both functional and anatomical imaging, and it has been applied to both the exclusion of illness in locations with residual structural abnormality and the localization of areas with elevated FDG with greater anatomical specificity [9]. Precise localization of OC recurrence affect both patient’s prognosis and therapy approach, according to Fulham et al, who evaluated the clinical effect of FDG PET on therapy plans[10]. Aim of the study To assess the added value of 18 FDG-PET/CT in the detection of MOC recurrence and its effect on patient management compared to CECT. Material and methods All 18 FDG PET/CT and CECT exams were done at the National Cancer Institute's Nuclear Medicine Unit at Cairo University. Our study was performed after receiving the institutional review board acceptance (protocol number: IRB#:11164-8-10-2023). Every patient gave his signed consent to share in this study after being informed. Patient population Patients with suspected recurrences of MOC guided by the clinical, laboratory and/or radiological data fulfill the inclusion criteria. Patients with ovarian cancer other than the mucinous type, concomitant cancer, uncontrolled diabetes, severe infections, and those lacking definitive pathology data, were excluded from the study. Also, patients with a suspected short life span of less than 6 months were also excluded from our study. Patient preparation Patients were instructed to avoid strenuous activity for few days before the exam to lessen 18 FDG uptake by skeletal muscles and follow a low-carb diet and fasting for 24 hours, and 4–6 hours before 18 FDG injection respectively. The peripheral blood glucose level should be verified to be less than 150 mg/dL. Oral diabetic drugs could be used as advised except prescriptions containing metformin, which should be stopped 48 hours before the study to lower the intestinal background activity produced by such medications. The day before the study, diabetic patients with type 1 diabetes mellitus should fast after midnight (except from drinking water) and scheduled in the morning before taking insulin and their acceptable blood glucose level was to be maintained at less than 180 mg/d. If there was hypoglycemia with symptoms or if the glucose level was more than 200 mg/dL, the exam should be rescheduled. (Serum creatinine level was also done for all patients before IV contrast injection and should not exceed the level of 1.7 mg/dl). Imaging Technique After I.V. injection of 18 F–FDG by a dose of 240–380 MBq, all patients were instructed to spend 45–60 minutes in a dimly lit room with a warm atmosphere. Also, patients were instructed to move as little as possible and rest quietly; no speaking, chewing, or reading was allowed. The patients were asked to urinate before being put on the PET/CT scanner. Scanning began with a non-enhanced, low-dose CT scan extending from the skull base down to the upper thighs, with a field of view of 50cm, 120 kV and 60 mAs, 0.9 pitch, and a 5 mm slice thickness. CT data were used for attenuation correction and anatomical localization. A three-dimensional whole-body PET scan was started immediately after the CT at the same acquisition range with 6–7 bed positions (2 minutes/position) using an integrated PET/CT system (Philips Medical Systems, equipped with a 16-slice CT)). A standard iterative reconstruction approach was utilized to reconstruct PET images that had been corrected for attenuation. Diagnostic CECT scan was carried out in the same session covering the same field of view. Iodinated contrast material was injected in a dose of 1.5–2 ml/kg by an automated injector at a 4 ml/s flow rate through a patent venous line inserted in the ante-cubital vein. The acquisition parameters were 5.0 mm collimator width, 120 kV, 120 mAs, 0.9 second gantry rotation time, and 5 mm slice thickness. Coronal and sagittal reconstructions were produced using the obtained raw data. Fusion images were generated for every set of PET and CT data. The CECT data set was automatically fused with the 3D PET images to generate contrast-enhanced anatomical images superimposed with FDG uptake using the integrated software interface supplied by the manufacturer company. Image interpretation A team of doctors with over 15 years of experience in nuclear medicine and radiology that were blind to the final pathology data and each other's assessments performed both visual and semi-quantitative analysis of the acquired PET/CT and CECT images for every patient. Using a region of interest drawn in the area of enhanced uptake, the maximum standardized uptake values (SUVmax) for each pathological lesion have been determined for semi-quantitative assessment. Malignant lesions were identified on 18 FDG PET/CT imaging as lesions with an SUVmax of at least 2.5 at the location of pathologic alterations [11]. Data Analysis The SUVmax values were recorded and located with focally increased FDG uptake were observed in order to conduct a qualitative and semi-quantitative analysis. The lesions was deemed abnormal if it showed greater FDG absorption on the attenuation-corrected pictures than the activity of the hepatic blood pool. In order to exclude the potential of physiological FDG uptake by specific organs such as adipose tissues, salivary glands and muscles. Areas of focused FDG uptake were compared with corresponding CT images using CT data. Increased focal FDG uptake (local or distant) was found and documented. The imaging data were compared to the outcomes of the histology and/or to clinical, radiological and laboratory follow-up data. True positives (TP) were lesions that demonstrated a decrease in CA-125 levels during ovarian cancer therapy (chemotherapy or radiation therapy) or that were validated by subsequent imaging methods like PET/CT. If the PET/CT scans were normal and no recurrence was seen during serial imaging and clinical follow-up, a true negative (TN) result was obtained. If further imaging modalities or clinical follow-up data demonstrated recurrence but the PET/CT scans were normal, the results were considered false-negative (FN). Positive PET/CT results that turned out to be benign or that were linked to a subsequent cancer were referred to as false-positive (FP) results. Statistical Analysis : Both the continuous and categorical variables were expressed as the mean ± SD, median (range) and number (%). To confirm that continuous variables were normal, the Shapiro-Wilk test was employed. The Wilcoxon signed rank test was used to compare the non-normally distributed data in two dependent groups. McNemar's test was used to compare the matched data. The Stuart-Maxwell test, a version of the McNemar test, was used to determine the marginal homogeneity of a square table with more than two rows and columns. The validity of CT and PET/CT was determined by comparing the diagnostic performance of sample 2x2 contingency tables created with the golden standard test as a reference test for mucinous ovarian cancer recurrence. The associated 95% confidence intervals for the accuracies, PPV, NPV, SP and SN were computed. The inter-rater agreement (Cohen's Kappa) test was used to calculate the requirements for qualifying for the strength of agreement, and the results were as follows: (K < 0.2 denotes poor quality), (K 0.21–0.40 fair), (K 0.41–0.60 moderate), (K 0.61–0.80 good), and (K 0.81–1.00 extremely good). P-value of less than 0.05 was deemed statistically significant for all two-sided tests. MedCalc 13 for Windows (MedCalc Software bvba, Ostend, Belgium) and SPSS 22.0 for Windows (SPSS Inc., Chicago, IL, USA) were used to analyze all of the data. Results A total of 59 patients with MOC and a mean age of 55.0 ± 13.0 years were enrolled in our study. Fifty-five (93.2%) underwent both surgery and chemotherapy, while four patients received chemotherapy alone. Forty-seven (79.6%) patients had recurrences, out of them 18 patients had local recurrences and 29 had distant recurrences. The mean CA-125 blood level as a tumor marker was 58.6 ± 36.7, which was high in 36 (61%) patients and normal in 23 (39%). The mean value of CA-125 was significantly higher in patients with MOC recurrence than those without (68.3 ± 34.6 versus 20.7 ± 11.4, respectively, p 0.001). On PET/CT and CECT, the mean maximal lesion size for the operative bed and lymph node recurrence was 5.7 ± 3.3 and 1.8 ± 1.9 cm, while the mean SUVmax was 7.1 ± 2.6 and 6.0 ± 6.2, respectively (Table 1) . Table (1): The characteristics of the studied mucinous ovarian carcinoma patients The rate of surgical bed recurrence was found to be similar for both PET/CT and CECT (18 patients each) (p-value 1.00). Out of them, 6 patients on PET/CT and 5 on CECT showed invasion of the nearby structures (p-value 1.00). PET/CT showed a significantly higher rate of distant mets detection compared to CECT at the omento-peritoneal and LNs [36 (61%) and 27 (45.8%) versus 22 (37.3%) and 18 (30.5%), with p-values of 0.0001 and 0.004, respectively]. The rates of distant mets diagnosis at the liver, lung, adrenals, bone, brain and subcutaneous tissue were comparable between both modalities, with an insignificant statistical difference (p-values > 0.05) (Table 2). Table (2): Comparison between CECT and PET/CT findings among the studied mucinous ovarian carcinoma patients PET/CT and CECT were highly concordant in the detection of both operative bed recurrence and nearby structure invasion (K 1.00 and 0.90, respectively, with p 0.001). There is only one (1.7%) discordant negative case on CECT ,but positive on PET/CT (p < 0.001). The detection of distant mets at the lung, bones, subcutaneous tissue and LNs showed strong agreement between both modalities (K 0.69–1.0, p < 0.001), while the omento-peritoneum and adrenals showed weak agreement (K.56 and 0.38, with p < 0.001 and 0.003, respectively). For more details, see Table 3 . Table (3): Agreement between CT and PET/CT findings among the studied mucinous ovarian carcinoma patients (N = 59). PET/CT had a lower FN rate than CECT (1.7% vs. 11.9%) and demonstrated greater SN, PPV, NPV, and accuracy, but the same SP in recurrence detection (97.9%, 90.2%, 87.5%, 89.8% and 58.3%, vs. 85.1%, 88.9%, 50%, 79.7% and 58.3% respectively). On comparing the diagnostic parameters with the gold standard PET/CT showed a lower P value than CECT (0.22 versus 0.77) ( table 4 ). Regarding the LNs mets detection, PET/CT displayed higher SN (96.2%), NPV (96.9%) and accuracy (95%) compared to 65.4%, 78% and 83.1% for CECT respectively, while CECT has a higher SP (97%) versus 94% for PET/CT. CECT showed a high false negative rate (23.7%) in the diagnosis of peritoneal deposits but PET/CT did not (table 4) . Table (4): Diagnostic performance of CECT and PET/CT in relation to the golden standard in diagnosis of mucinous ovarian carcinoma recurrence PET/CT upgraded patient management in 25.4% of patients, from no therapy to local and systemic therapy in one and seven patients respectively, and from local to systemic therapy in another seven patients (p 0.001) (Table 5) . Table (5): Comparison between therapy plan decisions based on CECT and PET/CT findings Figure (1) A 64-year-old Female patient who has a ovarian cancer, received CTH and referred for follow up. CECT (A, D, and G) images displayed a loculated right paracolic collection measuring 7.8x15.5 cm, small sub-centimetric right inguinal LN, and a right pelvic cystic lesion with a solid component measuring 4.2x4.3 cm. PET and PET/CT scans showed diffuse FDG uptake at the loculated right paracolic collection (SUVmax 8), the right ovarian mixed cystic and a solid lesion (SUVmax 13). Also, FDG-avid omento-peritoneal infiltrative thickening, multiple nodularity and serosal implants (SUVmax ~ 11.2) were seen in addition to active FDG uptake at the small right inguinal LN (SUVmax 5.5). Figure (2) A 58-year-old woman who had ovarian cancer was treated by pan-hysterectomy and chemotherapy. CECT images (A, D, G and J) showed small calcified sub-carinal LN (8 mm), diffuse minimal abdomino-pelvic thickening, more pronounced at the left hypochondrial area, diffuse and loculated abdominal ascites and sub-centimetric right external iliac LN. PET and the fused PET/CT image revealed avid FDG uptake at the calcified subcarinal LN with SUVmax 7.7 (C image). F and I images showed active diffuse omento-peritoneal thickening and nodularity with serosal implants, more prominent at Lt. hypochondrium and left lateral region with SUVmax 12 and 10.2. L images revealed sub-centimeteric FDG avid right external iliac LN with SUVmax ~ 5. Discussion Despite effective treatment and complete response in patients with OC, the recurrence rate is high (50–80%). Early diagnosis of recurrence in these patients is important as it has a close relation with prognosis and the choice of appropriate treatment. ( 12 – 15 ). Imaging techniques like CT and MRI can be used to detect OC recurrences. However, since OC mets primarily affect the omento-peritoneal region rather than parenchymal organs, the detection of small implanted mets on the visceral surface is challenging ( 16 – 18 ). Despite the limited value of F18-FDG PET-CT in evaluating the primary tumor, it has a particular value for identifying the LNs and distant mets, particularly when it comes to extra-abdominal spread.( 19 – 20 ). The value of 18FDG PET/CT is in detection of OC recurrence, as it is superior to both conventional imaging and the CA-125 assay. It has better SN and SP for both high and low-grade carcinomas ( 21 , 22 ). In addition to the higher efficiency of 18FDG PET/CT than CT and MRI in identifying recurrent OC, it can also identify recurrences of OC approximately six months before CT ( 23 ).Consequently, 18FDG PET/CT can be used effectively for surveillance of treated OC patients, particularly when conventional imaging methods had negative results but there is an increase in the CA-125 level or the clinical examination may indicate recurrence or progression ( 24 ). In the present study, 18FDG PET/CT showed higher SN than CECT (97.9% versus 85.1%) in detecting OC relapse at the patient level with a statistically insignificant difference when compared to the GS (P 0.77 and 0.21). These results support the findings of Sala et al who suggested that CECT and PET/CT may have comparable accuracy at detecting recurrent OC at the patient and regional levels ( 25 ). Similar to our research, multiple studies ( 26 – 30 ) evaluated recurrent OC by directly comparing PET/CT with CECT. They discovered that PET/CT had a higher SN than CECT at the patient level (74–100% vs. 53–76%, respectively) ( 26 , 27 , 28 , 31 , 32 ). In a recent meta-analysis, Gu et al ( 33 ) reported pooled accuracy, SN and SP of 96%, 91% and 88% respectively for PET/CT. And 88%, 79% and 84%, respectively for CECT with significant differences in SN and accuracy. However, in the current study the difference was only insignificant at the regional not the patient level. Additionally, Antunovic et al. proposed that PET-CT is of higher efficacy (80%) than both traditional imaging (62%) and CA-125 (64%) in identifying recurrences of epithelial OC ( 34 ). Furthermore, the results of PET-CT are independent of the tumor's histology. Sebastian et al., stated that PET-CT is significantly more accurate than CT in detecting OC recurrence, with lower inter-observer variability of results in case of PET-CT ( 35 ). Most cases of relapsed ovarian cancer are multifocal and approximately 75% of cases are located in the peritoneal cavity and retroperitoneal space ( 36 – 38 ). These findings are consistent with data from other literature that indicates the trans-coelomic spread is the most common method of OC dissemination ( 39 ). Kosinska et al found that multifocal relapse of OC was present in 77.61% of cases with localization of cancer in the peritoneum and/or the retroperitoneum in 84.13%. Distant organs and supra-diaphragmatic LNs mets was seem in only 15.87% of cases ( 40 ). These findings are in concordance with our study in which peritoneal metastases were seen in 59.3% and LN mets in (44%) of patients. However, Elsayed et al., found that the most frequent site of disease relapse was LNs, mainly the abdomino-pelvic nodes with a prevalence of 64%( 41 ). Furthermore, Dragosavac et al. observed that the LNs were the main site for recurrent disease ( 42 ). When it comes to identifying peritoneal implants with recurrent OC the SN and SP of 18FDG PET/CT are extremely high ( 43 – 46 ). Rubini et al. stated that 18FDG PET/CT has higher SN (85%) and SP (92.31%) than CT and MRI ( 47 ). Researchers in previous studies like our study directly compared CECT and PET/CT in the detection of OC recurrence at the regional level. They found that the accuracy and SN of PET/CT (92–96% and 75–97%, respectively) were greater to those of CECT (83–93% and 61–92%, respectively) ( 26 – 48 ). Like our study, exploratory surgery was not the gold standard. Coakley et al. showed 85–93% SN for peritoneal mets detection in OC through spiral CT with significantly lower SN for implants less than 1 cm ( 49 ). The current study revealed PET/CT has significantly higher SN than CECT in the detection of omento-peritoneal and LN mets, specifically the pelvic and abdominal LNs (100% and 96% versus 60% and 65%) with p-values of 0.0001 and 0.004 respectively, but PET-CT has not been found to be more effective than CECT in identifying LR or extra-abdominal mets, especially bone mets that may be due to the small patient’s number who proved to have extra pelvic and distant mets. The lower accuracy of CECT in the current study may be due to the smaller sample size. Sala et al. discovered, however, that while both CECT and PET/CT were successful in identifying lesions in the peritoneum and pelvic LNs, they were only moderately accurate in identifying pelvic LR, distant LNs invasion (above renal hila), distant liver and spleen mets ( 46 ). Furthermore, Sironi et al. ( 50 ) found that pelvic LR was less sensitive to PET/CT than peritoneal and LNs mets. But according to Rusu et al. ( 24 ) PET-CT is superior to traditional imaging for identifying distant and extra-abdominal mets, especially when there is involvement of the supra-diaphragmatic LNs. Additionally, Namet al. demonstrated that 3.8% of cases with additional synchronous tumors and 15.8% of instances of unanticipated extra-abdominal LNs expansion could be identified by PET-CT ( 51 ). In line with another study that assessed the clinical impact of FDG PET upon treatment strategy and found that accurate localization of OC recurrence impacts both patient outcome and treatment strategy ( 10 ). The current study demonstrated that PET-CT has a clinical impact on patient management, as the treatment strategy has been changed in 25.4% of patients based on the findings of FDG PET-CT compared to CECT. In contrast to the current study and earlier studies results Cho et al observed that, PET/CT showed a low degree of SN (58.2%.). Moreover, they failed to find any statistically significant differences in the diagnostic accuracy of CT, FDG-PET, or the combination of CT and FDG-PET modalities ( 52 ). Limitations: This analytical prospective study was carried out at a single center, which limit the selection criteria and may result in inherent selection bias. Our study focused on patients with MOC, which is uncommon histological type of ovarian cancer, resulting in a limited sample size. The gold standard (pathological confirmation) cannot be achieved for all lesions with enhanced contrast on CT and/or avid FDG uptake, as it is inappropriate and immoral. Conclusion Despite the common use of CECT and its comparable results with 18FDG-PET/CT in the evaluation of patients with suspected MOC recurrence, 18FDG-PET/CT achieved higher SN and diagnostic accuracy in detection of MOC recurrence, mainly the omento-peritoneal and nodal deposits. Which allows better guidance for proper therapy planning in these patients. Our results encourage the use of 18FDG PET/CT as the preferred imaging modality for MOC recurrence detection. Abbreviations PET–CT: Positron emotion tomography/computerized tomography 18 FDG: Florodeoxyglucose OC: Ovarian Cancer MOC Mucinous ovarian cancer CECT contrast enhanced computerized tomography MRI Magnetic resonance imaging US ultrasound Mets metastases LNs lymph nodes +ve : Positive -ve: Negative GS: Gold standard SPSS: Statistical Package for the Social Sciences; SN: Sensitivity SP: Specificity PPV: Positive predictive value NPV: Negative predictive value Declarations Funding: None Ethics declarations None Conflict of interest: The authors declare that they have no conflict of interest Ethical approval This research was authorized by the institutional review board, Faculty of Medicine, Zagzig University ( Study approved no. IRB#:11164-8-10-2023). Author contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by Omnia Mohamed Talaat and Shaimaa Farouk. Data analysis was done by Ismail Ali. 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CA 125, PET alone, PET-CT, CT and MRI in diagnosing recurrent ovarian carcinoma: a systematic review and meta-analysis . Eur J Radiol 2009 ; 71 ( 1 ): 164 – 174 Antunovic L, Cimitan M, Borsatti E, Baresic T, Sorio R, Giorda G, Steffan A, Balestreri L,Tatta R, Pepe G, et al. Revisiting the Clinical Value of 18F-FDG PET/CT in Detection of Recurrent Epithelial Ovarian Carcinomas. Clin. Nucl. Med. 2012, 37, e184–e188. [CrossRef] [PubMed]. Sebastian S, Lee S.I, Horowitz N.S, Scott J.A, Fischman A.J, Simeone J.F, Fuller A.F, Hahn, P.F. PET-CT vs. CT alone in ovarian cancer recurrence. Abdom. Imaging 2008, 33, 112–118. [CrossRef] [PubMed] Cengiz A, Koç ZP, Özcan Kara P, et al. The role of F-FDG PET/CT in detecting ovarian cancer recurrence in patients with elevated CA-125 levels. Mol Imaging Radionucl Ther. 2019; 28(1): 8–14, doi: 10.4274/mirt.galenos.2018.00710, indexed in Pubmed: 30942056. Gadducci A, Cosio S, Zola P, et al. Surveillance procedures for patients treated for epithelial ovarian cancer: a review of the literature. Int J Gynecol Cancer. 2007; 17(1): 21–31, doi: 10.1111/j.1525-1438.2007.00826.x, indexed in Pubmed: 17291227. Amate P, Huchon C, Dessapt AL, et al. Ovarian cancer: sites of recurrence. Int J Gynecol Cancer. 2013; 23(9): 1590–1596, doi: 10.1097/IGC. 0000000000000007, indexed in Pubmed: 24172095. Rizvi I, Gurkan UA, Tasoglu S, Alagic N, Celli JP, Mensah LB, Hasan T. (2013) Flow induces epithelial-mesenchymal transition, cellular heterogeneity and biomarker modulation in 3D ovarian cancer nodules. Proc Natl Acad Sci 110(22):E1974–E1983 Kosinska M, Misiewicz P, Kalita K, Fijuth J, Foks M, Kuncman L, Gottwald L. The value of [18F]FDG PET/CT examination in the detection and differentiation of recurrent ovarian cancer. Nucl Med Rev Cent East Eur. 2023;26(0):98-105. doi: 10.5603/NMR.2023.0013. PMID: 37525539. Elsayed, G.A., Abdullah, R.H., Elia, R.Z. et al. Role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography in the detection of recurrence and peritoneal metastasis from ovarian cancer in correlation with cancer antigen-125 tumor marker levels. Egypt J Radiol Nucl Med 55, 9 (2024). https://doi.org/10.1186/s43055-023-01153-3 Dragosavac S, Derchain S, Caserta NM, De Souza G (2013) Staging recurrent ovarian cancer with 18FDG PET/CT. Oncol Lett 5(2):593–597. Sala E, Kataoka M, Pandit-Taskar N, et al. Recurrent ovarian cancer: use of contrast-enhanced CT and PET/CT to accurately localize tumor recurrence and to predict patients’ survival. Radiology. 2010; 257(1): 125–134, doi:10.1148/radiol.10092279, indexed in Pubmed: 20697116. Gouhar G, Siam S, Sadek S, et al. Prospective assessment of 18F-FDG PET/CT in detection of recurrent ovarian cancer. Egypt J Radiol Nucl Med. 2013; 44(4): 913–922, doi: 10.1016/j.ejrnm.2013.08.005. Sanli Y, Turkmen C, Bakir B, et al. Diagnostic value of PET/CT is similar to that of conventional MRI and even better for detecting small peritoneal implants in patients with recurrent ovarian cancer. Nucl Med Commun. 2012; 33(5): 509–515, doi: 10.1097/MNM.0b013e32834fc5bf, indexed in Pubmed: 22357440. ElHariri M, Harira M, Riad M. Usefulness of PET–CT in the evaluation of suspected recurrent ovarian carcinoma. Egypt J Radiol Nucl Med. 2019; 50(1), doi: 10.1186/s43055-019-0002-2. Rubini G, Altini C, Notaristefano A, et al. Role of 18F-FDG PET/CT in diagnosing peritoneal carcinomatosis in the restaging of patient with ovarian cancer as compared to contrast enhanced CT and tumor marker Ca-125. Rev Esp Med Nucl Imagen Mol. 2014; 33(1): 22–27, doi: 10.1016/j.remn.2013.06.008, indexed in Pubmed: 23948509. Thrall MM , DeLoia JA , Gallion H , Avril N . Clinical use of combined positron emission tomography and computed tomography (FDGPET/CT) in recurrent ovarian cancer . Gynecol Oncol 2007 ; 105 ( 1 ): 17 – 22 Coakley, F.V.; Choi, P.H.; Gougoutas, C.A.; Pothuri, B.; Venkatraman, E.; Chi, D.; Bergman, A.; Hricak, H. Peritoneal metastases: Detection with spiral CT in patients with ovarian cancer. Radiology 2002, 223, 495–499. [CrossRef] [PubMed] Sironi S , Messa C , Mangili G , et al . Integrated FDG PET/CT in patients with persistent ovarian cancer: correlation with histologic findings. Radiology 2004 ; 233 ( 2 ): 433 – 440 Nam, E.J.; Yun, M.J.; Oh, Y.T.; Kim, J.W.; Kim, S.; Jung, Y.W.; Kim, S.W.; Kim, Y.T. Diagnosis and staging of primary ovarian cancer: Correlation between PET/CT, Doppler US, and CT or MRI. Gynecol. Oncol. 2010, 116, 389–394. [CrossRef] [PubMed] Cho SM, Ha HK, Byun JY, Lee JM, Kim CJ, Nam-Koong SE, Lee JM. Usefulness of FDG PET for assessment of early recurrent epithelial ovarian cancer. Am J Roentgenol 2002;179(2):391–395 Tables Table (1): The characteristics of the studied mucinous ovarian carcinoma patients Characteristics Total No = 59 No. % Mead &SD Age (years) 55.0±13.0 Primary therapy Surgery + Chemoth. 55 93.2% Chemotherapy only 4 6.8% CA125 level Within normal 23 39% Elevated 36 61% For all patients 58.6±36.7 With recurrence 68.3±34.6 Without recurrence 20.7±11.4 Max. Lesion size O. Bed 5.7±3.3 LNs 1.8±1.9 SUV max O. Bed 7.1±2.6 LNs 6.0±6.2 Numbers (percentages) were used to express categorical variables. The continuous variables were defined as mean ± SD and median (range). Table (2): Comparison between CECT and PET/CT findings among the studied mucinous ovarian carcinoma patients Findings CECT (N=59) PET/CT (N=59) p-value No. % No. % Operative bed recurrence Absent 41 69.5% 41 69.5% 1.000 a Present 18 30.5% 18 30.5% Nearby structures invasion Absent 54 91.5% 53 89.8% 1.000 a Present 5 8.5% 6 10.2% Sites of Nearby structures invasion Absent 54 91.5% 53 89.8% 0.317 b Uterus 2 3.4% 3 5.1% Rectum 1 1.7% 1 1.7% Bowel 2 3.4% 2 3.4% Omento-peritoneal mets Absent 37 62.7% 23 39% <0.001 a Present 22 37.3% 36 61% LNs mets Absent 41 69.5% 32 54.2% 0.004 a Present 18 30.5% 27 45.8% Pelvic LNs mets Absent 45 76.3% 38 64.4% 0.016 a Present 14 23.7% 21 35.6% Abdominal LNs mets Absent 48 81.4% 40 67.8% 0.008 a Present 11 18.6% 19 32.2% Distant LNs mets Absent 57 96.6% 56 94.9% 1.000 a Present 2 3.4% 3 5.1% Distant mets Absent 45 76.3% 44 74.6% 1.000 a Present 14 23.7% 15 25.4% Liver mets Absent 55 93.2% 53 89.8% 0.500 a Present 4 6.8% 6 10.2% Lung mets Absent 51 86.4% 51 86.4% 1.000 a Present 8 13.6% 8 13.6% Adrenal mets Absent 57 96.6% 56 94.9% 0.003 a Present 2 3.4% 3 5.1% Bone mets Absent 57 96.6% 57 96.6% <0.001 a Present 2 3.4% 2 3.4% Brain mets Absent 59 100% 58 98.3% 1.000 a Present 0 0% 1 1.7% Subcutaneous nodule Absent 58 98.3% 57 96.6% 1.000 a Present 1 1.7% 2 3.4% N: The overall patient number; The numerical representation of the qualitative data was expressed as numbers and percentages (%). The mean±SD and median (range) for continuous variables were reported. a: McNemar's test; b: Stuart Maxell test; c: Wilcoxon signed rank test; p-value<0.05 indicates significance. Table (3): Agreement between CT and PET/CT findings among the studied mucinous ovarian carcinoma patients (N=59). Findings Concordant +ve/+ve -ve/-ve Discordant +ve/-ve -ve/+ve K 95%CI p-value O. bed recurrence 59 (100%) 18 (30.5%) 41 (69.5%) 0 (0%) 0 (0%) 0 (0%) 1.000 <0.001 Nearby structures invasion 58 (98.3%) 5 (8.5) 53 (89.8) 1 (1.7%) 0 (0%) 1 (1.7%) 0.90 0.71 – 1.000 <0.001 Oment-peritoneal mets 45 (76.3%) 22 (37.3%) 23 (38.9%) 14 (23.7%) 0 (0%) 14 (23.7%) 0.55 0.37 – 0.74 <0.001 LNs mets 50 (84.7%) 18 (30.5%) 32 (54.2%) 9 (15.3%) 0 (0%) 9 (15.3%) 0.68 0.50 – 0.86 <0.001 Pelvic LNs 52 (88.1%) 14 (23.7%) 38 (64.4%) 7 (11.9%) 0 (0%) 7 (11.9%) 0.72 0.53 – 0.91 <0.001 Abd. LNs 51 (86.4%) 11 (18.6%) 40 (67.8%) 8 (13.6%) 0 (0%) 8 (13.6%) 0.65 0.44 – 0.86 <0.001 Distant LNs 58 (98.3%) 2 (3.4%) 56 (94.9%) 1 (1.7%) 0 (0%) 1 (1.7%) 0.79 0.40 – 1.00 <0.001 Distant mets 52 (88.1%) 11 (18.6%) 41 (69.5%) 7 (11.9%) 3 (5.1%) 4 (6.8%) 0.68 0.46 – 0.90 <0.001 Liver mets 57 (96.6%) 4 (6.8%) 53 (8.9%) 2 (3.4%) 0 (0%) 2 (3.4%) 0.78 0.49 – 0.98 <0.001 Lung mets 55 (93.2%) 6 (10.1%) 49 (83.1%) 4 (6.8%) 2 (3.4%) 2 (3.4%) 0.71 0.44 – 0.98 <0.001 Adrenal mets 56 (94.9%) 1 (1.7%) 55 (93.2%) 3 (5.1%) 1 (1.7%) 2 (3.4%) 0.38 0.00 – 0.93 0.003 Bone mets 59 (100%) 2 (3.4%) 57 (96.6%) 0 (0%) 0 (0%) 0 (0%) 1.00 <0.001 Brain mets 58 (98.3%) 0 (0%) 58 (98.3%) 1 (1.7%) 0 (0%) 1 (1.7%) 0.00 1.000 Subcutaneous Nodule 58 (98.3%) 1 (1.7%) 57 (96.6%) 1 (1.7%) 0 (0%) 1 (1.7%) 0.659 0.036 – 1.000 <0.001 -ve: absent finding; +ve: present finding; Numerator: CT finding; Denominator: PET/CT finding; K: Cohen's Kappa inter-rater agreement coefficient; 95%CI: 95% confidence interval; p-value< 0.05 is significant. Table (4): Diagnostic performance of CECT and PET/CT in relation to the golden standard in diagnosis of mucinous ovarian carcinoma recurrence Findings TP No.(%) FP No.(%) TN No.(%) FN No.(%) SN% (95%CI) SP% (95%CI) PPV% (95%CI) NPV% (95%CI) Acc% (95%CI) p-value CECT 40 (67.8%) 5 (8.4%) 7 (11.9%) 7 (11.9%) 85.1% (71.6-93.8) 58.3% (27.7-84.8) 88.9% (80.2-94) 50% (30.3-69.7) 79.7% (67.2-89) 0.774 PET/CT 46 77.9% 5 8.4% 7 11.9% 1 1.7% 97.9% 58.3% 90.2% 87.5% 89.8% 0.219 CECT LNs mtes 17 28.8% 1 1.7% 32 54.2% 9 15.3% 65.4% 97% 94.4% 78% 83.1% 0.07 PET/CT LNs mets 25 42.4% 2 3.4% 31 52.5% 1 1.7% 96.2% 94% 92.6% 96.9% 95% 1.00 CECT Pertionium mets 21 35.6% 1 1.7% 23 39% 14 23.7% 60% 96% 95.5% 62.2% 74.6% 0.001 PET/CT Peritonium mets 35 59.3% 1 1.7% 23 39% 0 0.0% 100% 96% 97.2% 100% 98.3% 1.00 Qualitative data were expressed as a number (percentage); TP: True positive; TN: True negative; FP: False positive; FN: False negative; SN: Sensitivity; SP: Specificity; PPV: Positive Predictive Value; NPV: Negative Predictive Value; Acc: Accuracy; %CI: 95% Confidence Interval ; p-value< 0.05 is significant. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3961163","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273321452,"identity":"30717cc6-efd8-473f-b69e-7031ee2c5db8","order_by":0,"name":"Ismail Ali","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACNhCRUMAgw8bAfPABA8MBYrUYMPCwMbAlGxClBQKAWhgYeMwkiNLCx3868cMDAzsePgYGs2qemjty/AzMDx/dwOcwidzNEgkGyUCHMaTd5jn2zFiygc3YOAevFt4NQC3MIC3HbvOwHU7ccICHTRqvFv6zm38kGNQDtTC2FfP8I0YLQ+42oC2HgVqY2Zh524jRIpG7zSLB4DgokJkl5/YdNpZsJuAX+f6zm2/+qKiWk2/g//jhzbfDcvzszQ8f49OCpPkBAxMPiMFMlHIoYPxBiupRMApGwSgYMQAAQixBw+O3UZsAAAAASUVORK5CYII=","orcid":"","institution":"Zagazig university faculty of medicine","correspondingAuthor":true,"prefix":"","firstName":"Ismail","middleName":"","lastName":"Ali","suffix":""},{"id":273321453,"identity":"114778d9-1f6d-4cd8-9dc6-80cc360a685a","order_by":1,"name":"Ibrahim Nasr","email":"","orcid":"","institution":"Zagaaig university faculty of medicine","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Nasr","suffix":""},{"id":273321454,"identity":"a7ac236d-19d4-409b-9ba7-224712762b61","order_by":2,"name":"shaimaa farouk","email":"","orcid":"","institution":"Zagazig university faculty of medicine","correspondingAuthor":false,"prefix":"","firstName":"shaimaa","middleName":"","lastName":"farouk","suffix":""},{"id":273321455,"identity":"2241326e-d53c-4dba-9201-ac3e49e8312e","order_by":3,"name":"Mai Elahmadawy","email":"","orcid":"","institution":"National Cancer Institute Cairo university","correspondingAuthor":false,"prefix":"","firstName":"Mai","middleName":"","lastName":"Elahmadawy","suffix":""},{"id":273321456,"identity":"cecd8b41-f553-4420-916d-b7248925764f","order_by":4,"name":"Omnia Talaat","email":"","orcid":"","institution":"National Cancer Institute Cairo university","correspondingAuthor":false,"prefix":"","firstName":"Omnia","middleName":"","lastName":"Talaat","suffix":""}],"badges":[],"createdAt":"2024-02-16 12:09:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3961163/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3961163/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51393749,"identity":"4b6f82dd-9051-4b88-8388-f81d8859056c","added_by":"auto","created_at":"2024-02-20 18:59:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100428,"visible":true,"origin":"","legend":"\u003cp\u003eA 64-year-old Female patient who has a cancer ovary, received CTH and referred for follow up. CECT (A, D, and G) images displayed a loculated right paracolic collection measuring 7.8x15.5 cm, small sub-centimetric right inguinal LN, and a right pelvic cystic lesion with a solid component measuring 4.2x4.3 cm. PET and PET/CT scans showed diffuse FDG uptake at the loculated right paracolic collection (SUVmax 8), the right ovarian mixed cystic, and a solid lesion (SUVmax 13). Also, FDG-avid omento-peritoneal infiltrative thickening, multiple nodularity, and serosal implants (SUVmax~11.2) were seen in addition to active FDG uptake at the small right inguinal LN (SUVmax 5.5).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3961163/v1/d966687ca95037308210f3ce.jpg"},{"id":51394176,"identity":"2918c184-19aa-415f-a5a7-36c4a474c699","added_by":"auto","created_at":"2024-02-20 19:07:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":119720,"visible":true,"origin":"","legend":"\u003cp\u003eA 58-year-old woman who had ovarian cancer was treated by pan-hysterectomy and chemotherapy. CECT images (A, D, G, and J) showed small calcified sub-carinal LN (8 mm), diffuse minimal abdomino-pelvic thickening, more pronounced at the left hypochondrial area, diffuse and loculated abdominal ascites, and sub-centimetric right external iliac LN. PET and the fused PET/CT image revealed avid FDG uptake at the calcified subcarinal LN with SUVmax 7.7 (C image). F and I images showed active diffuse omento-peritoneal thickening and nodularity with serosal implants, more prominent at the Lt. hypochondrium and left lateral region with SUVmax 12 and 10.2. L images revealed sub-centimeteric FDG avid right external iliac LN with SUVmax~5.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3961163/v1/d74a50ba70c7c5e7418fa72d.jpg"},{"id":52571071,"identity":"5e295bce-1e46-442c-a82c-08dd7eb01166","added_by":"auto","created_at":"2024-03-13 05:52:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1075544,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3961163/v1/9c8e31eb-3901-4c27-92f0-74f00f4cabfd.pdf"}],"financialInterests":"","formattedTitle":"18FDG-PET/CT versus Contrast Enhanced CT in detection of mucinous ovarian cancer recurrence: comparative study","fulltext":[{"header":"Background","content":"\u003cp\u003eThere are about 22,000 newly diagnosed cases of OC every year and it is the most common reason of cancer-related deaths amongst women [1]. Mucinous ovarian carcinoma is a unique and uncommon kind of OC [2,3]. It was previously believed that MOC accounted for a higher percentage of the diagnosed OC (\u0026ge;\u0026thinsp;10%) [4]. Presently MOC is considered as a rare form of OC as true primary MOC accounts for roughly 5% of OC cases [2,5]. Even with a good initial response, around 80% of patients eventually relapse and need further treatment [6].Clinical examination, assessment of the serum tumor marker (CA-125), and morphological imaging methods such computed tomography (CT), magnetic resonance imaging (MRI), and ultrasonography (US) are typically included in follow-up programs. These techniques do have certain drawbacks. The limits of the use of CA-125 are known as increased CA-125 levels cannot be used to distinguish between localized and diffuse tumor recurrence, nor normal CA-125 values can be used to rule out the existence of disease [7]. Furthermore, the diagnosis of tumor recurrence may be difficult to achieve with traditional imaging methods dependent on anatomical variations, such as the discovery of a new aberrant lesion or change in the size of an existing lesion. Furthermore, CT and MRI imaging cannot identify mets of normal-sized LNs and they are not very useful in accurately distinguishing a recurrence from a post-surgical change; neither immediately following treatment nor later on [8]. A solution to these issues has been suggested: FDG with PET. It has been shown to be extremely sensitive in identifying OC recurrence, particularly in individuals exhibiting an inexplicable rise in the level of tumor marker. It provides the advantages of both functional and anatomical imaging, and it has been applied to both the exclusion of illness in locations with residual structural abnormality and the localization of areas with elevated FDG with greater anatomical specificity [9]. Precise localization of OC recurrence affect both patient\u0026rsquo;s prognosis and therapy approach, according to Fulham et al, who evaluated the clinical effect of FDG PET on therapy plans[10].\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAim of the study\u003c/strong\u003e \u003cp\u003eTo assess the added value of \u003csup\u003e18\u003c/sup\u003eFDG-PET/CT in the detection of MOC recurrence and its effect on patient management compared to CECT.\u003c/p\u003e \u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eAll \u003csup\u003e18\u003c/sup\u003eFDG PET/CT and CECT exams were done at the National Cancer Institute's Nuclear Medicine Unit at Cairo University. Our study was performed after receiving the institutional review board acceptance (protocol number: IRB#:11164-8-10-2023). Every patient gave his signed consent to share in this study after being informed.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePatient population\u003c/strong\u003e \u003cp\u003ePatients with suspected recurrences of MOC guided by the clinical, laboratory and/or radiological data fulfill the inclusion criteria. Patients with ovarian cancer other than the mucinous type, concomitant cancer, uncontrolled diabetes, severe infections, and those lacking definitive pathology data, were excluded from the study. Also, patients with a suspected short life span of less than 6 months were also excluded from our study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePatient preparation\u003c/strong\u003e \u003cp\u003ePatients were instructed to avoid strenuous activity for few days before the exam to lessen \u003csup\u003e18\u003c/sup\u003eFDG uptake by skeletal muscles and follow a low-carb diet and fasting for 24 hours, and 4\u0026ndash;6 hours before \u003csup\u003e18\u003c/sup\u003eFDG injection respectively. The peripheral blood glucose level should be verified to be less than 150 mg/dL. Oral diabetic drugs could be used as advised except prescriptions containing metformin, which should be stopped 48 hours before the study to lower the intestinal background activity produced by such medications. The day before the study, diabetic patients with type 1 diabetes mellitus should fast after midnight (except from drinking water) and scheduled in the morning before taking insulin and their acceptable blood glucose level was to be maintained at less than 180 mg/d. If there was hypoglycemia with symptoms or if the glucose level was more than 200 mg/dL, the exam should be rescheduled. (Serum creatinine level was also done for all patients before IV contrast injection and should not exceed the level of 1.7 mg/dl).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImaging Technique\u003c/strong\u003e \u003cp\u003eAfter I.V. injection of \u003csup\u003e18\u003c/sup\u003eF\u0026ndash;FDG by a dose of 240\u0026ndash;380 MBq, all patients were instructed to spend 45\u0026ndash;60 minutes in a dimly lit room with a warm atmosphere. Also, patients were instructed to move as little as possible and rest quietly; no speaking, chewing, or reading was allowed. The patients were asked to urinate before being put on the PET/CT scanner. Scanning began with a non-enhanced, low-dose CT scan extending from the skull base down to the upper thighs, with a field of view of 50cm, 120 kV and 60 mAs, 0.9 pitch, and a 5 mm slice thickness. CT data were used for attenuation correction and anatomical localization. A three-dimensional whole-body PET scan was started immediately after the CT at the same acquisition range with 6\u0026ndash;7 bed positions (2 minutes/position) using an integrated PET/CT system (Philips Medical Systems, equipped with a 16-slice CT)). A standard iterative reconstruction approach was utilized to reconstruct PET images that had been corrected for attenuation. Diagnostic CECT scan was carried out in the same session covering the same field of view. Iodinated contrast material was injected in a dose of 1.5\u0026ndash;2 ml/kg by an automated injector at a 4 ml/s flow rate through a patent venous line inserted in the ante-cubital vein. The acquisition parameters were 5.0 mm collimator width, 120 kV, 120 mAs, 0.9 second gantry rotation time, and 5 mm slice thickness. Coronal and sagittal reconstructions were produced using the obtained raw data. Fusion images were generated for every set of PET and CT data. The CECT data set was automatically fused with the 3D PET images to generate contrast-enhanced anatomical images superimposed with FDG uptake using the integrated software interface supplied by the manufacturer company.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImage interpretation\u003c/strong\u003e \u003cp\u003eA team of doctors with over 15 years of experience in nuclear medicine and radiology that were blind to the final pathology data and each other's assessments performed both visual and semi-quantitative analysis of the acquired PET/CT and CECT images for every patient. Using a region of interest drawn in the area of enhanced uptake, the maximum standardized uptake values (SUVmax) for each pathological lesion have been determined for semi-quantitative assessment. Malignant lesions were identified on \u003csup\u003e18\u003c/sup\u003eFDG PET/CT imaging as lesions with an SUVmax of at least 2.5 at the location of pathologic alterations [11].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Analysis\u003c/strong\u003e \u003cp\u003eThe SUVmax values were recorded and located with focally increased FDG uptake were observed in order to conduct a qualitative and semi-quantitative analysis. The lesions was deemed abnormal if it showed greater FDG absorption on the attenuation-corrected pictures than the activity of the hepatic blood pool. In order to exclude the potential of physiological FDG uptake by specific organs such as adipose tissues, salivary glands and muscles. Areas of focused FDG uptake were compared with corresponding CT images using CT data. Increased focal FDG uptake (local or distant) was found and documented. The imaging data were compared to the outcomes of the histology and/or to clinical, radiological and laboratory follow-up data. True positives (TP) were lesions that demonstrated a decrease in CA-125 levels during ovarian cancer therapy (chemotherapy or radiation therapy) or that were validated by subsequent imaging methods like PET/CT. If the PET/CT scans were normal and no recurrence was seen during serial imaging and clinical follow-up, a true negative (TN) result was obtained. If further imaging modalities or clinical follow-up data demonstrated recurrence but the PET/CT scans were normal, the results were considered false-negative (FN). Positive PET/CT results that turned out to be benign or that were linked to a subsequent cancer were referred to as false-positive (FP) results.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e: Both the continuous and categorical variables were expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median (range) and number (%). To confirm that continuous variables were normal, the Shapiro-Wilk test was employed. The Wilcoxon signed rank test was used to compare the non-normally distributed data in two dependent groups. McNemar's test was used to compare the matched data. The Stuart-Maxwell test, a version of the McNemar test, was used to determine the marginal homogeneity of a square table with more than two rows and columns. The validity of CT and PET/CT was determined by comparing the diagnostic performance of sample 2x2 contingency tables created with the golden standard test as a reference test for mucinous ovarian cancer recurrence. The associated 95% confidence intervals for the accuracies, PPV, NPV, SP and SN were computed. The inter-rater agreement (Cohen's Kappa) test was used to calculate the requirements for qualifying for the strength of agreement, and the results were as follows: (K\u0026thinsp;\u0026lt;\u0026thinsp;0.2 denotes poor quality), (K 0.21\u0026ndash;0.40 fair), (K 0.41\u0026ndash;0.60 moderate), (K 0.61\u0026ndash;0.80 good), and (K 0.81\u0026ndash;1.00 extremely good). P-value of less than 0.05 was deemed statistically significant for all two-sided tests. MedCalc 13 for Windows (MedCalc Software bvba, Ostend, Belgium) and SPSS 22.0 for Windows (SPSS Inc., Chicago, IL, USA) were used to analyze all of the data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 59 patients with MOC and a mean age of 55.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0 years were enrolled in our study. Fifty-five (93.2%) underwent both surgery and chemotherapy, while four patients received chemotherapy alone. Forty-seven (79.6%) patients had recurrences, out of them 18 patients had local recurrences and 29 had distant recurrences. The mean CA-125 blood level as a tumor marker was 58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;36.7, which was high in 36 (61%) patients and normal in 23 (39%). The mean value of CA-125 was significantly higher in patients with MOC recurrence than those without (68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;34.6 versus 20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4, respectively, p 0.001). On PET/CT and CECT, the mean maximal lesion size for the operative bed and lymph node recurrence was 5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 and 1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 cm, while the mean SUVmax was 7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 and 6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2, respectively \u003cb\u003e(Table\u0026nbsp;1)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;(1): The characteristics of the studied mucinous ovarian carcinoma patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe rate of surgical bed recurrence was found to be similar for both PET/CT and CECT (18 patients each) (p-value 1.00). Out of them, 6 patients on PET/CT and 5 on CECT showed invasion of the nearby structures (p-value 1.00). PET/CT showed a significantly higher rate of distant mets detection compared to CECT at the omento-peritoneal and LNs [36 (61%) and 27 (45.8%) versus 22 (37.3%) and 18 (30.5%), with p-values of 0.0001 and 0.004, respectively]. The rates of distant mets diagnosis at the liver, lung, adrenals, bone, brain and subcutaneous tissue were comparable between both modalities, with an insignificant statistical difference (p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05) \u003cb\u003e(Table\u0026nbsp;2).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;(2): Comparison between CECT and PET/CT findings among the studied mucinous ovarian carcinoma patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePET/CT and CECT were highly concordant in the detection of both operative bed recurrence and nearby structure invasion (K 1.00 and 0.90, respectively, with p 0.001). There is only one (1.7%) discordant negative case on CECT ,but positive on PET/CT (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The detection of distant mets at the lung, bones, subcutaneous tissue and LNs showed strong agreement between both modalities (K 0.69\u0026ndash;1.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the omento-peritoneum and adrenals showed weak agreement (K.56 and 0.38, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and 0.003, respectively). For more details, see \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;(3): Agreement between CT and PET/CT findings among the studied mucinous ovarian carcinoma patients (N\u0026thinsp;=\u0026thinsp;59).\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePET/CT had a lower FN rate than CECT (1.7% vs. 11.9%) and demonstrated greater SN, PPV, NPV, and accuracy, but the same SP in recurrence detection (97.9%, 90.2%, 87.5%, 89.8% and 58.3%, vs. 85.1%, 88.9%, 50%, 79.7% and 58.3% respectively). On comparing the diagnostic parameters with the gold standard PET/CT showed a lower P value than CECT (0.22 versus 0.77) (\u003cb\u003etable 4\u003c/b\u003e). Regarding the LNs mets detection, PET/CT displayed higher SN (96.2%), NPV (96.9%) and accuracy (95%) compared to 65.4%, 78% and 83.1% for CECT respectively, while CECT has a higher SP (97%) versus 94% for PET/CT. CECT showed a high false negative rate (23.7%) in the diagnosis of peritoneal deposits but PET/CT did not \u003cb\u003e(table 4)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;(4): Diagnostic performance of CECT and PET/CT in relation to the golden standard in diagnosis of mucinous ovarian carcinoma recurrence\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePET/CT upgraded patient management in 25.4% of patients, from no therapy to local and systemic therapy in one and seven patients respectively, and from local to systemic therapy in another seven patients (p 0.001) \u003cb\u003e(Table\u0026nbsp;5)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;(5): Comparison between therapy plan decisions based on CECT and PET/CT findings\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure\u0026nbsp;(1)\u003c/strong\u003e \u003cp\u003eA 64-year-old Female patient who has a ovarian cancer, received CTH and referred for follow up. CECT (A, D, and G) images displayed a loculated right paracolic collection measuring 7.8x15.5 cm, small sub-centimetric right inguinal LN, and a right pelvic cystic lesion with a solid component measuring 4.2x4.3 cm. PET and PET/CT scans showed diffuse FDG uptake at the loculated right paracolic collection (SUVmax 8), the right ovarian mixed cystic and a solid lesion (SUVmax 13). Also, FDG-avid omento-peritoneal infiltrative thickening, multiple nodularity and serosal implants (SUVmax\u0026thinsp;~\u0026thinsp;11.2) were seen in addition to active FDG uptake at the small right inguinal LN (SUVmax 5.5).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure\u0026nbsp;(2)\u003c/strong\u003e \u003cp\u003eA 58-year-old woman who had ovarian cancer was treated by pan-hysterectomy and chemotherapy. CECT images (A, D, G and J) showed small calcified sub-carinal LN (8 mm), diffuse minimal abdomino-pelvic thickening, more pronounced at the left hypochondrial area, diffuse and loculated abdominal ascites and sub-centimetric right external iliac LN. PET and the fused PET/CT image revealed avid FDG uptake at the calcified subcarinal LN with SUVmax 7.7 (C image). F and I images showed active diffuse omento-peritoneal thickening and nodularity with serosal implants, more prominent at Lt. hypochondrium and left lateral region with SUVmax 12 and 10.2. L images revealed sub-centimeteric FDG avid right external iliac LN with SUVmax\u0026thinsp;~\u0026thinsp;5.\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite effective treatment and complete response in patients with OC, the recurrence rate is high (50\u0026ndash;80%). Early diagnosis of recurrence in these patients is important as it has a close relation with prognosis and the choice of appropriate treatment. (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Imaging techniques like CT and MRI can be used to detect OC recurrences. However, since OC mets primarily affect the omento-peritoneal region rather than parenchymal organs, the detection of small implanted mets on the visceral surface is challenging (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Despite the limited value of F18-FDG PET-CT in evaluating the primary tumor, it has a particular value for identifying the LNs and distant mets, particularly when it comes to extra-abdominal spread.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The value of 18FDG PET/CT is in detection of OC recurrence, as it is superior to both conventional imaging and the CA-125 assay. It has better SN and SP for both high and low-grade carcinomas (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to the higher efficiency of 18FDG PET/CT than CT and MRI in identifying recurrent OC, it can also identify recurrences of OC approximately six months before CT (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e).Consequently, 18FDG PET/CT can be used effectively for surveillance of treated OC patients, particularly when conventional imaging methods had negative results but there is an increase in the CA-125 level or the clinical examination may indicate recurrence or progression (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, 18FDG PET/CT showed higher SN than CECT (97.9% versus 85.1%) in detecting OC relapse at the patient level with a statistically insignificant difference when compared to the GS (P 0.77 and 0.21). These results support the findings of Sala et al who suggested that CECT and PET/CT may have comparable accuracy at detecting recurrent OC at the patient and regional levels (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Similar to our research, multiple studies (\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e30\u003c/span\u003e) evaluated recurrent OC by directly comparing PET/CT with CECT. They discovered that PET/CT had a higher SN than CECT at the patient level (74\u0026ndash;100% vs. 53\u0026ndash;76%, respectively) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In a recent meta-analysis, Gu et al (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e) reported pooled accuracy, SN and SP of 96%, 91% and 88% respectively for PET/CT. And 88%, 79% and 84%, respectively for CECT with significant differences in SN and accuracy. However, in the current study the difference was only insignificant at the regional not the patient level. Additionally, Antunovic et al. proposed that PET-CT is of higher efficacy (80%) than both traditional imaging (62%) and CA-125 (64%) in identifying recurrences of epithelial OC (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Furthermore, the results of PET-CT are independent of the tumor's histology. Sebastian et al., stated that PET-CT is significantly more accurate than CT in detecting OC recurrence, with lower inter-observer variability of results in case of PET-CT (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost cases of relapsed ovarian cancer are multifocal and approximately 75% of cases are located in the peritoneal cavity and retroperitoneal space (\u003cspan additionalcitationids=\"CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e38\u003c/span\u003e). These findings are consistent with data from other literature that indicates the trans-coelomic spread is the most common method of OC dissemination (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Kosinska et al found that multifocal relapse of OC was present in 77.61% of cases with localization of cancer in the peritoneum and/or the retroperitoneum in 84.13%. Distant organs and supra-diaphragmatic LNs mets was seem in only 15.87% of cases (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e40\u003c/span\u003e). These findings are in concordance with our study in which peritoneal metastases were seen in 59.3% and LN mets in (44%) of patients. However, Elsayed et al., found that the most frequent site of disease relapse was LNs, mainly the abdomino-pelvic nodes with a prevalence of 64%(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Furthermore, Dragosavac et al. observed that the LNs were the main site for recurrent disease (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e42\u003c/span\u003e). When it comes to identifying peritoneal implants with recurrent OC the SN and SP of 18FDG PET/CT are extremely high (\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Rubini et al. stated that 18FDG PET/CT has higher SN (85%) and SP (92.31%) than CT and MRI (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearchers in previous studies like our study directly compared CECT and PET/CT in the detection of OC recurrence at the regional level. They found that the accuracy and SN of PET/CT (92\u0026ndash;96% and 75\u0026ndash;97%, respectively) were greater to those of CECT (83\u0026ndash;93% and 61\u0026ndash;92%, respectively) (\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47\" citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Like our study, exploratory surgery was not the gold standard.\u003c/p\u003e \u003cp\u003eCoakley et al. showed 85\u0026ndash;93% SN for peritoneal mets detection in OC through spiral CT with significantly lower SN for implants less than 1 cm (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The current study revealed PET/CT has significantly higher SN than CECT in the detection of omento-peritoneal and LN mets, specifically the pelvic and abdominal LNs (100% and 96% versus 60% and 65%) with p-values of 0.0001 and 0.004 respectively, but PET-CT has not been found to be more effective than CECT in identifying LR or extra-abdominal mets, especially bone mets that may be due to the small patient\u0026rsquo;s number who proved to have extra pelvic and distant mets. The lower accuracy of CECT in the current study may be due to the smaller sample size. Sala et al. discovered, however, that while both CECT and PET/CT were successful in identifying lesions in the peritoneum and pelvic LNs, they were only moderately accurate in identifying pelvic LR, distant LNs invasion (above renal hila), distant liver and spleen mets (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Furthermore, Sironi et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e50\u003c/span\u003e) found that pelvic LR was less sensitive to PET/CT than peritoneal and LNs mets. But according to Rusu et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e) PET-CT is superior to traditional imaging for identifying distant and extra-abdominal mets, especially when there is involvement of the supra-diaphragmatic LNs. Additionally, Namet al. demonstrated that 3.8% of cases with additional synchronous tumors and 15.8% of instances of unanticipated extra-abdominal LNs expansion could be identified by PET-CT (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn line with another study that assessed the clinical impact of FDG PET upon treatment strategy and found that accurate localization of OC recurrence impacts both patient outcome and treatment strategy (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The current study demonstrated that PET-CT has a clinical impact on patient management, as the treatment strategy has been changed in 25.4% of patients based on the findings of FDG PET-CT compared to CECT.\u003c/p\u003e \u003cp\u003eIn contrast to the current study and earlier studies results Cho et al observed that, PET/CT showed a low degree of SN (58.2%.). Moreover, they failed to find any statistically significant differences in the diagnostic accuracy of CT, FDG-PET, or the combination of CT and FDG-PET modalities (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLimitations: This analytical prospective study was carried out at a single center, which limit the selection criteria and may result in inherent selection bias. Our study focused on patients with MOC, which is uncommon histological type of ovarian cancer, resulting in a limited sample size. The gold standard (pathological confirmation) cannot be achieved for all lesions with enhanced contrast on CT and/or avid FDG uptake, as it is inappropriate and immoral.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDespite the common use of CECT and its comparable results with 18FDG-PET/CT in the evaluation of patients with suspected MOC recurrence, 18FDG-PET/CT achieved higher SN and diagnostic accuracy in detection of MOC recurrence, mainly the omento-peritoneal and nodal deposits. Which allows better guidance for proper therapy planning in these patients. Our results encourage the use of 18FDG PET/CT as the preferred imaging modality for MOC recurrence detection.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePET\u0026ndash;CT: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Positron emotion tomography/computerized tomography\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e18\u003c/sup\u003eFDG: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Florodeoxyglucose\u003c/p\u003e\n\u003cp\u003eOC: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Ovarian Cancer\u003c/p\u003e\n\u003cp\u003eMOC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mucinous ovarian cancer\u003c/p\u003e\n\u003cp\u003eCECT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; contrast enhanced computerized tomography\u003c/p\u003e\n\u003cp\u003eMRI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eUS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ultrasound\u003c/p\u003e\n\u003cp\u003eMets \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; metastases\u003c/p\u003e\n\u003cp\u003eLNs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;lymph nodes\u003c/p\u003e\n\u003cp\u003e+ve : \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Positive\u003c/p\u003e\n\u003cp\u003e-ve: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Negative\u003c/p\u003e\n\u003cp\u003eGS: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Gold standard\u003c/p\u003e\n\u003cp\u003eSPSS: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Statistical Package for the Social Sciences;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSN: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Sensitivity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSP: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Specificity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPV: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Positive predictive value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNPV: \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Negative predictive value\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics declarations\u003c/strong\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflict of interest:\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no conflict of interest\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eThis research was authorized by the institutional review board, Faculty of Medicine, Zagzig University \u003cb\u003e(\u003c/b\u003eStudy approved no. IRB#:11164-8-10-2023).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection were performed by \u003cb\u003eOmnia Mohamed Talaat\u003c/b\u003e and \u003cb\u003eShaimaa Farouk.\u003c/b\u003e Data analysis was done by \u003cb\u003eIsmail Ali.\u003c/b\u003e The first draft of the manuscript was written by \u003cb\u003eIbrahim Nasr\u003c/b\u003e and \u003cb\u003eMai Amr.\u003c/b\u003e Review and editing of the final manuscript were approved by all authors\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eWe thank \u003cb\u003eDr. Mohammed Fathy\u003c/b\u003e for his efforts in statistical analysis and editing this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin. 2017; 67:7\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eCheasley, D.;Wakefield, M.J.; Ryland, G.L.; Allan, P.E.; Alsop, K.; Amarasinghe, K.C.; Ananda, S.; Anglesio, M.S.; Au-Yeung, G.; B\u0026ouml;hm, M.; et al. The molecular origin and taxonomy of mucinous ovarian carcinoma. Nat. Commun. 2019; 10, 1\u0026ndash;11. [CrossRef]\u003c/li\u003e\n\u003cli\u003ePeres, L.C.; Cushing-Haugen, K.L.; K\u0026ouml;bel, M.; Harris, H.R.; Berchuck, A.; Rossing, M.A.; Schildkraut, J.M.; Doherty, J.A. Invasive Epithelial Ovarian Cancer Survival by Histotype and Disease Stage. J. Natl. Cancer Inst. 2018; 111, 60\u0026ndash;68. [CrossRef]\u003c/li\u003e\n\u003cli\u003eMorice, P.; Gouy, S.; Leary, A. Mucinous Ovarian Carcinoma. N. Engl. J. Med. 2019; 380, 1256\u0026ndash;1266. [CrossRef] [PubMed]\u003c/li\u003e\n\u003cli\u003ePerren, T.J. Mucinous epithelial ovarian carcinoma. Ann. Oncol. 2016, 27 (Suppl. S1), i53\u0026ndash;i57. [CrossRef] [PubMed] A. Bilici, B.B. Ustaalioglu, M. 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A Comparative Study between 18F-FDG PET/CT and Conventional Imaging in the Evaluation of Progressive Disease and Recurrence in Ovarian Carcinoma: 2021 Jun 3;9(6):666.doi: 10.3390/healthcare9060666.\u003c/li\u003e\n\u003cli\u003eSala E, Kataoka M, Pandit-Taskar N, Ishill N, Mironov S, Moskowitz CS, Mironov O, Collins MA, Chi DS, Larson S, Hricak H. Recurrent ovarian cancer: use of contrast-enhanced CT and PET/CT to accurately localize tumor recurrence and to predict patients\u0026apos; survival. Radiology. 2010 Oct;257(1):125-34. doi: 10.1148/radiol.10092279. \u003c/li\u003e\n\u003cli\u003eKitajima K , Murakami K , Yamasaki E , et al . Performance of integrated FDG-PET/contrast enhanced CT in the diagnosis of recurrent ovarian cancer: comparison with integrated FDG-PET/non-contrast-enhanced CT and enhanced CT . Eur J Nucl Med Mol Imaging 2008 ; 35 ( 8 ): 1439 \u0026ndash; 1448 .\u003c/li\u003e\n\u003cli\u003eSoussan M , Wartski M , Cherel P , et al . 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Diagnosis and staging of primary ovarian cancer: Correlation between PET/CT, Doppler US, and CT or MRI. Gynecol. Oncol. 2010, 116, 389\u0026ndash;394. [CrossRef] [PubMed]\u003c/li\u003e\n\u003cli\u003eCho SM, Ha HK, Byun JY, Lee JM, Kim CJ, Nam-Koong SE, Lee JM. Usefulness of FDG PET for assessment of early recurrent epithelial ovarian cancer. Am J Roentgenol 2002;179(2):391\u0026ndash;395\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable (1):\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe characteristics of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;the studied mucinous ovarian carcinoma patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"509\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.772102161100197%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.2278978388998%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e\u0026nbsp;Total No = 59\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.103092783505154%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.195876288659793%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMead \u0026amp;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.772102161100197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.791748526522596%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.430255402750491%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.324165029469548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.68172888015717%\" valign=\"top\"\u003e\n \u003cp\u003e55.0\u0026plusmn;13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.772102161100197%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.791748526522596%\" valign=\"top\"\u003e\n \u003cp\u003eSurgery + Chemoth.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.430255402750491%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.324165029469548%\" valign=\"top\"\u003e\n \u003cp\u003e93.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.68172888015717%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eChemotherapy only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.103092783505154%\" valign=\"top\"\u003e\n \u003cp\u003e6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.195876288659793%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.772102161100197%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eCA125 level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.791748526522596%\" valign=\"top\"\u003e\n \u003cp\u003eWithin normal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.430255402750491%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.324165029469548%\" valign=\"top\"\u003e\n \u003cp\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.68172888015717%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.103092783505154%\" valign=\"top\"\u003e\n \u003cp\u003e61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.195876288659793%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eFor all patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.103092783505154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.195876288659793%\" valign=\"top\"\u003e\n \u003cp\u003e58.6\u0026plusmn;36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eWith recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.103092783505154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.195876288659793%\" valign=\"top\"\u003e\n \u003cp\u003e68.3\u0026plusmn;34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.329896907216494%\" valign=\"top\"\u003e\n \u003cp\u003eWithout recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.103092783505154%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.195876288659793%\" valign=\"top\"\u003e\n \u003cp\u003e20.7\u0026plusmn;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.772102161100197%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMax. Lesion size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.791748526522596%\" valign=\"top\"\u003e\n \u003cp\u003eO. Bed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.430255402750491%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.324165029469548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.68172888015717%\" valign=\"top\"\u003e\n \u003cp\u003e5.7\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.772102161100197%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.791748526522596%\" valign=\"top\"\u003e\n \u003cp\u003eLNs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.430255402750491%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.324165029469548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.68172888015717%\" valign=\"top\"\u003e\n \u003cp\u003e1.8\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.772102161100197%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV max\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.791748526522596%\" valign=\"top\"\u003e\n \u003cp\u003eO. Bed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.430255402750491%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.324165029469548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.68172888015717%\" valign=\"top\"\u003e\n \u003cp\u003e7.1\u0026plusmn;2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.772102161100197%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.791748526522596%\" valign=\"top\"\u003e\n \u003cp\u003eLNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.430255402750491%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.324165029469548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.68172888015717%\" valign=\"top\"\u003e\n \u003cp\u003e6.0\u0026plusmn;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNumbers (percentages) were used to express categorical variables. The continuous variables were defined as mean \u0026plusmn; SD and median (range).\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (2):\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComparison between CECT and PET/CT findings among the studied mucinous ovarian carcinoma patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.904761904761905%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCECT (N=59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.80952380952381%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePET/CT (N=59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperative bed recurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e69.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e69.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e30.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e30.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNearby structures invasion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e91.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e89.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e8.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e10.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSites of\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNearby structures invasion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e91.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e89.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.317\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eUterus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eRectum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eBowel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmento-peritoneal mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e62.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e37.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e61%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLNs mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e69.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e54.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e30.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e45.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePelvic LNs mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e76.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e64.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e23.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e35.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbdominal LNs mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e81.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e67.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e18.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e32.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistant LNs mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e94.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistant mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e76.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e74.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e23.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e25.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e93.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e89.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.500\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e6.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e10.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLung mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e86.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e86.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdrenal mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e94.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBone mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrain mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e98.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.476190476190474%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubcutaneous nodule\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.523809523809524%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e98.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.476190476190476%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e96.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.380952380952381%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.333333333333332%\" valign=\"top\"\u003e\n \u003cp\u003e3.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eN: The overall patient number; The numerical representation of the qualitative data was expressed as numbers and percentages (%). The mean\u0026plusmn;SD and median (range) for continuous variables were reported. a: McNemar\u0026apos;s test; b: Stuart Maxell test; c: Wilcoxon signed rank test; p-value\u0026lt;0.05 indicates significance.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (3):\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAgreement between CT and PET/CT findings\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eamong the studied mucinous ovarian carcinoma patients (N=59).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"761\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcordant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003e+ve/+ve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-ve/-ve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscordant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003e+ve/-ve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-ve/+ve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eO. bed recurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e(30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e(69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNearby structures invasion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e(98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003cp\u003e(89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.71 \u0026ndash; 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOment-peritoneal mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003cp\u003e(76.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e(37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e(38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.37 \u0026ndash; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLNs mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003cp\u003e(84.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e(30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003cp\u003e(54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.50 \u0026ndash; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePelvic LNs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e(88.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e(23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e(64.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.53 \u0026ndash; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbd. LNs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003cp\u003e(86.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e(67.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.44 \u0026ndash; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistant LNs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e(98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e(94.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.40 \u0026ndash; 1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistant mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e(88.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e(18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003cp\u003e(69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.46 \u0026ndash; 0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003cp\u003e(96.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003cp\u003e(8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.49 \u0026ndash; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLung mets\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003cp\u003e(93.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003cp\u003e(83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.44 \u0026ndash; 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdrenal mets\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e(94.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003cp\u003e(93.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.00 \u0026ndash; 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBone mets\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003cp\u003e(96.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrain mets\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e(98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e(98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.921052631578947%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubcutaneous\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNodule\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e(98.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003cp\u003e(96.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.394736842105264%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.289473684210526%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.894736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e0.036 \u0026ndash; 1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.421052631578947%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e-ve: absent finding; +ve: present finding; Numerator: CT finding; Denominator: PET/CT finding; K: Cohen\u0026apos;s Kappa inter-rater agreement coefficient; 95%CI: 95% confidence interval; p-value\u0026lt; 0.05 is significant.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable (4):\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDiagnostic performance of CECT and PET/CT in relation to the golden standard in diagnosis of mucinous ovarian carcinoma recurrence\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"762\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.992125984251969%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.692913385826771%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo.(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.480314960629921%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo.(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo.(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.480314960629921%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo.(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSN%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSP%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcc%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.955380577427822%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.992125984251969%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCECT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.692913385826771%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e(67.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.480314960629921%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.480314960629921%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\" valign=\"top\"\u003e\n \u003cp\u003e85.1%\u003c/p\u003e\n \u003cp\u003e(71.6-93.8)\u003c/p\u003e\n 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\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e95.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e62.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e74.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.955380577427822%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.992125984251969%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePET/CT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePeritonium mets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.692913385826771%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003cp\u003e59.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.480314960629921%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.26771653543307%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.480314960629921%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.498687664041995%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.711286089238845%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.955380577427822%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQualitative data were expressed as a number (percentage); TP: True positive; TN: True negative; FP: False positive; FN: False negative;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eSN: Sensitivity; SP: Specificity; PPV: Positive Predictive Value; NPV: Negative Predictive Value; Acc: Accuracy; %CI: 95% Confidence Interval\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e; p-value\u0026lt; 0.05 is significant.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cimg src=\"https://myfiles.space/user_files/132203_cef980177e9a226b/132203_custom_files/img1708450308.png\" style=\"width: 640px; height: 183.231px;\" width=\"640\" height=\"183.231\"\u003e\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\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":"Mucinous ovarian cancer, 18FDG PET/CT, CECT, and recurrence","lastPublishedDoi":"10.21203/rs.3.rs-3961163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3961163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives: \u003c/strong\u003eto assess the added value of \u003csup\u003e18\u003c/sup\u003eFDG-PET/CT in detection of mucinous ovarian cancer (MOC) recurrence and its effect on patient management compared to contrast enhanced computerized tomography (CECT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eAll\u003cstrong\u003e \u003c/strong\u003epatients underwent \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT and CECT for detection of\u0026nbsp; MOC recurrence. PET/CT and CT were interpreted separately and the significance of difference between them was evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study included 59 patients, out of them 18 and 29 patients were proven to have local and distant recurrence respectively. PET/CT demonstrated greater sensitivity (SN) , positive predictive value (PPV), negative predictive value (NPV) and accuracy, but the same specificity (SP) in recurrence detection (97.9%, 90.2%, 87.5%, 89.8%, and 58.3%, vs. 85.1%, 88.9%, 50%, 79.7%, and 58.3%, respectively) and showed significantly higher sensitivity for detection of omento-peritoneal and LNs metastases (mets) (36 and 27 versus 22 and 18, p- 0.0001 and 0.004, respectively). Both modalities were comparable in identifying distant organ mets (p \u0026gt;0.05). PET/CT changed patient management in 25.4% of patients,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003csup\u003e 18\u003c/sup\u003eFDG-PET/CT showed higher SN and accuracy than CECT in MOC recurrence detection, mainly the omento-peritoneal and nodal deposits, which allow better guidance for proper therapy planning.\u003c/p\u003e","manuscriptTitle":"18FDG-PET/CT versus Contrast Enhanced CT in detection of mucinous ovarian cancer recurrence: comparative study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 18:59:18","doi":"10.21203/rs.3.rs-3961163/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":"276de384-a1b5-4d08-9fec-02eb469f9c70","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-13T05:44:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-20 18:59:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3961163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3961163","identity":"rs-3961163","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}