Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma

Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma Xiaodan Chen, Qingyong Guo, Xiaorong Chen, Wanjing Zheng, Yaqing Kang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3099408/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2024 Read the published version in BMC Medical Imaging → Version 1 posted 4 You are reading this latest preprint version Abstract Introduction The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features. Methods We retrospectively evaluated clinical and MRI findings in 17 patients with UCS and 34 patients with EAC proven by histologically. The following clinical and pathological features were evaluated: age, post- or pre-menopausal, clinical presentation, invasion depth, FIGO stage, lymphaticmetastasis. The following MRI features were evaluated: tumor dimension, cystic degeneration or necrosis, hemorrhage, signalintensity (SI) on T2-weighted images (T2WI), relative SI of lesion to myometrium on T2WI, T1WI, DWI, ADCmax, ADCmin, ADCmean (RSI-T2, RSI-T1, RSI-DWI, RSI-ADCmax, RSI-ADCmin, RSI-ADCmean), ADCmax, ADCmin, ADCmean, the maximum, minimum and mean relative enhancement (RE) of lesion to myometrium on the arterial and venous phases (REAmax, REAmin, REAmean, REVmax, REVmin, REVmean). Receiver operating characteristic (ROC) analysis and the area under the curve (AUC) were used to evaluate prediction ability. Results The mean age of UCS was higher than EAC. UCS occurred more often in the postmenopausal patients. UCS and EAC did not significantly differ in depth of myometrial invasion, FIGO stage and lymphatic metastasis. The anterior-posterior and transverse dimensions were significantly larger in UCS than EAC. Cystic degeneration or necrosis and hemorrhage were more likely occurred in UCS than EAC. The tumor on T2WI was more heterogeneous in UCS. The RSI-T2, ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior, transverse dimensions, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively. Our study showed that REAmax threshold value of 0.23 can differentiate UCS from EAC with 94% sensitivity, 88% specificity, and 0.97AUC. Discussion Multiparametric MRI features may be utilized as a biomarker to distinguish UCS from EAC. Magnetic resonance imaging uterine carcinosarcoma endometrioid adenocarcinoma diagnosis differential diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Malignant Muellerian mixed tumors or malignant mesodermal mixed tumors were the previous names for the most prevalent malignant MEMTs, which are carcinosarcomas. A tumor with both a malignant mesenchymal and epithelial component is known as a uterine carcinosarcoma (UCS) [1]. Despite being among the most malignant neoplasms to develop in the uterus, carcinosarcomas are commonly misdiagnosed as endometrial carcinomas (EC) by dilatation and curettage or endometrial biopsy [2]. Due to the stark differences in their prognoses, the differential diagnosis of UCS and EC is essential. Due to the lack of distinct symptoms and clinical characteristics, such as vaginal bleeding, that distinguish UCS from EC is challenging [3]. UCS has a 5-year survival rate of just 25% overall, which is much lower than EC [2,4], which has an 83% 5-year survival rate [5]. UCS accounts for fewer than 5% of all uterine malignant tumors, although it kills more than 16% of uterine cancer patients [6]. According to morphological appearances, a variety of imaging characteristics of UCS have been reported [2,5,7]. It is believed that the aggressiveness of UCS is caused by the grade of its adenocarcinomas [8]. Adenocarcinoma of endometrial origin is the epithelial component, according to histopathological analysis. The majority of initial cytological diagnoses are adenocarcinomas because the sarcomatous component adheres more than the adenocarcinomatous component [2]. Immunohistochemical and molecular studies have suggested that the sarcoma component is in fact a metaplastic component derived from the carcinoma [9]. Therefore, UCS is expected to exhibit similar biological behavior to high-grade EC [9]. It has distinctive clinical and pathological characteristics which justify its separation from EC. UCS has a high incidence of lymphatic spread, peritoneal seeding, and a higher rate of lung metastases, so it is associated with a worse prognosis than EC [10]. The selection of anticarcinogen drugs is different for UCS and EC, and hormonal therapy is not applied to UCS [11,12]. Pre-operative diagnosis of UCS is suggested by imaging and is done through endometrial sampling. However, UCS is often diagnosed after hysterectomy because there is insufficient sensitivity in endometrial sampling to identify carcinosarcoma (23.5–58.8%) [13,14]. For the preoperative assessment of uterine carcinomas or sarcomas, magnetic resonance imaging (MRI) is frequently employed [15]. Therefore, MRI may play an important role to distinguish the two types of uterine tumor. Some authors describe MR observations of hemorrhage, necrosis, and exophytic mass on USC [5], they also mention that T1-weighted images (T1WI) and T2-weighted images (T2WI) might be difficult to distinguish between UCS and EC. In addition, as was noted in the present series, the conventional MRI findings of UCS are non-specific and cannot differentiate them from certain EC [2,3]. The prospective surgeries or therapeutic therapy of these individuals depend on the preoperative diagnosis, differential diagnosis, and staging. Therefore, it is very meaningful to analyze the imaging characteristics of UCS. The lengthy process and limited resources involved in pathological biopsy will provide biased results. MRI examination is a good supplement. The purpose of our study was to analyze the clinical and imaging features of UCS and endometrial adenocarcinoma (EAC, the most common pathological type of EC), and to explore the diagnostic and the differential diagnostic accuracy using multiparametric MRI. Materials and methods This study was observational and retrospective. The patients orally consented to the use of their medical records by telephone. All procedures including orally consent performed in studies involving human participants were approved by the Medical Ethics Committee of the First Affiliated Hospital of Fujian Medical University. Patients Patients with pathologically proven UCS or EAC from January 2014 to June 2022 were identified from our hospital. Patients who met the following criterias were included in the study: 1) those who preoperatively underwent standard pelvic MRI examination including T2WI, T1WI, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), contrast-enhanced (CE)-T1WI on the arterial and venous phases; 2) those with pathologically proven UCS or EAC after surgical resection. Patients who met the following criterias were excluded: 1) history of chemotherapy or radiation treatment before MRI examination; 2) those with poor image quality due to effect of artifact. Thus, a total of 51 patients were enrolled for analysis, including 17 UCS and 34 EAC patients. Clinical and pathological features The clinical and pathological features were recorded in our study, including age, premenopause or postmenopause, clinical manifestation, the level of CA125, pathological pattern, FIGO stage, lymphatic metastasis, lymph-vascular space invasion (LVSI). MRI techniques A 3.0 T MR scanner was used for every examination (MAGNETOM Verio, Siemens Healthineers). The standard dedicated pelvic MRI protocol consisted of the following sequences, transverse volumetric interpolated breath-hold examination with fat-suppression (VIBE)-T1WI, transverse and sagittal turbo spin echo (TSE)-T2WI, and DWI (b value = 50 and 800s/mm 2 ). For the arterial and venous phases, CE-T1WI was performed in the transverse and sagittal planes at 40–60 seconds and 90–110 seconds, after intravenous injections of gadobenate dimeglumine (MultiHance, Bracco, 0.2mmol/kg body weight, rate of 3.0mL/s). The MRI protocol is shown in Table 1 . Table 1 MRI protocol Sequences TR (ms) TE (ms) Slice Thickness (mm) Intersection Gap (mm) FOV (mm) Matrix TSE-T2WI 4000 96 5 1.0 360 x 360 384 x 384 x 70% DWI 6000 58 5 1.0 400 x 300 180 x 180 x 85% VIBE-T1WI 3.2 1.2 3 0 360 x 300 384 x 384 x 70% Measurement of MRI values All MRI were retrospectively reviewed by two radiologists with 5 and 15 years of experience in pelvic MRI, respectively, who were blinded to the clinical and the pathologic features (either UCS or EAC). Image findings assessed included tumor size (in anterior-posterior, longitudinal, and transverse dimensions) on the sagittal T2WI, the boundary was clear or unclear, presence or absence of tumor cystic degeneration and necrosis or hemorrhage, the signal intensity of the tumor was homogeneous or heterogeneous on T2WI, invasion the depth of the myometrium, adjacent tissue invasion, lymph node metastasis, FIGO stage, the presence or absence of feeding arteries, the degree of enhancement, whether there were areas of strong enhancement. Cystic degeneration or necrosis was defined as areas of high signal intensity on T2WI without enhancement after administration of contrast medium. Hemorrhage was defined as areas of high signal intensity on T1WI. We measured both tumor regions of interest (ROIs) and normal myometrium of uterus ROIs on T2WI (a), DWI (high-b-value) (b), ADC (c), T1WI (d), CE-T1WI (e, f) on the arterial phase and the venous phase, respectively (Fig. 1). The tumor ROI was placed as a single ROI at the level where the largest lesion could be measured on T2WI, T1WI, DWI. The maximum, mean, minimum ADC values (10 –3 mm 2 /s) and CE-T1WI on the arterial phase and the venous phase were measured in a circular ROI in the representative location as large as possible within the tumor (ADCmax, ADCmean, ADCmin, REAmax, REAmean, REAmin, REVmax, REVmean, REVmin, respectively). The ROIs were placed on solid portion of the tumors to avoid including necrosis, cystic degeneration, or hemorrhage as much as possible by reference to all MRI including T2WI, T1WI, CE-T1WI, DWI. The normal myometrium of uterus ROIs were measured on the same slice of the lesion. Calculated : The ratio of the tumor to the myometrium of uterus on T2WI, T1WI, DWI, ADC, CE-T1WI was calculated as follows: RSI (the relative signal intensity) = the signal intensity of tumor/normal myometrium of uterus (RSI-T2, RSI-T1, RSI-DWI, RSI-ADCmax, RSI-ADCmean, RSI-ADCmin); RE (the relative enhancement signal intensity) = (the contrast-enhanced SI of tumor - the unenhanced SI of tumor)/the contrast-enhanced SI of normal myometrium of uterus; The ROIs were drawn on the most, the average and the lowest enhancing component of the tumor on the contrast-enhanced T1WI obtained at the arterial and venous phases (REAmax, REAmean, REAmin, REVmax, REVmean, REVmin). REA = RE on the arterial phase, REV = RE on the venous phase. Statistical Analysis Categorical variables were described as frequencies and percentages. Continuous variables were described as means and standard deviations or medians and quartiles. The X 2 test was used to compare categorical characteristics between disease groups. Differences between continuous data were tested for significance using an independent t test. Receiver operating characteristic (ROC) analysis was used to assess the model’s performance, and the area under the curve (AUC) was used to evaluate the ability of prediction. The Youden index of sensitivity and specificity was used to determine the optimal cut-of values for UCS. All tests were two-sided, and p values of 0.05 or less were considered statistically significant. Statistical analysis was carried out using SPSS version 20.0. Results Clinical characteristics The preoperative clinical features of the 51 cases were reviewed (Table 2 ). The mean age of UCS patients was higher than that of EAC (p < 0.001). There were only 1 (5.9%) premenopausal woman with UCS and 15 (44.1%) premenopausal women with EAC (p < 0.001). 13(76.5%) patients with UCS and 28 (82.4%) patients with EAC suffered from abnormal vaginal bleeding, there was no statistical difference between the two groups (p = 0.325). The level of CA125 was not significantly different between the two diseases (p = 0.579). Pathological features UCS and EAC did not significantly differ in depth of myometrial invasion (p = 0.426) and FIGO stage (p = 0.214) on pathology. Lymphatic metastasis and LVSI were also similar between the two disease groups (p = 0.904, p = 0.692, respectively). Of UCS cases, 52.94% were heterologous, 23.53% were homologous, and the rest 23.53% were an unspecifed subtype. The proportion of grade 1–3 in the EAC cases was 17.65%, 29.41%, 52.94%, respectively. (Table 2 ) Table 2 Clinical and pathological features of UCS and EAC Characteristic UCS (n = 17) EAC (n = 34) p value Age, mean (range) 63.24 ± 2.93 54.82 ± 4.49 < 0.001* Postmenopausal 0.001* Yes 16 19 No 1 15 Clinical presentation, no. 0.325 Abnormal vaginal bleeding 13 28 Pelvic pain 3 6 Abdominal mass 1 0 CA125 0.579 Elevated 6 8 normal 11 26 Invasion depth 0.426 ≤ 1/2 myometrium 8 20 > 1/2 myometrium 9 14 FIGO stage, no. (%) 0.214 I 9 20 II 2 6 III 6 8 IV 0 0 lymphatic metastasis 0.904 Yes 3 8 No 14 26 LVSI 0.692 Yes 8 18 No 9 16 UCS subtype, no. (%) - Homologous 4(23.53%) - Heterologous 9(52.94%) - Unspecified 4(23.53%) - EAC classification, no. (%) - Grade 1 - 6(17.65%) Grade 2 - 10(29.41%) Grade 3 - 18(52.94%) MRI characteristics Table 3 summarized the MRI characteristics of the 17 UCS (Fig. 2 – 3 ) and 34 EAC (Fig. 4 ) cases. The anterior-posterior and transverse dimensions were significantly larger in UCS than in EAC (P = 0.011, P = 0.015, respectively). However, there was no significant difference between UCS and EAC in longitudinal dimension (P = 0.077). The boundary that was clear or unclear was not obviously different in the UCS and EAC (P = 0.910). The cystic degeneration or necrosis and intratumoral hemorrhage were more likely occurred in UCS than EAC patients (P < 0.001, P < 0.001, respectively) (Fig. 5). Regarding the signal intensity of T2WI, our study demonstrated that UCS was more heterogeneous than EAC (P < 0.001) (Fig. 5). The tumors of UCS had higher RSI-T2 than that of EAC (P < 0.001), however, there were no significant difference between UCS and EAC on RSI-T1, RSI-DWI (P = 0.202, P = 0.771, respectively). Both UCS and EAC showed low or equal signal intensity on T1WI and high signal intensity on DWI. Apart from that, ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean were significantly different between the two groups (P < 0.001, P < 0.001, P < 0.001, P = 0.019, respectively), the ADC values of UCS group were higher than EAC group. While ADCmin and RSI-ADCmin were similar between the two disease groups (p = 0.753, p = 0.106, respectively). The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. The feeding artery and areas of strong enhancement were more likely to appear in UCS than EAC (P = 0.021) (Fig. 5). Figure 6 showed ROC results for significant features. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior, transverse dimentions, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively (Table 4 ). Our study showed that REAmax threshold value of 0.23 can differentiate UCS from EAC with 94% sensitivity, 88% specificity, and 0.97 AUC. Table 3 MRI characteristics of UCS and EAC UCS (n = 17) EC (n = 34) p value Tumor dimension Anterior–posterior 4.11 ± 2.36 2.43 ± 0.87 0.011* Longitudinal 6.37 ± 3.67 4.61 ± 1.77 0.077 Transverse 4.97 ± 2.72 3.15 ± 0.98 0.015* Boundary 0.910 Unclear 5 8 Clear 12 26 Cystic degeneration or necrosis < 0.001* Yes 11 0 No 6 34 Hemorrhage < 0.001* Yes 8 2 No 9 33 Signalintensity on T2WI < 0.001* homogeneous 4 32 heterogeneous 13 2 RSI-T2(mean ± SD) 1.97 ± 0.47 1.41 ± 0.32 < 0.001* RSI-T1 0.87 ± 0.09 0.93 ± 0.18 0.202 RSI-DWI 2.76 ± 0.57 2.72 ± 0.49 0.771 ADCmax 1166.18 ± 325.37 752.35 ± 81.22 < 0.001* ADCmin 598.23 ± 145.71 608.94 ± 94.47 0.753 ADCmean 904.85 ± 187.24 672.06 ± 86.54 < 0.001* RSI-ADCmax 0.71 ± 0.15 0.53 ± 0.12 < 0.001* RSI-ADCmin 0.37 ± 0.11 0.43 ± 0.11 0.106 RSI-ADCmean 0.55 ± 0.10 0.47 ± 0.11 0.019* REAmax 0.69 ± 0.27 0.24 ± 0.10 < 0.001* REAmin 0.31 ± 0.13 0.11 ± 0.10 < 0.001* REAmean 0.44 ± 0.14 0.17 ± 0.09 < 0.001* REVmax 0.64 ± 0.25 0.29 ± 0.13 < 0.001* REVmin 0.29 ± 0.13 0.21 ± 0.12 0.031* REVmean 0.42 ± 0.16 0.24 ± 0.12 0.001* Feeding artery 0.021* Yes 6 2 No 11 32 Areas of strong enhancement < 0.001* Yes 13 0 No 4 34 Table 4 ROC curve parameters AUC P value 95% CI Anterior-posteriordimension 0.723 0.010* 0.555–0.892 Transversedimension 0.706 0.017* 0.535–0.877 RSI-T2 0.858 < 0.001* 0.739–0.977 ADCmax 0.955 < 0.001* 0.892-1.000 ADCmean 0.893 < 0.001* 0.786–0.999 RSI-ADCmax 0.841 < 0.001* 0.732–0.949 RSI-ADCmean 0.727 0.009 0.580–0.873 REAmax 0.969 < 0.001* 0.000–1.000 REAmin 0.882 < 0.001* 0.793–0.972 REAmean 0.941 < 0.001* 0.869-1.000 REVmax 0.913 < 0.001* 0.831–0.996 REVmin 0.689 0.029* 0.523–0.854 REVmean 0.799 0.001* 0.674–0.925 CI: confidence interval Discussion UCS is misdiagnosed as EC frequently at that time due to a lack of pathological specimens. A research found that 75% of UCS patients were preoperatively mistakenly diagnosed as EC [2]. UCS is typically diagnosed correctly depending on ultimate pathological findings following surgical resection [16]. UCS has an aggressive clinical course and a poor overall prognosis. Even if UCS is in stage I, the 5 year survival rate is still less than 50%. Therefore, uterine tumor imaging can play an important role if it can help the clinician make a correct diagnosis early on, before the operation. UCS often occurs in postmenopausal females. Their frequency increases with age. The typical clinical symptom is abnomal vaginal bleeding, other symptoms include abdominal mass, abdominal pain. According to histology, UCS is composed of epithelial and mesenchymal components. UCS is classified as heterologous or homologous according to the sort of cells that make up the sarcomatous component. Heterologous types include rhabdomyosarcoma, chondrosarcoma, osteosarcoma, or liposarcoma, and homologous types include fibrosarcoma, endometrial stromal sarcoma, or leiomyosarcoma. In either situation, the carcinomatous component may be made up of endometrioid, serous, or clear cell types. Recent investigations in immunohistochemistry, ultrastructure, and molecular biology have all pointed to carcinosarcomas being metaplastic carcinomas, with the mesenchymal component typically retaining at least some epithelial characteristics. As a result, some experts contend that UCS is better classified as a particular kind of EC. And cancerous components are the driving force for tumor progression. The risk factors and clinical manifestations of UCS are similar to those of EC [17]. Regard to the conspicuity of the tumor margin on T2WI, we predicted that UCS would reveal a clearer border between the tumor and myometrium because a carcinosarcoma is often a clearly defined exophytic mass [18]. However, there was no significant difference in this respect between UCS and EAC. This is probably due to the fact that MRI has a higher resolution of soft tissue and can clearly distinguish endometrial, myometry and interstitial limits. A number of MRI characteristics of UCS have been reported. Previous studies have shown that although UCS tends to appear as larger, heterogeneous tumors with a deep myometric invasion unlike EC, but MRI results are not specific and can reflect those of invasive EC [19]. In our study, 76.47% of UCS presented with mixed signals on T2WI, which is consistent with the complicated histopathological components. UCS has a combination of cancer and sarcoma, and sometimes even various sarcomas. The heterogeneous SI on T2WI has also been described as a feature of the UCS [2]. Hemorrhage, cystic degeneration or necrosis is common, which may lead to the heterogeneous SI of UCS on T2WI. EAC almost shows homogeneous SI on T2WI. Therefore, homogeneous SI on T2WI is a reliable indicator to distinguish between UCS and EAC. Our study also demonstrated that the UCS had higher RSI-T2 than EAC, while most EAC were equally or slightly higher signal intensity to the myometrium of uterus. Our observation of predominant hyperintensity of UCS on T2WI was in good agreement with previous reports [18]. There was not significantly different RSI-T1 between UCS and EAC. Both were of equally or slightly lower signal intensity on TIWI. Previous report showed a correlation between regions of medium to high signal intensity on T1WI of UCS with hemorrhage and necrosis. While presence of high signal area on T1WI is a rare but highly specific MRI feature of UCS that represents intratumoral hemorrhage [2]. There was not significantly different RSI-DWI between UCS and EAC. Both two were malignant tumors with high signal intensity on DWI. DWI is gradually recognized in body imaging for the identification of malignancies, and ADC values have been used to describe tumor functions [20–22]. DWI is a well-known method for finding uterine tumors and offers a large tissue contrast to evaluate the extent of muscle infiltration by these tumors [23,24]. Several recent reports have generally used ADC values obtained in uterine imaging to differentiate benign tumors from malignant ones [25,26]. UCS that contain cartilage, nerves, calcifications, necrosis, and hemorrhage would have high ADC values for component diversity. It was reported that the ADCmean of UCS was much higher than that of grade 2 or 3 EC [16]. Furthermore, the ADC map can also make a distinction between adenocarcinoma and sarcoma [27], since the ADC map was more heterogeneous in the sarcomatous component [28]. High ADC values have been reported to indicate high-grade malignancy with necrosis, which is often more common with UCS than EC [16]. This is similar with our findings that the ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. This result may possibly reflect the tissue heterogeneity of UCS including abundant microscopic necrotic regions and epithelial cystic components, which could increase the ADC values. The ADCmin and RSI-ADCmin values were measured in the solid portion of the tumor. Both are high-grade malignant tumor components, so they have similar minimum ADC values. Previous research on EC has demonstrated that significant difference in ADC values that help distinguish between histological grades, with high-grade tumors producing low ADC values and low-grade tumors producing high ADC values [25,29]. UCS was enhanced equally as much as or more strongly than uterine myometrium, and was enhanced more strongly than EC, in good agreement with previous reports [2,18]. Takemori et al. [30] showed that the sarcomatous component was enhanced more strongly than the carcinomatous component on contrast-enhanced T1WI, this may be because the sarcomatous component had substantial vascularity. Therefore, UCS was more likely to occur the feeding artery and areas of strong enhancement than EAC. Ohguri et al. [2] also reported that all four UCS showed areas of early and persistent marked enhancement similar to that of uterine myometrium and found that the portions with high signal intensity in the early phase dynamic study corresponded histologically to sarcomatous components with prominent vascularity. Matsuo et al. [31] reported that not only did the carcinoma component play a major role in tumor progression and survival, but also that the sarcoma component made a significant contribution. Tumour necrosis is assumed to be caused by chronic ischemic lesions caused by rapidly growing tumors [32]. Hypoxia is frequently present in necrosis, which causes the activation of hypoxia-inducible factors [33]. Ischemic regions lead to tumor progression by promoting overexpression of hypoxia-inducible factors under hypoxic circumstances [34]. These results indicate that the non-enhanced areas caused by necrosis probably reflect a highly aggressive tumor with active proliferation. As the EC is homogeneously enhanced lower than the myometry of the uterus in general, they observed that different patterns of contrast enhancement inside the anendometric tumor may increase the possibility of UCS. As a result, different patterns of enhancement within an endometrial mass may represent a mixture of different histopathologisms. UCS should be distinguished from EAC because their prognosis and treatment strategy are different [35]. However, the histologic results are sometimes misleading, as the biphasic nature may sometimes not be apparent until the entire tumor is investigated. In 13 of the 17 cases, there were significant areas of enhancement, while all EAC cases showed no obvious enhancement areas. There was a statistical difference between the two cases (P < 0.001). Tanaka et al. [18] reported that UCS mainly has strongly enhanced regions and unenhanced areas on T1WI within the mass. Therefore, a highly enhanced area can predict the possibility of UCS to diagnose a malignant tumor of the endometrium. Other studies have shown that the UCS generally displayed early hyper-enhancement relative to the myometrium persisting into the delayed phase, whereas EC more frequently had hypo-enhancement relative to the myometrium [36]. Our study showed that the REAmax, REAmin, REAmean, REVmax, REVmin and REVmeanof UCS were all higher than EAC. This result indicated that UCS was associated with higher enhancement during the arterial phase and the venous phasecompared to EAC. Emoto and colleagues reported that UCS had greater angiogenic activity than EC due to over-expression of VEGF in cancer cells and expression of the Ang-2 gene at the periphery of the tumour [37]. As previously published data have demonstrated that conventional contrast-enhanced MRI cannot distinguish UCS from EC, we believe that the unique ability and higher diagnostic accuracy of semiquantitative parameters of the REAmax, REAmin, REAmean, REVmax, REVmin and REVmean to differentiate UCS from EAC will have a significant impact in clinical practice. UCS often shows progressive or persistent enhancement, while EAC often shows mild enhancement [38]. This is another important differential point to distinguish UCS from EAC. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior, transverse dimensions, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively. Our study showed that REAmax threshold value of 0.23 could differentiate UCS from EAC with 94% sensitivity, 88% specifcity, and 0.97 AUC. The ROC curve has been widely applied in the evaluation of radiologic imaging, and diagnostic accuracy is characterized by the combination of sensitivity and specificity. A strongly enhanced area may predict the possibility of UCS to EAC. There are several limitations to our study. First, this is a retrospective study using a small sample size of 17 patients because of the rarity of UCS and therefore subject to potential selection bias. A larger sample of data is needed to confirm these findings. Second, studies were not all carried out on the same kind of MRI scanner, and there were some variations in protocol. Third, because some instances lacked the necessary sequences, we were unable to assess the value of MR spectroscopy or perfusion imaging. Fourth, we did not contrast the MRI findings with the pathological features. Future research should use radiological pathological correlation to validate the imaging characteristics of UCS identified in this study. In conclusion, UCS was more common in postmenopausal patients and the main manifestation was abnormal vaginal bleeding. The T2WI signal intensity was more heterogeneous in UCS than EAC. The ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. A strongly enhanced area may predict the possibility of UCS to EAC. Our study showed that REAmax threshold value of 0.23 can differentiate UCS from EAC with 94% sensitivity, 88% specifcity, and 0.97 AUC. Therefore, based on semiquantitative characteristics and the enhancement pattern, MRI may be utilized as a biomarker to distinguish UCS from EAC, which may help with appropriate preoperative characterisation and therapy stratification in these individuals. These results need to be confirmed by prospective studies. Declarations Acknowledgements The authors would like to thank all the participants Authors’ contributions Dairong Cao and Xiaodan Chen contributed to the study idea, designed the study and were involved at all stages. Xiaodan Chen and Qingyong Guo conducted the analysis and interpretation of results and wrote the manuscript. Xiaorong Chen was involved instudy design and provided clinical information. Wanjing Zheng was involved in data analysis and supervision. Yaqing Kang collected data and prepared the tables. All authors discussed the results, commented on the manuscript versions and approved this final version. Funding No funds, grants, or other support was received. Data Availability The datasets used during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The patients orally consented to the use of their medical records by telephone. All procedures including orally consent performed in studies involving human participants were in accordance with relevant guidelines and regulations and approved by the Medical Ethics Committee of the First Affiliated Hospital of Fujian Medical University. Consent for publication Not Applicable. Competing interests The authors declare no competing interest. References D'Angelo E, Prat J. Pathology of mixed Müllerian tumours [J]. Best Pract Res Clin Obstet Gynaecol, 2011, 25(6): 705-718. Ohguri T, Aoki T, Watanabe H, et al. MRI findings including gadolinium-enhanced dynamic studies of malignant, mixed mesodermal tumors of the uterus: differentiation from endometrial carcinomas [J]. Eur Radiol, 2002, 12(11): 2737-2742. Shapeero LG, Hricak H. Mixed müllerian sarcoma of the uterus: MR imaging findings [J]. AJR Am J Roentgenol, 1989, 153(2): 317-319. Sartori E, Bazzurini L, Gadducci A, et al. Carcinosarcoma of the uterus: a clinicopathological multicenter CTF study [J]. Gynecol Oncol, 1997, 67(1): 70-75. Tirumani SH, Ojili V, Shanbhogue AK, et al. Current concepts in the imaging of uterine sarcoma [J]. Abdom Imaging, 2013, 38(2): 397-411. Arend R, Doneza JA, Wright JD. Uterine carcinosarcoma [J]. Curr Opin Oncol, 2011, 23(5): 531-536. Genever AV, Abdi S. Can MRI predict the diagnosis of endometrial carcinosarcoma? [J]. Clin Radiol, 2011, 66(7): 621-624. Smith T, Moy L, Runowicz C. Müllerian mixed tumors: CT characteristics with clinical and pathologic observations [J]. AJR Am J Roentgenol, 1997, 169(2): 531-535. Wada H, Enomoto T, Fujita M, et al. Molecular evidence that most but not all carcinosarcomas of the uterus are combination tumors [J]. Cancer Res, 1997, 57(23): 5379-5385. Gadducci A, Sartori E, Landoni F, et al. The prognostic relevance of histological type in uterine sarcomas: a Cooperation Task Force (CTF) multivariate analysis of 249 cases [J]. Eur J Gynaecol Oncol, 2002, 23(4): 295-299. Koh WJ, Greer BE, Abu-Rustum NR, et al. Uterine neoplasms, version 1.2014 [J]. J Natl Compr Canc Netw, 2014, 12(2): 248-280. Homesley HD, Filiaci V, Markman M, et al. Phase III trial of ifosfamide with or without paclitaxel in advanced uterine carcinosarcoma: a Gynecologic Oncology Group Study [J]. J Clin Oncol, 2007, 25(5): 526-531. Bansal N, Herzog TJ, Burke W, et al. The utility of preoperative endometrial sampling for the detection of uterine sarcomas [J]. Gynecol Oncol, 2008, 110(1): 43-48. Sagae S, Yamashita K, Ishioka S, et al. Preoperative diagnosis and treatment results in 106 patients with uterine sarcoma in Hokkaido, Japan [J]. Oncology, 2004, 67(1): 33-39. Sala E, Rockall AG, Freeman SJ, et al. The added role of MR imaging in treatment stratification of patients with gynecologic malignancies: what the radiologist needs to know [J]. Radiology, 2013, 266(3): 717-740. Takeuchi M, Matsuzaki K, Harada M. Carcinosarcoma of the uterus: MRI findings including diffusion-weighted imaging and MR spectroscopy [J]. Acta Radiol, 2016, 57(10): 1277-1284. Amant F, Cadron I, Fuso L, et al. Endometrial carcinosarcomas have a different prognosis and pattern of spread compared to high-risk epithelial endometrial cancer [J]. Gynecol Oncol, 2005, 98(2): 274-280. Tanaka YO, Tsunoda H, Minami R, et al. Carcinosarcoma of the uterus: MR findings [J]. J Magn Reson Imaging, 2008, 28(2): 434-439. Teo SY, Babagbemi KT, Peters HE, et al. Primary malignant mixed mullerian tumor of the uterus: findings on sonography, CT, and gadolinium-enhanced MRI [J]. AJR Am J Roentgenol, 2008, 191(1): 278-283. Takahara T, Imai Y, Yamashita T, et al. Diffusion weighted whole body imaging with background body signal suppression (DWIBS): technical improvement using free breathing, STIR and high resolution 3D display [J]. Radiat Med, 2004, 22(4): 275-282. Galea N, Cantisani V, Taouli B. Liver lesion detection and characterization: role of diffusion-weighted imaging [J]. J Magn Reson Imaging, 2013, 37(6): 1260-1276. Inoue T, Kozawa E, Okada H, et al. Noninvasive evaluation of kidney hypoxia and fibrosis using magnetic resonance imaging [J]. J Am Soc Nephrol, 2011, 22(8): 1429-1434. Nougaret S, Reinhold C, Alsharif SS, et al. Endometrial Cancer: Combined MR Volumetry and Diffusion-weighted Imaging for Assessment of Myometrial and Lymphovascular Invasion and Tumor Grade [J]. Radiology, 2015, 276(3): 797-808. Andreano A, Rechichi G, Rebora P, et al. MR diffusion imaging for preoperative staging of myometrial invasion in patients with endometrial cancer: a systematic review and meta-analysis [J]. Eur Radiol, 2014, 24(6): 1327-1338. Fujii S, Matsusue E, Kigawa J, et al. Diagnostic accuracy of the apparent diffusion coefficient in differentiating benign from malignant uterine endometrial cavity lesions: initial results [J]. Eur Radiol, 2008, 18(2): 384-389. Thomassin-Naggara I, Dechoux S, Bonneau C, et al. How to differentiate benign from malignant myometrial tumours using MR imaging [J]. Eur Radiol, 2013, 23(8): 2306-2314. Kato H, Kanematsu M, Furui T, et al. Carcinosarcoma of the uterus: radiologic-pathologic correlations with magnetic resonance imaging including diffusion-weighted imaging [J]. Magn Reson Imaging, 2008, 26(10): 1446-1450. Hernández Mateo P, Méndez Fernández R, Serrano Tamayo E. Uterine sarcoma vs adenocarcinoma: can MRI distinguish between them? [J]. Radiologia, 2016, 58(3): 199-206. Zhang GF, Zhang H, Tian XM, et al. Magnetic resonance and diffusion-weighted imaging in categorization of uterine sarcomas: correlation with pathological findings [J]. Clin Imaging, 2014, 38(6): 836-844. Takemori M, Nishimura R, Yasuda D, et al. Carcinosarcoma of the uterus: magnetic resonance imaging [J]. Gynecol Obstet Invest, 1997, 43(2): 139-141. Matsuo K, Takazawa Y, Ross MS, et al. Significance of histologic pattern of carcinoma and sarcoma components on survival outcomes of uterine carcinosarcoma [J]. Ann Oncol, 2016, 27(7): 1257-1266. Caruso RA, Branca G, Fedele F, et al. Mechanisms of coagulative necrosis in malignant epithelial tumors (Review) [J]. Oncol Lett, 2014, 8(4): 1397-1402. Tomes L, Emberley E, Niu Y, et al. Necrosis and hypoxia in invasive breast carcinoma [J]. Breast Cancer Res Treat, 2003, 81(1): 61-69. Bertout JA, Patel SA, Simon MC. The impact of O2 availability on human cancer [J]. Nat Rev Cancer, 2008, 8(12): 967-975. Vaidya AP, Horowitz NS, Oliva E, et al. Uterine malignant mixed mullerian tumors should not be included in studies of endometrial carcinoma [J]. Gynecol Oncol, 2006, 103(2): 684-687. Bharwani N, Newland A, Tunariu N, et al. MRI appearances of uterine malignant mixed müllerian tumors [J]. AJR Am J Roentgenol, 2010, 195(5): 1268-1275. Emoto M, Charnock-Jones DS, Licence DR, et al. Localization of the VEGF and angiopoietin genes in uterine carcinosarcoma [J]. Gynecol Oncol, 2004, 95(3): 474-482. Kamishima Y, Takeuchi M, Kawai T, et al. A predictive diagnostic model using multiparametric MRI for differentiating uterine carcinosarcoma from carcinoma of the uterine corpus [J]. Jpn J Radiol, 2017, 35(8): 472-483. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2024 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Major revision 09 Jul, 2023 Editor assigned by journal 02 Jul, 2023 Submission checks completed at journal 02 Jul, 2023 First submitted to journal 23 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-3099408","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":215038205,"identity":"5ea69d42-bbd1-4bfe-9ae1-8babaeb006ba","order_by":0,"name":"Xiaodan Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaodan","middleName":"","lastName":"Chen","suffix":""},{"id":215038206,"identity":"7e038a01-cef4-4cca-a7d1-f598f6d3ecde","order_by":1,"name":"Qingyong Guo","email":"","orcid":"","institution":"Fujian Women and Children Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qingyong","middleName":"","lastName":"Guo","suffix":""},{"id":215038207,"identity":"cb83edb8-5d3f-4276-ae6b-5edc48d80ca6","order_by":2,"name":"Xiaorong Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaorong","middleName":"","lastName":"Chen","suffix":""},{"id":215038208,"identity":"f1d2425a-4de1-47de-a2cb-26537b07e002","order_by":3,"name":"Wanjing Zheng","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wanjing","middleName":"","lastName":"Zheng","suffix":""},{"id":215038209,"identity":"502a6500-88cf-4248-b255-9959ac5fb3f9","order_by":4,"name":"Yaqing Kang","email":"","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yaqing","middleName":"","lastName":"Kang","suffix":""},{"id":215038210,"identity":"7671f7c8-9fed-4566-a441-247ff238e02b","order_by":5,"name":"dairong cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDCCA4wNUBbzgQMffpCmhS3x4MweorTAWTzGhznYiNDBd/xw24ePO2rl+dt7Phxm4GGQ5xc7gF+L5JnE5pkzzxw3nHHm7IbDBRYMhjNnJ+DXYnAgsZmZt+1YAsON3A2HZ/AwJBjcJqTl/EOIFvkbOQ8O87ARo+UG2JaaBIMbOQzEaZG88bCZcWbbAcONZ44ZAANZgrBf+M6nP2b42FYnL3e8+fGHDz9s5PmlCWiBgsMwhgRRykGgjmiVo2AUjIJRMAIBACfLTXYKWYcRAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"dairong","middleName":"","lastName":"cao","suffix":""}],"badges":[],"createdAt":"2023-06-23 08:44:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3099408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3099408/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12880-024-01225-4","type":"published","date":"2024-02-19T15:01:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39705932,"identity":"e792a6bd-0aa0-423e-b39c-6c1b4f0aa47b","added_by":"auto","created_at":"2023-07-07 15:21:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":304926,"visible":true,"origin":"","legend":"\u003cp\u003eshows how the ROI is selected and measured.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3099408/v1/813373561e4829247c1e0055.png"},{"id":39705934,"identity":"62149553-4b25-4bdc-8ff4-a8df0250666e","added_by":"auto","created_at":"2023-07-07 15:21:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":204047,"visible":true,"origin":"","legend":"\u003cp\u003eImages of uterine carcinosarcoma (UCS). (a-b) Sagittal and axial T2WI of a 54-year-old woman shows a large intrauterine mass with heterogeneous high signal intensity. (c) Diffusion-weighted imaging (DWI) shows a high signal intensity tumor. (d) Apparent diffusion coefficient (ADC) displays mainly a low signal intensity tumor, some parts of high signal intensity. (e) Axial T1WI shows intratumoral hemorrhage of high signal intensity area in the mass. (f-g) Axial and sagittal contrast enhanced T1WI show a heterogeneous medium enhancement tumor, there was partial area without enhancement in the lesion.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3099408/v1/2a0edb9d82efe568ba3a0a29.png"},{"id":39707723,"identity":"a8c3416a-ea6a-4d0d-b0fb-1de63ee65095","added_by":"auto","created_at":"2023-07-07 15:29:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":246126,"visible":true,"origin":"","legend":"\u003cp\u003eImages of UCS. (a, b) Sagittal and axial T2WI of a 36-year-old woman shows a well-defined lesion with heterogeneous high signal intensity. (c) DWI shows a high signal intensity tumor. (d) ADC displays mainly a high signal intensity tumor, some parts of low signal intensity. (e) Axial T1WI shows low signal intensity in the mass. (f-g) Axial and sagittal contrast enhanced T1WI, there is an area in the mass showing strong enhancement similar to the myometrium.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3099408/v1/8b525248b40d3977cfb50684.png"},{"id":39707725,"identity":"b5ab773f-9cf8-46b7-94d7-673a55fa27a0","added_by":"auto","created_at":"2023-07-07 15:29:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":230367,"visible":true,"origin":"","legend":"\u003cp\u003eA 51-year-old woman with endometrioid adenocarcinoma (EAC). (a) Sagittal T2WI shows a homogeneous and slightly higher signal tumor. (b) Sagittal contrast enhanced T1WI, there is homogeneous mild enhancement in the mass lower than the myometrium. (c) DWI shows a obvious high signal intensity tumor. (d) ADC displays homogeneous low signal intensity in the tumor.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3099408/v1/a393e5163e07a8c0e6bb909e.png"},{"id":39705937,"identity":"cd52686a-67a2-4d05-ac7e-5048fb38781e","added_by":"auto","created_at":"2023-07-07 15:21:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148630,"visible":true,"origin":"","legend":"\u003cp\u003eImages of UCS. (a) Sagittal T2WI shows a clear boundary and mixed signal tumor with high signal intensity of cystic degeneration or necrosis. (b) Sagittal contrast enhanced T1WI shows the feeding artery in the tumor. (c) Axial T1WI shows high signal intensity of hemorrhage in the mass. (d) Axial contrast enhanced T1WI, the tumor shows heterogeneous enhancement partly stronger than the myometrium in the tumor.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3099408/v1/c6c86280b112870f5673127f.png"},{"id":39707722,"identity":"6b8a682b-642f-43c6-b33a-28162d596541","added_by":"auto","created_at":"2023-07-07 15:29:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57652,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves show the threshold values of the anterior-posterior, transverse dimensions, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean for differentiating UCS from EAC. The areas under the curve (AUCs) are 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for these parameters, respectively.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3099408/v1/7104f4dd612a83f536d36fd2.png"},{"id":51648508,"identity":"1e9b272a-10b4-44b4-9799-5fa074276113","added_by":"auto","created_at":"2024-02-26 15:13:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1416261,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3099408/v1/e4c936e7-638d-4b64-9514-4b77bdfeca76.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalignant Muellerian mixed tumors or malignant mesodermal mixed tumors were the previous names for the most prevalent malignant MEMTs, which are carcinosarcomas. A tumor with both a malignant mesenchymal and epithelial component is known as a uterine carcinosarcoma (UCS) [1]. Despite being among the most malignant neoplasms to develop in the uterus, carcinosarcomas are commonly misdiagnosed as endometrial carcinomas (EC) by dilatation and curettage or endometrial biopsy [2]. Due to the stark differences in their prognoses, the differential diagnosis of UCS and EC is essential. Due to the lack of distinct symptoms and clinical characteristics, such as vaginal bleeding, that distinguish UCS from EC is challenging [3].\u003c/p\u003e \u003cp\u003eUCS has a 5-year survival rate of just 25% overall, which is much lower than EC [2,4], which has an 83% 5-year survival rate [5]. UCS accounts for fewer than 5% of all uterine malignant tumors, although it kills more than 16% of uterine cancer patients [6]. According to morphological appearances, a variety of imaging characteristics of UCS have been reported [2,5,7]. It is believed that the aggressiveness of UCS is caused by the grade of its adenocarcinomas [8]. Adenocarcinoma of endometrial origin is the epithelial component, according to histopathological analysis. The majority of initial cytological diagnoses are adenocarcinomas because the sarcomatous component adheres more than the adenocarcinomatous component [2].\u003c/p\u003e \u003cp\u003eImmunohistochemical and molecular studies have suggested that the sarcoma component is in fact a metaplastic component derived from the carcinoma [9]. Therefore, UCS is expected to exhibit similar biological behavior to high-grade EC [9]. It has distinctive clinical and pathological characteristics which justify its separation from EC. UCS has a high incidence of lymphatic spread, peritoneal seeding, and a higher rate of lung metastases, so it is associated with a worse prognosis than EC [10]. The selection of anticarcinogen drugs is different for UCS and EC, and hormonal therapy is not applied to UCS [11,12].\u003c/p\u003e \u003cp\u003ePre-operative diagnosis of UCS is suggested by imaging and is done through endometrial sampling. However, UCS is often diagnosed after hysterectomy because there is insufficient sensitivity in endometrial sampling to identify carcinosarcoma (23.5\u0026ndash;58.8%) [13,14]. For the preoperative assessment of uterine carcinomas or sarcomas, magnetic resonance imaging (MRI) is frequently employed [15]. Therefore, MRI may play an important role to distinguish the two types of uterine tumor. Some authors describe MR observations of hemorrhage, necrosis, and exophytic mass on USC [5], they also mention that T1-weighted images (T1WI) and T2-weighted images (T2WI) might be difficult to distinguish between UCS and EC. In addition, as was noted in the present series, the conventional MRI findings of UCS are non-specific and cannot differentiate them from certain EC [2,3].\u003c/p\u003e \u003cp\u003eThe prospective surgeries or therapeutic therapy of these individuals depend on the preoperative diagnosis, differential diagnosis, and staging. Therefore, it is very meaningful to analyze the imaging characteristics of UCS. The lengthy process and limited resources involved in pathological biopsy will provide biased results. MRI examination is a good supplement. The purpose of our study was to analyze the clinical and imaging features of UCS and endometrial adenocarcinoma (EAC, the most common pathological type of EC), and to explore the diagnostic and the differential diagnostic accuracy using multiparametric MRI.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThis study was observational and retrospective. The patients orally consented to the use of their medical records by telephone. All procedures including orally consent performed in studies involving human participants were approved by the Medical Ethics Committee of the First Affiliated Hospital of Fujian Medical University.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePatients\u003c/h2\u003e\n \u003cp\u003ePatients with pathologically proven UCS or EAC from January 2014 to June 2022 were identified from our hospital. Patients who met the following criterias were included in the study: 1) those who preoperatively underwent standard pelvic MRI examination including T2WI, T1WI, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), contrast-enhanced (CE)-T1WI on the arterial and venous phases; 2) those with pathologically proven UCS or EAC after surgical resection. Patients who met the following criterias were excluded: 1) history of chemotherapy or radiation treatment before MRI examination; 2) those with poor image quality due to effect of artifact. Thus, a total of 51 patients were enrolled for analysis, including 17 UCS and 34 EAC patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical and pathological features\u003c/h2\u003e\n \u003cp\u003eThe clinical and pathological features were recorded in our study, including age, premenopause or postmenopause, clinical manifestation, the level of CA125, pathological pattern, FIGO stage, lymphatic metastasis, lymph-vascular space invasion (LVSI).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eMRI techniques\u003c/h2\u003e\n \u003cp\u003eA 3.0 T MR scanner was used for every examination (MAGNETOM Verio, Siemens Healthineers). The standard dedicated pelvic MRI protocol consisted of the following sequences, transverse volumetric interpolated breath-hold examination with fat-suppression (VIBE)-T1WI, transverse and sagittal turbo spin echo (TSE)-T2WI, and DWI (b value\u0026thinsp;=\u0026thinsp;50 and 800s/mm\u003csup\u003e2\u003c/sup\u003e). For the arterial and venous phases, CE-T1WI was performed in the transverse and sagittal planes at 40\u0026ndash;60 seconds and 90\u0026ndash;110 seconds, after intravenous injections of gadobenate dimeglumine (MultiHance, Bracco, 0.2mmol/kg body weight, rate of 3.0mL/s). The MRI protocol is shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMRI protocol\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSequences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTR (ms)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTE (ms)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlice Thickness (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntersection Gap (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFOV (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMatrix\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSE-T2WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360 x 360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e384 x 384 x 70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400 x 300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 x 180 x 85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVIBE-T1WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360 x 300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e384 x 384 x 70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasurement of MRI values\u003c/h2\u003e\n \u003cp\u003eAll MRI were retrospectively reviewed by two radiologists with 5 and 15 years of experience in pelvic MRI, respectively, who were blinded to the clinical and the pathologic features (either UCS or EAC).\u003c/p\u003e\n \u003cp\u003eImage findings assessed included tumor size (in anterior-posterior, longitudinal, and transverse dimensions) on the sagittal T2WI, the boundary was clear or unclear, presence or absence of tumor cystic degeneration and necrosis or hemorrhage, the signal intensity of the tumor was homogeneous or heterogeneous on T2WI, invasion the depth of the myometrium, adjacent tissue invasion, lymph node metastasis, FIGO stage, the presence or absence of feeding arteries, the degree of enhancement, whether there were areas of strong enhancement. Cystic degeneration or necrosis was defined as areas of high signal intensity on T2WI without enhancement after administration of contrast medium. Hemorrhage was defined as areas of high signal intensity on T1WI.\u003c/p\u003e\n \u003cp\u003eWe measured both tumor regions of interest (ROIs) and normal myometrium of uterus ROIs on T2WI (a), DWI (high-b-value) (b), ADC (c), T1WI (d), CE-T1WI (e, f) on the arterial phase and the venous phase, respectively (Fig.\u0026nbsp;1). The tumor ROI was placed as a single ROI at the level where the largest lesion could be measured on T2WI, T1WI, DWI. The maximum, mean, minimum ADC values (10\u003csup\u003e\u0026ndash;3\u003c/sup\u003emm\u003csup\u003e2\u003c/sup\u003e/s) and CE-T1WI on the arterial phase and the venous phase were measured in a circular ROI in the representative location as large as possible within the tumor (ADCmax, ADCmean, ADCmin, REAmax, REAmean, REAmin, REVmax, REVmean, REVmin, respectively). The ROIs were placed on solid portion of the tumors to avoid including necrosis, cystic degeneration, or hemorrhage as much as possible by reference to all MRI including T2WI, T1WI, CE-T1WI, DWI. The normal myometrium of uterus ROIs were measured on the same slice of the lesion.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCalculated\u003c/strong\u003e: The ratio of the tumor to the myometrium of uterus on T2WI, T1WI, DWI, ADC, CE-T1WI was calculated as follows: RSI (the relative signal intensity)\u0026thinsp;=\u0026thinsp;the signal intensity of tumor/normal myometrium of uterus (RSI-T2, RSI-T1, RSI-DWI, RSI-ADCmax, RSI-ADCmean, RSI-ADCmin); RE (the relative enhancement signal intensity) = (the contrast-enhanced SI of tumor - the unenhanced SI of tumor)/the contrast-enhanced SI of normal myometrium of uterus; The ROIs were drawn on the most, the average and the lowest enhancing component of the tumor on the contrast-enhanced T1WI obtained at the arterial and venous phases (REAmax, REAmean, REAmin, REVmax, REVmean, REVmin). REA\u0026thinsp;=\u0026thinsp;RE on the arterial phase, REV\u0026thinsp;=\u0026thinsp;RE on the venous phase.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eCategorical variables were described as frequencies and percentages. Continuous variables were described as means and standard deviations or medians and quartiles. The X\u003csup\u003e2\u003c/sup\u003e test was used to compare categorical characteristics between disease groups. Differences between continuous data were tested for significance using an independent t test. Receiver operating characteristic (ROC) analysis was used to assess the model\u0026rsquo;s performance, and the area under the curve (AUC) was used to evaluate the ability of prediction. The Youden index of sensitivity and specificity was used to determine the optimal cut-of values for UCS. All tests were two-sided, and p values of 0.05 or less were considered statistically significant. Statistical analysis was carried out using SPSS version 20.0.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eClinical characteristics\u003c/p\u003e\n\u003cp\u003eThe preoperative clinical features of the 51 cases were reviewed (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The mean age of UCS patients was higher than that of EAC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were only 1 (5.9%) premenopausal woman with UCS and 15 (44.1%) premenopausal women with EAC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). 13(76.5%) patients with UCS and 28 (82.4%) patients with EAC suffered from abnormal vaginal bleeding, there was no statistical difference between the two groups (p\u0026thinsp;=\u0026thinsp;0.325). The level of CA125 was not significantly different between the two diseases (p\u0026thinsp;=\u0026thinsp;0.579).\u003c/p\u003e\n\u003cp\u003ePathological features\u003c/p\u003e\n\u003cp\u003eUCS and EAC did not significantly differ in depth of myometrial invasion (p\u0026thinsp;=\u0026thinsp;0.426) and FIGO stage (p\u0026thinsp;=\u0026thinsp;0.214) on pathology. Lymphatic metastasis and LVSI were also similar between the two disease groups (p\u0026thinsp;=\u0026thinsp;0.904, p\u0026thinsp;=\u0026thinsp;0.692, respectively). Of UCS cases, 52.94% were heterologous, 23.53% were homologous, and the rest 23.53% were an unspecifed subtype. The proportion of grade 1\u0026ndash;3 in the EAC cases was 17.65%, 29.41%, 52.94%, respectively. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical and pathological features of UCS and EAC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUCS (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEAC (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, mean (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.82\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostmenopausal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical presentation, no.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbnormal vaginal bleeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePelvic pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbdominal mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInvasion depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;1/2 myometrium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1/2 myometrium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIGO stage, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elymphatic\u0026nbsp;metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCS subtype, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHomologous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(23.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeterologous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(52.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(23.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEAC classification, no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(17.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(29.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(52.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMRI characteristics\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e summarized the MRI characteristics of the 17 UCS (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and 34 EAC (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) cases. The anterior-posterior and transverse dimensions were significantly larger in UCS than in EAC (P\u0026thinsp;=\u0026thinsp;0.011, P\u0026thinsp;=\u0026thinsp;0.015, respectively). However, there was no significant difference between UCS and EAC in longitudinal dimension (P\u0026thinsp;=\u0026thinsp;0.077). The boundary that was clear or unclear was not obviously different in the UCS and EAC (P\u0026thinsp;=\u0026thinsp;0.910). The cystic degeneration or necrosis and intratumoral hemorrhage were more likely occurred in UCS than EAC patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively) (Fig. 5). Regarding the signal intensity of T2WI, our study demonstrated that UCS was more heterogeneous than EAC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. 5). The tumors of UCS had higher RSI-T2 than that of EAC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), however, there were no significant difference between UCS and EAC on RSI-T1, RSI-DWI (P\u0026thinsp;=\u0026thinsp;0.202, P\u0026thinsp;=\u0026thinsp;0.771, respectively). Both UCS and EAC showed low or equal signal intensity on T1WI and high signal intensity on DWI. Apart from that, ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean were significantly different between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, P\u0026thinsp;=\u0026thinsp;0.019, respectively), the ADC values of UCS group were higher than EAC group. While ADCmin and RSI-ADCmin were similar between the two disease groups (p\u0026thinsp;=\u0026thinsp;0.753, p\u0026thinsp;=\u0026thinsp;0.106, respectively). The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. The feeding artery and areas of strong enhancement were more likely to appear in UCS than EAC (P\u0026thinsp;=\u0026thinsp;0.021) (Fig. 5). Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e showed ROC results for significant features. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior, transverse dimentions, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Our study showed that REAmax threshold value of 0.23 can differentiate UCS from EAC with 94% sensitivity, 88% specificity, and 0.97 AUC.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMRI characteristics of UCS and EAC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUCS (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEC (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor dimension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnterior\u0026ndash;posterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLongitudinal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransverse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBoundary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCystic degeneration or necrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignalintensity on T2WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehomogeneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eheterogeneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-T2(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-T1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-DWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADCmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1166.18\u0026thinsp;\u0026plusmn;\u0026thinsp;325.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e752.35\u0026thinsp;\u0026plusmn;\u0026thinsp;81.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADCmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e598.23\u0026thinsp;\u0026plusmn;\u0026thinsp;145.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e608.94\u0026thinsp;\u0026plusmn;\u0026thinsp;94.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADCmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e904.85\u0026thinsp;\u0026plusmn;\u0026thinsp;187.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e672.06\u0026thinsp;\u0026plusmn;\u0026thinsp;86.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-ADCmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-ADCmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-ADCmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREAmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREAmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREAmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREVmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREVmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeeding\u0026nbsp;artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAreas of strong enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eROC curve\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eparameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnterior-posteriordimension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.555\u0026ndash;0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransversedimension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.535\u0026ndash;0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.739\u0026ndash;0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADCmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADCmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.786\u0026ndash;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-ADCmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.732\u0026ndash;0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRSI-ADCmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.580\u0026ndash;0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREAmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u0026ndash;1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREAmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.793\u0026ndash;0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREAmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.869-1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u0026ndash;0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREVmin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.523\u0026ndash;0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eREVmean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.674\u0026ndash;0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eCI: confidence interval\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUCS is misdiagnosed as EC frequently at that time due to a lack of pathological specimens. A research found that 75% of UCS patients were preoperatively mistakenly diagnosed as EC [2]. UCS is typically diagnosed correctly depending on ultimate pathological findings following surgical resection [16]. UCS has an aggressive clinical course and a poor overall prognosis. Even if UCS is in stage I, the 5 year survival rate is still less than 50%. Therefore, uterine tumor imaging can play an important role if it can help the clinician make a correct diagnosis early on, before the operation. UCS often occurs in postmenopausal females. Their frequency increases with age. The typical clinical symptom is abnomal vaginal bleeding, other symptoms include abdominal mass, abdominal pain.\u003c/p\u003e \u003cp\u003eAccording to histology, UCS is composed of epithelial and mesenchymal components. UCS is classified as heterologous or homologous according to the sort of cells that make up the sarcomatous component. Heterologous types include rhabdomyosarcoma, chondrosarcoma, osteosarcoma, or liposarcoma, and homologous types include fibrosarcoma, endometrial stromal sarcoma, or leiomyosarcoma. In either situation, the carcinomatous component may be made up of endometrioid, serous, or clear cell types. Recent investigations in immunohistochemistry, ultrastructure, and molecular biology have all pointed to carcinosarcomas being metaplastic carcinomas, with the mesenchymal component typically retaining at least some epithelial characteristics. As a result, some experts contend that UCS is better classified as a particular kind of EC. And cancerous components are the driving force for tumor progression. The risk factors and clinical manifestations of UCS are similar to those of EC [17].\u003c/p\u003e \u003cp\u003eRegard to the conspicuity of the tumor margin on T2WI, we predicted that UCS would reveal a clearer border between the tumor and myometrium because a carcinosarcoma is often a clearly defined exophytic mass [18]. However, there was no significant difference in this respect between UCS and EAC. This is probably due to the fact that MRI has a higher resolution of soft tissue and can clearly distinguish endometrial, myometry and interstitial limits. A number of MRI characteristics of UCS have been reported. Previous studies have shown that although UCS tends to appear as larger, heterogeneous tumors with a deep myometric invasion unlike EC, but MRI results are not specific and can reflect those of invasive EC [19]. In our study, 76.47% of UCS presented with mixed signals on T2WI, which is consistent with the complicated histopathological components. UCS has a combination of cancer and sarcoma, and sometimes even various sarcomas. The heterogeneous SI on T2WI has also been described as a feature of the UCS [2]. Hemorrhage, cystic degeneration or necrosis is common, which may lead to the heterogeneous SI of UCS on T2WI. EAC almost shows homogeneous SI on T2WI. Therefore, homogeneous SI on T2WI is a reliable indicator to distinguish between UCS and EAC. Our study also demonstrated that the UCS had higher RSI-T2 than EAC, while most EAC were equally or slightly higher signal intensity to the myometrium of uterus. Our observation of predominant hyperintensity of UCS on T2WI was in good agreement with previous reports [18]. There was not significantly different RSI-T1 between UCS and EAC. Both were of equally or slightly lower signal intensity on TIWI. Previous report showed a correlation between regions of medium to high signal intensity on T1WI of UCS with hemorrhage and necrosis. While presence of high signal area on T1WI is a rare but highly specific MRI feature of UCS that represents intratumoral hemorrhage [2].\u003c/p\u003e \u003cp\u003eThere was not significantly different RSI-DWI between UCS and EAC. Both two were malignant tumors with high signal intensity on DWI. DWI is gradually recognized in body imaging for the identification of malignancies, and ADC values have been used to describe tumor functions [20\u0026ndash;22]. DWI is a well-known method for finding uterine tumors and offers a large tissue contrast to evaluate the extent of muscle infiltration by these tumors [23,24]. Several recent reports have generally used ADC values obtained in uterine imaging to differentiate benign tumors from malignant ones [25,26]. UCS that contain cartilage, nerves, calcifications, necrosis, and hemorrhage would have high ADC values for component diversity. It was reported that the ADCmean of UCS was much higher than that of grade 2 or 3 EC [16]. Furthermore, the ADC map can also make a distinction between adenocarcinoma and sarcoma [27], since the ADC map was more heterogeneous in the sarcomatous component [28]. High ADC values have been reported to indicate high-grade malignancy with necrosis, which is often more common with UCS than EC [16]. This is similar with our findings that the ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. This result may possibly reflect the tissue heterogeneity of UCS including abundant microscopic necrotic regions and epithelial cystic components, which could increase the ADC values. The ADCmin and RSI-ADCmin values were measured in the solid portion of the tumor. Both are high-grade malignant tumor components, so they have similar minimum ADC values. Previous research on EC has demonstrated that significant difference in ADC values that help distinguish between histological grades, with high-grade tumors producing low ADC values and low-grade tumors producing high ADC values [25,29].\u003c/p\u003e \u003cp\u003e UCS was enhanced equally as much as or more strongly than uterine myometrium, and was enhanced more strongly than EC, in good agreement with previous reports [2,18]. Takemori et al. [30] showed that the sarcomatous component was enhanced more strongly than the carcinomatous component on contrast-enhanced T1WI, this may be because the sarcomatous component had substantial vascularity. Therefore, UCS was more likely to occur the feeding artery and areas of strong enhancement than EAC. Ohguri et al. [2] also reported that all four UCS showed areas of early and persistent marked enhancement similar to that of uterine myometrium and found that the portions with high signal intensity in the early phase dynamic study corresponded histologically to sarcomatous components with prominent vascularity.\u003c/p\u003e \u003cp\u003eMatsuo et al. [31] reported that not only did the carcinoma component play a major role in tumor progression and survival, but also that the sarcoma component made a significant contribution. Tumour necrosis is assumed to be caused by chronic ischemic lesions caused by rapidly growing tumors [32]. Hypoxia is frequently present in necrosis, which causes the activation of hypoxia-inducible factors [33]. Ischemic regions lead to tumor progression by promoting overexpression of hypoxia-inducible factors under hypoxic circumstances [34]. These results indicate that the non-enhanced areas caused by necrosis probably reflect a highly aggressive tumor with active proliferation.\u003c/p\u003e \u003cp\u003eAs the EC is homogeneously enhanced lower than the myometry of the uterus in general, they observed that different patterns of contrast enhancement inside the anendometric tumor may increase the possibility of UCS. As a result, different patterns of enhancement within an endometrial mass may represent a mixture of different histopathologisms. UCS should be distinguished from EAC because their prognosis and treatment strategy are different [35]. However, the histologic results are sometimes misleading, as the biphasic nature may sometimes not be apparent until the entire tumor is investigated. In 13 of the 17 cases, there were significant areas of enhancement, while all EAC cases showed no obvious enhancement areas. There was a statistical difference between the two cases (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Tanaka et al. [18] reported that UCS mainly has strongly enhanced regions and unenhanced areas on T1WI within the mass. Therefore, a highly enhanced area can predict the possibility of UCS to diagnose a malignant tumor of the endometrium.\u003c/p\u003e \u003cp\u003eOther studies have shown that the UCS generally displayed early hyper-enhancement relative to the myometrium persisting into the delayed phase, whereas EC more frequently had hypo-enhancement relative to the myometrium [36]. Our study showed that the REAmax, REAmin, REAmean, REVmax, REVmin and REVmeanof UCS were all higher than EAC. This result indicated that UCS was associated with higher enhancement during the arterial phase and the venous phasecompared to EAC. Emoto and colleagues reported that UCS had greater angiogenic activity than EC due to over-expression of VEGF in cancer cells and expression of the Ang-2 gene at the periphery of the tumour [37]. As previously published data have demonstrated that conventional contrast-enhanced MRI cannot distinguish UCS from EC, we believe that the unique ability and higher diagnostic accuracy of semiquantitative parameters of the REAmax, REAmin, REAmean, REVmax, REVmin and REVmean to differentiate UCS from EAC will have a significant impact in clinical practice. UCS often shows progressive or persistent enhancement, while EAC often shows mild enhancement [38]. This is another important differential point to distinguish UCS from EAC. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior, transverse dimensions, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively. Our study showed that REAmax threshold value of 0.23 could differentiate UCS from EAC with 94% sensitivity, 88% specifcity, and 0.97 AUC. The ROC curve has been widely applied in the evaluation of radiologic imaging, and diagnostic accuracy is characterized by the combination of sensitivity and specificity. A strongly enhanced area may predict the possibility of UCS to EAC.\u003c/p\u003e \u003cp\u003eThere are several limitations to our study. First, this is a retrospective study using a small sample size of 17 patients because of the rarity of UCS and therefore subject to potential selection bias. A larger sample of data is needed to confirm these findings. Second, studies were not all carried out on the same kind of MRI scanner, and there were some variations in protocol. Third, because some instances lacked the necessary sequences, we were unable to assess the value of MR spectroscopy or perfusion imaging. Fourth, we did not contrast the MRI findings with the pathological features. Future research should use radiological pathological correlation to validate the imaging characteristics of UCS identified in this study.\u003c/p\u003e \u003cp\u003eIn conclusion, UCS was more common in postmenopausal patients and the main manifestation was abnormal vaginal bleeding. The T2WI signal intensity was more heterogeneous in UCS than EAC. The ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. A strongly enhanced area may predict the possibility of UCS to EAC. Our study showed that REAmax threshold value of 0.23 can differentiate UCS from EAC with 94% sensitivity, 88% specifcity, and 0.97 AUC. Therefore, based on semiquantitative characteristics and the enhancement pattern, MRI may be utilized as a biomarker to distinguish UCS from EAC, which may help with appropriate preoperative characterisation and therapy stratification in these individuals. These results need to be confirmed by prospective studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the participants\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDairong Cao and Xiaodan Chen contributed to the study idea, designed the study and were involved at all stages. Xiaodan Chen and Qingyong Guo conducted the analysis and interpretation of results and wrote the manuscript. Xiaorong Chen was involved instudy design and provided clinical information. Wanjing Zheng was involved in data analysis and supervision. Yaqing Kang collected data and prepared the tables. All authors discussed the results, commented on the manuscript versions and approved this final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funds, grants, or other support was received.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients orally consented to the use of their medical records by telephone. All procedures including orally consent performed in studies involving human participants were in accordance with relevant guidelines and regulations\u0026nbsp;and approved by the Medical Ethics Committee of the First Affiliated Hospital of Fujian Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eD\u0026apos;Angelo E, Prat J. Pathology of mixed M\u0026uuml;llerian tumours [J]. Best Pract Res Clin Obstet Gynaecol, 2011, 25(6): 705-718.\u003c/li\u003e\n\u003cli\u003eOhguri T, Aoki T, Watanabe H, et al. 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Jpn J Radiol, 2017, 35(8): 472-483.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Magnetic resonance imaging, uterine carcinosarcoma, endometrioid adenocarcinoma, diagnosis, differential diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-3099408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3099408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe retrospectively evaluated clinical and MRI findings in 17 patients with UCS and 34 patients with EAC proven by histologically. The following clinical and pathological features were evaluated: age, post- or pre-menopausal, clinical presentation, invasion depth, FIGO stage, lymphaticmetastasis. The following MRI features were evaluated: tumor dimension, cystic degeneration or necrosis, hemorrhage, signalintensity (SI) on T2-weighted images (T2WI), relative SI of lesion to myometrium on T2WI, T1WI, DWI, ADCmax, ADCmin, ADCmean (RSI-T2, RSI-T1, RSI-DWI, RSI-ADCmax, RSI-ADCmin, RSI-ADCmean), ADCmax, ADCmin, ADCmean, the maximum, minimum and mean relative enhancement (RE) of lesion to myometrium on the arterial and venous phases (REAmax, REAmin, REAmean, REVmax, REVmin, REVmean). Receiver operating characteristic (ROC) analysis and the area under the curve (AUC) were used to evaluate prediction ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe mean age of UCS was higher than EAC. UCS occurred more often in the postmenopausal patients. UCS and EAC did not significantly differ in depth of myometrial invasion, FIGO stage and lymphatic metastasis. The anterior-posterior and transverse dimensions were significantly larger in UCS than EAC. Cystic degeneration or necrosis and hemorrhage were more likely occurred in UCS than EAC. The tumor on T2WI was more heterogeneous in UCS. The RSI-T2, ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior, transverse dimensions, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively. Our study showed that REAmax threshold value of 0.23 can differentiate UCS from EAC with 94% sensitivity, 88% specificity, and 0.97AUC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion \u003c/strong\u003eMultiparametric MRI features may be utilized as a biomarker to distinguish UCS from EAC.\u003c/p\u003e","manuscriptTitle":"Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-07 15:21:47","doi":"10.21203/rs.3.rs-3099408/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-07-09T07:46:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-03T03:31:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-02T14:58:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2023-06-23T08:37:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20299900-272b-4e06-8dc1-709b4f386326","owner":[],"postedDate":"July 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-02-26T15:08:02+00:00","versionOfRecord":{"articleIdentity":"rs-3099408","link":"https://doi.org/10.1186/s12880-024-01225-4","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2024-02-19 15:01:50","publishedOnDateReadable":"February 19th, 2024"},"versionCreatedAt":"2023-07-07 15:21:47","video":"","vorDoi":"10.1186/s12880-024-01225-4","vorDoiUrl":"https://doi.org/10.1186/s12880-024-01225-4","workflowStages":[]},"version":"v1","identity":"rs-3099408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3099408","identity":"rs-3099408","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}