MRI-based Radiomics and ADC Values Are Related to Recurrence of Endometrial Carcinoma：A Preliminary Analysis 

MRI-based Radiomics and ADC Values Are Related to Recurrence of Endometrial Carcinoma：A Preliminary Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MRI-based Radiomics and ADC Values Are Related to Recurrence of Endometrial Carcinoma：A Preliminary Analysis Kaiyue Zhang, Yu Zhang, Xin Fang, Jiangning Dong, Liting Qian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-776782/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC). Methods: One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed. Baseline clinicopathological features and ADC values were analyzed. Radiomic parameters were extracted on T2 weighted imaging and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). Kaplan–Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radiology with recurrence. Results: ADC values showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, lymphovascular space invasion (LVSI), ADC value and radscore were associated with recurrence. In multivariate Cox analysis, pathological types, ADC and radscore were independent risk factors for recurrence. Conclusions: ADC value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan. Cancer Biology Endometrial neoplasms Recurrence Risk Factors Apparent diffusion coefficient Radiomics Figures Figure 1 Figure 2 Figure 3 1. Background Endometrial carcinoma (EC) is the sixth most commonly diagnosed cancer in women. With the increasing prevalence of risk factors such as physical inactivity and overweight, the incidence of endometrial cancer continues to rise [ 1 , 2 ]. Surgery is the main method for the initial treatment of EC including laparoscopic or robotic resection of uterus, cervix, fallopian tube and ovary, and sentinel lymph node assessment[ 3 ]. Although most endometrial cancers can be diagnosed at an early stage and have 5-year survival rate of over 90%, recurrence and final mortality approximately occur in 20% of endometrioid carcinoma (type I) and 50% of non-endometrioid carcinoma (type II)[ 4 – 6 ]. Women with recurrent or metastatic diseases have 5-year survival rates as low as 17%-55%[ 7 , 8 ]. Unfortunately, little progress has been made in improving the survival rate of EC in the past decades. Therefore, early identification of risk factors for recurrence is an important challenge to improve the prognosis of EC patients. Clinicopathological factors, such as FIGO stage, pathological type and muscular invasion, etc. are common prognostic factors that affect the formulation of surgical plan, but they can only be accurately evaluated after surgery[ 9 ]. Due to the limitations of specimen collection, preoperative endometrial biopsy may be difficult to fully reflect the characteristics and heterogeneity of the tumor. Therefore, it is necessary to develop an early, comprehensive and non-invasive evaluation method to evaluate the possible adverse prognosis of EC. MRI is an important tool to preliminarily assess the extent of EC lesions. The apparent dispersion coefficient (ADC) of diffusion-weighted imaging (DWI) can reflect the malignancy of the tumor, which has been proved to be valuable in the diagnosis, typing and grade of EC[ 10 , 11 ], but its role in the prognostic assessment of EC remains unclear. Radiomics mines pixel distribution features from radiological images and transforms them into quantitative data, reflecting the heterogeneity within tumors [ 12 ]. Radiomic parameters derived from MRI, CT and PET-CT have been suggested as effective tools for diagnosis, risk assessment or treatment response of malignant tumors[ 13 – 15 ]. The purpose of this study is to explore whether postoperative recurrence of patients with EC can be reflected in MRI-based ADC value and radiomics. 2. Methods 2.1 Patient population This retrospective study was approved by the institutional review board and the informed consent was waived. Patients with EC who underwent 3.0 MRI before treatment in our institution from January 2015 to December 2019 were included retrospectively. Inclusion criteria are as follows: (a) Pathologically confirmed EC; (b) available clinical and postoperative pathological data; (c) MRI was performed within 1 month before surgery; (d) surgical treatment and follow-up were performed in our hospital. Exclusion criteria are as follows: (a) diagnosis of malignant tumor other than endometrial cancer (n = 4); (b) distant metastasis occurred at the time of diagnosis (IVB stage) (n = 5); (c) tumors were invisible on MRI or MRI with serious motion artifact (n = 24); (d) incomplete pathological report or medical record (n = 14); (e) patients lost to follow-up (n = 20). Finally, a total of 174 patients diagnosed with EC were identified. The last follow-up was in March 2021. 2.2 MR Imaging All patients underwent pelvic MRI and DWI before operation. MRI was performed on a 3.0T MRI system (Signa Excite HD, GE, Milwaukee, WI, USA) using an 8-channel body coil. Prior to MRI examinations, patients fasted for at least 4 hours and were given intramuscular 15 mg shyoscine butylbromide half an hour before examination. During image acquisition, the patient remained supine with a semi-filled bladder. Scanning sequence and parameters: (1) Axial T1-weighted images (T1WI): a field of view (FOV): 38 cm × 26 cm, repetition time (TR): 500 ms, echo time (TE): 7.2 ms, slice thickness: 6 mm, inter-slice gap: 2 mm, matrix size: 352 × 192. (2) Axial and axial fast spin-echo (FSE) T2WI: FOV: 24 cm × 24 cm, TR: 4600 ms, TE: 68 ms, slice thickness: 3 mm, inter-slice gap: 1 mm, matrix size: 320 × 256; (3) Oblique sagittal T2WI: FOV: 26 cm × 24 cm, TR: 4600 ms, TE: 68 ms, slice thickness: 6 mm, inter-slice gap: 2 mm, matrix size: 320 × 256; (4) Axial DWI: FOV: 38 cm × 26 cm, TR: 4000 ms, TE: 65 ms, slice thickness: 4 mm, inter-slice gap: 1 mm, matrix size: 96 × 130, b-value: 0 and 1000 mm 2 /s. 2.3 Histologic and pathologic diagnosis All surgical specimens were examined and reported by gynecologic pathologists. Tumor staging were performed according to standard 2018 FIGO criteria. Myometrial invasion , lymphovascular space invasion (LVSI) and lymph node metastasis (LNM) were confirmed under microscope according to the corresponding diagnostic criteria. Postoperative follow-up of patients: 3 to 6 months for the first 2–3 years, 6 months until 5 years, and then annually, any time when there are related symptoms such as vaginal bleeding, abdominal distension and pain. Surveillance included gynecological examination, imaging and pathological biopsy if necessary. 2.4 Image Analysis The images were assessed by two radiologists with 8 and 10 years (reader 1 and reader 2) of professional experience in pelvic MRI independently. Moreover, they were both blinded to each other’s results, and pathological and clinical data. Tumor was defined as a mass with signal hyperintensity on DWI and hypointensity on ADC map, compared with the signal of surrounding adjacent tissues. Meanwhile, T2WI sequence was referenced. ADC value measurements of the tumor were executed on an ADC map using GE Advantage Workstation 4.6, Function Tool software. The region of interest (ROI) was manually delineated in the slice containing the largest tumor layer, including the largest tumor area as much as possible and avoiding bleeding and necrotic tissue (Figure 1.A−C). Each reader measured ADC values for each patient three times, and the average value was taken. Tumor segmentation were performed on axial T2WI sequence using ITK-SNAP (Version 3.6.0, http:// www.itksnap.org) software by reader 2. ROIs were sketched manually on all MRI levels containing tumor, and the ROIs covered the whole tumor as much as possible. Then the ROIs of all layers were fused to get the three-dimensional volume of interest (VOI). Finally, radiomic parameters were extract from VOIs using AK (Analysis Kit, Kinetics Version 2.1, GE Healthcare) software, following the IBSI standards, with a total of 1130 (Figure 1.D−E). To investigate the stability of radiomic features extracted by different readers and the same reader, 30 patients were randomly selected for tumor segmentation 2 weeks later. 2.5 Statistical Analysis The data were analyzed with SPSS v. 26.0 (Chicago, IL, USA) and R (Version 3.6.1, http://www.r-project.org) .Patient clinicopathological characteristics were compared using a t -test (age, ADC and radscore) or chi-square test (FIGO stage, tumor size, pathological types, myometrial invasion, LVSI and LNM) . The intraclass correlation coefficient (ICC) was used to evaluate the intra-observer and inter-observer consistency of ADC values and radiomic parameters. The ADC values having higher intra-observer ICC was retained. The radiomic features with intra-observer and inter-observer ICC >0.75 were considered stable and were retained for subsequent analyses. Logistic regression analysis was used to select radiomic parameters. The radscore was calculated based on the regression coefficient of the selected radiomic features using multi-factor linear weighting. ADC value and radscore were compared by t-test. Receiver operating characteristic (ROC) curve and Youden index were used to obtain cut-off values of ADC vale and radscore. Spearman's bivariate correlation test was used to analyze the correlation between ADC value, radscore and recurrence. The Kaplan–Meier method was used to calculate survival rate and draw survival curve, Log–rank method was used for univariate analysis. Variables with P < 0.05 were included in a multivariate Cox regression model and independent prognostic factors of survival were identified with P<0.05. 3. Results 3.1 Patient characteristics and outcomes A total of 174 patients with EC treated in our institution were included, 162 patients underwent laparoscopic surgery and 12 patients had open laparotomy. Among all the patients, 48 patients received postoperative radiotherapy, and 105 patients received adjuvant chemotherapy or concurrent chemotherapy. The median follow-up of 174 patients was 31 months (range, 4–69 months). The median follow-up time of recurrent cohort was 18 months (range, 4–50 months). Tumor recurrence was recorded in a total of 26 (14.9%) patients of the 174 cases. There were 3 (11.5%) isolated pelvis recurrences, 1 (3.9%) isolated vaginal recurrence, 5 isolated abdominal failure, 6 (23.1%) combined pelvic and distant failure, and 11 (42.3%) distant failure. The 3-year and 5-year disease-free survival (DFS) of the entire cohort were 91.2% (95%CI: 86.9-95.5%) and 75.2% (95%CI: 64.8-85.6%). The comparison of clinicopathological features between non-recurrent and recurrent cohorts are shown in Table 1. The onset age of recurrent group was significantly higher than that of non-recurrent group (60.1±11.5 vs 55.6±8.5, P=0.018). There was a significant difference in FIGO staging between recurrence group and non-recurrence group (P<0.001). The pathological types were 137 cases of endometrioid carcinoma and 58 cases of non-endometrioid carcinoma, and there were more recurrences in patients with non-endometrioid carcinoma compared to those with endometrioid carcinoma (37.8% vs 9.5%, P<0.001). Pathological evaluation showed that 38.1% of all patients included had deep myometrial invasion (≥50%), and there were more recurrences in patients with myometrial invasion ≥ 50% compared to those with < 50% (31.2% vs 9.5%, P<0.001). The recurrence rate of LVSI present patients was significantly higher than that of LVSI absent patients (37.9% vs 11.0%, P<0.001). However, there was no significant difference between the recurrence and no-recurrence groups with regards to tumor size and LNM. Table 1 Clinicopathological characteristics of recurrent and non-recurrent endometrial carcinoma characteristics Total(n=174) No recurrence(n=147) Recurrence(n=27) p -value Age at diagnosis (yr) 56.3±9.1 55.6±8.5 60.1±11.5 0.018 FIGO stage <0.001 ⅠA 97 88(90.7%) 9(9.3%) ⅠB 19 15(78.9%) 4(21.1%) Ⅱ 31 29(93.5%) 2(6.5%) ⅢA 8 4(50.0%) 4(50.0%) ⅢC1 10 8(80.0%) 2(20.0%) ⅢC2 6 3(50.0%) 3(50.0%) ⅣA 3 0 3(100.0%) Tumor size (cm) 0.227 <3.5 83(47.7%) 73(88.0%) 10(12.0%) ≥3.5 91(52.3%) 74(81.3%) 17(18.7%) Pathological types <0.001 Endometrioid carcinoma 137(78.7%) 124(90.5%) 13(9.5%) Non-endometrioid carcinoma 37(21.3%) 23(62.2%) 14(37.8%) Myometrial invasion <0.001 <50% 126(72.4%) 114(90.5%) 12(9.5%) ≥50% 48(38.1%) 33(68.8%) 15(31.2%) LVSI <0.001 Absent 145(83.3%) 129(89.0%) 16(11.0%) Present 29(16.7%) 18(62.1%) 11(37.9%) LNM 0.068 Absent 158(90.8%) 136(86.1%) 22(13.9%) Present 16(9.2%) 11(68.8%) 5(31.2%) LVSI: lymphovascular space invasion; LNM: lymph node metastasis. (page 9, line 12) 3.2 Extraction of ADC value and radiomic features A total of 1130 radiomic features were obtained from axial T2 sequence, of which 112 features had both intra-observer and inter-observer ICC > 0.75. Through multivariate logistic regression, 4 radiomic features were obtained as independent discriminant features, which were glcm_DifferenceEntropy, firstorder_Uniformity, wavelet-HHL_firstorder_Mean and lbp-3D-m1_firstorder_MeanAbsoluteDeviation (An additional movie file shows this in more detail [see Additional file 1]), then the radiomics score (radscore) of each patient was calculated as a new variable, according to the linear combination of regression coefficients. The radscore of recurrence group was significantly higher than that of non-recurrence group, and the score was positively correlated with recurrence (Table 2). ADC in recurrence group was significantly lower than that in non-recurrence group, and was negatively correlated with recurrence. Table 2 ROC curve results of ADC values and radiomic parameters No recurrence Recurrence p value AUC Cutoff value Sensitivity (%) Specificity (%) Correlation with recurrence status (rs) ADC 0.908±0.177 0.779±0.150 <0.001 0.703 0.817 70.1 63 -0.263 Radscore -2.65±1.48 -0.89±1.37 <0.001 0.813 -1.342 66.7 85 0.402 ADC: apparent diffusion coefficient; AUC: area under curve. ROC curve results of ADC and radscore for predicting recurrence are shown in table 3. The cut-off values of ADC and radscore were 0.817 and -1.342, according to Youden index. The ROCs of ADC and radscore are shown in Figure 2. 3.3 Univariate and multivariate analysis of factors associated with recurrence Univariate analysis revealed that five factors were predictive of recurrence; FIGO stage (hazard ratio [HR]=5.203; 95% CI=2.43–11.14; P<0.001), pathological types (HR=5.20; 95% CI=2.43–11.14; P<0.001), depth of muscular invasion (HR=3.33; 95% CI=1.56–7.13; P=0.002), LVSI (HR=4.44; 95% CI=2.04–9.66; P<0.001), ADC (HR=3.37; 95% CI=1.54–7.37; P=0.02) and radscore (HR=8.67; 95% CI=3.89–19.34;P<0.001) were significantly correlated with disease-free survival (Figure 3). There was no association between the risk of recurrence and age, tumor size and LNM (Table 3). In multivariate analysis, non-endometrioid (HR=3.05; 95% CI=1.36–6.87; P=0.007), ADC (HR=2.37; 95% CI=1.07-5.24; P=0.034) and radscore (HR=3.86; 95% CI=1.57-9.49; P=0.003) were independent predictors of recurrence (Table 4). Table 3 Univariate analysis of recurrence factors for DFS of endometrial carcinoma Predictive factors Recurrence HR 95%CI p -value Age at diagnosis 1.460 0.67–3.26 0.354 FIGO stage (Ⅰ–Ⅱ vs. Ⅲ–Ⅳ) 5.203 2.43–11.14 <0.001 Tumor size (<3.5 cm vs. ≥3.5 cm) 1.606 0.74–3.51 0.235 Pathological types (endometrioid vs. non-endometrioid) 5.20 2.43–11.14 <0.001 Myometrial invasion (<50% vs. ≥50%) 3.33 1.56–7.13 0.002 LVSI (absent vs. present) 4.44 2.04–9.66 <0.001 LNM (absent vs. present) 2.26 0.86–6.00 0.1 ADC (<0.817 vs. ≥0.817) 3.37 1.54–7.37 0.02 Radscore (<-1.34 vs. ≥-1.34) 8.67 3.89–19.34 <0.001 LVSI: lymphovascular space invasion; LNM: lymph node metastasis; ADC: apparent diffusion coefficient; HR: hazard ratio; CI: confidence interval. Table 4 Multivariate analysis of recurrence factors for DFS of endometrial carcinoma Predictive factors Recurrence HR 95%CI P value FIGO stage (Ⅰ–Ⅱ vs. Ⅲ–Ⅳ) 1.90 0.79–5.17 0.209 Pathological types (endometrioid vs. non-endometrioid) 3.05 1.36–6.87 0.007 Myometrial invasion (<50% vs. ≥50%) 1.19 0.45–3.16 0.723 LVSI (absent vs. present) 2.26 0.84–6.09 0.11 ADC (<0.817 vs. ≥0.817) 2.37 1.07-5.24 0.034 Radscore (<-1.34 vs. ≥-1.34) 3.86 1.57-9.49 0.003 LVSI: lymphovascular space invasion; ADC: apparent diffusion coefficient; HR: hazard ratio; CI: confidence interval. 4. Discussion Traditionally, prognostic factors for EC include surgical stage, histological factors[16, 17], and molecular factors such as P53 mutation[18]. This information is often only available from histological evaluation after surgery, so preoperative risk assessment of recurrence is limited. As a routine preoperative imaging examination of EC, MRI can provide anatomical information, and DWI and radiomics analysis may reveal the prognostic information of EC. In addition to clinical and pathological factors, we also investigated the correlation between ADC value and radscore and recurrence in this study. We identified risk factors associated with recurrence and reduced survival of EC after surgery, including FIGO stage, pathologic type, depth of basal invasion, LVSI, ADC values, and radscore. Non-endometrioid cancer, ADC value and Radscore were independent predictors of EC recurrence, which can be used as clinically relevant tumor markers in preoperative risk stratification and prognosis assessment of EC. Although the number of recurrent patients is small in our study, we identified the clinical and pathological risk factors related to recurrence of EC patients including FIGO stage, pathological type, myometrial invasion depth and LVSI. These factors have been associated with recurrence or poor outcome in EC in other studies. Women who were initially diagnosed with advanced disease (FIGO Ⅲ−Ⅳ) had a higher risk of recurrence and were more likely to develop extrapervic metastases[8]. Patients with low-grade (grade 1-2) endometrioid cancer (type I) often have better outcomes than those with high-grade (grade 3) endometrioid cancer and non-endometrioid cancer (type II)[19]. A study exploring recurrence factors for stage 1 endometrioid adenocarcinoma observed that large tumor size, and muscular invasion were the most important predictors of recurrence[9]. The study of Bosse et al. [16] confirmed that substantial LVSI was the strongest prognostic factor for recurrence and metastasis and overall survival of EC. Notably, we did not find age of onset, tumor size, or LNM as factors related to the increased risk of recurrence. The subjects of our study were concentrated in the 50−60 years old, which was a homogeneous population, with few patients under 50 years old or over 70 years old. In our study, the tumor size was defined as the maximum diameter of the tumor, which was often the extent to which tumor tissue invaded the endometrium rather than the depth. So, there was no significant difference in tumor size between the relapsed and non-relapsed groups, and tumor size had no significant effect on survival. Less cases with positive lymph node metastasis may be the reason why LNM had no effect on survival time. ADC can reflect the diffusion degree of water molecules. The present study indicated that lower ADC was significantly related to the recurrence of EC and ADC was shown to be an independent predictor of DFS in multivariate analysis, which has been suggested in previous studies[20, 21]. ADC is closely related to cell density. Yan et al.[20] and Reyes-Pérez et al.[21] explained that the decrease of ADC can act as markers for tumor’s high cellularity, proliferation, perfusion and less extracellular space, which means higher tumor load, tumor residue and recurrence. Traditionally, clinicopathological factors such as tumor grade, subtype, depth of myometrial invasion, and nodal status have an impact on prognosis of EC. Several studies have reported that ADC was associated with histopathological risk factors of endometrial cancer[10, 22, 23], so it is not difficult to explain the correlation between ADC and prognosis and recurrence. Chen et al.[10] reported that type II EC had lower ADC values, and ADC values were important when identifying type II and type I ECs. The study of Jiang et al.[22] indicated that the ADC values of high grade, stage IB and high Ki-67 expression patients were significantly lower than those of low grade, stage IA and low Ki-67 expression patients with EC. Zhang et al.[23] reported that lower ADC were observed in tumor with deep myometrial invasion and LVSI than tumor without deep myometrial invasion and LVSI. Therefore, the lower ADC indicates that there may be more high-risk factors for endometrial cancer, and the greater the chance of recurrence. Heterogeneity is an important characteristic of malignant tumors and the basis of tumor recurrence and metastasis[24, 25]. Radiomics can extract features that cannot be observed by naked eyes and convert them into data information to quantify the heterogeneity in the image[26, 27]. Radiomics derived from MRI have been proposed as a reliable tool for accurate diagnosis and risk assessment in several cancer types, e.g. in cervical[28], brain[29], and breast[30]. Similarly, a study has reported that radiomics analysis based on MRI was an effective tool for preoperative risk stratification in EC[31]. In this context, MRI-based radiomics analysis was available to predict recurrence in patients with EC, and four radiomic parameters were selected to predict recurrence. Among these, firstorder_Uniformity was used to describe the uniformity of image array. Glcm_DifferenceEntropy was a measure of the randomness/variability in neighborhood intensity value differences. Wavelet and lbp features were the observation of the whole tumor range and the extraction of high-dimensional features. The radscore was calculated according to the combination of regression coefficients of each parameter, which was an independent predictor of relapse in both univariate and multivariate analyses. Patients were grouped into risk groups based on radscore, and patients with higher radscore had worse DFS. Sigmund et al[32]. reported that thirteen MRI-derived tumor radiomic parameters significantly predicted reduced recurrence- and progression-free survival in univariable Cox regression analysis, and T1c_Kurtosis2 was the top-ranked prognostic texture parameter independently predicted reduced survival. The study of Yan et al.[14] manifested that MRI-based radiomics achieved high diagnostic performance for predicting LVSI of EC preoperatively, and was helpful for early identification of poor prognosis. In addition, the whole-tumor radiomic features were found to significantly predict progression-free survival at hazard ratios of 4.6-9.8 in the research of Fasmer et al [33] , albeit in a small sample size. Although the sample size, MRI sequence, and radiomic parameter extraction methods were different in the previous study, we cannot deny that radiomics and texture analysis may mine more prognostic information than clinical factors, and can be used as a biomarker to assist clinical practice. There are some limitations in our research. This is a single-center small sample study, which needs to be further verified by a large multi-center database. The absence of stratification of staging and pathologic types may influence the estimation of survival and recurrence outcomes. A short follow-up time and less recurrence cases may limit the predictive value of the covariates. 5. Conclusion In conclusion, EC patients are at certain risk of recurrence, and early identification of risk factors for recurrence and enhanced treatment intensity are significant to improve prognosis. ADC values and MRI-derived radiomics may provide additional prognostic information in addition to traditional prognostic factors. Abbreviations Abbreviations Full name ADC apparent diffusion coefficient MRI magnetic resonance imaging EC endometrial carcinoma radscore radiomic score LVSI lymphovascular space invasion DWI diffusion-weighted imaging T1WI T1-weighted images FOV field of view TR repetition time TE echo time FSE fast spin-echo LNM lymph node metastasis ROI region of interest VOI volume of interest ICC intraclass correlation coefficient ROC receiver operating characteristic DFS disease-free survival AUC area under curve Declarations Ethics approval and consent to participate Ethics Committee of Anhui Provincial Cancer Hospital approved this retrospective study and the informed consent was waived (Ethics Approval No. 2021-FLK-01). Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declare that they have no competing interests. Funding 2020 SKY Image Research Fund (NO. Z-2014-07-2003-11) : Data analysis and manuscript publication. Authors' contributions Kaiyue Zhang: Investigation, Data acquisition, Formal analysis, Methodology, Software, Visualization, Writing - original draft, Writing - editing; Yu Zhang: Data curation, Project administration, Formal analysis, Methodology, Writing – review; Xin Fang: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Validation, Visualization, Writing – review; Jiangning Dong: Conceptualization, Project administration, Supervision, Validation, Writing - review. Liting Qian: Conceptualization, Project administration, Supervision, Validation, Writing – review. The author(s) read and approved the final manuscript. Acknowledgements The authors thank GE Healthcare for the technical assistance. Authors' information Kaiyue Zhang: MD, Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China. Yu Zhang: Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China. Xin Fang: MD, Department of Radiology, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhudong Road, Hefei 230031, China. Jiangning Dong: MD, Department of Radiology, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhudong Road, Hefei 230031, China. Liting Qian: PhD, Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Oncol Lett 2020; 20: 1033–1054.doi: 10.3892/ol.2020.11690 . Bokhman JV. Two pathogenetic types of endometrial carcinoma. Gynecol Oncol 1983; 15: 10–17.doi: 10.1016/0090-8258(83)90111-7 . Yan BC, Xiao ML, Li Y, Wei Qiang J. The diagnostic performance of ADC value for tumor grade, deep myometrial invasion and lymphovascular space invasion in endometrial cancer: a meta-analysis. Acta Radiol 2019; 284185119841988.doi: 10.1177/0284185119841988 . Reyes-Pérez JA, Villaseñor-Navarro Y, Jiménez de Los Santos ME, Pacheco-Bravo I, Calle-Loja M, Sollozo-Dupont I. The apparent diffusion coefficient (ADC) on 3-T MRI differentiates myometrial invasion depth and histological grade in patients with endometrial cancer. Acta Radiol. 2020 Sep;61(9):1277–1286. doi: 10.1177/0284185119898658 . Jiang JX, Zhao JL, Zhang Q, Qing JF, Zhang SQ, Zhang YM, Wu XH. Endometrial carcinoma: diffusion-weighted imaging diagnostic accuracy and correlation with Ki-67 expression. Clin Radiol. 2018 Apr;73(4):413 .e1-413.e6 . doi: 10.1016/j.crad.2017.11.011. Zhang Q, Ouyang H, Ye F, Chen S, Xie L, Zhao X, et al. Multiple mathematical models of diffusion-weighted imaging for endometrial cancer characterization: Correlation with prognosis-related risk factors. Eur J Radiol. 2020 Sep;130:109102. doi: 10.1016/j.ejrad.2020.109102 . Hua X, Zhao W, Pesatori AC, Consonni D, Caporaso NE, Zhang T, et al. Genetic and epigenetic intratumor heterogeneity impacts prognosis of lung adenocarcinoma. Nat Commun. 2020 May 18;11(1):2459. doi: 10.1038/s41467-020-16295-5 . Andor N, Graham TA, Jansen M, Xia LC, Aktipis CA, Petritsch C, et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat Med. 2016 Jan;22(1):105–13. doi: 10.1038/nm.3984 . Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol. 2017 Jan;72(1):3–10. doi: 10.1016/j.crad.2016.09.013 . Wu J, Tha KK, Xing L, Li R. Radiomics and radiogenomics for precision radiotherapy. J Radiat Res. 2018 Mar 1;59(suppl_1):i25-i31. doi: 10.1093/jrr/rrx102 . Fang J, Zhang B, Wang S, Jin Y, Wang F, Ding Y, et al. Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer. Theranostics. 2020 Jan 16;10(5):2284–2292. doi: 10.7150/thno.37429 . Li ZZ, Liu PF, An TT, Yang HC, Zhang W, Wang JX. Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients. Transl Oncol. 2021 Jun;14(6):101065. doi: 10.1016/j.tranon.2021.101065 . Kim S, Kim MJ, Kim EK, Yoon JH, Park VY. MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer. Sci Rep. 2020 Feb 28;10(1):3750. doi: 10.1038/s41598-020-60822-9 . Ueno Y, Forghani B, Forghani R, Dohan A, Zeng XZ, Chamming's F, et al. Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis. Radiology. 2017 Sep;284(3):748–757. doi: 10.1148/radiol.2017161950 . Ytre-Hauge S, Dybvik JA, Lundervold A, Salvesen ØO, Krakstad C, Fasmer KE, et al. Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. J Magn Reson Imaging. 2018 Dec;48(6):1637–1647. doi: 10.1002/jmri.26184 . Fasmer KE, Hodneland E, Dybvik JA, Wagner-Larsen K, Trovik J, Salvesen Ø, et al. Whole-Volume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer. J Magn Reson Imaging. 2021 Mar;53(3):928–937. doi: 10.1002/jmri.27444 . Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 14 Sep, 2021 Reviews received at journal 31 Aug, 2021 Reviewers agreed at journal 16 Aug, 2021 Reviewers invited by journal 16 Aug, 2021 Editor assigned by journal 09 Aug, 2021 Editor invited by journal 09 Aug, 2021 Submission checks completed at journal 09 Aug, 2021 First submitted to journal 02 Aug, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-776782","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":44679156,"identity":"16487fe3-bac8-401c-be23-b6e2585d596b","order_by":0,"name":"Kaiyue Zhang","email":"","orcid":"","institution":"Anhui Provincial Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kaiyue","middleName":"","lastName":"Zhang","suffix":""},{"id":44679157,"identity":"f09a1aaa-3119-4dfc-8957-cb2c2acea6a3","order_by":1,"name":"Yu Zhang","email":"","orcid":"","institution":"Anhui Provincial Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":44679158,"identity":"a2737a04-e61e-47d0-b143-54a7facdc78c","order_by":2,"name":"Xin Fang","email":"","orcid":"","institution":"First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Fang","suffix":""},{"id":44679159,"identity":"c601ed87-f79e-402e-808d-183639eb5ceb","order_by":3,"name":"Jiangning Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDCCA0AswWDDw8/fQJqWNBnJGQdI0cLAcNjGoCGBSB18x3sPv7DccZ7HgOEA44ePOURokTxzLs1C8sxtHnPmBmbJmduI0GJwI8fMQLLtNo9lwwE2Zl4StJzjMTiQQLwW4weSbQdI0CJ55owZg2RbMo/kjIPNxPmF73iP8WfJNjt7fv7mgx8+EqMFCNikJcA0YwNx6oGA+eMHotWOglEwCkbBiAQA4143YBSNXOcAAAAASUVORK5CYII=","orcid":"","institution":"Anhui Provincial Hospital Affiliated to Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jiangning","middleName":"","lastName":"Dong","suffix":""},{"id":44679161,"identity":"25ab63bf-c439-4322-9348-d747c89edf43","order_by":4,"name":"Liting Qian","email":"","orcid":"","institution":"Anhui Provincial Hospital Affiliated to Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liting","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2021-08-02 15:29:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-776782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-776782/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12326867,"identity":"1330941d-6100-40c7-9f1e-87ac5c1204ab","added_by":"auto","created_at":"2021-08-11 13:42:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":418921,"visible":true,"origin":"","legend":"MR images of a 52-yearold woman with endometrioid carcinoma of stage ⅠA. (A) Axial T2-weighted image demonstrated a mass in the uterine cavity, approximately 5.9 cm × 4.0 cm in size. (B) DWI (b = 1000 s/ mm2) showed the method of placing ROI within the tumor area. (C) ADC image showed that the ADC value was 0.710 × 10-3 mm2/s. (D) T2-weighted image showed the ROI of tumor segmentation. (E) The three-dimensional volume of interest.","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-776782/v1/b0b6d35c04eb7cf47ecebf91.png"},{"id":12326866,"identity":"fde925bd-e810-4663-be61-8203cd1d7ce2","added_by":"auto","created_at":"2021-08-11 13:42:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20368,"visible":true,"origin":"","legend":"ROC curve analysis of ADC and radscore predicting recurrence. (A) ADC predicting recurrence, AUC = 0.703, and (B) radscore predicting recurrence, AUC = 0.813.","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-776782/v1/e54f0c26ac1586dc5286952f.png"},{"id":12326865,"identity":"f5d750c2-6e14-41c3-bd0d-ccb7af1b0faa","added_by":"auto","created_at":"2021-08-11 13:42:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26485,"visible":true,"origin":"","legend":"DFS curves of patients with EC. (A): endometrioid vs. non-endometrioid, (B): ADC \u003c 0.817 vs. ≥ 0.817, and (C): radscore \u003c -1.342 vs. ≥ -1.342。","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-776782/v1/07d4176c2ce3b26bc6ac31cf.png"},{"id":13708695,"identity":"afe1932f-b3ec-4ec0-93d3-f9557f8157e2","added_by":"auto","created_at":"2021-09-17 14:09:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":914824,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-776782/v1/7f207bbc-4037-44b0-b9e4-aae93e9254f4.pdf"},{"id":12326868,"identity":"41f78a80-c20f-432e-8e90-30bea58268ba","added_by":"auto","created_at":"2021-08-11 13:42:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15648,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-776782/v1/88777fc2c424220c588d71d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"MRI-based Radiomics and ADC Values Are Related to Recurrence of Endometrial Carcinoma：A Preliminary Analysis ","fulltext":[{"header":"1. Background","content":"\u003cp\u003eEndometrial carcinoma (EC) is the sixth most commonly diagnosed cancer in women. With the increasing prevalence of risk factors such as physical inactivity and overweight, the incidence of endometrial cancer continues to rise [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Surgery is the main method for the initial treatment of EC including laparoscopic or robotic resection of uterus, cervix, fallopian tube and ovary, and sentinel lymph node assessment[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although most endometrial cancers can be diagnosed at an early stage and have 5-year survival rate of over 90%, recurrence and final mortality approximately occur in 20% of endometrioid carcinoma (type I) and 50% of non-endometrioid carcinoma (type II)[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Women with recurrent or metastatic diseases have 5-year survival rates as low as 17%-55%[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Unfortunately, little progress has been made in improving the survival rate of EC in the past decades. Therefore, early identification of risk factors for recurrence is an important challenge to improve the prognosis of EC patients.\u003c/p\u003e\n\u003cp\u003eClinicopathological factors, such as FIGO stage, pathological type and muscular invasion, etc. are common prognostic factors that affect the formulation of surgical plan, but they can only be accurately evaluated after surgery[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Due to the limitations of specimen collection, preoperative endometrial biopsy may be difficult to fully reflect the characteristics and heterogeneity of the tumor. Therefore, it is necessary to develop an early, comprehensive and non-invasive evaluation method to evaluate the possible adverse prognosis of EC. MRI is an important tool to preliminarily assess the extent of EC lesions. The apparent dispersion coefficient (ADC) of diffusion-weighted imaging (DWI) can reflect the malignancy of the tumor, which has been proved to be valuable in the diagnosis, typing and grade of EC[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], but its role in the prognostic assessment of EC remains unclear.\u003c/p\u003e\n\u003cp\u003eRadiomics mines pixel distribution features from radiological images and transforms them into quantitative data, reflecting the heterogeneity within tumors [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Radiomic parameters derived from MRI, CT and PET-CT have been suggested as effective tools for diagnosis, risk assessment or treatment response of malignant tumors[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. The purpose of this study is to explore whether postoperative recurrence of patients with EC can be reflected in MRI-based ADC value and radiomics.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch2\u003e2.1 Patient population\u003c/h2\u003e\n\u003cp\u003eThis retrospective study was approved by the institutional review board and the informed consent was waived.\u0026nbsp;Patients with EC who underwent 3.0 MRI before treatment in our institution from January 2015 to December 2019 were included retrospectively. Inclusion criteria are as follows: (a) Pathologically confirmed EC; (b) available clinical and postoperative pathological data;\u0026nbsp;(c) MRI was performed within 1 month before surgery; (d) surgical treatment and follow-up were performed in our hospital. Exclusion criteria are as follows: (a)\u0026nbsp;diagnosis of malignant tumor other than endometrial cancer (n = 4); (b) distant metastasis occurred at the time of diagnosis (IVB stage) (n = 5); (c) tumors were invisible on MRI or MRI with serious motion artifact (n = 24); (d) incomplete pathological report or medical record (n = 14); (e)\u0026nbsp;patients lost to follow-up (n = 20).\u0026nbsp;Finally, a total of 174 patients diagnosed with EC were identified.\u0026nbsp;The last follow-up was in\u0026nbsp;March 2021.\u003c/p\u003e\n\u003ch2\u003e2.2 MR Imaging\u003c/h2\u003e\n\u003cp\u003eAll patients underwent pelvic MRI and DWI before operation. MRI was performed on a 3.0T MRI system (Signa Excite HD, GE, Milwaukee, WI, USA) using an 8-channel body coil. Prior to MRI examinations, patients fasted for at least 4 hours and were given intramuscular 15 mg shyoscine butylbromide half an hour before examination. During image acquisition, the patient remained supine with a semi-filled bladder.\u003c/p\u003e\n\u003cp\u003eScanning sequence and parameters: (1) Axial T1-weighted images (T1WI): a field of view (FOV): \u0026nbsp;38 cm \u0026times; 26 cm, repetition time (TR): 500 ms, echo time (TE): 7.2 ms, slice thickness: 6 mm, inter-slice gap: 2 mm, matrix size: 352 \u0026times; 192. (2) Axial and axial fast spin-echo (FSE) T2WI: FOV: 24 cm \u0026times; 24 cm, TR: 4600 ms, TE: 68 ms, slice thickness: 3 mm, inter-slice gap: 1 mm, matrix size: 320 \u0026times; 256; (3) Oblique sagittal T2WI: FOV: 26 cm \u0026times; 24 cm, TR: 4600 ms, TE: 68 ms, slice thickness: 6 mm, inter-slice gap: 2 mm, matrix size: 320 \u0026times; 256; (4) Axial DWI: FOV: 38 cm \u0026times; 26 cm, TR: 4000 ms, TE: 65 ms, slice thickness: 4 mm, inter-slice gap: 1 mm, matrix size: 96 \u0026times; 130, b-value: 0 and 1000 mm\u003csup\u003e2\u003c/sup\u003e/s.\u003c/p\u003e\n\u003ch2\u003e2.3\u0026nbsp;Histologic and pathologic diagnosis\u003c/h2\u003e\n\u003cp\u003eAll surgical specimens were examined and reported by gynecologic pathologists.\u0026nbsp;Tumor staging were performed according to standard 2018 FIGO criteria. Myometrial invasion , lymphovascular space invasion (LVSI) and lymph node metastasis (LNM) were confirmed under microscope according to the corresponding diagnostic criteria.\u003c/p\u003e\n\u003cp\u003ePostoperative follow-up of patients: 3 to 6 months for the first 2\u0026ndash;3 years, 6 months until 5 years, and then annually, any time when there are related symptoms such as vaginal bleeding, abdominal distension and pain. Surveillance included gynecological examination, imaging and pathological biopsy if necessary.\u003c/p\u003e\n\u003ch2\u003e2.4 Image Analysis\u003c/h2\u003e\n\u003cp\u003eThe images were assessed by two radiologists with 8 and 10 years (reader 1 and reader 2) of professional experience in pelvic MRI independently.\u0026nbsp;Moreover, they were both blinded to each other\u0026rsquo;s results, and pathological and clinical data. Tumor was defined as a mass with signal hyperintensity on DWI and hypointensity on ADC map, compared with the signal of surrounding adjacent tissues. Meanwhile, T2WI sequence was referenced. ADC value measurements of the tumor were executed on an ADC map\u0026nbsp;using GE Advantage Workstation 4.6, Function Tool software. The region of interest (ROI) was manually delineated in the slice containing the largest tumor layer, including the largest tumor area as much as possible and avoiding bleeding and necrotic tissue (Figure 1.A\u0026minus;C).\u0026nbsp;Each reader measured ADC values for each patient three times, and the average value was taken.\u003c/p\u003e\n\u003cp\u003eTumor segmentation were performed on axial T2WI sequence using ITK-SNAP (Version 3.6.0, http:// www.itksnap.org) software by reader 2. ROIs were sketched manually on all MRI levels containing tumor, and the ROIs covered the whole tumor as much as possible. Then the ROIs of all layers were fused to get the three-dimensional volume of interest (VOI). Finally, radiomic parameters were extract from VOIs using AK (Analysis Kit, Kinetics Version 2.1, GE Healthcare) software, following the IBSI standards, with a total of 1130 (Figure 1.D\u0026minus;E). To investigate the stability of radiomic features extracted by different readers and the same reader, 30 patients were randomly selected for tumor segmentation 2 weeks later. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eThe data were analyzed with SPSS v. 26.0 (Chicago, IL, USA) and R (Version 3.6.1, http://www.r-project.org) .Patient clinicopathological characteristics were compared using a \u003cem\u003et\u003c/em\u003e-test (age, ADC and radscore) or chi-square test (FIGO stage, tumor size, pathological types, myometrial invasion, LVSI and LNM) . The intraclass correlation coefficient (ICC) was used to evaluate the intra-observer and inter-observer consistency of ADC values and radiomic parameters. The ADC values having higher intra-observer ICC was retained. The radiomic features with intra-observer and inter-observer ICC \u0026gt;0.75 were considered stable and were retained for subsequent analyses. Logistic regression analysis was used to select radiomic parameters. The radscore was calculated based on the regression coefficient of the selected radiomic features using multi-factor linear weighting. ADC value and radscore were compared by t-test. Receiver operating characteristic (ROC) curve and Youden index were used to obtain cut-off values of ADC vale and radscore. Spearman\u0026apos;s bivariate correlation test was used to analyze the correlation between ADC value, radscore and recurrence. The Kaplan\u0026ndash;Meier method was used to calculate survival rate and draw survival curve, Log\u0026ndash;rank method was used for univariate analysis. Variables with P \u0026lt; 0.05 were included in a multivariate Cox regression model and independent prognostic factors of survival were identified with P\u0026lt;0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Patient characteristics and outcomes\u003c/h2\u003e\n\u003cp\u003eA total of 174 patients with EC treated in our institution were included, 162 patients underwent laparoscopic surgery and 12 patients had open laparotomy. Among all the patients, 48 patients received postoperative radiotherapy, and 105 patients received adjuvant chemotherapy or concurrent chemotherapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe median follow-up of 174 patients was 31 months (range, 4\u0026ndash;69 months). The median follow-up time of recurrent cohort was 18 months (range, 4\u0026ndash;50 months). Tumor recurrence was recorded in a total of 26 (14.9%) patients of the 174 cases. There were 3 (11.5%) isolated pelvis recurrences, 1 (3.9%) isolated vaginal recurrence, 5 isolated abdominal failure, 6 (23.1%) combined pelvic and distant failure, and 11 (42.3%) distant failure. The 3-year and 5-year disease-free survival (DFS) of the entire cohort were 91.2% (95%CI: 86.9-95.5%) and 75.2% (95%CI: 64.8-85.6%).\u003c/p\u003e\n\u003cp\u003eThe comparison of clinicopathological features between non-recurrent and recurrent cohorts are shown in Table 1. The onset age of recurrent group was significantly higher than that of non-recurrent group (60.1\u0026plusmn;11.5 vs 55.6\u0026plusmn;8.5, P=0.018). There was a significant difference in FIGO staging between recurrence group and non-recurrence group (P\u0026lt;0.001). The pathological types were 137 cases of endometrioid carcinoma and 58 cases of non-endometrioid carcinoma, and there were more recurrences in patients with non-endometrioid carcinoma compared to those with endometrioid carcinoma (37.8% vs 9.5%, P\u0026lt;0.001). Pathological evaluation showed that 38.1% of all patients included had deep myometrial invasion (\u0026ge;50%), and there were more recurrences in patients with myometrial invasion \u0026ge; 50% compared to those with \u0026lt; 50% (31.2% vs 9.5%, P\u0026lt;0.001). The recurrence rate of LVSI present patients was significantly higher than that of LVSI absent patients (37.9% vs 11.0%, P\u0026lt;0.001). However, there was no significant difference between the recurrence and no-recurrence groups with regards to tumor size and LNM.\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 1\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eClinicopathological characteristics of recurrent and non-recurrent endometrial carcinoma\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" style=\"border-collapse: collapse; margin: 0px auto;\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003echaracteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003eTotal(n=174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003eNo recurrence(n=147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003eRecurrence(n=27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eAge at diagnosis (yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e56.3\u0026plusmn;9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e55.6\u0026plusmn;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e60.1\u0026plusmn;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eFIGO stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eⅠA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e88(90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e9(9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eⅠB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e15(78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e4(21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e29(93.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e2(6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eⅢA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e4(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e4(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eⅢC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e8(80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e2(20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eⅢC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e3(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e3(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eⅣA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e3(100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eTumor size (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003e\u0026lt;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e83(47.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e73(88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e10(12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003e\u0026ge;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e91(52.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e74(81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e17(18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003ePathological types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eEndometrioid\u0026nbsp;carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e137(78.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e124(90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e13(9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eNon-endometrioid carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e37(21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e23(62.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e14(37.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eMyometrial invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003e\u0026lt;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e126(72.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e114(90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e12(9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003e\u0026ge;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e48(38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e33(68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e15(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e145(83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e129(89.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e16(11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e29(16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e18(62.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e11(37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eLNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e158(90.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e136(86.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e22(13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.38373751783167%\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.835948644793152%\"\u003e\n \u003cp\u003e16(9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.53780313837375%\"\u003e\n \u003cp\u003e11(68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.5988%;\" valign=\"top\" width=\"19.686162624821684%\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4938%;\" valign=\"top\" width=\"10.556348074179743%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 99.8457%;\"\u003eLVSI: lymphovascular space invasion; LNM: lymph node metastasis. (page 9, line 12)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003e3.2 Extraction of ADC value and radiomic features\u003c/h2\u003e\n\u003cp\u003eA total of 1130 radiomic features were obtained from axial T2 sequence, of which 112 features had both intra-observer and inter-observer ICC \u0026gt; 0.75. Through multivariate logistic regression, 4 radiomic features were obtained as independent discriminant features, which were glcm_DifferenceEntropy, firstorder_Uniformity, wavelet-HHL_firstorder_Mean and lbp-3D-m1_firstorder_MeanAbsoluteDeviation (An additional movie file shows this in more detail [see Additional file 1]), then the radiomics score (radscore) of each patient was calculated as a new variable, according to the linear combination of regression coefficients. The radscore of recurrence group was significantly higher than that of non-recurrence group, and the score was positively correlated with recurrence (Table 2). ADC in recurrence group was significantly lower than that in non-recurrence group, and was negatively correlated with recurrence.\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 2\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eROC curve results of ADC values and radiomic parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" style=\"border-collapse: collapse; margin: 0px auto;\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"9.566326530612244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.11734693877551%\"\u003e\n \u003cp\u003eNo recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.11734693877551%\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.056122448979592%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.760204081632653%\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.525510204081633%\"\u003e\n \u003cp\u003eCutoff value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.96938775510204%\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.96938775510204%\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.918367346938776%\"\u003e\n \u003cp\u003eCorrelation with\u003c/p\u003e\n \u003cp\u003erecurrence status (rs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"9.566326530612244%\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.11734693877551%\"\u003e\n \u003cp\u003e0.908\u0026plusmn;0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.11734693877551%\"\u003e\n \u003cp\u003e0.779\u0026plusmn;0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.056122448979592%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.760204081632653%\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.525510204081633%\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.96938775510204%\"\u003e\n \u003cp\u003e70.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.96938775510204%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.918367346938776%\"\u003e\n \u003cp\u003e-0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"9.566326530612244%\"\u003e\n \u003cp\u003eRadscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.11734693877551%\"\u003e\n \u003cp\u003e-2.65\u0026plusmn;1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.11734693877551%\"\u003e\n \u003cp\u003e-0.89\u0026plusmn;1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.056122448979592%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.760204081632653%\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.525510204081633%\"\u003e\n \u003cp\u003e-1.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.96938775510204%\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.96938775510204%\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.918367346938776%\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 99.4845%;\"\u003eADC: apparent diffusion coefficient; AUC: area under curve.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eROC curve results of ADC and radscore for predicting recurrence are shown in table 3. The cut-off values of ADC and radscore were 0.817 and -1.342, according to Youden index. The ROCs of ADC and radscore are shown in Figure 2.\u003c/p\u003e\n\u003ch2\u003e3.3 Univariate and multivariate analysis of factors associated with recurrence\u003c/h2\u003e\n\u003cp\u003eUnivariate analysis revealed that five factors were predictive of recurrence; FIGO stage (hazard ratio [HR]=5.203; 95% CI=2.43\u0026ndash;11.14; P\u0026lt;0.001), pathological types (HR=5.20; 95% CI=2.43\u0026ndash;11.14; P\u0026lt;0.001), depth of muscular invasion (HR=3.33; 95% CI=1.56\u0026ndash;7.13; P=0.002), LVSI (HR=4.44; 95% CI=2.04\u0026ndash;9.66; P\u0026lt;0.001), ADC (HR=3.37; 95% CI=1.54\u0026ndash;7.37; P=0.02) and radscore (HR=8.67; 95% CI=3.89\u0026ndash;19.34;P\u0026lt;0.001) were significantly correlated with\u0026nbsp;disease-free survival (Figure 3).\u003c/p\u003e\n\u003cp\u003eThere was no association between the risk of recurrence and age, tumor size and LNM (Table 3). In multivariate analysis, non-endometrioid (HR=3.05; 95% CI=1.36\u0026ndash;6.87;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eP=0.007), ADC (HR=2.37; 95% CI=1.07-5.24; P=0.034) and radscore (HR=3.86; 95% CI=1.57-9.49; P=0.003) were independent predictors of recurrence (Table 4).\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 3\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eUnivariate analysis of recurrence factors for DFS of endometrial carcinoma\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" style=\"border-collapse: collapse; margin: 0px auto;\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"54.07725321888412%\"\u003e\n \u003cp\u003ePredictive factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"45.92274678111588%\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"23.60248447204969%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"46.8944099378882%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.503105590062113%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eAge at diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e1.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e0.67\u0026ndash;3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eFIGO stage (Ⅰ\u0026ndash;Ⅱ vs. Ⅲ\u0026ndash;Ⅳ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e5.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e2.43\u0026ndash;11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eTumor size (\u0026lt;3.5 cm vs. \u0026ge;3.5 cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e1.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e0.74\u0026ndash;3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003ePathological types (endometrioid vs. non-endometrioid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e2.43\u0026ndash;11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eMyometrial invasion (\u0026lt;50% vs. \u0026ge;50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e1.56\u0026ndash;7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eLVSI (absent vs. present)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e2.04\u0026ndash;9.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eLNM (absent vs. present)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e0.86\u0026ndash;6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eADC (\u0026lt;0.817 vs. \u0026ge;0.817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e1.54\u0026ndash;7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eRadscore (\u0026lt;-1.34 vs. \u0026ge;-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.857142857142858%\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.571428571428573%\"\u003e\n \u003cp\u003e3.89\u0026ndash;19.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 99.7423%;\"\u003eLVSI: lymphovascular space invasion; LNM: lymph node metastasis; ADC: apparent diffusion coefficient; HR: hazard ratio; CI: confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 4\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eMultivariate analysis of recurrence factors for DFS of endometrial carcinoma\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" style=\"border-collapse: collapse; margin: 0px auto;\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"54.07725321888412%\"\u003e\n \u003cp\u003ePredictive factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" width=\"45.92274678111588%\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.39751552795031%\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"44.099378881987576%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"29.503105590062113%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eFIGO stage (Ⅰ\u0026ndash;Ⅱ vs. Ⅲ\u0026ndash;Ⅳ)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.142857142857142%\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.285714285714285%\"\u003e\n \u003cp\u003e0.79\u0026ndash;5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003ePathological types (endometrioid vs. non-endometrioid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.142857142857142%\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.285714285714285%\"\u003e\n \u003cp\u003e1.36\u0026ndash;6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eMyometrial invasion (\u0026lt;50% vs. \u0026ge;50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.142857142857142%\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.285714285714285%\"\u003e\n \u003cp\u003e0.45\u0026ndash;3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eLVSI (absent vs. present)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.142857142857142%\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.285714285714285%\"\u003e\n \u003cp\u003e0.84\u0026ndash;6.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eADC (\u0026lt;0.817 vs. \u0026ge;0.817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.142857142857142%\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.285714285714285%\"\u003e\n \u003cp\u003e1.07-5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54%\"\u003e\n \u003cp\u003eRadscore (\u0026lt;-1.34 vs. \u0026ge;-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.142857142857142%\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.285714285714285%\"\u003e\n \u003cp\u003e1.57-9.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.571428571428571%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 99.7423%;\"\u003eLVSI: lymphovascular space invasion; ADC: apparent diffusion coefficient; HR: hazard ratio; CI: confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTraditionally, prognostic factors for EC include surgical stage, histological factors[16, 17], and molecular factors such as P53 mutation[18]. This information is often only available from histological evaluation after surgery, so preoperative risk assessment of recurrence is limited. As a routine preoperative imaging examination of EC, MRI can provide anatomical information, and DWI and radiomics analysis may reveal the prognostic information of EC. In addition to clinical and pathological factors, we also investigated the correlation between ADC value and radscore and recurrence in this study. We identified risk factors associated with recurrence and reduced survival of EC after surgery, including FIGO stage, pathologic type, depth of basal invasion, LVSI, ADC values, and radscore.\u0026nbsp;Non-endometrioid cancer, ADC value and Radscore were independent predictors of EC recurrence, which can be used as clinically relevant tumor markers in preoperative risk stratification and prognosis assessment of EC.\u003c/p\u003e\n\u003cp\u003eAlthough the number of recurrent patients is small in our study, we identified the clinical and pathological risk factors related to recurrence of EC patients including FIGO stage, pathological type, myometrial invasion depth and LVSI. These factors have been associated with recurrence or poor outcome in EC in other studies. Women who were initially diagnosed with advanced disease (FIGO Ⅲ\u0026minus;Ⅳ) had a higher risk of recurrence and were more likely to develop extrapervic metastases[8]. Patients with low-grade (grade 1-2) endometrioid cancer (type I) often have better outcomes than those with high-grade (grade 3) endometrioid cancer and non-endometrioid cancer (type II)[19]. A study exploring recurrence factors for stage 1 endometrioid adenocarcinoma observed that large tumor size, and muscular invasion were the most important predictors of recurrence[9]. The study of Bosse et al. [16] confirmed that substantial LVSI was the strongest prognostic factor for recurrence and metastasis and overall survival of EC.\u003c/p\u003e\n\u003cp\u003eNotably, we did not find age of onset, tumor size, or LNM as factors related to the increased risk of recurrence. The subjects of our study were concentrated in the 50\u0026minus;60 years old, which was a homogeneous population, with few patients under 50 years old or over 70 years old. In our study, the tumor size was defined as the maximum diameter of the tumor, which was often the extent to which tumor tissue invaded the endometrium rather than the depth. So, there was no significant difference in tumor size between the relapsed and non-relapsed groups, and tumor size had no significant effect on survival. Less cases with positive lymph node metastasis may be the reason why LNM had no effect on survival time.\u003c/p\u003e\n\u003cp\u003eADC can reflect the diffusion degree of water molecules. The present study indicated that lower ADC was significantly related to the recurrence of EC and ADC was shown to be an independent predictor of DFS in multivariate analysis, which has been suggested in previous studies[20, 21]. ADC is closely related to cell density. Yan et al.[20] and Reyes-P\u0026eacute;rez et al.[21] explained that the decrease of ADC can act as markers for tumor\u0026rsquo;s high cellularity, proliferation, perfusion and less extracellular space, which means higher tumor load, tumor residue and recurrence. Traditionally, clinicopathological factors such as tumor grade, subtype, depth of myometrial invasion, and nodal status have an impact on prognosis of EC. Several studies have reported that ADC was associated with histopathological risk factors of endometrial cancer[10, 22, 23], so it is not difficult to explain the correlation between ADC and prognosis and recurrence. Chen et al.[10] reported that type II EC had lower ADC values, and ADC values were important when identifying type II and type I ECs. The study of Jiang et al.[22] indicated that the ADC values of high grade, stage IB and high Ki-67 expression patients were significantly lower than those of low grade, stage IA and low Ki-67 expression patients with EC. Zhang et al.[23] reported that lower ADC were observed in tumor with deep myometrial invasion and LVSI than tumor without deep myometrial invasion and LVSI. Therefore, the lower ADC indicates that there may be more high-risk factors for endometrial cancer, and the greater the chance of recurrence.\u003c/p\u003e\n\u003cp\u003eHeterogeneity is an important characteristic of malignant tumors and the basis of tumor recurrence and metastasis[24, 25]. Radiomics can extract features that cannot be observed by naked eyes and convert them into data information to quantify the heterogeneity in the image[26, 27]. Radiomics derived from MRI have been proposed as a reliable tool for accurate diagnosis and risk assessment in several cancer types, e.g. in cervical[28], brain[29], and breast[30]. Similarly, a study has reported that radiomics analysis based on MRI was an effective tool for preoperative risk stratification in EC[31]. In this context, MRI-based radiomics analysis was available to predict recurrence in patients with EC, and four radiomic parameters were selected to predict recurrence. Among these, firstorder_Uniformity was used to describe the uniformity of image array. Glcm_DifferenceEntropy was a measure of the randomness/variability in neighborhood intensity value differences. Wavelet and lbp features were the observation of the whole tumor range and the extraction of high-dimensional features. The radscore was calculated according to the combination of regression coefficients of each parameter, which was an independent predictor of relapse in both univariate and multivariate analyses. Patients were grouped into risk groups based on radscore, and patients with higher radscore had worse DFS. Sigmund et al[32]. reported that thirteen MRI-derived tumor radiomic parameters significantly predicted reduced recurrence- and progression-free survival in univariable Cox regression analysis, and T1c_Kurtosis2 was the top-ranked prognostic texture parameter independently predicted reduced survival. The study of Yan et al.[14] manifested that MRI-based radiomics achieved high diagnostic performance for predicting LVSI of EC preoperatively, and was helpful for early identification of poor prognosis. In addition, the whole-tumor radiomic features were found to significantly predict progression-free survival at hazard ratios of 4.6-9.8 in the research of Fasmer et al [33] , albeit in a small sample size. Although the sample size, MRI sequence, and radiomic parameter extraction methods were different in the previous study, we cannot deny that radiomics and texture analysis may mine more prognostic information than clinical factors, and can be used as a biomarker to assist clinical practice.\u003c/p\u003e\n\u003cp\u003eThere are some limitations in our research. This is a single-center small sample study, which needs to be further verified by a large multi-center database. The absence of stratification of staging and pathologic types may influence the estimation of survival and recurrence outcomes. A short follow-up time and less recurrence cases may limit the predictive value of the covariates.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, EC patients are at certain risk of recurrence, and early identification of risk factors for recurrence and enhanced treatment intensity are significant to improve prognosis. ADC values and MRI-derived radiomics may provide additional prognostic information in addition to traditional prognostic factors.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eADC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eapparent diffusion coefficient\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003emagnetic resonance imaging\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eendometrial carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eradscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eradiomic score\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eLVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003elymphovascular space invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eDWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003ediffusion-weighted imaging\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eT1WI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eT1-weighted images\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eFOV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003efield of view\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003erepetition time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eecho time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eFSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003efast spin-echo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eLNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003elymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eROI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eregion of interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eVOI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003evolume of interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eICC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eintraclass correlation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003ereceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003edisease-free survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003earea under curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eEthics Committee of Anhui Provincial Cancer Hospital approved this retrospective study and the informed consent was waived (Ethics Approval No. 2021-FLK-01).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003e2020 SKY Image Research Fund (NO. Z-2014-07-2003-11) : Data analysis and manuscript publication.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eKaiyue Zhang: Investigation, Data acquisition, Formal analysis, Methodology, Software, Visualization, Writing - original draft, Writing - editing; Yu Zhang: Data curation, Project administration, Formal analysis, Methodology, Writing \u0026ndash; review; Xin Fang: Conceptualization, Data curation, Formal analysis, Methodology, \u0026nbsp;Supervision, Validation, Visualization, Writing \u0026ndash; review; Jiangning Dong: Conceptualization, Project administration, Supervision, Validation, Writing - review. Liting Qian: Conceptualization, Project administration, Supervision, Validation, Writing \u0026ndash; review. The author(s) read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank GE Healthcare for the technical assistance.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; information\u003c/h2\u003e\n\u003cp\u003eKaiyue Zhang: MD, Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China.\u003c/p\u003e\n\u003cp\u003eYu Zhang: Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China.\u003c/p\u003e\n\u003cp\u003eXin Fang: MD, Department of Radiology, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhudong Road, Hefei 230031, China.\u003c/p\u003e\n\u003cp\u003eJiangning Dong: MD, Department of Radiology, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, 107 Huanhudong Road, Hefei 230031, China.\u003c/p\u003e\n\u003cp\u003eLiting Qian: PhD, Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, 17 Lujiang Road, Hefei, Anhui 230001, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Sci Rep. 2020 Feb 28;10(1):3750. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-60822-9\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUeno Y, Forghani B, Forghani R, Dohan A, Zeng XZ, Chamming's F, et al. Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis. Radiology. 2017 Sep;284(3):748\u0026ndash;757. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2017161950\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYtre-Hauge S, Dybvik JA, Lundervold A, Salvesen \u0026Oslash;O, Krakstad C, Fasmer KE, et al. Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. 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J Magn Reson Imaging. 2021 Mar;53(3):928\u0026ndash;937. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jmri.27444\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endometrial neoplasms, Recurrence, Risk Factors, Apparent diffusion coefficient, Radiomics","lastPublishedDoi":"10.21203/rs.3.rs-776782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-776782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eTo identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eOne hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed. Baseline clinicopathological features and ADC values were analyzed. Radiomic parameters were extracted on T2 weighted imaging and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). Kaplan–Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radiology with recurrence.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e ADC values showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, lymphovascular space invasion (LVSI), ADC value and radscore were associated with recurrence. In multivariate Cox analysis, pathological types, ADC and radscore were independent risk factors for recurrence.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eADC value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan.\u003c/p\u003e","manuscriptTitle":"MRI-based Radiomics and ADC Values Are Related to Recurrence of Endometrial Carcinoma：A Preliminary Analysis ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-11 13:42:00","doi":"10.21203/rs.3.rs-776782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-09-14T09:07:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-08-31T18:52:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26958e27-af60-44e2-aab5-add90f92f6b8","date":"2021-08-16T14:34:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-08-16T13:19:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-08-09T11:12:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-08-09T09:30:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-08-09T09:26:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2021-08-02T15:22:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39e1fbd3-0a9d-485b-8b2a-b320e0feb441","owner":[],"postedDate":"August 11th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":6371662,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2021-11-10T05:59:06+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-11 13:42:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-776782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-776782","identity":"rs-776782","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}