Feasibility of magnetic resonance imaging compilation (MAGIC) for evaluating the depth of myometrial invasion and predicting the pathological subtypes of endometrial cancer: A comparison with high resolution T2WI and DWI

Feasibility of magnetic resonance imaging compilation (MAGIC) for evaluating the depth of myometrial invasion and predicting the pathological subtypes of endometrial cancer: A comparison with high resolution T2WI and DWI | 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 Feasibility of magnetic resonance imaging compilation (MAGIC) for evaluating the depth of myometrial invasion and predicting the pathological subtypes of endometrial cancer: A comparison with high resolution T2WI and DWI Yue Li, Yu Wang, Weiyin Vivian Liu, Hui Liu, Xiaorong Ou, Yixin Liu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7818425/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To investigate the feasibility of MAGiC (sy-T2WI; T1, T2 and PD maps) for evaluating the depth of myometrial invasion (DMI) and predicting the pathological subtypes of endometrial cancer (EC) in comparison to hr-T2WI and DWI. Methods 68 out of 131 consecutive EC patients were prospectively recruited to perform MAGiC, hr-T2WI and DWI on 3.0T MRI before surgery. DMI (DMI-: < 50%, DMI-II: ≥ 50%) and histologic subtypes (type-I and type-II) were confirmed by operation and as the reference standard. For DMI, the diagnostic performance was assessed and compared across imaging protocols (sy-T2WI vs. hr-T2WI; sy-T2WI + DWI vs. hr-T2WI + DWI). For histologic typing, the predictive performance of T1, T2, PD maps and their combinations was evaluated against ADC values using area under the curve (AUC). Results For DMI, the diagnostic accuracy, sensitivity and specificity were 77.9%, 66.7% and 81.1% for sy-T2WI, 79.4%, 80.0% and 79.2% for hr-T2WI; 82.2%, 86.7% and 88.7% for sy-T2WI + DWI, and 91.2%, 86.7% and 92.5% for hr-T2WI + DWI. No statistically significant differences were observed between sy-T2WI vs. hr-T2WI or between their combinations with DWI (P > 0.05). For histologic subtypes, the combination of T2 and PD maps outperformed ADC alone (AUC: 0.873 vs. 0.778; p = 0.003), although T2 and PD showed similar AUC to ADC (both p > 0.05). Conclusions MAGiC provides a comparable or superior performance to conventional hr-T2WI and DWI both DMI evaluation and histologic subtypes prediction in EC, highlighting its potential as a robust non-contrast MR protocol in clinical practice. Magnetic Resonance Imaging Compilation Depth of Myometrial Invasion Pathological Subtypes Endometrial Cancer Comparative Studies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Endometrial cancer (EC) is the most prevalent gynaecologic malignancy, with a rising incidence in China due to obesity, diabetes and population aging [ 1 , 2 ]. Optimal treatment strategies including surgery alone and surgery followed by adjuvant chemotherapy/radiotherapy[ 3 ] rely on various prognostic factors including histologic type, depth of myometrial invasion (DMI), lymphovascular invasion [ 4 ]. Among these, histologic type and DMI are strongly correlated with lymph node metastases and overall patient survival, and play a vital role in tailoring individualized treatment strategies. Approximately 80%-90% of EC cases are endometrial adenocarcinomas (EACs), while the remaining 10%-20% are non-endometrial adenocarcinomas (Non-EACs)[ 5 ]. EACs are further graded histologically from G1 to G3 based on the proportion of solid non-squamous within the tumor [ 6 ]. G1-2 EACs are associated favorable prognosis and are typically managed with surgery alone, reflecting their low-risk histological and estrogen-dependent pathophysiology [ 7 , 8 ]. In contrast, G3 EACs and non-EACs are non-estrogen dependent and linked to poorer outcomes. These subtypes warrant tumor excision along with complete lymphadenectomy due to their increased risk for lymphovascular invasion, intraperitoneal spread and distant metastases [ 7 , 8 ]. Non-EACs includes serous, endometrioid, clear cell, mixed, undifferentiated and high-grade endometrioid carcinomas. These tumors are prone to recurrence and mortality, with over 50% non-EACs harboring TP53 mutation [ 1 , 8 ]. DMI is categorized into two levels based on tumor infiltration into the myometrium: DMI-I (superficial, (< 50% invasion of the myometrium) and DMI-II (deep invasion, ≥ 50% invasion). DMI-II is strongly correlated with higher risks of lymph-vascular space invasion (LVSI), nodal metastases and disease recurrence [ 9 , 10 ]. Consequently, patients with DMI-II are typically recommended for extended lymphadenectomy during surgery [ 9 ]. Pelvic MRI is widely adopted tool for the preoperative assessment of histologic type (type I and II) and DMI (DMI-I and DMI-II), primarily using high-resolution T2-weighted imaging (hr-T2WI)) and diffusion weighted imaging (DWI) [ 11 , 12 ]. Hr-T2WI can depicts DMI with an accuracy ranging from 68% to 82.1%[ 11 , 12 ]. The diagnostic performance improves further when hr-T2WI is combined with DWI, rather than with dynamic contrast-enhanced MR [ 12 , 13 ]. Apparent diffusion coefficient (ADC) values derived from DWI offer a quantitative biomarker, reflecting tumor cellularity and microstructural properties. Notably, ADC values are reported to be significantly higher in type I EC compared to type II tumor [ 5 , 14 – 16 ], supporting their potential use in non-invasive tumor characterization. Recently, magnetic resonance imaging compilation (MAGiC) has emerged as a novel imaging technique capable of generating both synthetic morphologic images (synthetic T2WI, sy-T2WI) and quantitative images (synthetic T1, T2 and proton density [PD] maps) in a single acquisition [ 17 ]. While MAGiC has shown promising diagnostic value in breast, prostate, rectum pathologies [ 17 – 19 ], its application in the assessment of DMI and histologic classification in EC remains unexplored. This study aims to evaluate the feasibility of MAGiC in assessing DMI (DMI-I vs. DMI-II) and predicting histologic type (type I: G1-G2 EACs; type II: G3 EACs and non-EACs) in endometrial cancer patients. Additionally, we compare the imaging quality of sy-T2WI with hr-T2WI. 2. Methods 2.1 Participants This prospective study was approved by the institutional review board, with all participants providing written informed consent. From October 2022 to June 2024, we prospectively enrolled 131 consecutive patients (mean age: 56.3 ± 10.2 years; range: 37–75 years) with histologically conformed EC who underwent preoperative pelvic MRI. Inclusion criteria required patients to be ≥ 18 years old with confirmed EC, no prior pelvic therapy, and complete preoperative MRI (including MAGiC, hr-T2WI and DWI sequences) within 10 days before treatment. Exclusion criteria eliminated patients with MRI contraindication (n = 2) such as claustrophobia, poor image quality(n = 10), lack of discernible lesion on MRI (n = 20), prior treatment history (n = 11), confounding uterine pathology (n = 4, 3 large uterine fibroids and 1 adenomyosis), or unavailable histopathological correlation (n = 16). After exclusions, 68 patients (mean age: 56.3 ± 10.2 years) were included in the final analysis (Fig. 1 ). 2.2 Pelvic MRI scan All pelvic MRI examinations were performed on a 3.0T scanner (Signa Premier, GE Healthcare, USA) using a 32-channal body coil. The imaging protocol included MAGiC, conventional hr-T2WI (oblique axial, sagittal and coronal), DWI (b = 0 and 800 s/mm 2 ), and dynamic contrast enhanced MRI (DCE-MRI). MAGiC and DWI were acquired with identical orientation, slice thickness, and gap as the oblique axial hr-T2WI (perpendicular to the uterine axis)[ 10 ]. Acquisition parameters are detailed in table 1. Synthetic T2WI (sy-T2WI) and T1, T2, PD maps were generated using a vendor-supplied multi-delay multi-echo sequence. ADC maps were calculated from DWI data. After MAGiC and DWI, DCE-MRI was performed with an extracellular contrast agent (15 mL of gadodiamide, Omniscan, GE Healthcare) injected at a rate of 0.2 mL/kg and followed by 20 mL of 0.9% saline (injection rate: 2 mL/s). The detailed scan time was 40 minutes (the net scan time: 4:36, MAGiC; 3:08, hr-T2WI; 2:32, DWI). Synthetic morphologic images (sy-T2WI), and quantitative parametric maps (synthetic T1, T2, PD maps) (Fig. 2 ) were generated by a vendor-provided MAGiC sequence, and ADC maps calculated from DWI (b = 0, 800 s/mm²) on the scanner console. 2.3 Morphologic images 2.3.1 Image quality Image quality was systematically evaluated using a 5-point Likert scale across four domains: lesion edge sharpness, lesion conspicuity, motion artifacts and overall image quality [ 20 ] (Supplementary Table 1). Two radiologists respectively with 10 and 5 years of gynecologic MRI experience were blinded to patient’s information and assessed sy-T2WI and hr-T2WI in separate weekly sessions. 2.3.2 DMI evaluation DMI (DMI-1vs.DMI-II) was evaluated on sy-T2WI and hr-T2WI in the first week by two radiologist (reader 3 and reader 4, with 4 and 6 years of gynecologic MRI experience, respectively). In the second week, the same readers assessed sy-T2WI + DWI and hr-T2WI + DWI, as DWI can enhance the accuracy of DMI diagnosis when combined with T2WI [17] . The readers were blinded to patient information and histopathologic reports to ensure objectivity. Initially, the size, extent, and boundary of the lesions were identified separately on sy-T2WI, hr-T2WI and DWI. DMI was classified as DMI-I if the tumor invasion was up to 50% of the myometrial thickness and as DMI-II when the invasion extended beyond 50% of the myometrium thickness [ 13 ].Representative cases of pathology-confirmed DMI-I and DMI-II were illustrated on sy-T2WI, hr-T2WI, and DWI (Fig. 3 ). 2.4 Quantitative images After identifying the size, extent, and boundary of the lesions on sy-T2WI and DWI, regions of interests (ROIs) were manually drawn on three consecutive slices of sy-T2WI, including the largest mass slice in the tumor’s center while avoiding necrotic areas [ 21 ] (Fig. 4 ). For small tumors, ROI(s) were drawn only on the visible tumor slice(s). These ROIs were then copied to T1, T2, PD and ADC map to extract corresponding mean values (Fig. 4 ). The average T1, T2, PD and ADC values were calculated for each EC patient. Two radiologists (Reader 3 and 4, with 5 and 3 years of gynecologic MRI respectively) performed the ROI delineation while blinded to clinical and pathological data. Discrepancies in interpretation were resolved by a senior radiologist with over 30 years of experience in gynecologic MRI. In this study, ROI sizes ranged from 0.37cm 2 to 13.5 cm 2 with an average size of 5.21 ± 2.94 cm 2 for EC subjects. 2.5 Pathologic Analyses Pathological examination was performed by an experienced pathologist with 12 years of gynecologic pathology experience, who remained blinded to the patient’s clinical and MRI data. Pathological characteristics – including DMI, histological types, tumor grade and other features – were assessed according to the 2023 revised FIGO staging system for endometrial carcinoma [ 22 ]. EC encompassed grade (G) 1–3 endometrial adenocarcinomas (EACs) and non- endometrial adenocarcinomas (non-EACs) [ 5 , 6 ]. The FIGO criteria defined G1-3 EACs as exhibiting ≤ 5%, 6–50% and > 50% solid non-glandular growth in the majority of cells, respectively (Fig. 5 A-C)Non-EACs includes serous, endometrioid, clear cell, mixed histologic type and others (Fig. 5 D). All patients were categorized into type I (estrogen-dependent type: G1-G2 EACs) and type II (no estrogen-dependent type: G3 EACs + non-EACs) based on pathologic tissue types, and further classified as DMI-I and DMI-II based on tumor invasive depth in the myometrium (Fig. 5 ). 2.5 Statistical analysis All statistical analyses were performed using SPSS Statistics (version 26.0; IBM Corp., Armonk, NY, USA) and GraphPad (version 9.0.1). A two-tailed p-value < 0.05 was considered statistically significant. For qualitative image parameters were evaluated: (1) lesion edge sharpness, (2) lesion conspicuity, (3) motion artifacts, and (4) overall image quality, with comparisons performed using the Mann-Whitney U test. Interobserver agreement of image quality for sy-T2WI and hr-T2WI was evaluated respectively using weighted kappa (κ) statistics (0.20: poor; 0.21–0.40: fair; 0.41–0.60: moderate; 0.61–0.80: good; >0.80: excellent agreement). Diagnostic accuracy metrics (accuracy, sensitivity and specificity) for DMI classification (DMI-I vs DMI-II) were compared across four imaging protocols: (1) sy-T2WI, (2) hr-T2WI, (3) sy-T2WI + DWI, and (4) hr-T2WI + DWI using Chi-square test. Cohen kappa coefficient evaluated with pathological findings, categorized as: κ < 0.40 (poor); 0.4–0.75 (acceptable); ≥0.75 (good consistency). Pairwise comparison of diagnostic performance were performed between sy-T2WI vs hr-T2W and sy-T2WI + DWI and hr-T2WI + DWI. In terms of histological type prediction, quantitative MR parameters (sy-T1, T2, PD and ADC values) were compared for their ability to differentiate type I vs type II tumors. The DeLong test was employed to compare area under the curve (AUC) values and 95% confidence intervals (CI). 3. Results 3.1 Demographic and pathological characteristics The study cohort comprised 68 histologically confirmed EC patients (mean age: 56.3 ± 10.2 years; range: 37–75 years), consisting of 62 subjects with EACs ( G1 = 31; G2 = 23; G3 = 8) and 6 non-EACs (serous carcinoma = 5, clear cell carcinoma = 1). Based on the dualistic classification, the population included 54 type I tumors (estrogen-dependent; mean age: 52.4 ± 3.2 years; range: 37–75 years) and 14 type II tumors (non-estrogen-dependent; mean age: 58.3 ± 4.8 years; range: 39–66 years). Myometrial invasion analysis revealed 53 DMI-I cases (mean age: 53.4 ± 4.7 years; range: 37–68 years) and 15 DMI-II cases (mean age: 56.4 ± 2.2 years; range: 38–73 years). 3.2 Comparative image quality assessment Both Sy-T2WI and hr-T2WI demonstrated comparable performance across all evaluation image quality parameters: sharpness of the lesion edge, lesion conspicuity, motion artifacts and overall image quality (all P > 0.05) (Table 2 , Fig. 6 ). Inter-observer reliability analysis revealed good and excellent agreement for sy-T2WI (κ = 0.754–0.826, all P<0.001) and consistently excellent agreement for hr-T2WI (κ = 0.814–0.863, all P<0.001)(Table 2 ). Particularly strong concordance was observed for lesion conspicuity assessment on sy-T2WI (κ = 0.802; P < 0.0001) and hr-T2WI (κ = 0.814; P < 0.0001) (Table 2 ). Table 2 Comparison of the image quality scores between sy-T2WI and hr-T2WI. sy-T2WI hr-T2Wl P value (overall) Agreement for sy-T2WI Agreement for hr-T2WI Sharpness of the lesion edge Reader 1 5(4,5) 5(4,5) 0.654 0.754(<0.0001*) 0.825(<0.0001*) Reader 2 5(4,5) 5(3,5) 0.552 Lesion conspicuity Reader 1 5(4,5) 5(3,5) 0.234 0.802(<0.0001*) 0.814(<0.0001*) Reader 2 5(4,5) 5(3,5) 0.327 Motion artifacts Reader 1 5(4,5) 5(4,5) 0.326 0.789(<0.0001*) 0.853(<0.0001*) Reader 2 5(4,5) 5(4,5) 0.484 Overall image quality Reader 1 5(4,5) 5(4,5) 0.426 0.826(<0.0001*) 0.863(<0.0001*) Reader 2 5(4,5) 5(4,5) 0.289 Note. A 5-point scale were used and Median (min, max) values are given in sharpness of the lesion edge, lesion conspicuity, motion artifacts and overall image quality. The inter-observer agreement was assessed on sy-T2WI and hr-T2WI respectively and * p value indicates a significant difference. 3.3 DMI evaluation For DMI, the diagnostic accuracy, sensitivity and specificity were 77.9%, 66.7% and 81.1% for sy-T2WI, 79.4%, 80.0% and 79.2% for hr-T2WI, 82.2%, 86.7% and 88.7% for sy-T2WI + DWI, and 91.2%, 86.7% and 92.5% for hr-T2WI + DWI. There was no significant difference between sy-T2WI and hr-T2WI (all P > 0.05) as well as sy-T2WI + DWI and hr-T2WI + DWI (all P > 0.05) (Table 3 ). Table 3 The diagnostic performance of various evaluated methods and the difference between them with comparison of pathological findings. The evaluation with imaging Pathological findings Diagnostic performance for DMI Motheds classification DMI-I DMI-II Acc Sen Spec Kapple value(95%CI) Sy-T2WI DMI-I 43 5 77.9% 66.7% 81.1% 0.427*(0.305–0.549) DMI-II 10 10 Hr-T2WI DMI-I 42 3 79.4% 80.0% 79.2% 0.497*(0.385–0.609) DMI-II 11 12 P Value 0.583 0.625 0.784 N/A Sy-T2WI + DWI DMI-I 47 2 88.2% 86.7% 88.7% 0.688*(0.507–0.789) DMI-II 6 13 Hr-T2WI + DWI DMI-I 49 2 91.2% 86.7% 92.5% 0.755*(0.661–0.849) DMI-II 4 13 P Value 0.697 0.551 0.687 N/A Note. ACC: accuracy; Sen: Sensitivity; Spec: Specificity; *: there was a statistical difference by kappa value; 95% CI: 95% confidence interval. 3.3 Quantitative images The quantitative MR analysis revealed mean values of T1 = 1234.68 ± 87.93 msec, P2 = 85.68 ± 3.27 ms, PD = 79.32 ± 3.61 p.u and ADC = 0.90 ± 0.10×10 − 3 mm 2 /s across enrolled EC patients. Significant differences were observed between histological types, with type I tumors with higher T2 (94.36 ± 1.44 msec vs 77.59 ± 4.15 msec), PD (81.57 ± 1.27 p.u. vs 77.59 ± 4.15 p.u.) and ADC (0.88 ± 0.81×10 − 3 mm 2 /s vs 0.79 ± 0.76×10 − 3 mm 2 /s) except T1 value (1335.5 ± 67.87 vs 1391.1 ± 19.09 msec; p = 0.695) (Table 4 ). ROC analysis demonstrated good diagnostic performance for individual parameters: T2 (AUC: 0.849; 95% CI: 0.770–0.928), PD (AUC: 0.731; 95% CI: 0.639–0.823) and ADC value (AUC: 0.778; 95% CI: 0.713–0.843; all p > 0.05). Notably, the combined T2 + PD model showed superior prediction performance (AUC: 0.873; 95%CI: 0.796–0.950) compared to ADC alone (0.778;95%CI: 0.713–0.843, p = 0.003), highlighting the added value of multiparametric MR analysis for differentiating type I from type II EC tumors (Table 5 , Fig. 7 ). Table 4 Comparisons of quantifications parameter (T1, T2, PD and ADC) between type I and type II Parameter Type Ⅰ Type Ⅱ P value T1 (msec) 1335.5 ± 67.87 1391.1 ± 19.09 0.695 T2 (msec) 94.36 ± 1.44 77.59 ± 4.15 0.000* PD (pu) 81.57 ± 1.27 71.80 ± 5.50 0.002* ADC(10-3mm2/s) 0.88 ± 0.81 0.79 ± 0.76 0.001* Data are mean ± standard deviation. EACs: Endometrial adenocarcinoma. EACs: endometrial adenocarcinoma; Type I: G1 + G2 EACs; Type II: G3 + non-EACs ADC = apparent diffusion coefficient;PD = proton density༛ T1 = T1 relaxation time;T2 = T2 relaxation time. *P values less than 0.05 were considered to indicate statistical significance. Table 5 Predictive performance of MAGiC-derive parameters in differentiating Type I from Type II Histogram metrics YI CV SEN SPE AUC (95%CI) P value (VS ADC) ADC (10 − 3 mm 2 /s) 0.464 0.821 71.4% 75.0% 0.778(0.713–0.843) reference T2 (msec) 0.714 86.2 88.1% 83.3%* 0.849(0.770–0.928) P = 0.056 PD (pu) 0.488 72.1 90.5% 58.3% 0.731(0.639–0.823) P = 0.645 T2 + PD 0.727 N/A 91.7%* 81.0% 0.873 (0.796–0.950) * P = 0.030 NOTE.YI = Youden index; CV = cutoff value; SEN = sensitivity;SPE = specificity; AUC = area under the curve. 4. Discussion This study was first application of MAGiC-generating synthetic morphologic images (sy-T2WI) and quantitative synthetic images (synthetic T1, T2 and PD maps) to assess DMI in comparison to conventional hr-T2WI and pathological type prediction with superior performance compared to ADC values in EC patients. The clinical utility of synthetic morphological imaging primarily depends on image quality. Consistent with previous studies in rectum and head, sy-T2WI presented comparable performance to hr-T2WI in lesion edge sharpness, lesion conspicuity, motion artifacts and overall image quality, with good inter-reader agreement [ 19 , 23 ]. For DMI evaluation, sy-T2WI had similar diagnostic accuracy, sensitivity and sensitivity of 77.9%, 66.7% and 81.1% to hr-T2WI when referenced against histological findings (all p > 0.05), aligning with previous endometrial carcinoma assessments [ 24 ]. The combination of sy-T2WI with DWI further improved diagnostic performance (accuracy = 82.2%, sensitivity = 86.7%, sensitivity 88.7%), achieving comparable results to hr-T2WI + DWI (all p > 0.05), in line with reports emphasizing the capability of T2WI-DWI fusion for DMI [ 13 , 24 ]. The hypointense junctional zone served as a consistent anatomical landmark for DMI assessment on both sy-T2WI and hr-T2WI[ 25 ]. According to pathological findings, the intermediate agreement analysis revealed fair consistency for sy-T2WI alone (κ = 0.427) and good consistency for sy-T2WI + DWI κ = 0.668), monitoring the performance of conventional hr-T2WI combinations. These findings position sy-T2WI as a reliable alternative to hr-T2WI for preoperative DMI assessment and surgical planning in EC. Our quantitative analysis revealed T2 and ADC values consistent with literature reports [ 26 , 27 ], while PD and T1 maps were not previously reported in EC. In agreement with previous studies [ 28 , 29 ], we observed significantly higher T2, PD and ADC values in type I versus than in type II tumors (all p < 0.05). The observed T2 reductions in higher-grade tumors likely reflect decreased free water content, increased cellularity, and reduced extracellular space associated with more aggressive histology (type II: G3 EACs and non-EACs) but no different T1 relaxation time between histological subtypes. Similarly, decreased PD value correlated with the more malignant components, reflecting tissue water content [ 30 ]. The inverse relationship between ADC values and tumor grade in our cohort further supports the role of quantitative MR parameters in tumor characterization [ 31 ]. The combination of T2 and PD maps demonstrated superior predictive performance for histological typing compared to ADC alone (p 0.05). These findings suggested that MAGiC-derived parameters may serve as viable alternative to ADC for preoperative tumor characterization [ 32 – 35 ], with potential application extending to other tumor types as demonstrated in salivary gland lesion differentiation [ 36 ]. Several limitations should be acknowledged in our study. First, the single-center design with a relatively constrained sample size may limit the comprehensive assessment of DMI and histologic subtypes. Validation through larger, multicenter cohorts would strengthen the generalizability of our findings and mitigate potential selection bias. Second, direct comparison between synthetic and conventional T1 and T2 mapping was not performed, primarily due to an impractical clinical scan time (up to 30 minutes per patient for conventional mapping) despite established MAGiC applications in brain, breast, rectal, and prostate imaging. Third, our ROI analysis was restricted to three consecutive slices per patient, which may not fully capture intratumoral heterogeneity and could lead to partial loss of biological information. 5. Conclusions MAGiC enables simultaneous acquisition of diagnostic-quality synthetic T2-weighted images and quantitative parametric maps. sy-T2WI demonstrates equivalent image quality and DMI diagnostic accuracy to conventional hr-T2WI, particularly when combined with DWI. Significant differences in T2 and PD values between type I and type II tumors (p < 0.05), combined with the superior prediction performance of T2 + PD analysis versus ADC (p < 0.05), established MAGiC as a comprehensive technique for preoperative EC morphological and quantitative assessment in EC management. 6. Abbreviations MAGiC Magnetic resonance imaging compilation DMI The depth of myometrial invasion EC Endometrial cancer AUC Area under the receiver operating characteristic curve EACs Endometrial adenocarcinomas Non-EACs non-endometrial adenocarcinomas LVSI lymph-vascular space invasion 7. Declarations 7.1 Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. This prospective study was approved by the ethics committee of Xiangya Hospital of Central South University (No. 2021101117), with all participants providing written informed consent. 7.2 Consent for publication All authors named in this manuscript consented to its publication and take full responsibility for its content. All authors read and approved the final manuscript. 7.3 Availability of data and materials The datasets generated or analyzed during the study are available from the corresponding author on reasonable request. 7.4 Competing interests The authors declare no competing interests. 7.5 Funding This work was supported National Geriatric Disease Clinical Medical Research Center Foundation (grant number 2022LNJJ08) and Healthcare Commission Project of Hunan Province (grant number 20233006). 7.6 Authors' contributions All authors contributed to this study. Yue Li and Yu Wang contributed to designing the study, data analysis and interpretation, preparing the initial draft. Yigang Pei and Weiyin Vivian Liu contributed to critically revising the final manuscript. Xiaorong Ou and Yixin Liu: image analysis and evaluation; Jiaxin Zhou and Wenguang Liu: data acquisition; Jinbiao Chen: data analysis; Qiongqiong He: analysis and interpretation of pathological data; All authors read and approved the final manuscript. 7.7 Acknowledgments The authors thank all those who helped us during the writing of this research. We also thank the Department of gynecology and Pathology of our hospital for their valuable help and feedback. References Crosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. Lancet. 2022;399:1412–28. Nougaret S, Reinhold C, Alsharif SS, Addley H, Arceneau J, Molinari N, et al. Endometrial Cancer: Combined MR Volumetry and Diffusion-weighted Imaging for Assessment of Myometrial and Lymphovascular Invasion and Tumor Grade. Radiology. 2015;276:797–808. Wang X, Zhang H, Di W, Li W. 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Sequential multi-parametric MRI in assessment of the histological subtype and features in the malignant pleural mesothelioma xenografts. Heliyon. 2023;9:e15237. Jiang W, Du S, Gao S, Xie L, Xie Z, Wang M, et al. Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response. Insights Imaging. 2023;14:162. Takumi K, Nakanosono R, Nagano H, Hakamada H, Kanzaki F, Kamimura K, et al. Multiparametric approach with synthetic MR imaging for diagnosing salivary gland lesions. Jpn J Radiol. 2024;42:983–92. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1Theimagequalityscores.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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20:31:09","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128325,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/b3df61ff06b5281a918bb88d.html"},{"id":94797337,"identity":"ce12ea57-25f1-4ce1-9665-cbedb6e38fdd","added_by":"auto","created_at":"2025-10-30 20:31:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248169,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of patient enrollment.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/5df2fd8659a87239590f4852.png"},{"id":94825297,"identity":"255392e4-b129-430b-9b0c-c5c62c3932bc","added_by":"auto","created_at":"2025-10-31 06:50:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":695806,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative synthetic magnetic resonance images of a 44-year-old female patient with EC, highlighting the mass (arrow) on (A) sy-T2WI, (B) T1 map, (C) T2 map, and (D) PD map.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/ed3865e674487133c0b0c603.png"},{"id":94797345,"identity":"6290bf66-26fb-4415-8e1b-da6b48827a61","added_by":"auto","created_at":"2025-10-30 20:31:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":863665,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative pathology-confirmed cases of DMI-I and DMI-II (HE staining, 100´ magnification) on sy-T2WI, hr-T2WI and DWI. The lesion invasion of DMI-I and DMI-II was less than and more than 50% of the myometrial thickness (arrow) as seen on sy-T2WI(A, E), hr-T2WI(B, F), MUSE DWI (C, G), and pathology (D, H), accordingly.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/da979ef547aaa3c1a29e9732.png"},{"id":94797342,"identity":"382425c4-de6d-40d2-8488-5cb60611f3cd","added_by":"auto","created_at":"2025-10-30 20:31:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":458053,"visible":true,"origin":"","legend":"\u003cp\u003eThe schematic diagram depicts three-slice regions of interests (ROIs) manually sketched on sy-T2WI. The last column displays the manually-sketched ROIs at three slices of the mass on sy-T2WI, encompassing the slice with the largest mass area along with its adjacent upper and lower slices. These ROIs were then copied to T1, T2, PD and ADC maps to retrieve the corresponding T1, T2, PD and ADC values for each CC patient. The corresponding hr-T2WI (first column), sy-T2WI (second column) and DWI (third column) are also shown for reference.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/f4bf3eda8ff44d7d4e797b7c.png"},{"id":94826082,"identity":"8b77fbac-0654-43c6-9391-02e3c76b4511","added_by":"auto","created_at":"2025-10-31 06:51:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":947068,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent tissue types in EC patients using HE staining (100 × magnification). (A) G1 endometrial adenocarcinomas (EACs) show no obvious solid non-glandular in the majority of acinar structures (G1 criteria: \u0026lt; 5% solid non-glandular) (G1 criteria: . (B) G2 EACs feature approximately 30-40% solid non-glandular components in the tumor (G2 criteria: 6-50% solid non-glandular). (C) G3 EACs presented about 70-80% solid non-glandular tissue (G3 criteria: \u0026gt; 50% solid non-glandular). (D) A high-grade serous carcinoma (white arrow) which largely replaced glandular tissue, classifying it as non- endometrial adenocarcinomas (non-EACs).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/e68c6b177f709b0e5d7048b3.png"},{"id":94826012,"identity":"23da8030-22b8-4509-83e4-6dbcacea4bc8","added_by":"auto","created_at":"2025-10-31 06:50:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":948295,"visible":true,"origin":"","legend":"\u003cp\u003eImage quality for synthetic T2WI (sy-T2WI; the first column) and conventional T2WI (hr-T2WI; the second column) of a 61-year-old female patient with EC at the sharpness of the lesion edge (5 vs. 5), lesion conspicuity (5 vs. 5), motion artifacts (5 vs. 5) and overall image quality (5 vs.5).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/56a52bf7929b164d2fa2ec39.png"},{"id":94797351,"identity":"886b433c-6749-41f4-8090-72f8e9c135ec","added_by":"auto","created_at":"2025-10-30 20:31:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":213201,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves showed the predictive performance of various models for pathological type classification. The T2 map (AUC: 0.849) and PD map (AUC: 0.731) presented comparable diagnostic performance to ADC values (AUC: 0.778). Notably, the combined T2+PD model exhibited superior predictive capability compared to ADC alone (AUC: 0.873 vs. 0.778; p = 0.03), highlighting the added value of multiparametric synthetic MR analysis.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/0daa9f86b532bfd53c6fb54f.png"},{"id":96919732,"identity":"6250655e-70b3-46da-8495-423678958bfe","added_by":"auto","created_at":"2025-11-27 14:14:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6289601,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/cf1fbb13-d33b-45b6-90bc-be3f21438801.pdf"},{"id":94797338,"identity":"615ed201-bbe5-4d50-8054-e3777ca8e96c","added_by":"auto","created_at":"2025-10-30 20:31:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13119,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1Theimagequalityscores.docx","url":"https://assets-eu.researchsquare.com/files/rs-7818425/v1/c0051bf1c163c05cd9c126a7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Feasibility of magnetic resonance imaging compilation (MAGIC) for evaluating the depth of myometrial invasion and predicting the pathological subtypes of endometrial cancer: A comparison with high resolution T2WI and DWI","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is the most prevalent gynaecologic malignancy, with a rising incidence in China due to obesity, diabetes and population aging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Optimal treatment strategies including surgery alone and surgery followed by adjuvant chemotherapy/radiotherapy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] rely on various prognostic factors including histologic type, depth of myometrial invasion (DMI), lymphovascular invasion [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, histologic type and DMI are strongly correlated with lymph node metastases and overall patient survival, and play a vital role in tailoring individualized treatment strategies.\u003c/p\u003e\u003cp\u003eApproximately 80%-90% of EC cases are endometrial adenocarcinomas (EACs), while the remaining 10%-20% are non-endometrial adenocarcinomas (Non-EACs)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. EACs are further graded histologically from G1 to G3 based on the proportion of solid non-squamous within the tumor [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. G1-2 EACs are associated favorable prognosis and are typically managed with surgery alone, reflecting their low-risk histological and estrogen-dependent pathophysiology [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In contrast, G3 EACs and non-EACs are non-estrogen dependent and linked to poorer outcomes. These subtypes warrant tumor excision along with complete lymphadenectomy due to their increased risk for lymphovascular invasion, intraperitoneal spread and distant metastases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Non-EACs includes serous, endometrioid, clear cell, mixed, undifferentiated and high-grade endometrioid carcinomas. These tumors are prone to recurrence and mortality, with over 50% non-EACs harboring TP53 mutation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDMI is categorized into two levels based on tumor infiltration into the myometrium: DMI-I (superficial, (\u0026lt;\u0026thinsp;50% invasion of the myometrium) and DMI-II (deep invasion, \u0026ge;\u0026thinsp;50% invasion). DMI-II is strongly correlated with higher risks of lymph-vascular space invasion (LVSI), nodal metastases and disease recurrence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, patients with DMI-II are typically recommended for extended lymphadenectomy during surgery [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePelvic MRI is widely adopted tool for the preoperative assessment of histologic type (type I and II) and DMI (DMI-I and DMI-II), primarily using high-resolution T2-weighted imaging (hr-T2WI)) and diffusion weighted imaging (DWI) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Hr-T2WI can depicts DMI with an accuracy ranging from 68% to 82.1%[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The diagnostic performance improves further when hr-T2WI is combined with DWI, rather than with dynamic contrast-enhanced MR [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Apparent diffusion coefficient (ADC) values derived from DWI offer a quantitative biomarker, reflecting tumor cellularity and microstructural properties. Notably, ADC values are reported to be significantly higher in type I EC compared to type II tumor [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], supporting their potential use in non-invasive tumor characterization.\u003c/p\u003e\u003cp\u003eRecently, magnetic resonance imaging compilation (MAGiC) has emerged as a novel imaging technique capable of generating both synthetic morphologic images (synthetic T2WI, sy-T2WI) and quantitative images (synthetic T1, T2 and proton density [PD] maps) in a single acquisition [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While MAGiC has shown promising diagnostic value in breast, prostate, rectum pathologies [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], its application in the assessment of DMI and histologic classification in EC remains unexplored.\u003c/p\u003e\u003cp\u003eThis study aims to evaluate the feasibility of MAGiC in assessing DMI (DMI-I vs. DMI-II) and predicting histologic type (type I: G1-G2 EACs; type II: G3 EACs and non-EACs) in endometrial cancer patients. Additionally, we compare the imaging quality of sy-T2WI with hr-T2WI.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003eThis prospective study was approved by the institutional review board, with all participants providing written informed consent. From October 2022 to June 2024, we prospectively enrolled 131 consecutive patients (mean age: 56.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 years; range: 37\u0026ndash;75 years) with histologically conformed EC who underwent preoperative pelvic MRI. Inclusion criteria required patients to be \u0026ge;\u0026thinsp;18 years old with confirmed EC, no prior pelvic therapy, and complete preoperative MRI (including MAGiC, hr-T2WI and DWI sequences) within 10 days before treatment. Exclusion criteria eliminated patients with MRI contraindication (n\u0026thinsp;=\u0026thinsp;2) such as claustrophobia, poor image quality(n\u0026thinsp;=\u0026thinsp;10), lack of discernible lesion on MRI (n\u0026thinsp;=\u0026thinsp;20), prior treatment history (n\u0026thinsp;=\u0026thinsp;11), confounding uterine pathology (n\u0026thinsp;=\u0026thinsp;4, 3 large uterine fibroids and 1 adenomyosis), or unavailable histopathological correlation (n\u0026thinsp;=\u0026thinsp;16). After exclusions, 68 patients (mean age: 56.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 years) were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Pelvic MRI scan\u003c/h2\u003e\u003cp\u003eAll pelvic MRI examinations were performed on a 3.0T scanner (Signa Premier, GE Healthcare, USA) using a 32-channal body coil. The imaging protocol included MAGiC, conventional hr-T2WI (oblique axial, sagittal and coronal), DWI (b\u0026thinsp;=\u0026thinsp;0 and 800 s/mm\u003csup\u003e2\u003c/sup\u003e), and dynamic contrast enhanced MRI (DCE-MRI). MAGiC and DWI were acquired with identical orientation, slice thickness, and gap as the oblique axial hr-T2WI (perpendicular to the uterine axis)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Acquisition parameters are detailed in table 1. Synthetic T2WI (sy-T2WI) and T1, T2, PD maps were generated using a vendor-supplied multi-delay multi-echo sequence. ADC maps were calculated from DWI data. After MAGiC and DWI, DCE-MRI was performed with an extracellular contrast agent (15 mL of gadodiamide, Omniscan, GE Healthcare) injected at a rate of 0.2 mL/kg and followed by 20 mL of 0.9% saline (injection rate: 2 mL/s). The detailed scan time was 40 minutes (the net scan time: 4:36, MAGiC; 3:08, hr-T2WI; 2:32, DWI). Synthetic morphologic images (sy-T2WI), and quantitative parametric maps (synthetic T1, T2, PD maps) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were generated by a vendor-provided MAGiC sequence, and ADC maps calculated from DWI (b\u0026thinsp;=\u0026thinsp;0, 800 s/mm\u0026sup2;) on the scanner console.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Morphologic images\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Image quality\u003c/h2\u003e\u003cp\u003eImage quality was systematically evaluated using a 5-point Likert scale across four domains: lesion edge sharpness, lesion conspicuity, motion artifacts and overall image quality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] (Supplementary Table\u0026nbsp;1). Two radiologists respectively with 10 and 5 years of gynecologic MRI experience were blinded to patient\u0026rsquo;s information and assessed sy-T2WI and hr-T2WI in separate weekly sessions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 DMI evaluation\u003c/h2\u003e\u003cp\u003eDMI (DMI-1vs.DMI-II) was evaluated on sy-T2WI and hr-T2WI in the first week by two radiologist (reader 3 and reader 4, with 4 and 6 years of gynecologic MRI experience, respectively). In the second week, the same readers assessed sy-T2WI\u0026thinsp;+\u0026thinsp;DWI and hr-T2WI\u0026thinsp;+\u0026thinsp;DWI, as DWI can enhance the accuracy of DMI diagnosis when combined with T2WI \u003csup\u003e[17]\u003c/sup\u003e. The readers were blinded to patient information and histopathologic reports to ensure objectivity. Initially, the size, extent, and boundary of the lesions were identified separately on sy-T2WI, hr-T2WI and DWI. DMI was classified as DMI-I if the tumor invasion was up to 50% of the myometrial thickness and as DMI-II when the invasion extended beyond 50% of the myometrium thickness [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Representative cases of pathology-confirmed DMI-I and DMI-II were illustrated on sy-T2WI, hr-T2WI, and DWI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Quantitative images\u003c/h2\u003e\u003cp\u003eAfter identifying the size, extent, and boundary of the lesions on sy-T2WI and DWI, regions of interests (ROIs) were manually drawn on three consecutive slices of sy-T2WI, including the largest mass slice in the tumor\u0026rsquo;s center while avoiding necrotic areas [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For small tumors, ROI(s) were drawn only on the visible tumor slice(s). These ROIs were then copied to T1, T2, PD and ADC map to extract corresponding mean values (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The average T1, T2, PD and ADC values were calculated for each EC patient. Two radiologists (Reader 3 and 4, with 5 and 3 years of gynecologic MRI respectively) performed the ROI delineation while blinded to clinical and pathological data. Discrepancies in interpretation were resolved by a senior radiologist with over 30 years of experience in gynecologic MRI. In this study, ROI sizes ranged from 0.37cm\u003csup\u003e2\u003c/sup\u003e to 13.5 cm \u003csup\u003e2\u003c/sup\u003e with an average size of 5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94 cm \u003csup\u003e2\u003c/sup\u003e for EC subjects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Pathologic Analyses\u003c/h2\u003e\u003cp\u003ePathological examination was performed by an experienced pathologist with 12 years of gynecologic pathology experience, who remained blinded to the patient\u0026rsquo;s clinical and MRI data. Pathological characteristics \u0026ndash; including DMI, histological types, tumor grade and other features \u0026ndash; were assessed according to the 2023 revised FIGO staging system for endometrial carcinoma [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. EC encompassed grade (G) 1\u0026ndash;3 endometrial adenocarcinomas (EACs) and non- endometrial adenocarcinomas (non-EACs) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The FIGO criteria defined G1-3 EACs as exhibiting\u0026thinsp;\u0026le;\u0026thinsp;5%, 6\u0026ndash;50% and \u003cb\u003e\u0026gt;\u003c/b\u003e\u0026thinsp;50% solid non-glandular growth in the majority of cells, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C)Non-EACs includes serous, endometrioid, clear cell, mixed histologic type and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). All patients were categorized into type I (estrogen-dependent type: G1-G2 EACs) and type II (no estrogen-dependent type: G3 EACs\u0026thinsp;+\u0026thinsp;non-EACs) based on pathologic tissue types, and further classified as DMI-I and DMI-II based on tumor invasive depth in the myometrium (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using SPSS Statistics (version 26.0; IBM Corp., Armonk, NY, USA) and GraphPad (version 9.0.1). A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eFor qualitative image parameters were evaluated: (1) lesion edge sharpness, (2) lesion conspicuity, (3) motion artifacts, and (4) overall image quality, with comparisons performed using the Mann-Whitney U test. Interobserver agreement of image quality for sy-T2WI and hr-T2WI was evaluated respectively using weighted kappa (κ) statistics (0.20: poor; 0.21\u0026ndash;0.40: fair; 0.41\u0026ndash;0.60: moderate; 0.61\u0026ndash;0.80: good; \u0026gt;0.80: excellent agreement).\u003c/p\u003e\u003cp\u003eDiagnostic accuracy metrics (accuracy, sensitivity and specificity) for DMI classification (DMI-I vs DMI-II) were compared across four imaging protocols: (1) sy-T2WI, (2) hr-T2WI, (3) sy-T2WI\u0026thinsp;+\u0026thinsp;DWI, and (4) hr-T2WI\u0026thinsp;+\u0026thinsp;DWI using Chi-square test. Cohen kappa coefficient evaluated with pathological findings, categorized as: κ\u0026thinsp;\u0026lt;\u0026thinsp;0.40 (poor); 0.4\u0026ndash;0.75 (acceptable); \u0026ge;0.75 (good consistency). Pairwise comparison of diagnostic performance were performed between sy-T2WI vs hr-T2W and sy-T2WI\u0026thinsp;+\u0026thinsp;DWI and hr-T2WI\u0026thinsp;+\u0026thinsp;DWI.\u003c/p\u003e\u003cp\u003eIn terms of histological type prediction, quantitative MR parameters (sy-T1, T2, PD and ADC values) were compared for their ability to differentiate type I vs type II tumors. The DeLong test was employed to compare area under the curve (AUC) values and 95% confidence intervals (CI).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic and pathological characteristics\u003c/h2\u003e\u003cp\u003eThe study cohort comprised 68 histologically confirmed EC patients (mean age: 56.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 years; range: 37\u0026ndash;75 years), consisting of 62 subjects with EACs ( G1\u0026thinsp;=\u0026thinsp;31; G2\u0026thinsp;=\u0026thinsp;23; G3\u0026thinsp;=\u0026thinsp;8) and 6 non-EACs (serous carcinoma\u0026thinsp;=\u0026thinsp;5, clear cell carcinoma\u0026thinsp;=\u0026thinsp;1). Based on the dualistic classification, the population included 54 type I tumors (estrogen-dependent; mean age: 52.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 years; range: 37\u0026ndash;75 years) and 14 type II tumors (non-estrogen-dependent; mean age: 58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 years; range: 39\u0026ndash;66 years). Myometrial invasion analysis revealed 53 DMI-I cases (mean age: 53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 years; range: 37\u0026ndash;68 years) and 15 DMI-II cases (mean age: 56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 years; range: 38\u0026ndash;73 years).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Comparative image quality assessment\u003c/h2\u003e\u003cp\u003eBoth Sy-T2WI and hr-T2WI demonstrated comparable performance across all evaluation image quality parameters: sharpness of the lesion edge, lesion conspicuity, motion artifacts and overall image quality (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Inter-observer reliability analysis revealed good and excellent agreement for sy-T2WI (κ\u0026thinsp;=\u0026thinsp;0.754\u0026ndash;0.826, all P\u0026lt;0.001) and consistently excellent agreement for hr-T2WI (κ\u0026thinsp;=\u0026thinsp;0.814\u0026ndash;0.863, all P\u0026lt;0.001)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Particularly strong concordance was observed for lesion conspicuity assessment on sy-T2WI (κ\u0026thinsp;=\u0026thinsp;0.802; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and hr-T2WI (κ\u0026thinsp;=\u0026thinsp;0.814; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e Comparison of the image quality scores between sy-T2WI and hr-T2WI.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003esy-T2WI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehr-T2Wl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value (overall)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAgreement\u003c/p\u003e\u003cp\u003efor sy-T2WI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAgreement\u003c/p\u003e\u003cp\u003efor hr-T2WI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSharpness of the lesion edge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.754(\u0026lt;0.0001*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.825(\u0026lt;0.0001*)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(3,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLesion conspicuity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(3,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.802(\u0026lt;0.0001*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.814(\u0026lt;0.0001*)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(3,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMotion artifacts\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.789(\u0026lt;0.0001*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.853(\u0026lt;0.0001*)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall image quality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.826(\u0026lt;0.0001*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.863(\u0026lt;0.0001*)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReader 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5(4,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote. A 5-point scale were used and Median (min, max) values are given in sharpness of the lesion edge, lesion conspicuity, motion artifacts and overall image quality. The inter-observer agreement was assessed on sy-T2WI and hr-T2WI respectively and * p value indicates a significant difference.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 DMI evaluation\u003c/h2\u003e\u003cp\u003eFor DMI, the diagnostic accuracy, sensitivity and specificity were 77.9%, 66.7% and 81.1% for sy-T2WI, 79.4%, 80.0% and 79.2% for hr-T2WI, 82.2%, 86.7% and 88.7% for sy-T2WI\u0026thinsp;+\u0026thinsp;DWI, and 91.2%, 86.7% and 92.5% for hr-T2WI\u0026thinsp;+\u0026thinsp;DWI. There was no significant difference between sy-T2WI and hr-T2WI (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) as well as sy-T2WI\u0026thinsp;+\u0026thinsp;DWI and hr-T2WI\u0026thinsp;+\u0026thinsp;DWI (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe diagnostic performance of various evaluated methods and the difference between them with comparison of pathological findings.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eThe evaluation with imaging\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePathological findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eDiagnostic performance for DMI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMotheds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eclassification\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDMI-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDMI-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAcc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSpec\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKapple value(95%CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSy-T2WI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDMI-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e77.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e66.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e81.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.427*(0.305\u0026ndash;0.549)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDMI-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHr-T2WI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDMI-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e79.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e80.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e79.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.497*(0.385\u0026ndash;0.609)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDMI-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSy-T2WI\u0026thinsp;+\u0026thinsp;DWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDMI-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e88.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e86.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e88.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.688*(0.507\u0026ndash;0.789)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDMI-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHr-T2WI\u0026thinsp;+\u0026thinsp;DWI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDMI-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e91.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e86.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e92.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.755*(0.661\u0026ndash;0.849)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDMI-II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eNote. ACC: accuracy; Sen: Sensitivity; Spec: Specificity; *: there was a statistical difference by kappa value; 95% CI: 95% confidence interval.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Quantitative images\u003c/h2\u003e\u003cp\u003eThe quantitative MR analysis revealed mean values of T1\u0026thinsp;=\u0026thinsp;1234.68\u0026thinsp;\u0026plusmn;\u0026thinsp;87.93 msec, P2\u0026thinsp;=\u0026thinsp;85.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27 ms, PD\u0026thinsp;=\u0026thinsp;79.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61 p.u and ADC\u0026thinsp;=\u0026thinsp;0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s across enrolled EC patients. Significant differences were observed between histological types, with type I tumors with higher T2 (94.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44 msec vs 77.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15 msec), PD (81.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27 p.u. vs 77.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15 p.u.) and ADC (0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s vs 0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s) except T1 value (1335.5\u0026thinsp;\u0026plusmn;\u0026thinsp;67.87 vs 1391.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.09 msec; p\u0026thinsp;=\u0026thinsp;0.695) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). ROC analysis demonstrated good diagnostic performance for individual parameters: T2 (AUC: 0.849; 95% CI: 0.770\u0026ndash;0.928), PD (AUC: 0.731; 95% CI: 0.639\u0026ndash;0.823) and ADC value (AUC: 0.778; 95% CI: 0.713\u0026ndash;0.843; all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, the combined T2\u0026thinsp;+\u0026thinsp;PD model showed superior prediction performance (AUC: 0.873; 95%CI: 0.796\u0026ndash;0.950) compared to ADC alone (0.778;95%CI: 0.713\u0026ndash;0.843, p\u0026thinsp;=\u0026thinsp;0.003), highlighting the added value of multiparametric MR analysis for differentiating type I from type II EC tumors (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e Comparisons of quantifications parameter (T1, T2, PD and ADC) between type I and type II\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eType Ⅰ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eType Ⅱ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1 (msec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1335.5\u0026thinsp;\u0026plusmn;\u0026thinsp;67.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1391.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 (msec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77.59\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD (pu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC(10-3mm2/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. EACs: Endometrial adenocarcinoma.\u003c/p\u003e\u003cp\u003eEACs: endometrial adenocarcinoma; Type I: G1\u0026thinsp;+\u0026thinsp;G2 EACs; Type II: G3\u0026thinsp;+\u0026thinsp;non-EACs\u003c/p\u003e\u003cp\u003eADC\u0026thinsp;=\u0026thinsp;apparent diffusion coefficient;PD\u0026thinsp;=\u0026thinsp;proton density༛\u003c/p\u003e\u003cp\u003eT1\u0026thinsp;=\u0026thinsp;T1 relaxation time;T2\u0026thinsp;=\u0026thinsp;T2 relaxation time.\u003c/p\u003e\u003cp\u003e*P values less than 0.05 were considered to indicate statistical significance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e Predictive performance of MAGiC-derive parameters in differentiating Type I from Type II\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistogram metrics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSEN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSPE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAUC (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value (VS ADC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADC (10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e mm\u003csup\u003e2\u003c/sup\u003e/s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.778(0.713\u0026ndash;0.843)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ereference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2 (msec)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83.3%*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.849(0.770\u0026ndash;0.928)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD (pu)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.731(0.639\u0026ndash;0.823)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.645\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u0026thinsp;+\u0026thinsp;PD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.7%*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.873 (0.796\u0026ndash;0.950) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNOTE.YI\u0026thinsp;=\u0026thinsp;Youden index; CV\u0026thinsp;=\u0026thinsp;cutoff value; SEN\u0026thinsp;=\u0026thinsp;sensitivity;SPE\u0026thinsp;=\u0026thinsp;specificity; AUC\u0026thinsp;=\u0026thinsp;area under the curve.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study was first application of MAGiC-generating synthetic morphologic images (sy-T2WI) and quantitative synthetic images (synthetic T1, T2 and PD maps) to assess DMI in comparison to conventional hr-T2WI and pathological type prediction with superior performance compared to ADC values in EC patients.\u003c/p\u003e\u003cp\u003eThe clinical utility of synthetic morphological imaging primarily depends on image quality. Consistent with previous studies in rectum and head, sy-T2WI presented comparable performance to hr-T2WI in lesion edge sharpness, lesion conspicuity, motion artifacts and overall image quality, with good inter-reader agreement [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For DMI evaluation, sy-T2WI had similar diagnostic accuracy, sensitivity and sensitivity of 77.9%, 66.7% and 81.1% to hr-T2WI when referenced against histological findings (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), aligning with previous endometrial carcinoma assessments [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The combination of sy-T2WI with DWI further improved diagnostic performance (accuracy\u0026thinsp;=\u0026thinsp;82.2%, sensitivity\u0026thinsp;=\u0026thinsp;86.7%, sensitivity 88.7%), achieving comparable results to hr-T2WI\u0026thinsp;+\u0026thinsp;DWI (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), in line with reports emphasizing the capability of T2WI-DWI fusion for DMI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The hypointense junctional zone served as a consistent anatomical landmark for DMI assessment on both sy-T2WI and hr-T2WI[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. According to pathological findings, the intermediate agreement analysis revealed fair consistency for sy-T2WI alone (κ\u0026thinsp;=\u0026thinsp;0.427) and good consistency for sy-T2WI\u0026thinsp;+\u0026thinsp;DWI κ\u0026thinsp;=\u0026thinsp;0.668), monitoring the performance of conventional hr-T2WI combinations. These findings position sy-T2WI as a reliable alternative to hr-T2WI for preoperative DMI assessment and surgical planning in EC.\u003c/p\u003e\u003cp\u003eOur quantitative analysis revealed T2 and ADC values consistent with literature reports [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], while PD and T1 maps were not previously reported in EC. In agreement with previous studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], we observed significantly higher T2, PD and ADC values in type I versus than in type II tumors (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The observed T2 reductions in higher-grade tumors likely reflect decreased free water content, increased cellularity, and reduced extracellular space associated with more aggressive histology (type II: G3 EACs and non-EACs) but no different T1 relaxation time between histological subtypes. Similarly, decreased PD value correlated with the more malignant components, reflecting tissue water content [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The inverse relationship between ADC values and tumor grade in our cohort further supports the role of quantitative MR parameters in tumor characterization [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe combination of T2 and PD maps demonstrated superior predictive performance for histological typing compared to ADC alone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with an AUC of 0.873 versus 0.849 for T2 mapping alone (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings suggested that MAGiC-derived parameters may serve as viable alternative to ADC for preoperative tumor characterization [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], with potential application extending to other tumor types as demonstrated in salivary gland lesion differentiation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged in our study. First, the single-center design with a relatively constrained sample size may limit the comprehensive assessment of DMI and histologic subtypes. Validation through larger, multicenter cohorts would strengthen the generalizability of our findings and mitigate potential selection bias. Second, direct comparison between synthetic and conventional T1 and T2 mapping was not performed, primarily due to an impractical clinical scan time (up to 30 minutes per patient for conventional mapping) despite established MAGiC applications in brain, breast, rectal, and prostate imaging. Third, our ROI analysis was restricted to three consecutive slices per patient, which may not fully capture intratumoral heterogeneity and could lead to partial loss of biological information.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eMAGiC enables simultaneous acquisition of diagnostic-quality synthetic T2-weighted images and quantitative parametric maps. sy-T2WI demonstrates equivalent image quality and DMI diagnostic accuracy to conventional hr-T2WI, particularly when combined with DWI. Significant differences in T2 and PD values between type I and type II tumors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), combined with the superior prediction performance of T2\u0026thinsp;+\u0026thinsp;PD analysis versus ADC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), established MAGiC as a comprehensive technique for preoperative EC morphological and quantitative assessment in EC management.\u003c/p\u003e"},{"header":"6. Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAGiC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMagnetic resonance imaging compilation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe depth of myometrial invasion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEndometrial cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the receiver operating characteristic curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEACs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEndometrial adenocarcinomas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNon-EACs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-endometrial adenocarcinomas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLVSI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elymph-vascular space invasion\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"7. Declarations","content":"\u003cp\u003e\u003cstrong\u003e7.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. This prospective study was approved by the ethics committee of Xiangya Hospital of Central South University (No. 2021101117), with all participants providing written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors named in this manuscript consented to its publication and take full responsibility for its content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated or analyzed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported National Geriatric Disease Clinical Medical Research Center Foundation (grant number 2022LNJJ08) and Healthcare Commission Project of Hunan Province (grant number 20233006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.6\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to this study. Yue Li and Yu Wang contributed to designing the study, data analysis and interpretation, preparing the initial draft. Yigang Pei and Weiyin Vivian Liu contributed to critically revising the final manuscript. Xiaorong Ou and Yixin Liu: image analysis and evaluation; Jiaxin Zhou and Wenguang Liu: data acquisition; Jinbiao Chen: data analysis; Qiongqiong He: analysis and interpretation of pathological data; All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.7\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all those who helped us during the writing of this research. We also thank the Department of gynecology and Pathology of our hospital for their valuable help and feedback.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCrosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. 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Onco Targets Ther. 2017;10:5937\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNurdillah I, Rizuana IH, Suraya A, Syazarina SO. A Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging and T2-Weighted Imaging in Determining the Depth of Myometrial Invasion in Endometrial Carcinoma-A Retrospective Study. J Pers Med. 2022;12:1268.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S, Liu J, Zhang F, Yang M, Zhang Z, Liu J, et al. Novel T2 Mapping for Evaluating Cervical Cancer Features by Providing Quantitative T2 Maps and Synthetic Morphologic Images: A Preliminary Study. J Magn Reson Imaging. 2020;52:1859\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu L, Lu W, Wang F, Wang Y, Wu PY, Zhou J, et al. Study of T2 mapping in quantifying and discriminating uterine lesions under different magnetic field strengths: 1.5 T vs. 3.0 T. 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Heliyon. 2023;9:e15237.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang W, Du S, Gao S, Xie L, Xie Z, Wang M, et al. Correlation between synthetic MRI relaxometry and apparent diffusion coefficient in breast cancer subtypes with different neoadjuvant therapy response. Insights Imaging. 2023;14:162.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakumi K, Nakanosono R, Nagano H, Hakamada H, Kanzaki F, Kamimura K, et al. Multiparametric approach with synthetic MR imaging for diagnosing salivary gland lesions. Jpn J Radiol. 2024;42:983\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Magnetic Resonance Imaging Compilation, Depth of Myometrial Invasion, Pathological Subtypes, Endometrial Cancer, Comparative Studies","lastPublishedDoi":"10.21203/rs.3.rs-7818425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7818425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo investigate the feasibility of MAGiC (sy-T2WI; T1, T2 and PD maps) for evaluating the depth of myometrial invasion (DMI) and predicting the pathological subtypes of endometrial cancer (EC) in comparison to hr-T2WI and DWI.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e68 out of 131 consecutive EC patients were prospectively recruited to perform MAGiC, hr-T2WI and DWI on 3.0T MRI before surgery. DMI (DMI-: \u0026lt; 50%, DMI-II: \u0026ge; 50%) and histologic subtypes (type-I and type-II) were confirmed by operation and as the reference standard. For DMI, the diagnostic performance was assessed and compared across imaging protocols (sy-T2WI vs. hr-T2WI; sy-T2WI\u0026thinsp;+\u0026thinsp;DWI vs. hr-T2WI\u0026thinsp;+\u0026thinsp;DWI). For histologic typing, the predictive performance of T1, T2, PD maps and their combinations was evaluated against ADC values using area under the curve (AUC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFor DMI, the diagnostic accuracy, sensitivity and specificity were 77.9%, 66.7% and 81.1% for sy-T2WI, 79.4%, 80.0% and 79.2% for hr-T2WI; 82.2%, 86.7% and 88.7% for sy-T2WI\u0026thinsp;+\u0026thinsp;DWI, and 91.2%, 86.7% and 92.5% for hr-T2WI\u0026thinsp;+\u0026thinsp;DWI. No statistically significant differences were observed between sy-T2WI vs. hr-T2WI or between their combinations with DWI (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For histologic subtypes, the combination of T2 and PD maps outperformed ADC alone (AUC: 0.873 vs. 0.778; p\u0026thinsp;=\u0026thinsp;0.003), although T2 and PD showed similar AUC to ADC (both p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eMAGiC provides a comparable or superior performance to conventional hr-T2WI and DWI both DMI evaluation and histologic subtypes prediction in EC, highlighting its potential as a robust non-contrast MR protocol in clinical practice.\u003c/p\u003e","manuscriptTitle":"Feasibility of magnetic resonance imaging compilation (MAGIC) for evaluating the depth of myometrial invasion and predicting the pathological subtypes of endometrial cancer: A comparison with high resolution T2WI and DWI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 20:31:03","doi":"10.21203/rs.3.rs-7818425/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43687ebb-7da8-40f7-bd49-d2e7cdb4bb16","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T01:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 20:31:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7818425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7818425","identity":"rs-7818425","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}