Acoustic radiation force impulse elastography for differentiating adenomyosis and uterine leiomyoma: A prospective histopathology-validated study

Acoustic radiation force impulse elastography for differentiating adenomyosis and uterine leiomyoma: A prospective histopathology-validated study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Acoustic radiation force impulse elastography for differentiating adenomyosis and uterine leiomyoma: A prospective histopathology-validated study LATİF HACIOĞLU, Onur KARAASLAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-10619173/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Purpose To evaluate the diagnostic performance of Acoustic Radiation Force Impulse (ARFI) elastography in differentiating adenomyosis from uterine leiomyoma and to compare its diagnostic accuracy with magnetic resonance imaging (MRI). Methods This prospective study included 164 patients with histopathologically confirmed diagnoses after hysterectomy: adenomyosis (n = 53), leiomyoma (n = 56), and controls (n = 55). All participants underwent preoperative ARFI elastography and MRI. Shear wave elastography (SWE) values were compared among groups, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results Mean SWE values were significantly higher in the leiomyoma group (3.09 ± 0.78 m/s) than in the adenomyosis (2.80 ± 0.83 m/s; p = 0.040) and control groups (2.57 ± 0.84 m/s; p < 0.001). No significant difference was observed between adenomyosis and controls (p = 0.158). For differentiating leiomyoma from controls, the area under the curve (AUC) was 0.677 (95% CI, 0.581–0.773), with 71.4% sensitivity and 62.8% specificity. For differentiating leiomyoma from adenomyosis, the AUC was 0.619 (95% CI, 0.508–0.730), with 67.1% sensitivity and 59.0% specificity. MRI showed high diagnostic accuracy for both conditions and enabled adenomyosis subtype classification, whereas SWE values did not differ significantly among adenomyosis subtypes. Conclusion ARFI elastography showed moderate performance in differentiating uterine leiomyoma from normal myometrium and adenomyosis but poor performance in distinguishing adenomyosis from normal myometrium. ARFI should be considered a complementary tool to MRI, particularly when MRI is contraindicated or unavailable. Adenomyosis Leiomyoma Elastography Magnetic Resonance Image Figures Figure 1 Figure 2 Introduction Adenomyosis and uterine leiomyoma are among the most common benign gynecological conditions affecting women of reproductive age. Adenomyosis is defined by the presence of ectopic endometrial glands and stroma within the myometrium, surrounded by hyperplastic smooth muscle cells; its histopathological prevalence ranges from 5% to 70% [1–5]. Uterine leiomyoma, a monoclonal benign smooth muscle tumor, is the most frequent pelvic neoplasm in women, affecting approximately 50–60% of this population [2,6]. Despite often presenting with overlapping clinical symptoms, these two entities require accurate differentiation, as their treatment strategies differ markedly [7–9]. Among current imaging modalities, magnetic resonance imaging (MRI) offers high diagnostic accuracy for differentiating adenomyosis from uterine leiomyoma. However, its broad clinical application is constrained by high costs, lengthy scan times, claustrophobia, and contraindications such as incompatible implants [10–13]. Conversely, transvaginal ultrasonography (TVUS) is more accessible and economical, but its diagnostic reliability is operator-dependent and may be compromised—particularly in the presence of coexisting leiomyomas—leading to reduced sensitivity for adenomyosis detection [4,14–17]. These limitations underscore the ongoing need for objective, reproducible, and non-invasive imaging tools capable of enhancing preoperative diagnostic accuracy. In recent years, elastography—an ultrasound-based technique that evaluates tissue mechanical properties such as stiffness and elasticity—has emerged as a promising diagnostic modality [18–21]. This method relies on measuring tissue deformation or the propagation velocity of shear waves generated by an applied mechanical force [22]. Acoustic Radiation Force Impulse (ARFI) elastography is one of the most advanced iterations of this technology. By emitting short, high-intensity acoustic pulses via the ultrasound transducer, ARFI quantifies tissue stiffness by measuring the propagation velocity of induced shear waves (m/s) [23,24]. Compared to strain elastography, ARFI offers distinct advantages, including no need for external compression and the capacity to deliver objective, operator-independent, and reproducible quantitative data [25]. Although ARFI elastography has demonstrated high diagnostic accuracy in various settings—most notably in liver fibrosis assessment—evidence for its utility in uterine pathologies remains limited and largely derived from small-scale studies [18–22]. Given their distinct histopathological features, uterine leiomyomas are expected to show greater stiffness, whereas adenomyosis may exhibit different elasticity profiles due to structural changes induced within the myometrium [26–32]. Nevertheless, prospective, histopathology-validated studies directly comparing these two conditions with each other and with normal uterine tissue remain scarce. This study aimed to evaluate the diagnostic performance of ARFI elastography for uterine leiomyoma and adenomyosis using histopathologically confirmed cases, and to assess its contribution to their differential diagnosis. We hypothesized that ARFI elastography would effectively distinguish uterine leiomyoma from both adenomyosis and normal myometrium. Additionally, we sought to determine whether SWE values differ across MRI-defined adenomyosis subtypes, and to establish the sensitivity and specificity of ARFI elastography for these conditions. The novelty of this study lies in its prospective, single-blinded, histopathology-validated design, which integrates a comprehensive assessment of ARFI elastography with MRI-based adenomyosis subtyping and histopathological correlation. We expect our findings to help define the clinical utility of ARFI elastography as a complementary imaging tool in the preoperative workup of uterine pathologies—especially when MRI is unavailable or contraindicated. Materials and methods This prospective, single-blind, case-control study was conducted at the Department of Obstetrics and Gynecology, Van Yüzüncü Yıl University Faculty of Medicine, Türkiye, between April 1, 2021, and April 1, 2022, and adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. This study is entirely observational and does not involve any intervention in the treatment of participants. The decision for surgery was made by the attending surgeons based on clinical indications, independently of the research team. The investigators played no role in the surgical decision-making process or in the surgical procedures themselves. Therefore, this study does not meet the definition of a clinical trial and is not subject to registration requirements under ICMJE criteria. The study protocol was approved by the local Clinical Research Ethics Committee (Approval No. 01, March 31, 2021), and all procedures complied with the Declaration of Helsinki. Written informed consent was obtained from all participants. We initially enrolled 256 consecutive women aged 40–70 years scheduled for hysterectomy at the gynecology outpatient clinic. Exclusion criteria included endometrial carcinoma, hysterectomy for uterine prolapse, age outside the predefined range, and morbid obesity (body mass index ≥ 40 kg/m²), the latter due to its potential to compromise elastographic measurements. Of these, 92 patients were excluded from the final analysis: 44 did not undergo surgery (12 for uncontrolled hypertension, 19 for COVID-19 positivity, and 13 for poorly controlled diabetes), 10 withdrew consent for surgery, 25 could not undergo MRI (8 due to claustrophobia and 17 who refused), and 13 declined elastographic examination. The remaining 164 patients comprised the final study population, with histopathologically confirmed diagnoses of adenomyosis (n = 53, 32.3%), uterine leiomyoma (n = 56, 34.1%), or neither (controls, n = 55, 33.5%) (Fig. 1 ). Cases were defined as patients diagnosed with adenomyosis and/or leiomyoma based on clinical, ultrasonographic, and laboratory findings, all of whom were scheduled for hysterectomy. Controls were patients without any uterine pathology (including leiomyoma, adenomyosis, or endometrial carcinoma) who underwent hysterectomy for other benign indications. All patients underwent contrast-enhanced pelvic MRI before hysterectomy, followed by ARFI elastography. MRI was performed using a 1.5-T scanner (Magnetom Avanto, Siemens Healthcare, Erlangen, Germany) with a phased-array pelvic coil. T2-weighted turbo spin-echo (TSE) images were acquired in the axial, sagittal, and coronal planes (TR/TE: 4000–6000/100–120 ms; slice thickness: 4 mm; interslice gap: 1 mm; field of view: 240 × 240 mm; matrix: 320 × 256). Axial T1-weighted images were obtained with TR/TE 600/10 ms and 4-mm slice thickness. Diffusion-weighted imaging (DWI) was performed with b-values of 0, 50, 400, and 800 s/mm². Contrast-enhanced imaging used dynamic T1-weighted volumetric interpolated breath-hold examination (VIBE) sequences following intravenous gadobutrol (Gadovist®, Bayer Healthcare, Berlin, Germany) at 0.1 mmol/kg. A radiologist with 10 years of experience in pelvic MRI, blinded to clinical, ultrasonographic, and histopathological data, interpreted all examinations. Adenomyosis was classified using the MRI-based system proposed by Kishi et al. [32], with minor modifications. Subtypes were defined as follows: Type I (intrinsic)—lesions arising from the inner myometrium, closely associated with the endometrium and junctional zone (JZ), without outer myometrial involvement; Type II (extrinsic)—lesions originating from the outer myometrium, potentially involving the serosa while sparing the JZ; Type III (intramural)—isolated lesions confined to the myometrium with no continuity with the inner or outer layers; and Type IV (indeterminate)—lesions not fitting Types I–III, generally representing advanced or heterogeneous disease. ARFI elastography was performed by the same radiologist (8 years of ultrasonography and 6 years of elastography experience), who was blinded to clinical and imaging data (single-blind design). Examinations used a Siemens ACUSON S2000™ ultrasound system (Siemens Healthcare, Erlangen, Germany) with a 4-MHz 6C1 convex transducer. Patients were examined supine. After conventional B-mode ultrasonography to locate the uterus, shear wave elastography (SWE) measurements were obtained using Virtual Touch™ IQ application in Shear Wave mode. During image acquisition, patients were asked to hold their breath and remain still. The built-in quality map guided selection of appropriate regions, with measurement depth standardized to 3–6 cm and the region of interest (ROI) fixed at 10 × 10 mm. SWE measurements were taken from six standardized uterine regions: the anterior and posterior myometrium at the fundus, corpus, and isthmus. Three consecutive measurements were obtained from each region, yielding 18 measurements per patient (3 levels × 2 walls × 3 replicates). Measurements were repeated if the quality map indicated motion artifacts or poor signal. Fewer than 5% of all measurements (n < 9) were technically unsuccessful and excluded. The mean value of all valid measurements was calculated as the final SWE velocity (m/s) for each patient. After hysterectomy, all surgical specimens underwent routine histopathological evaluation by a single gynecologic pathologist with 15 years of experience, blinded to clinical and imaging data (including ultrasonography, MRI, and elastography). Histopathological diagnosis served as the reference (gold) standard for final classification. Based on these findings, patients were assigned to one of three groups: adenomyosis, leiomyoma, or control. Statistical Analysis Sample size was calculated using G*Power software (version 3.1.9.7; Heinrich Heine University, Düsseldorf, Germany). Based on the primary outcome (SWE values), a minimum of 52 patients per group was estimated to detect differences among the three groups (adenomyosis, leiomyoma, control), assuming an effect size (Cohen's f) of 0.25, a two-sided alpha of 0.05, and a power (1 − β) of 80%. Normality was assessed using the Kolmogorov–Smirnov test (for n > 50) and by evaluating skewness and kurtosis. As all continuous variables were approximately normally distributed, parametric tests were used. Continuous variables are presented as mean ± standard deviation (SD), and categorical variables as frequency (n) and percentage (%). Group comparisons for continuous variables used one-way analysis of variance (ANOVA), followed by Duncan's post hoc test for pairwise comparisons. Categorical variables were compared with the chi-square test. Diagnostic performance of ARFI elastography (mean SWE velocity, m/s) for differentiating adenomyosis and leiomyoma was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC), 95% confidence intervals (CI), sensitivity, specificity, and optimal cut-off values were calculated. Optimal cut-offs were determined using the Youden index (J = sensitivity + specificity − 1), which maximizes combined sensitivity and specificity. AUCs were compared using the DeLong test. All tests were two-sided, with p < 0.05 considered statistically significant. Analyses were conducted using IBM SPSS Statistics (version 25.0; IBM Corp., Armonk, NY, USA) and MedCalc Statistical Software (version 20.0; MedCalc Software Ltd., Ostend, Belgium). Results Of the 256 initially enrolled patients, 92 were excluded per the predefined criteria, leaving 164 patients with histopathologically confirmed diagnoses: 53 with adenomyosis, 56 with leiomyoma, and 55 controls (Fig. 1 ). The three groups differed significantly in age, body mass index (BMI), gravidity, and parity (all p < 0.05), but not in number of abortions (p = 0.681). Patients with leiomyoma were significantly younger than the other groups, whereas those with adenomyosis had the highest mean BMI. Laboratory parameters also differed significantly across groups for hemoglobin, white blood cell, and platelet counts (Table 1 ). These baseline characteristics are summarized descriptively, while the primary analyses focused on SWE measurements and diagnostic performance. Table 1 Baseline demographic, obstetric, and laboratory characteristics of the study population Control Adenomyosis Leiomyoma p Mean SD Mean SD Mean SD Age (years) 52.76ᵃ 8.76 50.68ᵃ 6.72 47.80ᵇ 5.79 0.002 BMI (kg/m²) 25.48ᵇ 5.09 28.32ᵃ 4.92 23.94ᵇ 3.45 0.001 Gravidity 4.93ᵃᵇ 2.24 5.21ᵃ 3.08 3.98ᵇ 2.57 0.042 Parity 4.78ᵃᵇ 2.14 5.13ᵃ 3.08 3.84ᵇ 2.37 0.025 Abortions 0.15 0.52 0.08 0.33 0.14 0.52 0.681 Hemoglobin (g/dL) 12.42ᵇ 1.15 12.55ᵃᵇ 1.24 12.97ᵃ 1.28 0.048 WBC (×10³/µL) 8.13ᵇ 2.86 7.51ᵇ 2.12 10.67ᵃ 2.65 0.001 Platelet count (×10³/µL) 287.49ᵃ 104.80 247.19ᵇ 59.70 265.89ᵃᵇ 82.93 0.049 ᵃᵇ Values with different superscript letters indicate statistically significant differences between groups according to the Bonferroni post hoc pairwise comparison test Mean SWE values were significantly higher in the leiomyoma group than in both the adenomyosis (p = 0.040) and control groups (p < 0.001). No significant difference was found between adenomyosis and controls (p = 0.158) (Table 2 ). Table 2 Comparison of Elastosonography Measurements Between Groups Comparison Group Mean Standard Deviation p Adenomyosis vs. Leiomyoma Adenomyosis 2.80 0.83 0.040 Leiomyoma 3.09 0.78 Control vs. Adenomyosis Control 2.57 0.84 0.158 Adenomyosis 2.80 0.83 Control vs. Leiomyoma Control 2.57 0.84 0.001 Leiomyoma 3.09 0.78 ROC analysis for differentiating leiomyoma from controls yielded an AUC of 0.677 (95% CI, 0.581–0.773), with an optimal cut-off of 2.67 m/s (sensitivity 71.4%, specificity 62.8%). For differentiating leiomyoma from adenomyosis, the AUC was 0.619 (95% CI, 0.508–0.730), with an optimal cut-off of 2.91 m/s (sensitivity 67.1%, specificity 59.0%) (Fig. 2 ). MRI correctly identified all cases of adenomyosis and leiomyoma. Among controls, 90.9% (50/55) were correctly classified as having a normal uterus; three were misclassified as leiomyoma and two as Type II adenomyosis (Table 3 ). Table 3 Distribution of MRI Findings According to Study Groups MRI Control Adenomyosis Leiomyoma p N Row % Column % N Row % Column % N Row % Column % Type 1 0 0.0% 0.0% 14 100.0% 26.4% 0 0.0% 0.0% 0.001 Type 2 2 12.5% 3.6% 14 87.5% 26.4% 0 0.0% 0.0% Type 3 0 0.0% 0.0% 10 100.0% 18.9% 0 0.0% 0.0% Type 4 0 0.0% 0.0% 15 100.0% 28.3% 0 0.0% 0.0% Normal Uterus 50 100.0% 90.9% 0 0.0% 0.0% 0 0.0% 0.0% Leiomyoma 3 5.1% 5.5% 0 0.0% 0.0% 56 94.9% 100.0% Within the adenomyosis group, subgroup analysis revealed no significant differences in SWE values among Type I, II, III, or IV subtypes (*p* > 0.05). Overall, MRI accurately distinguished the three groups, with 100% sensitivity for both adenomyosis (53/53) and leiomyoma (56/56), and 90.9% specificity for normal uteri (50/55) (Table 3 ). Discussion The principal finding of this study is that ARFI elastography showed significant diagnostic utility in differentiating leiomyomas from both normal myometrium and adenomyosis, but failed to reliably distinguish adenomyosis from normal uterine tissue. Moreover, ARFI elastography did not contribute meaningfully to subclassifying adenomyosis, whereas MRI remained the reference standard with superior diagnostic accuracy. Collectively, these results suggest that ARFI elastography should be viewed not as a replacement for MRI, but as a complementary imaging tool in selected clinical contexts. The increased stiffness of leiomyomas relative to normal myometrium aligns with their histopathological profile. Leiomyomas are fibrotic lesions rich in extracellular matrix components such as collagen, fibronectin, and proteoglycans [26–28, 33, 34]—features that likely underpin the higher SWE values observed in this group. Tessarolo et al. [35], in a pioneering histopathology-validated study, reported that adenomyotic tissue appeared stiffer than normal myometrium on strain elastography, suggesting its potential as a diagnostic tool. However, strain elastography is semi-quantitative and operator-dependent, underscoring the need for standardization. In contrast, our quantitative ARFI-SWE approach—which reduces operator dependency—found no stiffness difference between adenomyosis and normal myometrium, while confirming significantly higher SWE values in leiomyomas. This discrepancy may reflect differences in elastography technique, measurement principles, patient selection, and the exclusive use of hysterectomy specimens with histopathological confirmation in our cohort. Overall, our findings imply that ARFI-SWE may yield more consistent results for leiomyomas than for adenomyosis. The lack of a stiffness difference between adenomyosis and normal myometrium contrasts with several earlier reports. For instance, Acar et al. [36] found significantly increased myometrial stiffness in histopathologically confirmed adenomyosis using transvaginal SWE. Several methodological factors may explain this discrepancy. First, Acar et al. measured directly from adenomyotic lesions transvaginally, whereas we obtained transabdominal measurements from standardized uterine regions using ARFI. Second, concomitant leiomyomas in some of our adenomyosis cases may have confounded the assessment of pure adenomyotic tissue. Third, lesion distribution (focal vs. diffuse), disease extent, hormonal status, and the degree of fibrotic remodeling may all influence myometrial stiffness. Collectively, these observations suggest that elastographic evaluation of adenomyosis is highly technique-, location-, and patient-dependent, reinforcing the need for standardized acquisition protocols. Similarly, Pongpunprut et al. [37], in a cross-sectional study, reported higher SWE values in leiomyomas than in normal myometrium, along with increased stiffness in adenomyotic tissue. While our leiomyoma findings align with theirs, we did not observe a significant stiffness difference between adenomyosis and controls. This discrepancy may stem from methodological differences, including our exclusive inclusion of histopathologically confirmed cases, distinct patient selection criteria, variations in measurement protocols, and the presence of concurrent leiomyomas in some adenomyosis patients. Görgülü and Okçu [38] compared SWE, strain elastography, and MRI-derived apparent diffusion coefficient (ADC) values in patients undergoing hysterectomy for adenomyosis or leiomyomas. They concluded that while SWE was useful for differentiating leiomyomas from adenomyosis, MRI exhibited superior diagnostic performance—a finding consistent with ours. In our cohort, SWE values were significantly higher in the leiomyoma group than in both adenomyosis and normal myometrium, with no difference between the latter two. Moreover, the moderate AUC values from our ROC analysis reinforce that ARFI elastography should be viewed as an adjunctive, rather than a standalone, diagnostic tool. The modest AUC values from our ROC analysis suggest that ARFI elastography may aid in diagnosing leiomyomas but is not sufficiently robust for independent clinical decision-making. In contrast, MRI's superiority in diagnosing adenomyosis is well established in histopathology-based studies and meta-analyses [14–17,39]. In our cohort, MRI achieved high diagnostic accuracy for both adenomyosis and leiomyomas, whereas ARFI elastography showed only moderate performance for leiomyomas and failed to reliably distinguish adenomyosis from normal myometrium. These findings reinforce that elastography cannot replace MRI within the current diagnostic framework. Kishi et al. [40] proposed a four-subtype MRI-based classification of adenomyosis, suggesting that distinct morphological subtypes may reflect different anatomical origins and pathogenetic mechanisms. However, this classification relies solely on morphology and does not incorporate biomechanical tissue properties. In our study, SWE values did not differ significantly across MRI-defined adenomyosis subtypes. This suggests that morphological heterogeneity does not necessarily translate into measurable stiffness differences, and underscores the need for future classification systems that integrate both morphological and biomechanical features. In their systematic review and meta-analysis, Brunelli et al. [41] concluded that ultrasound elastography shows promise for diagnosing adenomyosis, but highlighted substantial methodological heterogeneity across studies regarding techniques, measurement protocols, and patient selection. They recommended further high-quality prospective research before routine clinical adoption. Our findings align with this view. While ARFI elastography showed significant diagnostic utility for differentiating leiomyomas from normal myometrium and adenomyosis, it did not achieve sufficient accuracy to distinguish adenomyosis from normal uterine tissue. Thus, our study supports a role for ARFI elastography in evaluating leiomyomas, while emphasizing the need for standardized imaging protocols before wider application in adenomyosis. Clinical implications Our findings suggest that ARFI elastography may offer supplementary diagnostic information in the preoperative evaluation of suspected leiomyomas, particularly when distinction from adenomyosis is challenging. Quantitative stiffness assessment could complement conventional imaging and aid clinical decisions. However, given its modest accuracy, ARFI elastography should not guide surgical decisions independently. Key strengths include the prospective design, histopathological confirmation as the reference standard, single-blinded methodology, combined evaluation of ARFI and MRI findings, and quantitative ROC-based performance assessment. All elastographic examinations were conducted by a single experienced radiologist, enhancing measurement standardization and reducing interobserver variability. Importantly, unlike many prior studies, our investigation integrated elastography with both histopathology and MRI, providing robust evidence on ARFI's diagnostic utility in differentiating leiomyomas from adenomyosis. Study limitations This study has several limitations. First, it was single-center and used a single radiologist for all elastographic examinations, precluding evaluation of inter- and intraobserver reproducibility. Second, the relatively small sample and exclusive inclusion of hysterectomy candidates may have introduced selection bias, limiting generalizability to conservatively managed patients or those without surgical indications. Third, SWE measurements can be influenced by menstrual cycle phase, and we did not standardize acquisition timing, which may have contributed to variability. Fourth, using a single elastography platform and software may limit reproducibility across different systems and transducers. Finally, the optimal cut-off values identified here were not externally validated in an independent cohort; thus, they should be interpreted cautiously before routine use. Large-scale, prospective, multicenter validation studies are needed to confirm the generalizability and external validity of our findings. Conclusion In this prospective, histopathology-validated study, ARFI elastography demonstrated moderate diagnostic accuracy for differentiating leiomyomas from normal myometrium and adenomyosis, but performed poorly in distinguishing adenomyosis from normal uterine tissue. These findings indicate that ARFI elastography cannot replace MRI. However, it may serve as a useful complementary tool in preoperative evaluation, particularly when MRI is contraindicated or unavailable. Prospective multicenter studies with standardized protocols are needed to validate these findings and establish the role of ARFI elastography in routine clinical practice. Abbreviations ARFI, Acoustic Radiation Force Impulse; SWE, Shear Wave Elastography; MRI, Magnetic Resonance Imaging; TVUS, Transvaginal Ultrasonography; US, Ultrasonography; BMI, Body Mass Index; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; CI, Confidence Interval; ROI, Region of Interest; JZ, Junctional Zone; TSE, Turbo Spin-Echo; DWI, Diffusion-Weighted Imaging; VIBE, Volumetric Interpolated Breath-hold Examination; ADC, Apparent Diffusion Coefficient; ANOVA, One-Way Analysis of Variance; WBC, White Blood Cell; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology. Declarations Author Contributions: Conceptualization, L.H, O.K; methodology, L.H, O.K.; software, L.H, O.K; validation, L.H, O.K; formal analysis, L.H, O.K; investigation, L.H, O.K; resources, L.H, O.K; data curation, L.H, O.K; writing— original draft preparation, L.H, O.K; writing—review and editing, L.H, O.K, visualization, İ.E.P; supervision, L.H, O.K.; All authors have read and agreed to the published version of the manuscript. Funding: There is no funding Data availability : The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Declarations Ethics approval and consent to participate : This prospective, single-blind, case-control study was conducted at the Department of Obstetrics and Gynecology, Van Yüzüncü Yıl University Faculty of Medicine, Türkiye, between April 1, 2021, and April 1, 2022. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for observational studies. The study protocol was approved by the Clinical Research Ethics Committee of Van Yüzüncü Yıl University (Approval No. 01, March 31, 2021). All study procedures were performed in accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants after they received detailed information regarding the study. Consent for publication: Not applicable. Acknowledgments: The authors acknowledge the use of DeepSeek and ChatGPT for assistance with English language editing, grammar refinement, and literature search during the preparation of this manuscript. Competing interests: The authors declare no competing interests References Brosens JJ, Barker FG, de Souza NM. Myometrial zonal differentiation and uterine junctional zone hyperplasia in the non-pregnant uterus. 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EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 1: Basic principles and technology. Ultraschall Med. 2013;34(2):169-184. doi:10.1055/s-0033-1335205 D'Onofrio M, Crosara S, De Robertis R, et al. Acoustic radiation force impulse of the liver. World J Gastroenterol. 2013;19(30):4841-4849. doi:10.3748/wjg.v19.i30.4841 Lazebnik RS. Tissue Strain Analytics Virtual Touch Tissue Imaging and Quantification. Siemens, 2008 Nightingale K, Soo MS, Nightingale R, Trahey G. Acoustic radiation force impulse imaging: in vivo demonstration of clinical feasibility. Ultrasound Med Biol. 2002;28(2):227-235. doi:10.1016/s0301-5629(01)00499-9 Kim JJ, Sefton EC. The role of progesterone signaling in the pathogenesis of uterine leiomyoma. Mol Cell Endocrinol. 2012;358(2):223-231. doi:10.1016/j.mce.2011.05.044 Parker WH. Etiology, symptomatology, and diagnosis of uterine myomas. Fertil Steril. 2007;87(4):725-736. doi:10.1016/j.fertnstert.2007.01.093 Sankaran S, Manyonda IT. Medical management of fibroids. Best Pract Res Clin Obstet Gynaecol. 2008;22(4):655-676. doi:10.1016/j.bpobgyn.2008.03.001 Novak ER. Adenomyosis (adenomyoma) uteri. In: Novak ER, Woodruff JD, eds. Novak's gynecologic and obstetric pathology. Philadelphia, Pa: Saunders, 1979; 280-290. Mark AS, Hricak H, Heinrichs LW, et al. Adenomyosis and leiomyoma: differential diagnosis with MR imaging. Radiology. 1987;163(2):527-529. doi:10.1148/radiology.163.2.3562836 Togashi K, Nishimura K, Itoh K, et al. Adenomyosis: diagnosis with MR imaging. Radiology. 1988;166(1 Pt 1):111-114. doi:10.1148/radiology.166.1.3336669 Outwater EK, Siegelman ES, Van Deerlin V. Adenomyosis: current concepts and imaging considerations. AJR Am J Roentgenol. 1998;170(2):437-441. doi:10.2214/ajr.170.2.9456960 Prayson RA, Hart WR. Pathologic considerations of uterine smooth muscle tumors. Obstet Gynecol Clin North Am. 1995;22(4):637-657. Yamashita Y, Torashima M, Takahashi M, et al. Hyperintense uterine leiomyoma at T2-weighted MR imaging: differentiation with dynamic enhanced MR imaging and clinical implications. Radiology. 1993;189(3):721-725. doi:10.1148/radiology.189.3.8234695 Tessarolo M, Bonino L, Camanni M, Deltetto F. Elastosonography: a possible new tool for diagnosis of adenomyosis?. Eur Radiol. 2011;21(7):1546-1552. doi:10.1007/s00330-011-2064-z Acar S, Millar E, Mitkova M, Mitkov V. Value of ultrasound shear wave elastography in the diagnosis of adenomyosis. Ultrasound. 2016;24(4):205-213. doi:10.1177/1742271X16673677 Pongpunprut S, Panburana P, Wibulpolprasert P, et al. A Comparison of Shear Wave Elastography between Normal Myometrium, Uterine Fibroids, and Adenomyosis: A Cross-Sectional Study. Int J Fertil Steril. 2022;16(1):49-54. doi:10.22074/IJFS.2021.523075.1074 Görgülü FF, Okçu NT. Which imaging method is better for the differentiation of adenomyosis and uterine fibroids?. J Gynecol Obstet Hum Reprod. 2021;50(5):102002. doi:10.1016/j.jogoh.2020.102002 Reinhold C, McCarthy S, Bret PM, et al. Diffuse adenomyosis: comparison of endovaginal US and MR imaging with histopathologic correlation. Radiology. 1996;199(1):151-158. doi:10.1148/radiology.199.1.8633139 Kishi Y, Suginami H, Kuramori R, Yabuta M, Suginami R, Taniguchi F. Four subtypes of adenomyosis assessed by magnetic resonance imaging and their specification. Am J Obstet Gynecol. 2012;207(2):114.e1-114.e1147. doi:10.1016/j.ajog.2012.06.027 Brunelli AC, Brito LGO, Moro FAS, Jales RM, Yela DA, Benetti-Pinto CL. Ultrasound Elastography for the Diagnosis of Endometriosis and Adenomyosis: A Systematic Review with Meta-analysis. Ultrasound Med Biol. 2023;49(3):699-709. doi:10.1016/j.ultrasmedbio.2022.11.006 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-10619173","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":695952358,"identity":"5868c28d-755c-4f1f-a6cf-d6fd6598daf7","order_by":0,"name":"LATİF HACIOĞLU","email":"data:image/png;base64,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","orcid":"","institution":"Van Yüzüncü Yıl Üniversitesi","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"LATİF","middleName":"","lastName":"HACIOĞLU","suffix":""},{"id":695952359,"identity":"6b1f1f6d-6792-446e-99f1-b9b67d63a7ba","order_by":1,"name":"Onur KARAASLAN","email":"","orcid":"","institution":"Van Yüzüncü Yıl Üniversitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Onur","middleName":"","lastName":"KARAASLAN","suffix":""}],"badges":[],"createdAt":"2026-08-06 20:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-10619173/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-10619173/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":117444534,"identity":"4a555b2a-81af-4f00-b3cf-eb0d8eb59b56","added_by":"auto","created_at":"2026-08-15 02:25:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116132,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Flow Diagram\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-10619173/v1/e62c4e4de41426dd224d1c76.png"},{"id":117444469,"identity":"ddf33c4b-18fb-47f6-9b9b-60ed3a86913a","added_by":"auto","created_at":"2026-08-15 02:25:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191869,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of the Optimal Cut-off Value for Elastography Based on Receiver Operating Characteristic (ROC) Analysis\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-10619173/v1/5abc345e2054ea75fedaae59.png"},{"id":117444545,"identity":"907eb987-eec4-45f2-9df9-8d0b57433433","added_by":"auto","created_at":"2026-08-15 02:25:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":500631,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-10619173/v1/be99e562-314d-4798-8ad0-e9f1b75c08c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Acoustic radiation force impulse elastography for differentiating adenomyosis and uterine leiomyoma: A prospective histopathology-validated study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdenomyosis and uterine leiomyoma are among the most common benign gynecological conditions affecting women of reproductive age. Adenomyosis is defined by the presence of ectopic endometrial glands and stroma within the myometrium, surrounded by hyperplastic smooth muscle cells; its histopathological prevalence ranges from 5% to 70% [1\u0026ndash;5]. Uterine leiomyoma, a monoclonal benign smooth muscle tumor, is the most frequent pelvic neoplasm in women, affecting approximately 50\u0026ndash;60% of this population [2,6]. Despite often presenting with overlapping clinical symptoms, these two entities require accurate differentiation, as their treatment strategies differ markedly [7\u0026ndash;9].\u003c/p\u003e \u003cp\u003eAmong current imaging modalities, magnetic resonance imaging (MRI) offers high diagnostic accuracy for differentiating adenomyosis from uterine leiomyoma. However, its broad clinical application is constrained by high costs, lengthy scan times, claustrophobia, and contraindications such as incompatible implants [10\u0026ndash;13]. Conversely, transvaginal ultrasonography (TVUS) is more accessible and economical, but its diagnostic reliability is operator-dependent and may be compromised\u0026mdash;particularly in the presence of coexisting leiomyomas\u0026mdash;leading to reduced sensitivity for adenomyosis detection [4,14\u0026ndash;17]. These limitations underscore the ongoing need for objective, reproducible, and non-invasive imaging tools capable of enhancing preoperative diagnostic accuracy.\u003c/p\u003e \u003cp\u003eIn recent years, elastography\u0026mdash;an ultrasound-based technique that evaluates tissue mechanical properties such as stiffness and elasticity\u0026mdash;has emerged as a promising diagnostic modality [18\u0026ndash;21]. This method relies on measuring tissue deformation or the propagation velocity of shear waves generated by an applied mechanical force [22]. Acoustic Radiation Force Impulse (ARFI) elastography is one of the most advanced iterations of this technology. By emitting short, high-intensity acoustic pulses via the ultrasound transducer, ARFI quantifies tissue stiffness by measuring the propagation velocity of induced shear waves (m/s) [23,24]. Compared to strain elastography, ARFI offers distinct advantages, including no need for external compression and the capacity to deliver objective, operator-independent, and reproducible quantitative data [25].\u003c/p\u003e \u003cp\u003eAlthough ARFI elastography has demonstrated high diagnostic accuracy in various settings\u0026mdash;most notably in liver fibrosis assessment\u0026mdash;evidence for its utility in uterine pathologies remains limited and largely derived from small-scale studies [18\u0026ndash;22]. Given their distinct histopathological features, uterine leiomyomas are expected to show greater stiffness, whereas adenomyosis may exhibit different elasticity profiles due to structural changes induced within the myometrium [26\u0026ndash;32]. Nevertheless, prospective, histopathology-validated studies directly comparing these two conditions with each other and with normal uterine tissue remain scarce.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the diagnostic performance of ARFI elastography for uterine leiomyoma and adenomyosis using histopathologically confirmed cases, and to assess its contribution to their differential diagnosis. We hypothesized that ARFI elastography would effectively distinguish uterine leiomyoma from both adenomyosis and normal myometrium. Additionally, we sought to determine whether SWE values differ across MRI-defined adenomyosis subtypes, and to establish the sensitivity and specificity of ARFI elastography for these conditions.\u003c/p\u003e \u003cp\u003eThe novelty of this study lies in its prospective, single-blinded, histopathology-validated design, which integrates a comprehensive assessment of ARFI elastography with MRI-based adenomyosis subtyping and histopathological correlation. We expect our findings to help define the clinical utility of ARFI elastography as a complementary imaging tool in the preoperative workup of uterine pathologies\u0026mdash;especially when MRI is unavailable or contraindicated.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e This prospective, single-blind, case-control study was conducted at the Department of Obstetrics and Gynecology, Van Y\u0026uuml;z\u0026uuml;nc\u0026uuml; Yıl University Faculty of Medicine, T\u0026uuml;rkiye, between April 1, 2021, and April 1, 2022, and adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. This study is entirely observational and does not involve any intervention in the treatment of participants. The decision for surgery was made by the attending surgeons based on clinical indications, independently of the research team. The investigators played no role in the surgical decision-making process or in the surgical procedures themselves. Therefore, this study does not meet the definition of a clinical trial and is not subject to registration requirements under ICMJE criteria. The study protocol was approved by the local Clinical Research Ethics Committee (Approval No. 01, March 31, 2021), and all procedures complied with the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eWe initially enrolled 256 consecutive women aged 40\u0026ndash;70 years scheduled for hysterectomy at the gynecology outpatient clinic. Exclusion criteria included endometrial carcinoma, hysterectomy for uterine prolapse, age outside the predefined range, and morbid obesity (body mass index\u0026thinsp;\u0026ge;\u0026thinsp;40 kg/m\u0026sup2;), the latter due to its potential to compromise elastographic measurements.\u003c/p\u003e \u003cp\u003eOf these, 92 patients were excluded from the final analysis: 44 did not undergo surgery (12 for uncontrolled hypertension, 19 for COVID-19 positivity, and 13 for poorly controlled diabetes), 10 withdrew consent for surgery, 25 could not undergo MRI (8 due to claustrophobia and 17 who refused), and 13 declined elastographic examination. The remaining 164 patients comprised the final study population, with histopathologically confirmed diagnoses of adenomyosis (n\u0026thinsp;=\u0026thinsp;53, 32.3%), uterine leiomyoma (n\u0026thinsp;=\u0026thinsp;56, 34.1%), or neither (controls, n\u0026thinsp;=\u0026thinsp;55, 33.5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCases were defined as patients diagnosed with adenomyosis and/or leiomyoma based on clinical, ultrasonographic, and laboratory findings, all of whom were scheduled for hysterectomy. Controls were patients without any uterine pathology (including leiomyoma, adenomyosis, or endometrial carcinoma) who underwent hysterectomy for other benign indications.\u003c/p\u003e \u003cp\u003eAll patients underwent contrast-enhanced pelvic MRI before hysterectomy, followed by ARFI elastography. MRI was performed using a 1.5-T scanner (Magnetom Avanto, Siemens Healthcare, Erlangen, Germany) with a phased-array pelvic coil. T2-weighted turbo spin-echo (TSE) images were acquired in the axial, sagittal, and coronal planes (TR/TE: 4000\u0026ndash;6000/100\u0026ndash;120 ms; slice thickness: 4 mm; interslice gap: 1 mm; field of view: 240 \u0026times; 240 mm; matrix: 320 \u0026times; 256). Axial T1-weighted images were obtained with TR/TE 600/10 ms and 4-mm slice thickness. Diffusion-weighted imaging (DWI) was performed with b-values of 0, 50, 400, and 800 s/mm\u0026sup2;. Contrast-enhanced imaging used dynamic T1-weighted volumetric interpolated breath-hold examination (VIBE) sequences following intravenous gadobutrol (Gadovist\u0026reg;, Bayer Healthcare, Berlin, Germany) at 0.1 mmol/kg.\u003c/p\u003e \u003cp\u003eA radiologist with 10 years of experience in pelvic MRI, blinded to clinical, ultrasonographic, and histopathological data, interpreted all examinations. Adenomyosis was classified using the MRI-based system proposed by Kishi et al. [32], with minor modifications. Subtypes were defined as follows: Type I (intrinsic)\u0026mdash;lesions arising from the inner myometrium, closely associated with the endometrium and junctional zone (JZ), without outer myometrial involvement; Type II (extrinsic)\u0026mdash;lesions originating from the outer myometrium, potentially involving the serosa while sparing the JZ; Type III (intramural)\u0026mdash;isolated lesions confined to the myometrium with no continuity with the inner or outer layers; and Type IV (indeterminate)\u0026mdash;lesions not fitting Types I\u0026ndash;III, generally representing advanced or heterogeneous disease.\u003c/p\u003e \u003cp\u003eARFI elastography was performed by the same radiologist (8 years of ultrasonography and 6 years of elastography experience), who was blinded to clinical and imaging data (single-blind design). Examinations used a Siemens ACUSON S2000\u0026trade; ultrasound system (Siemens Healthcare, Erlangen, Germany) with a 4-MHz 6C1 convex transducer. Patients were examined supine. After conventional B-mode ultrasonography to locate the uterus, shear wave elastography (SWE) measurements were obtained using Virtual Touch\u0026trade; IQ application in Shear Wave mode.\u003c/p\u003e \u003cp\u003eDuring image acquisition, patients were asked to hold their breath and remain still. The built-in quality map guided selection of appropriate regions, with measurement depth standardized to 3\u0026ndash;6 cm and the region of interest (ROI) fixed at 10 \u0026times; 10 mm. SWE measurements were taken from six standardized uterine regions: the anterior and posterior myometrium at the fundus, corpus, and isthmus. Three consecutive measurements were obtained from each region, yielding 18 measurements per patient (3 levels \u0026times; 2 walls \u0026times; 3 replicates). Measurements were repeated if the quality map indicated motion artifacts or poor signal. Fewer than 5% of all measurements (n\u0026thinsp;\u0026lt;\u0026thinsp;9) were technically unsuccessful and excluded. The mean value of all valid measurements was calculated as the final SWE velocity (m/s) for each patient.\u003c/p\u003e \u003cp\u003eAfter hysterectomy, all surgical specimens underwent routine histopathological evaluation by a single gynecologic pathologist with 15 years of experience, blinded to clinical and imaging data (including ultrasonography, MRI, and elastography). Histopathological diagnosis served as the reference (gold) standard for final classification. Based on these findings, patients were assigned to one of three groups: adenomyosis, leiomyoma, or control.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSample size was calculated using G*Power software (version 3.1.9.7; Heinrich Heine University, D\u0026uuml;sseldorf, Germany). Based on the primary outcome (SWE values), a minimum of 52 patients per group was estimated to detect differences among the three groups (adenomyosis, leiomyoma, control), assuming an effect size (Cohen's f) of 0.25, a two-sided alpha of 0.05, and a power (1\u0026thinsp;\u0026minus;\u0026thinsp;β) of 80%.\u003c/p\u003e \u003cp\u003eNormality was assessed using the Kolmogorov\u0026ndash;Smirnov test (for n\u0026thinsp;\u0026gt;\u0026thinsp;50) and by evaluating skewness and kurtosis. As all continuous variables were approximately normally distributed, parametric tests were used.\u003c/p\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables as frequency (n) and percentage (%). Group comparisons for continuous variables used one-way analysis of variance (ANOVA), followed by Duncan's post hoc test for pairwise comparisons. Categorical variables were compared with the chi-square test. Diagnostic performance of ARFI elastography (mean SWE velocity, m/s) for differentiating adenomyosis and leiomyoma was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC), 95% confidence intervals (CI), sensitivity, specificity, and optimal cut-off values were calculated. Optimal cut-offs were determined using the Youden index (J\u0026thinsp;=\u0026thinsp;sensitivity\u0026thinsp;+\u0026thinsp;specificity\u0026thinsp;\u0026minus;\u0026thinsp;1), which maximizes combined sensitivity and specificity. AUCs were compared using the DeLong test.\u003c/p\u003e \u003cp\u003eAll tests were two-sided, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Analyses were conducted using IBM SPSS Statistics (version 25.0; IBM Corp., Armonk, NY, USA) and MedCalc Statistical Software (version 20.0; MedCalc Software Ltd., Ostend, Belgium).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the 256 initially enrolled patients, 92 were excluded per the predefined criteria, leaving 164 patients with histopathologically confirmed diagnoses: 53 with adenomyosis, 56 with leiomyoma, and 55 controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe three groups differed significantly in age, body mass index (BMI), gravidity, and parity (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but not in number of abortions (p\u0026thinsp;=\u0026thinsp;0.681). Patients with leiomyoma were significantly younger than the other groups, whereas those with adenomyosis had the highest mean BMI. Laboratory parameters also differed significantly across groups for hemoglobin, white blood cell, and platelet counts (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These baseline characteristics are summarized descriptively, while the primary analyses focused on SWE measurements and diagnostic performance.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographic, obstetric, and laboratory characteristics of the study population\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAdenomyosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLeiomyoma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.76ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.68ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.80ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.48ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.32ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.94ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGravidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.93ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.21ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.98ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.78ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.13ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.84ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbortions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemoglobin (g/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.42ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.55ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.97ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.13ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.51ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.67ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlatelet count (\u0026times;10\u0026sup3;/\u0026micro;L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287.49ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e247.19ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e265.89ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e82.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eᵃᵇ Values with different superscript letters indicate statistically significant differences between groups according to the Bonferroni post hoc pairwise comparison test\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean SWE values were significantly higher in the leiomyoma group than in both the adenomyosis (p\u0026thinsp;=\u0026thinsp;0.040) and control groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference was found between adenomyosis and controls (p\u0026thinsp;=\u0026thinsp;0.158) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Elastosonography Measurements Between Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAdenomyosis vs. Leiomyoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenomyosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeiomyoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eControl vs. Adenomyosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenomyosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eControl vs. Leiomyoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeiomyoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\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\u003eROC analysis for differentiating leiomyoma from controls yielded an AUC of 0.677 (95% CI, 0.581\u0026ndash;0.773), with an optimal cut-off of 2.67 m/s (sensitivity 71.4%, specificity 62.8%). For differentiating leiomyoma from adenomyosis, the AUC was 0.619 (95% CI, 0.508\u0026ndash;0.730), with an optimal cut-off of 2.91 m/s (sensitivity 67.1%, specificity 59.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMRI correctly identified all cases of adenomyosis and leiomyoma. Among controls, 90.9% (50/55) were correctly classified as having a normal uterus; three were misclassified as leiomyoma and two as Type II adenomyosis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of MRI Findings According to Study Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAdenomyosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eLeiomyoma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRow %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eColumn %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRow %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eColumn %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRow %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eColumn %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal Uterus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLeiomyoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWithin the adenomyosis group, subgroup analysis revealed no significant differences in SWE values among Type I, II, III, or IV subtypes (*p* \u0026gt; 0.05).\u003c/p\u003e \u003cp\u003eOverall, MRI accurately distinguished the three groups, with 100% sensitivity for both adenomyosis (53/53) and leiomyoma (56/56), and 90.9% specificity for normal uteri (50/55) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe principal finding of this study is that ARFI elastography showed significant diagnostic utility in differentiating leiomyomas from both normal myometrium and adenomyosis, but failed to reliably distinguish adenomyosis from normal uterine tissue. Moreover, ARFI elastography did not contribute meaningfully to subclassifying adenomyosis, whereas MRI remained the reference standard with superior diagnostic accuracy. Collectively, these results suggest that ARFI elastography should be viewed not as a replacement for MRI, but as a complementary imaging tool in selected clinical contexts.\u003c/p\u003e \u003cp\u003eThe increased stiffness of leiomyomas relative to normal myometrium aligns with their histopathological profile. Leiomyomas are fibrotic lesions rich in extracellular matrix components such as collagen, fibronectin, and proteoglycans [26\u0026ndash;28, 33, 34]\u0026mdash;features that likely underpin the higher SWE values observed in this group.\u003c/p\u003e \u003cp\u003eTessarolo et al. [35], in a pioneering histopathology-validated study, reported that adenomyotic tissue appeared stiffer than normal myometrium on strain elastography, suggesting its potential as a diagnostic tool. However, strain elastography is semi-quantitative and operator-dependent, underscoring the need for standardization. In contrast, our quantitative ARFI-SWE approach\u0026mdash;which reduces operator dependency\u0026mdash;found no stiffness difference between adenomyosis and normal myometrium, while confirming significantly higher SWE values in leiomyomas. This discrepancy may reflect differences in elastography technique, measurement principles, patient selection, and the exclusive use of hysterectomy specimens with histopathological confirmation in our cohort. Overall, our findings imply that ARFI-SWE may yield more consistent results for leiomyomas than for adenomyosis.\u003c/p\u003e \u003cp\u003eThe lack of a stiffness difference between adenomyosis and normal myometrium contrasts with several earlier reports. For instance, Acar et al. [36] found significantly increased myometrial stiffness in histopathologically confirmed adenomyosis using transvaginal SWE. Several methodological factors may explain this discrepancy. First, Acar et al. measured directly from adenomyotic lesions transvaginally, whereas we obtained transabdominal measurements from standardized uterine regions using ARFI. Second, concomitant leiomyomas in some of our adenomyosis cases may have confounded the assessment of pure adenomyotic tissue. Third, lesion distribution (focal vs. diffuse), disease extent, hormonal status, and the degree of fibrotic remodeling may all influence myometrial stiffness. Collectively, these observations suggest that elastographic evaluation of adenomyosis is highly technique-, location-, and patient-dependent, reinforcing the need for standardized acquisition protocols.\u003c/p\u003e \u003cp\u003eSimilarly, Pongpunprut et al. [37], in a cross-sectional study, reported higher SWE values in leiomyomas than in normal myometrium, along with increased stiffness in adenomyotic tissue. While our leiomyoma findings align with theirs, we did not observe a significant stiffness difference between adenomyosis and controls. This discrepancy may stem from methodological differences, including our exclusive inclusion of histopathologically confirmed cases, distinct patient selection criteria, variations in measurement protocols, and the presence of concurrent leiomyomas in some adenomyosis patients.\u003c/p\u003e \u003cp\u003eG\u0026ouml;rg\u0026uuml;l\u0026uuml; and Ok\u0026ccedil;u [38] compared SWE, strain elastography, and MRI-derived apparent diffusion coefficient (ADC) values in patients undergoing hysterectomy for adenomyosis or leiomyomas. They concluded that while SWE was useful for differentiating leiomyomas from adenomyosis, MRI exhibited superior diagnostic performance\u0026mdash;a finding consistent with ours. In our cohort, SWE values were significantly higher in the leiomyoma group than in both adenomyosis and normal myometrium, with no difference between the latter two. Moreover, the moderate AUC values from our ROC analysis reinforce that ARFI elastography should be viewed as an adjunctive, rather than a standalone, diagnostic tool.\u003c/p\u003e \u003cp\u003eThe modest AUC values from our ROC analysis suggest that ARFI elastography may aid in diagnosing leiomyomas but is not sufficiently robust for independent clinical decision-making. In contrast, MRI's superiority in diagnosing adenomyosis is well established in histopathology-based studies and meta-analyses [14\u0026ndash;17,39]. In our cohort, MRI achieved high diagnostic accuracy for both adenomyosis and leiomyomas, whereas ARFI elastography showed only moderate performance for leiomyomas and failed to reliably distinguish adenomyosis from normal myometrium. These findings reinforce that elastography cannot replace MRI within the current diagnostic framework.\u003c/p\u003e \u003cp\u003eKishi et al. [40] proposed a four-subtype MRI-based classification of adenomyosis, suggesting that distinct morphological subtypes may reflect different anatomical origins and pathogenetic mechanisms. However, this classification relies solely on morphology and does not incorporate biomechanical tissue properties. In our study, SWE values did not differ significantly across MRI-defined adenomyosis subtypes. This suggests that morphological heterogeneity does not necessarily translate into measurable stiffness differences, and underscores the need for future classification systems that integrate both morphological and biomechanical features.\u003c/p\u003e \u003cp\u003eIn their systematic review and meta-analysis, Brunelli et al. [41] concluded that ultrasound elastography shows promise for diagnosing adenomyosis, but highlighted substantial methodological heterogeneity across studies regarding techniques, measurement protocols, and patient selection. They recommended further high-quality prospective research before routine clinical adoption. Our findings align with this view. While ARFI elastography showed significant diagnostic utility for differentiating leiomyomas from normal myometrium and adenomyosis, it did not achieve sufficient accuracy to distinguish adenomyosis from normal uterine tissue. Thus, our study supports a role for ARFI elastography in evaluating leiomyomas, while emphasizing the need for standardized imaging protocols before wider application in adenomyosis.\u003c/p\u003e\n\u003ch3\u003eClinical implications\u003c/h3\u003e\n\u003cp\u003eOur findings suggest that ARFI elastography may offer supplementary diagnostic information in the preoperative evaluation of suspected leiomyomas, particularly when distinction from adenomyosis is challenging. Quantitative stiffness assessment could complement conventional imaging and aid clinical decisions. However, given its modest accuracy, ARFI elastography should not guide surgical decisions independently.\u003c/p\u003e \u003cp\u003eKey strengths include the prospective design, histopathological confirmation as the reference standard, single-blinded methodology, combined evaluation of ARFI and MRI findings, and quantitative ROC-based performance assessment. All elastographic examinations were conducted by a single experienced radiologist, enhancing measurement standardization and reducing interobserver variability. Importantly, unlike many prior studies, our investigation integrated elastography with both histopathology and MRI, providing robust evidence on ARFI's diagnostic utility in differentiating leiomyomas from adenomyosis.\u003c/p\u003e\n\u003ch3\u003eStudy limitations\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations. First, it was single-center and used a single radiologist for all elastographic examinations, precluding evaluation of inter- and intraobserver reproducibility. Second, the relatively small sample and exclusive inclusion of hysterectomy candidates may have introduced selection bias, limiting generalizability to conservatively managed patients or those without surgical indications. Third, SWE measurements can be influenced by menstrual cycle phase, and we did not standardize acquisition timing, which may have contributed to variability. Fourth, using a single elastography platform and software may limit reproducibility across different systems and transducers. Finally, the optimal cut-off values identified here were not externally validated in an independent cohort; thus, they should be interpreted cautiously before routine use. Large-scale, prospective, multicenter validation studies are needed to confirm the generalizability and external validity of our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this prospective, histopathology-validated study, ARFI elastography demonstrated moderate diagnostic accuracy for differentiating leiomyomas from normal myometrium and adenomyosis, but performed poorly in distinguishing adenomyosis from normal uterine tissue. These findings indicate that ARFI elastography cannot replace MRI. However, it may serve as a useful complementary tool in preoperative evaluation, particularly when MRI is contraindicated or unavailable. Prospective multicenter studies with standardized protocols are needed to validate these findings and establish the role of ARFI elastography in routine clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARFI, Acoustic Radiation Force Impulse; SWE, Shear Wave Elastography; MRI, Magnetic Resonance Imaging; TVUS, Transvaginal Ultrasonography; US, Ultrasonography; BMI, Body Mass Index; ROC, Receiver Operating Characteristic; AUC, Area Under the Curve; CI, Confidence Interval; ROI, Region of Interest; JZ, Junctional Zone; TSE, Turbo Spin-Echo; DWI, Diffusion-Weighted Imaging; VIBE, Volumetric Interpolated Breath-hold Examination; ADC, Apparent Diffusion Coefficient; ANOVA, One-Way Analysis of Variance; WBC, White Blood Cell; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, L.H, O.K; methodology, L.H, O.K.; software, L.H, O.K; validation, L.H, O.K; formal analysis, L.H, O.K; investigation, L.H, O.K; resources, L.H, O.K; data curation, L.H, O.K; writing— original draft preparation, L.H, O.K; writing—review and editing, L.H, O.K, visualization, İ.E.P; supervision, L.H, O.K.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e There is no funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This prospective, single-blind, case-control study was conducted at the Department of Obstetrics and Gynecology, Van Yüzüncü Yıl University Faculty of Medicine, Türkiye, between April 1, 2021, and April 1, 2022. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for observational studies. The study protocol was approved by the Clinical Research Ethics Committee of Van Yüzüncü Yıl University (Approval No. 01, March 31, 2021). All study procedures were performed in accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants after they received detailed information regarding the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors acknowledge the use of DeepSeek and ChatGPT for assistance with English language editing, grammar refinement, and literature search during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrosens JJ, Barker FG, de Souza NM. Myometrial zonal differentiation and uterine junctional zone hyperplasia in the non-pregnant uterus. Hum Reprod Update 1998; 4: 496-502. doi:10.1093/humupd/4.5.496\u003c/li\u003e\n \u003cli\u003eBenagiano G, Brosens I, Lippi D. 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Best Pract Res Clin Obstet Gynaecol. 2008;22(4):655-676. doi:10.1016/j.bpobgyn.2008.03.001\u003c/li\u003e\n \u003cli\u003eNovak ER. Adenomyosis (adenomyoma) uteri. In: Novak ER, Woodruff JD, eds. Novak\u0026apos;s gynecologic and obstetric pathology. Philadelphia, Pa: Saunders, 1979; 280-290.\u003c/li\u003e\n \u003cli\u003eMark AS, Hricak H, Heinrichs LW, et al. Adenomyosis and leiomyoma: differential diagnosis with MR imaging. Radiology. 1987;163(2):527-529. doi:10.1148/radiology.163.2.3562836\u003c/li\u003e\n \u003cli\u003eTogashi K, Nishimura K, Itoh K, et al. Adenomyosis: diagnosis with MR imaging. Radiology. 1988;166(1 Pt 1):111-114. doi:10.1148/radiology.166.1.3336669\u003c/li\u003e\n \u003cli\u003eOutwater EK, Siegelman ES, Van Deerlin V. Adenomyosis: current concepts and imaging considerations. AJR Am J Roentgenol. 1998;170(2):437-441. doi:10.2214/ajr.170.2.9456960\u003c/li\u003e\n \u003cli\u003ePrayson RA, Hart WR. Pathologic considerations of uterine smooth muscle tumors. 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Int J Fertil Steril. 2022;16(1):49-54. doi:10.22074/IJFS.2021.523075.1074\u003c/li\u003e\n \u003cli\u003eG\u0026ouml;rg\u0026uuml;l\u0026uuml; FF, Ok\u0026ccedil;u NT. Which imaging method is better for the differentiation of adenomyosis and uterine fibroids?. J Gynecol Obstet Hum Reprod. 2021;50(5):102002. doi:10.1016/j.jogoh.2020.102002\u003c/li\u003e\n \u003cli\u003eReinhold C, McCarthy S, Bret PM, et al. Diffuse adenomyosis: comparison of endovaginal US and MR imaging with histopathologic correlation. Radiology. 1996;199(1):151-158. doi:10.1148/radiology.199.1.8633139\u003c/li\u003e\n \u003cli\u003eKishi Y, Suginami H, Kuramori R, Yabuta M, Suginami R, Taniguchi F. Four subtypes of adenomyosis assessed by magnetic resonance imaging and their specification. Am J Obstet Gynecol. 2012;207(2):114.e1-114.e1147. doi:10.1016/j.ajog.2012.06.027\u003c/li\u003e\n \u003cli\u003eBrunelli AC, Brito LGO, Moro FAS, Jales RM, Yela DA, Benetti-Pinto CL. Ultrasound Elastography for the Diagnosis of Endometriosis and Adenomyosis: A Systematic Review with Meta-analysis. Ultrasound Med Biol. 2023;49(3):699-709. doi:10.1016/j.ultrasmedbio.2022.11.006\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bratislava-medical-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bratislava Medical Journal](https://link.springer.com/journal/44411)","snPcode":"44411","submissionUrl":"https://submission.springernature.com/new-submission/44411/3","title":"Bratislava Medical Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adenomyosis, Leiomyoma, Elastography, Magnetic Resonance Image","lastPublishedDoi":"10.21203/rs.3.rs-10619173/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-10619173/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo evaluate the diagnostic performance of Acoustic Radiation Force Impulse (ARFI) elastography in differentiating adenomyosis from uterine leiomyoma and to compare its diagnostic accuracy with magnetic resonance imaging (MRI).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective study included 164 patients with histopathologically confirmed diagnoses after hysterectomy: adenomyosis (n\u0026thinsp;=\u0026thinsp;53), leiomyoma (n\u0026thinsp;=\u0026thinsp;56), and controls (n\u0026thinsp;=\u0026thinsp;55). All participants underwent preoperative ARFI elastography and MRI. Shear wave elastography (SWE) values were compared among groups, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMean SWE values were significantly higher in the leiomyoma group (3.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78 m/s) than in the adenomyosis (2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83 m/s; p\u0026thinsp;=\u0026thinsp;0.040) and control groups (2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84 m/s; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference was observed between adenomyosis and controls (p\u0026thinsp;=\u0026thinsp;0.158). For differentiating leiomyoma from controls, the area under the curve (AUC) was 0.677 (95% CI, 0.581\u0026ndash;0.773), with 71.4% sensitivity and 62.8% specificity. For differentiating leiomyoma from adenomyosis, the AUC was 0.619 (95% CI, 0.508\u0026ndash;0.730), with 67.1% sensitivity and 59.0% specificity. MRI showed high diagnostic accuracy for both conditions and enabled adenomyosis subtype classification, whereas SWE values did not differ significantly among adenomyosis subtypes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eARFI elastography showed moderate performance in differentiating uterine leiomyoma from normal myometrium and adenomyosis but poor performance in distinguishing adenomyosis from normal myometrium. ARFI should be considered a complementary tool to MRI, particularly when MRI is contraindicated or unavailable.\u003c/p\u003e","manuscriptTitle":"Acoustic radiation force impulse elastography for differentiating adenomyosis and uterine leiomyoma: A prospective histopathology-validated study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-08-15 02:23:41","doi":"10.21203/rs.3.rs-10619173/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-08-20T05:57:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-08-16T05:23:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11108241067822645724016037266619305190","date":"2026-08-15T10:41:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153455055082242817138564456014217623326","date":"2026-08-11T16:23:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-08-10T22:28:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-08-09T10:19:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-08-09T10:18:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bratislava Medical Journal","date":"2026-08-06T20:22:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bratislava-medical-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bratislava Medical Journal](https://link.springer.com/journal/44411)","snPcode":"44411","submissionUrl":"https://submission.springernature.com/new-submission/44411/3","title":"Bratislava Medical Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1a89ac70-63ae-4c45-8346-ed2bdb902a98","owner":[],"postedDate":"August 15th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-08-20T05:57:44+00:00","index":55,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-08-16T05:23:15+00:00","index":54,"fulltext":""},{"type":"reviewerAgreed","content":"11108241067822645724016037266619305190","date":"2026-08-15T10:41:36+00:00","index":53,"fulltext":""},{"type":"reviewerAgreed","content":"153455055082242817138564456014217623326","date":"2026-08-11T16:23:58+00:00","index":52,"fulltext":""},{"type":"reviewersInvited","content":"43","date":"2026-08-10T22:28:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-08-09T10:19:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-08-09T10:18:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bratislava Medical Journal","date":"2026-08-06T20:22:55+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-08-15T02:23:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-08-15 02:23:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-10619173","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-10619173","identity":"rs-10619173","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}