Pretreatment MRI Parameters as Predictive Biomarkers for Hormonal Therapy Response in Adenomyosis: A Comprehensive Analysis

Pretreatment MRI Parameters as Predictive Biomarkers for Hormonal Therapy Response in Adenomyosis: A Comprehensive Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pretreatment MRI Parameters as Predictive Biomarkers for Hormonal Therapy Response in Adenomyosis: A Comprehensive Analysis Kazuhiko Morikawa, Akira Baba, Shun Kusada, Satoshi Matsushima, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9201156/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objectives To evaluate whether pretreatment magnetic resonance imaging (MRI) parameters can predict response to hormonal therapy in patients with adenomyosis. Methods This retrospective study included 78 patients with MRI-diagnosed adenomyosis who underwent pelvic MRI before hormonal therapy between October 2018 and July 2025. Quantitative MRI parameters included T2 signal intensity ratios, diffusion-weighted imaging (DWI) signal intensity ratios, normalized apparent diffusion coefficient (ADC), and uterine morphological parameters. Adenomyosis subtypes were classified according to the modified Kishi criteria. Clinical response was evaluated 3–6 months after treatment initiation based on improvements in dysmenorrhea and/or hemoglobin levels. Results Of the 78 patients, 32 received gonadotropin-releasing hormone (GnRH) agonist or antagonist therapy, and 46 received dienogest (DNG). In the GnRH cohort, 31 of the 32 patients achieved treatment effectiveness. In the DNG cohort, 30 patients achieved treatment effectiveness, and 16 did not. MRI-based adenomyosis subtype, lesion distribution, and uterine morphological parameters were not significantly associated with treatment effectiveness in DNG-treated patients. However, absolute ADC values were significantly higher in the effective group (1.03 vs. 0.89 ×10⁻³ mm²/s, P = 0.036), as was the ADC signal intensity ratio relative to the endometrium (ADC signal intensity ratio [SIR endo ]: 0.92 vs. 0.85, P = 0.034). Receiver operating characteristic curve analysis demonstrated moderate discrimination between both parameters (area under the curve = 0.70). Optimal cut-off values were 0.951 × 10⁻³ mm²/s for ADC and 0.952 for ADC SIR endo . Conclusion Quantitative diffusion MRI parameters were associated with DNG treatment effectiveness, whereas conventional morphological features were not. Diffusion-weighted MRI may provide complementary imaging biomarkers for adenomyosis stratification. Adenomyosis Diffusion-weighted imaging Apparent diffusion coefficient Dienogest Progestins Treatment outcome Figures Figure 1 Figure 2 Figure 3 1. Introduction Adenomyosis is a benign gynecological condition characterized by the presence of ectopic endometrial glands and stroma within the myometrium and is commonly associated with dysmenorrhea, menorrhagia, and uterine enlargement, resulting in substantial impairment of the quality of life [ 1 – 3 ]. Management strategies must be individualized according to patient age, reproductive status, symptom severity, and desire for future fertility [ 1 ]. In clinical practice, medical therapy is generally considered the first-line treatment for patients who wish to avoid surgery or preserve fertility [ 1 – 4 ]. Commonly used hormonal therapies include oral progestins such as dienogest (DNG), the levonorgestrel-releasing intrauterine system (LNG-IUS), oral contraceptives, and gonadotropin-releasing hormone (GnRH) agonists or antagonists [ 1 – 3 ]. Among these options, LNG-IUS is widely recommended as the first-line treatment [ 1 , 3 ]. DNG is also considered effective and designated as a first-line therapy in the Society of Obstetricians and Gynecologists of Canada (SOGC) guidelines [ 3 ], although irregular bleeding is frequently reported [ 1 – 3 ]. Increased incidence of unexpected bleeding has been reported in specific adenomyosis subtypes [ 1 , 4 ]. In contrast, GnRH agonists are considered second-line agents by the SOGC guidelines [ 3 ], and while effective in controlling symptoms and reducing uterine size, their long-term use is not recommended because of hypoestrogenic adverse effects [ 1 ]. The current guidelines lack objective imaging-based criteria for individualized treatment selection; therefore, the management is largely guided by clinical assessment and patient symptoms [ 1 – 3 ]. Asian guidelines suggest that GnRH agonists may be more appropriate for patients with markedly enlarged uteri (> 10 cm in longitudinal diameter) and severe anemia (hemoglobin < 8 g/dL), whereas DNG may be less suitable for patients with intrinsic or diffuse adenomyosis with menorrhagia [ 1 ]. However, evidence supporting differential treatment efficacy by adenomyosis subtype remains limited [ 5 , 6 ]. Intrinsic adenomyosis has been identified as an independent risk factor for serious, unpredictable bleeding during DNG therapy [ 4 ], whereas extrinsic adenomyosis is often associated with dysmenorrhea related to coexisting deep endometriosis. Patients with the extrinsic subtype tend to respond well to DNG therapy [ 5 , 6 ]. Whether this subtype predicts therapeutic response versus tolerability remains unclear. Magnetic resonance imaging (MRI) is considered the most accurate modality for the diagnosis and characterization of adenomyosis because of its superior soft-tissue contrast and multiplanar capability [ 1 – 3 ]. Recent studies have suggested that MRI provides objective morphological information about adenomyosis through both morphological and quantitative parameters. Diffusion-weighted imaging (DWI) enables quantitative assessment of tissue water diffusion through the apparent diffusion coefficient (ADC). ADC values have been used to characterize adenomyosis and differentiate it from other uterine lesions, such as leiomyoma or malignant tumors [ 7 , 8 ]. The T2-weighted signal intensity ratio correlates with smooth muscle density and predicts treatment outcomes, such as response to uterine artery embolization [ 9 ], suggesting its utility in disease stratification and therapeutic planning. If pretreatment MRI features can stratify patients according to the likelihood of response, a more rational selection of hormonal therapy may be possible. This study aimed to evaluate pretreatment MRI features, including adenomyosis subtypes and quantitative parameters, and to determine their association with the clinical response to hormonal therapy. 2. Methods 2.1 Patients This retrospective study was approved by the institutional review board of our institution (approval number: 37–556), which waived the requirement for informed consent because of its retrospective design. From the institutional imaging database, 365 patients diagnosed with adenomyosis by using MRI between October 2018 and July 2025 were initially identified. Inclusion criteria were: (1) MRI-confirmed adenomyosis; (2) pretreatment MRI within 6 months before initiation of hormonal therapy; (3) hormonal therapy with GnRH agonist/antagonist or DNG (LNG-IUS and combined oral contraceptives were excluded owing to different therapeutic mechanisms); (4) complete clinical data; and (5) age ≥ 18 years and premenopausal status. The exclusion criteria were as follows: inadequate image quality precluding reliable assessment, absence of measurable adenomyotic lesions, coexistence of gynecologic malignancy, concurrent pregnancy, and incomplete clinical follow-up. After applying these criteria, 78 patients were included in the final analysis (Fig. 1 ). 2.2 MRI Acquisition MRI examinations were performed using a 3.0-T scanner (MAGNETOM Vida and MAGNETOM Skyra; Siemens Healthineers, Erlangen, Germany) or a 1.5-T scanner (MAGNETOM Avanto; Siemens Healthineers) equipped with a phased-array body coil. The imaging protocol included axial T1-weighted images acquired with or without fat suppression, axial and sagittal T2-weighted fast spin-echo images, axial DWI with b-values of 50 and 1000 s/mm², and coronal T2-weighted fast spin-echo images or half-Fourier acquisition of single-shot turbo spin-echo (HASTE) sequences. ADC maps were automatically generated on the operating console. All transverse images were acquired with a section thickness of 3–6 mm and an inter-slice gap of 0–2.4 mm. 2.3 Diagnostic Criteria and Classification The MRI diagnosis of adenomyosis was based on established criteria [ 1 , 2 ]: junctional zone thickness > 12 mm, difference between the thickest and thinnest junctional zones > 5 mm, asymmetry of the anterior and posterior myometrium, junctional zone-to-myometrium ratio > 40%, and/or intramyometrial endometrial cysts. Adenomyosis subtypes were classified according to the modified Kishi criteria [ 5 , 10 ] as intrinsic, extrinsic, intramural, or penetrating. Lesion distribution was categorized as focal or diffuse based on a 25% involvement threshold [ 11 ]. The lesion location was recorded as anterior, posterior, lateral, fundal, or unclassifiable (in cases with multiple or diffuse involvements). The presence of coexisting conditions (ovarian endometriotic cysts, deep endometriosis, and uterine myomas) was also documented. 2.4 Clinical Evaluation Clinical variables extracted from the medical records included age at menarche, menstrual cycle characteristics, history of infertility, and history of gynecologic surgery. The severity of dysmenorrhea was assessed using a modified Andersch–Milsom scale [ 12 ]. Menorrhagia or heavy menstrual bleeding was recorded and defined clinically as excessive menstrual bleeding affecting the quality of life, in accordance with the Federation of Gynecology and Obstetrics (FIGO) criteria [ 13 ]. Irregular menstrual bleeding was assessed using the FIGO abnormal uterine bleeding (AUB) System [ 13 ]. Because the FIGO classification does not provide an integrated severity grading system for irregular bleeding patterns, the overall severity was categorized as none, mild, or severe based on the combination of these parameters and their impact on the quality of life based on the FIGO heavy menstrual bleeding criteria [ 13 ]. Serum hemoglobin and CA125 levels were also recorded. Clinical efficacy was primarily assessed 3–6 months after the initiation of therapy, based on improvements in dysmenorrhea, menstrual blood loss, and/or hemoglobin levels. Treatment effectiveness was defined as sustained symptom improvement with continuation of treatment. Treatment failure included inadequate symptom relief or discontinuation owing to adverse effects. Serious, unpredictable bleeding was defined according to the previously reported criteria [ 4 ]. 2.5 Imaging Analysis All imaging analyses were performed on a picture archiving and communication system workstation by two radiologists with 10 and 15 years of experience in imaging diagnosis, who were blinded to the clinical information. Discrepancies in the qualitative imaging findings were resolved by consensus. Uterine morphological parameters (maximum myometrial wall thickness, uterine body length excluding the cervix, and anteroposterior diameter) were measured on sagittal T2-weighted images, and uterine volume was calculated using the ellipsoid formula (Fig. 2 ). Quantitative analysis involved the measurement of signal intensities on T2-weighted images, DWI, and ADC maps using standardized regions of interest (ROIs). The signal intensity ratios were calculated relative to the endometrium, outer myometrium, and gluteus maximus muscles according to previous reports [ 14 – 17 ]. Circular ROIs of ≥ 100 mm² were manually placed on adenomyotic lesions at the section showing optimal lesion visualization, avoiding adjacent tissues and maintaining approximately 1–2 mm from lesion margins to minimize partial volume effects [ 7 ]. Each ROI measurement was repeated three times, and the mean value was used for the analysis. A second ROI (≥ 100 mm²) was placed on the gluteus maximus muscle on the same slice. The third and fourth ROIs were placed on the normal endometrium and outer myometrium, respectively, with the largest possible area on the same or adjacent slice (Fig. 3 ). ROIs were copied to identical locations across the T2-weighted images, DWI, and ADC maps using a copy function. Manual adjustments were made when the ROIs were misaligned in the endometrium or the normal myometrium. The signal intensity ratios were calculated as follows: SIR glu = SI adenomyosis / SI gluteal muscle ; SIR endo = SI adenomyosis / SI endometrium ; and SIR myo = SI adenomyosis / SI outer myometrium . 2.6 Statistical Analysis Statistical analyses were performed using the R software (version 4.1.3; R Foundation for Statistical Computing, Vienna, Austria). Data normality was assessed using the Kolmogorov–Smirnov test. Continuous variables were compared using the Mann–Whitney U test, and categorical variables were analyzed using the chi-square test or Fisher's exact test, as appropriate. Interobserver agreement for quantitative MRI parameters was evaluated using the intraclass correlation coefficient (ICC). Parameters with an ICC greater than 0.75 were considered to have good reproducibility, and the mean values between readers were used for subsequent analyses. Receiver operating characteristic (ROC) curve analysis was performed for the selected quantitative parameters, with optimal cut-off values determined using the Youden index. Statistical significance was set at P < 0.05. Based on these optimal cut-off values, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated for quantitative parameters with area under the curve (AUC) ≥ 0.7. 3. Results 3.1 Patient Demographics and Clinical Characteristics Seventy-eight patients with MRI-diagnosed adenomyosis, who underwent hormonal therapy, were included in the final analysis (Figure 1). The patient demographics, baseline clinical characteristics, and qualitative MRI findings are summarized in Online Resource 1 and Tables 1–3. Of the 78 patients, 32 received GnRH agonist or antagonist therapy and 46 received DNG. In the GnRH cohort, 31 patients achieved effective treatment. Because only one patient in the GnRH cohort did not achieve treatment effectiveness, a formal comparison between the effective and ineffective groups within this cohort was not feasible; subsequent analyses of predictive MRI parameters were therefore focused on the DNG-treated cohort. Within the DNG cohort, 30 patients achieved treatment effectiveness, and 16 did not. 3.1.1 Comparison Between GnRH and DNG Groups Patients treated with GnRH agonists or antagonists (median, 46 years [range, 31–54 years]) were slightly older than those treated with DNG (median, 43 years [range, 22–52 years]) (P = 0.0018), and the frequency of previous hormonal therapy was higher in the GnRH group (21.9% vs. 4.4%, P = 0.028). Similarly, the frequency of adverse effects other than irregular bleeding was higher in the GnRH group (28.1% vs. 6.5%, P = 0.022). Baseline demographic characteristics, including age at menarche, menstrual cycle length, duration of menstruation, prevalence of infertility, and history of gynecological surgery, did not differ significantly between the groups. Pretreatment menorrhagia was significantly more frequent in the GnRH group than in the DNG group (84.4% vs. 60.9%, P = 0.042), whereas pretreatment serum hemoglobin levels did not differ significantly (11.2 g/dL [5.2–14.0] vs. 11.9 g/dL [6.4–14.1], P = 0.068). Following treatment, improvement in the modified Andersch–Milsom dysmenorrhea scale score was greater in the GnRH group (P = 0.006). Irregular bleeding during treatment occurred more frequently in patients receiving DNG (21.8% vs. 78.3%, P < 0.001), whereas unpredictable bleeding events were uncommon in both groups. Regarding MRI-based adenomyosis subtypes, penetrating adenomyosis was more frequently observed in the GnRH group (62.5% vs. 34.8%), whereas extrinsic adenomyosis was more common in the DNG group (28.1% vs. 47.8%); however, these differences were not statistically significant (P = 0.087). Coexisting intramural myomas ≥4 cm in diameter were more frequently observed in the GnRH group (31.3% vs. 4.3%, P = 0.047), consistent with the larger uterine size observed in this group (Online Resource 1). 3.1.2 Comparison Between DNG-Effective and Ine ffective Groups Within the DNG-treated cohort, no significant differences were observed between the effective (n = 30) and ineffective (n = 16) groups with respect to age, age at menarche, menstrual cycle length, menstruation duration, infertility, history of gynecological surgery, or history of hormonal therapy (Table 1). Irregular bleeding during treatment was more frequent in the ineffective treatment group (66.7% vs. 100%; P = 0.009). Regarding MRI-based adenomyosis subtypes, extrinsic and penetrating subtypes were predominant in the DNG cohort, accounting for 47.8% and 34.8% of the patients, respectively. No statistically significant differences in subtype distribution were observed between the effective and ineffective groups. However, ineffective treatment showed a trend toward a higher proportion of diffuse disease involvement (46.7% vs. 75.0%). 3.2 Quantitative MRI Morphologic Measurements Interobserver agreement for uterine morphologic parameters demonstrated good reliability for all parameters, with ICC exceeding 0.75 (Online Resource 2 and Table 2). Patients receiving GnRH therapy exhibited significantly larger uterine dimensions than those receiving DNG therapy, including greater maximum myometrial thickness, uterine body length, anteroposterior diameter, and uterine body volume (all P < 0.05). In contrast, no significant differences in the uterine size indices were observed between the DNG-effective and DNG-ineffective groups (Table 2). 3.3 Quantitative Signal Intensity Analysis and ADC Measurements The interobserver agreement for the quantitative MRI parameters was good to excellent. Pretreatment signal intensity ratios on T2-weighted imaging and DWI did not differ significantly between the GnRH agonist and DNG groups or within the DNG-treated cohort. The quantitative MRI findings are summarized in Online Resource 3 and Table 3. When comparing DNG-effective and ineffective groups, significant differences were observed in absolute ADC values (1.03 [0.62–1.52] ×10⁻³ mm²/s vs. 0.89 [0.70–1.59] ×10⁻³ mm²/s, P = 0.036) and ADC signal intensity ratio relative to the endometrium (ADC SIR endo : 0.92 [0.58–1.72] vs. 0.85 [0.38–0.94], P = 0.034). ROC analysis demonstrated that both the mean ADC value and mean ADC SIR endo provided moderate discrimination for predicting treatment effectiveness, with an area under the curve of 0.70 for each parameter (Table 4). Using the Youden index, the optimal cut-off value for ADC was 0.951 ×10⁻³ mm²/s, yielding a sensitivity of 70%, specificity of 75%, accuracy of 71.7%, a PPV of 0.84, and NPV of 0.57. The optimal cut-off value for ADC SIR endo was 0.952, yielding a sensitivity of 40%, specificity of 100%, accuracy of 60%, PPV of 1.00, and NPV of 0.46 (Table 4). 4. Discussion This study examined the relationship between pretreatment MRI features and hormonal therapy response in patients with adenomyosis, with a particular focus on a DNG-treated cohort. Three principal findings were identified in this study. First, the adenomyosis subtype according to the modified Kishi classification and distribution pattern (diffuse vs. focal) did not differ significantly between the DNG-effective and ineffective groups. Second, the pretreatment uterine morphological indices were not associated with DNG effectiveness. Third, both the mean ADC and ADC SIR endo showed moderate predictive performance for DNG effectiveness (AUC = 0.70 each). Clinically, the most relevant finding was the association between pretreatment ADC values and DNG effectiveness; the optimal cut-off value for ADC was 0.951 ×10⁻³ mm²/s (sensitivity, 70%; specificity, 75%) and 0.952 for ADC SIR endo (sensitivity, 40%; specificity, 100%). In our cohort, the MRI-based intrinsic/extrinsic subtype classification was not associated with DNG effectiveness, whereas lower ADC values were observed in the ineffective group. This discrepancy suggests that ADC may capture microstructural features beyond the gross subtype classification. Histologically, adenomyosis is characterized by infiltration of basalis-derived endometrial glands and stroma into the myometrium, accompanied by reactive hypertrophic and hyperplastic changes in the surrounding myometrium [ 18 , 19 ]. Intrinsic and extrinsic adenomyosis may also differ in stromal architecture, progesterone receptor expression, and fibrosis patterns (intrinsic, filamentous fibrosis; extrinsic, dense fibrosis) [ 18 , 19 ]. Although a direct histologic–ADC correlation has not yet been systematically established and the specific microstructural determinants of diffusion properties remain incompletely understood, differences in glandular proliferation, stromal composition, and fibrosis may contribute to variations in tissue microstructure influencing diffusion characteristics on MRI. DWI studies have demonstrated heterogeneous signal intensity and ADC values in adenomyosis [ 7 , 8 ]. In general, high cellularity and reduced extracellular space are associated with restricted diffusion, whereas edema, cystic changes, and stromal expansion may increase the ADC [ 8 ]. Yajima et al. reported significantly higher ADC values in high-intensity adenomyosis, largely attributable to T2 shine-through-related edema, congestion, or decidual change [ 7 ]. Therefore, diffusion metrics likely reflect both microstructural organization and tissue water content rather than glandular density alone. Although physiological variations in junctional zone thickness and ADC have been reported in healthy women during the menstrual cycle [ 20 ], Kido et al. found no association between the menstrual cycle phase or hormonal status and a low-signal-intensity layer at the endometrial–myometrial junction on ADC maps in adenomyosis [ 21 ], suggesting that certain endometrial–myometrial junction diffusion features may represent relatively stable structural characteristics. Nakai et al. described a proliferative ("fish-in-a-net") variant characterized by high ADC values and a favorable response to hormonal therapy [ 22 ]. Altogether, these observations support the hypothesis that lesions with relatively abundant glandular or stromal components are more susceptible to progestin-based suppression. Previous studies have shown that intrinsic adenomyosis is less responsive to systemic progestins than extrinsic disease in a large MRI-based cohort [ 5 ], whereas intrinsic localization was reported to be favorable for LNG-IUS response, and extrinsic or advanced disease predicted resistance [ 23 ]. In contrast, the MRI-based subtype classification did not predict DNG effectiveness in our cohort. These conflicting findings suggest that treatment response may not be determined solely by morphological differences in lesion localization or distribution, but may also be influenced by the underlying histologic heterogeneity. DNG, acting via progesterone receptor activation, induces decidualization and subsequent atrophy of ectopic endometrial tissue, while suppressing proliferation and local inflammatory activity [ 24 , 25 ]. Receptor-level heterogeneity may further contribute to treatment variability, as intrinsic adenomyosis exhibits reduced progesterone receptor expression relative to estrogen receptors in both glandular and stromal components, compared with extrinsic disease [ 19 ]. Together, these findings suggest that anatomical classification alone may not fully explain the therapeutic heterogeneity. To our knowledge, quantitative ADC thresholds predicting response to DNG have not been previously established. Therefore, the high-specificity cut-off for ADC SIR endo (100% specificity) may be useful for identifying patients at a high risk of non-response. In Japan, DNG is contraindicated in patients with marked uterine enlargement (> 10 cm) or severe anemia [ 1 ]. Furthermore, patients with intrinsic subtype adenomyosis are at an increased risk of DNG-related serious unpredictable bleeding [ 4 ]; in such cases, alternative treatments, including GnRH agonists, may warrant consideration [ 1 , 4 ]. In the dataset (Table 2 ), morphological parameters were significantly larger in the GnRH-treated group than in the DNG-treated group, reflecting the clinical selection patterns in which GnRH is preferentially used for patients with a greater disease burden. However, within the DNG-treated cohort, the uterine size indices and morphological subtypes did not differ between the effective and ineffective groups. Uterine size and subtype may influence treatment selection; however, they are not predictors of treatment response to DNG. In contrast, quantitative imaging biomarkers, such as ADC values, may provide additional predictive information regarding therapeutic effectiveness beyond morphological assessment alone. This study had several important limitations. First, the single-center retrospective design limits the generalizability and introduces a potential selection bias owing to clinician-driven treatment allocation, particularly given the greater uterine size and bleeding severity in the GnRH group. Second, the sample size was moderate, especially in the subgroup analyses, with limited statistical power, and precluded multivariate modeling. Third, MRI acquisition across different scanner platforms may have introduced measurement variability, and retrospectively placed ROIs, although blinded to the outcomes, may have introduced subtle bias. Fourth, follow-up was restricted to 3–6 months, and the lack of histopathological correlation limited the mechanistic interpretation of the ADC findings. Despite these limitations, this study provides preliminary quantitative evidence that ADC parameters may differentiate DNG responders from non-responders, and supports future prospective multicenter validation. In conclusion, quantitative ADC parameters were associated with DNG effectiveness, whereas conventional morphological features, including the subtype and uterine size, were not predictive in the DNG-treated cohort. These findings indicate that diffusion-weighted MRI may be useful for treatment stratification, although prospective multicenter validation is warranted. Declarations Author Contribution Kazuhiko Morikawa, Akira Baba, Shun Kusada, Satoshi Matsushima and Hiroya Ojiri contributed to conceptualization, manuscript writing, and editing. Yohei Ohki, Megumi Shiraishi, Yoshitake Miyamoto, Aya Igarashi, Yumari Kusano and Ayako Kawabata contributed to collecting and compiling patient data. All authors reviewed the manuscript. 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DNG (n=46) Effective (n=30) Ineffective (n=16) P value Age (years) [range] 43 [22-52] 42 [35-50] 0.954 Age at menarche (years) [range] 13 [10-15] (n=19) 14 [9-16] (n=13) 0.479 Menstrual cycle length (days) [range] 28 [25-30] (n=20) 29 [26-30] (n=10) 0.119 Duration of menstruation (days) [range] 6 [4-10] (n=19) 6.5 [4-7] (n=12) 0.95 Infertility 7 (7/30, 23.3%) 4 (4/16, 25.0%) 1 History of gynecologic surgery or disease 9 (9/30, 30.0%) 5 (5/16, 31.3%) 1 History of hormonal therapy 2 (2/30, 6.7%) 0 0.536 ≥1-month drug-free interval before MRI 1 (1/2, 50%) (n=2) － － Modified Andersch–Milsom scale Pre-treatment [Pts count of each grade 0/1/2/3] 2 [1/5/19/5] 2 [0/3/11/2] 1 Post-treatment [Pts count of grade 0/1/2/3] 1 [11/19/0/0] 2 [0/5/9/2] P<0.001 Menorrhagia Pre-treatment 16 (16/30, 53.3%) 12 (12/16, 75.0%) 0.21 Post-treatment 0 10 (10/16, 62.5%) P<0.001 Serum hemoglobin (g/dL) Pre-treatment [range] 11.8 [9.3-14.1] (n=25) 12.0 [6.4-13.8] (n=11) 0.823 Post-treatment [range] 12.9 [10.3-14.5] (n=23) 12.0 [6.8-13.6] (n=13) 0.0128 Serum CA125 (U/mL) Pre-treatment [range] 45.0 [11-253] (n=21) 65.8 [25-1083] (n=10) 0.398 Post-treatment [range] 42 [13-82] (n=9) 74 [15-149] (n=6) 0.175 Adverse effects except irregular bleeding 2 (2/30, 6.7%) 1 (1/16, 6.3%) 1 Irregular bleeding during treatment [Pts count of mild/severe bleeding] 20 (20/30, 66.7%) [19/1] 16 (16/16, 100%) [12/4] 0.0088 Unpredictable bleeding during treatment 1 (1/30, 3.3%) 2 (2/16, 12.5%) 0.274 Treatment period (days) [range] 193.5 [35-350] 141.5 [22–686] 0.863 Initial hormonal therapy result success 28 (28/30, 93.3%) 0 failure 2 (2/30, 6.7%) 16 (16/16, 100%) <0.001 Location 1 subtype 0.802 Intrinsic 5 (5/30, 16.7%) 2 (2/16, 12.5%) Extrinsic 15 (15/30, 50.0%) 7 (7/16, 43.8%) Intramural 1 (1/30, 3.3%) 0 Penetrating 9 (9/30, 30.0%) 7 (7/16, 43.8%) Location 2 distribution pattern 0.117 Diffuse 14 (14/30, 46.7%) 12 (12/16, 75.0%) Focal 16 (16/30, 53.3%) 4 (4/16, 25.0%) Location 3 main distribution 0.965 Anterior 4 (4/30, 13.3%) 3 (3/16, 16.7%) Posterior 20 (20/30, 66.7%) 10 (10/16, 62.5%) Lateral 1 (1/30, 3.3%) 0 Fundus 4 (4/30, 13.3%) 2 (2/16, 12.5%) unclassifiable 1 (1/30, 3.3%) 1 (1/16, 6.3%) Ovarian endometriosis 21 (21/30, 70.0%) 13 (13/16, 81.3%) 0.498 Deep endometriosis 19 (19/30, 63.3%) 9 (9/16, 56.3%) 0.754 Uterine myoma 13 (13/30, 43.3%) 8 (8/16, 50.0%) 0.76 Subendometrial myoma >2cm 1 (1/30, 3.3%) 1 (1/16, 6.3%) 1 Intramural myoma ≥4 cm 2 (2/30, 6.7%) 0 0.509 Shrinking in endometriotic cyst after treatment [enlarged/unchanged/reduced] [0/17/4] (n=21) [4/4/4] (n=12) 0.00446 Shrinking in leiomyoma size after treatment [enlarged/unchanged/reduced] [2/9/3] (n=14) [0/8/1] (n=9) 0.485 Data are presented as numbers (percentage) or median (range), as appropriate. Continuous variables were compared using the Mann–Whitney U test, and categorical variables were analyzed using the chi-squared test or Fisher's exact test. Treatment failure includes discontinuation or modification due to adverse effects. DNG, dienogest. Table 2. Comparison of quantitative uterine morphologic parameters in the DNG-treated group. Parameters Effective (n=30) Ineffective (n=16) P value ICC value maximum wall thickness on sagittal (mm) maximum diameter of uterine body (mm) maximum AP diameter (mm) uterine body volume (cm3) Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Readers mean 33 [14-95] 33.5 [16-92] 33.3 [15-93.5] 65 [41-120] 64 [35-117] 64.8 [41.5-118.5] 54.5 [32-120] 55 [38-105] 54.3 [40-112.5] 115.5 [38-554] 111.5 [32-494] 112 [37-489.5] 37.5 [19-66] 37.5 [20-67] 37.5 [19.5 -66.5] 70 [41-104] 69.5 [47-103] 69.8 [44-103.5] 62 [42-87] 60 [40-87] 61.5 [41.5-87] 141 [38-460] 138 [42-470] 137.5 [40-465] 0.572 0.533 0.316 0.533 0.94 0.92 0.94 0.98 Quantitative MRI parameters are expressed as median (range). Interobserver agreement was assessed using the intraclass correlation coefficient. AP, anteroposterior; DNG, dienogest; ICC, intraclass correlation coefficient. Table 3. Comparison of pretreatment signal intensity values and signal intensity ratios in the DNG-treated group. Parameters Effective (n=30) Ineffective (n=16) P value ICC value T2WI intensity value T2WI SIR endo T2WI SIR myo T2WI SIR glu DWI intensity value DWI SIR endo DWI SIR myo DWI SIR glu ADC value ADC SIR endo ADC SIR myo ADC SIR glu Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Reader 1 Reader 2 Reader 1 Reader 2 Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Reader 1 Reader 2 Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Readers mean Reader 1 Reader 2 Readers mean 151.5 [38-476] 152 [56-597] 151 [50-536.5] 0.39 [0.12-1.45] 0.43 [0.24-0.90] 0.60 [0.22-2.08] 0.64 [0.36-1.77] 1.07 [0.29-2.82] 1.00 [0.28-2.66] 28 [13-230] 31.5 [14-225] 29 [13.5-227.5] 0.51 [0.18-0.78] 0.54 [0.25-0.73] 1.00 [0.42-1.69] 1.05 [0.65-1.61] 1.66 [0.94-2.45] 1.77 [0.89-2.55] 1.74 [0.91-2.50] 1.02 [0.59-1.47] 1.03 [0.65-1.56] 1.03 [0.62-1.52] 0.90 [0.56-1.61] 0.90 [0.57-1.83] 0.92 [0.58-1.72] 0.74 [0.53-1.19] 0.80 [0.54-1.52] 0.78 [0.55-1.36] 0.85 [0.50-3.32] 0.88 [0.55-1.63] 0.86 [0.53-2.05] 154.5 [70-261] 155 [74-275] 154.5 [72-265.5] 0.42 [0.18-0.96] 0.42 [0.20-1.03] 0.67 [0.39-1.29] 0.73 [0.42-1.62] 1.25 [0.61-1.72] 1.38 [0.74-2.08] 30 [11-119] 26.5 [11-128] 28.8 [11-123.5] 0.55 [0.29-0.77] 0.56 [0.29-0.71] 1.03 [0.69-1.55] 1.04 [0.55-1.55] 1.62 [0.95-2.81] 1.68 [0.95-3.11] 1.62 [0.95-2.96] 0.88 [0.70-1.56] 0.90 [0.70-1.62] 0.89 [0.70-1.59] 0.79 [0.37-1.12] 0.76 [0.40-1.05] 0.85 [0.38-0.94] 0.72 [0.47-1.27] 0.68 [0.54-1.05] 0.71 [0.52-1.15] 0.81 [0.63-2.06] 0.84 [0.62-1.02] 0.82 [0.63-1.46] 0.845 0.8 0.533 0.821 0.803 0.782 0.747 0.963 0.572 0.972 0.518 0.782 0.819 0.641 0.782 0.747 0.963 0.572 0.724 0.041 0.039 0.0362 0.159 0.028 0.034 0.44 0.049 0.236 0.366 0.187 0.298 0.88 0.74 0.57 0.66 0.99 0.66 0.70 0.79 0.90 0.79 0.81 0.89 Quantitative parameters were analyzed using the Mann–Whitney U test. Interobserver agreement was evaluated using the intraclass correlation coefficient. ADC, apparent diffusion coefficient; DNG, dienogest; DWI, diffusion-weighted imaging; endo, endometrium; glu, gluteal muscle; myo, myometrium; SIR, signal intensity ratio. Table 4. Diagnostic performance of quantitative MRI parameters for predicting treatment effectiveness in the DNG-treated group. Parameters AUC Sensitivity (%) Specificity (%) Accuracy (%) PPV NPV Cut off value P value ADC value (Readers mean) 0.70 70 75 71.7 0.84 0.57 0.951 0.0362 ADC SIR endo (Readers mean) 0.70 40 100 60 1.00 0.46 0.952 0.034 Diagnostic performance metrics were derived from ROC curve analysis. Optimal cut-off values were determined using the Youden index. Sensitivity, specificity, accuracy, PPV, and NPV were calculated based on these cut-off values. ADC, apparent diffusion coefficient; AUC, area under the curve; DNG, dienogest; endo, endometrium; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic; SIR, signal intensity ratio. Additional Declarations No competing interests reported. Supplementary Files ESM1.pdf Online Resource 1 (Supplementary Table 1).Demographic, clinical, and radiological characteristics of the study population. ESM2.pdf Online Resource 2 (Supplementary Table 2).Comparison of quantitative uterine morphological parameters between the GnRH- and DNG-treated groups. ESM3.pdf Online Resource 3 (Supplementary Table 3).Comparison of pretreatment signal intensity values and signal intensity ratios between the GnRH- and DNG-treated groups. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviews received at journal 28 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers invited by journal 28 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 23 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9201156","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614781161,"identity":"b2a40fda-083b-4aa5-8c2c-dd2da34e8fe7","order_by":0,"name":"Kazuhiko Morikawa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACCTBpkAAkmA8YJBxg4EESJqiFLYEULQwgLTwGDAwHiHCYZAN34uOKgjR5g+NnPhQ8OGMjw8B++AGD5Q7cWqQZeDcbnjHIMdxwJneDQcKNNB4GnjQDBskzuLXIMfBuk2wwqGDcdgCk5cNhoF9ygJa3EdZiv+38mwdALf95GPjf4NciDdGSk7jtRg4w4G4c4GGQIGCLZDPQLw0Gacn7bzwzMEg4k8zDJvHM4AA+v0gc7934sOFPsu3M/uRnhj+O2dnz8yc/fCyJJ8QYmBFMNgMwCcSHJRvwaEHW/QDGYvxIpJZRMApGwSgYEQAAoKBPjLlID24AAAAASUVORK5CYII=","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Kazuhiko","middleName":"","lastName":"Morikawa","suffix":""},{"id":614781162,"identity":"f0c031d3-0cdd-4d7e-b70c-b18693221b84","order_by":1,"name":"Akira Baba","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Akira","middleName":"","lastName":"Baba","suffix":""},{"id":614781163,"identity":"885530a8-c95e-4e75-bfa8-8d19e38e3abf","order_by":2,"name":"Shun Kusada","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shun","middleName":"","lastName":"Kusada","suffix":""},{"id":614781164,"identity":"72af5e68-9157-4d57-87cc-8d5e78618f45","order_by":3,"name":"Satoshi Matsushima","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Matsushima","suffix":""},{"id":614781165,"identity":"05408d23-57e8-456f-8c2a-651f2056664a","order_by":4,"name":"Yohei Ohki","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yohei","middleName":"","lastName":"Ohki","suffix":""},{"id":614781166,"identity":"b7c98c07-16e3-4e05-a9d5-fcdc84c448a2","order_by":5,"name":"Megumi Shiraishi","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Megumi","middleName":"","lastName":"Shiraishi","suffix":""},{"id":614781168,"identity":"eea77617-3f98-4070-b339-b6964891b87c","order_by":6,"name":"Yoshitake Miyamoto","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yoshitake","middleName":"","lastName":"Miyamoto","suffix":""},{"id":614781171,"identity":"93e6c0dc-a07a-44e1-9bb4-b1f8f12b9aa3","order_by":7,"name":"Aya Igarashi","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Aya","middleName":"","lastName":"Igarashi","suffix":""},{"id":614781172,"identity":"760afebb-2fee-4d3d-8cce-28516617db37","order_by":8,"name":"Yumari Kusano","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yumari","middleName":"","lastName":"Kusano","suffix":""},{"id":614781174,"identity":"94215d8c-649a-46cc-9739-9f6a388de4db","order_by":9,"name":"Ayako Kawabata","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ayako","middleName":"","lastName":"Kawabata","suffix":""},{"id":614781176,"identity":"d84696e7-b0f9-4814-8f15-ef7ba33cdc28","order_by":10,"name":"Hiroya Ojiri","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hiroya","middleName":"","lastName":"Ojiri","suffix":""}],"badges":[],"createdAt":"2026-03-23 13:25:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9201156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9201156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106071805,"identity":"35de0b53-ce9a-4b05-a7e9-30c07de12364","added_by":"auto","created_at":"2026-04-03 06:43:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183240,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patient selection\u003c/p\u003e\n\u003cp\u003eFlow diagram illustrating patient inclusion and exclusion criteria\u003c/p\u003e\n\u003cp\u003eDNG, dienogest; GnRH, gonadotropin-releasing hormone; LNG-IUS, levonorgestrel-releasing intrauterine system; MRI, magnetic resonance imaging; OC, oral contraceptive\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9201156/v1/06683af32665c0f294ceade3.png"},{"id":106095456,"identity":"68b8865e-7b14-40fc-bb0b-54ad60892f90","added_by":"auto","created_at":"2026-04-03 11:47:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":360378,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of uterine body size\u003c/p\u003e\n\u003cp\u003e(a) Long-axis and anteroposterior diameters were measured on sagittal T2-weighted images (b) The maximum transverse diameter was measured on axial planes\u003c/p\u003e\n\u003cp\u003eThe maximum uterine body diameter was defined as the longest measurable distance on any imaging plane. Uterine body volume was calculated as maximum transverse diameter × long-axis diameter × anteroposterior diameter × 0.523, using the sagittal image showing the largest uterine body\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9201156/v1/7ca06d324404f5b7f7a5afe1.png"},{"id":106071809,"identity":"d59c4bff-1d0c-4b85-bbd6-1d871de95c79","added_by":"auto","created_at":"2026-04-03 06:43:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":473512,"visible":true,"origin":"","legend":"\u003cp\u003eRegion of interest (ROI) placement for quantitative signal intensity measurements\u003c/p\u003e\n\u003cp\u003e(a–c) Images from a woman in her 30s with adenomyosis who showed ineffective response to DNG therapy: (a) T2-weighted image, (b) diffusion-weighted image, and (c) ADC map\u003c/p\u003e\n\u003cp\u003eROIs were placed in the adenomyosis lesion, endometrium, normal outer myometrium, and gluteal muscle. The ADC value of the adenomyosis lesion was 0.79 ×10⁻³ mm²/s. (d–f) Images from a woman in her 30s with adenomyosis who showed an effective response to DNG therapy: (d) T2-weighted image, (e) diffusion-weighted image, and (f) ADC map\u003c/p\u003e\n\u003cp\u003eThe ADC value of the adenomyosis lesion was 1.08 ×10⁻³ mm²/s\u003c/p\u003e\n\u003cp\u003eADC, apparent diffusion coefficient; DNG, dienogest.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9201156/v1/fe2cfdf5fce7e2497f2b3657.png"},{"id":106096571,"identity":"0ad19e4b-9de4-4342-8792-4e436e0ec52d","added_by":"auto","created_at":"2026-04-03 11:55:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2267357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9201156/v1/416cb441-5222-4260-816b-2add12a72f49.pdf"},{"id":106071806,"identity":"422a6940-0b2b-4b22-8076-0a3149907373","added_by":"auto","created_at":"2026-04-03 06:43:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":184464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource 1 (Supplementary Table 1).\u003c/strong\u003eDemographic, clinical, and radiological characteristics of the study population.\u003c/p\u003e","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9201156/v1/8729982c293d2652cdd152a8.pdf"},{"id":106094206,"identity":"40f7ab2e-aeb5-4b5f-a97f-7544b39a1f78","added_by":"auto","created_at":"2026-04-03 11:41:44","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":170958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource 2 (Supplementary Table 2).\u003c/strong\u003eComparison of quantitative uterine morphological parameters between the GnRH- and DNG-treated groups.\u003c/p\u003e","description":"","filename":"ESM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9201156/v1/e3d8f38a0cf94cc88ca0e976.pdf"},{"id":106094582,"identity":"a082c68c-b0bb-4bcb-bd4b-80fd77e7c531","added_by":"auto","created_at":"2026-04-03 11:42:55","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":181994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOnline Resource 3 (Supplementary Table 3).\u003c/strong\u003eComparison of pretreatment signal intensity values and signal intensity ratios between the GnRH- and DNG-treated groups.\u003c/p\u003e","description":"","filename":"ESM3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9201156/v1/89029f638f91d2bd266e2f64.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pretreatment MRI Parameters as Predictive Biomarkers for Hormonal Therapy Response in Adenomyosis: A Comprehensive Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdenomyosis is a benign gynecological condition characterized by the presence of ectopic endometrial glands and stroma within the myometrium and is commonly associated with dysmenorrhea, menorrhagia, and uterine enlargement, resulting in substantial impairment of the quality of life [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Management strategies must be individualized according to patient age, reproductive status, symptom severity, and desire for future fertility [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In clinical practice, medical therapy is generally considered the first-line treatment for patients who wish to avoid surgery or preserve fertility [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCommonly used hormonal therapies include oral progestins such as dienogest (DNG), the levonorgestrel-releasing intrauterine system (LNG-IUS), oral contraceptives, and gonadotropin-releasing hormone (GnRH) agonists or antagonists [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among these options, LNG-IUS is widely recommended as the first-line treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. DNG is also considered effective and designated as a first-line therapy in the Society of Obstetricians and Gynecologists of Canada (SOGC) guidelines [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], although irregular bleeding is frequently reported [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Increased incidence of unexpected bleeding has been reported in specific adenomyosis subtypes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In contrast, GnRH agonists are considered second-line agents by the SOGC guidelines [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and while effective in controlling symptoms and reducing uterine size, their long-term use is not recommended because of hypoestrogenic adverse effects [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current guidelines lack objective imaging-based criteria for individualized treatment selection; therefore, the management is largely guided by clinical assessment and patient symptoms [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Asian guidelines suggest that GnRH agonists may be more appropriate for patients with markedly enlarged uteri (\u0026gt;\u0026thinsp;10 cm in longitudinal diameter) and severe anemia (hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;8 g/dL), whereas DNG may be less suitable for patients with intrinsic or diffuse adenomyosis with menorrhagia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, evidence supporting differential treatment efficacy by adenomyosis subtype remains limited [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIntrinsic adenomyosis has been identified as an independent risk factor for serious, unpredictable bleeding during DNG therapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], whereas extrinsic adenomyosis is often associated with dysmenorrhea related to coexisting deep endometriosis. Patients with the extrinsic subtype tend to respond well to DNG therapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Whether this subtype predicts therapeutic response versus tolerability remains unclear.\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging (MRI) is considered the most accurate modality for the diagnosis and characterization of adenomyosis because of its superior soft-tissue contrast and multiplanar capability [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent studies have suggested that MRI provides objective morphological information about adenomyosis through both morphological and quantitative parameters. Diffusion-weighted imaging (DWI) enables quantitative assessment of tissue water diffusion through the apparent diffusion coefficient (ADC). ADC values have been used to characterize adenomyosis and differentiate it from other uterine lesions, such as leiomyoma or malignant tumors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The T2-weighted signal intensity ratio correlates with smooth muscle density and predicts treatment outcomes, such as response to uterine artery embolization [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], suggesting its utility in disease stratification and therapeutic planning. If pretreatment MRI features can stratify patients according to the likelihood of response, a more rational selection of hormonal therapy may be possible.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate pretreatment MRI features, including adenomyosis subtypes and quantitative parameters, and to determine their association with the clinical response to hormonal therapy.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the institutional review board of our institution (approval number: 37\u0026ndash;556), which waived the requirement for informed consent because of its retrospective design. From the institutional imaging database, 365 patients diagnosed with adenomyosis by using MRI between October 2018 and July 2025 were initially identified. Inclusion criteria were: (1) MRI-confirmed adenomyosis; (2) pretreatment MRI within 6 months before initiation of hormonal therapy; (3) hormonal therapy with GnRH agonist/antagonist or DNG (LNG-IUS and combined oral contraceptives were excluded owing to different therapeutic mechanisms); (4) complete clinical data; and (5) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years and premenopausal status. The exclusion criteria were as follows: inadequate image quality precluding reliable assessment, absence of measurable adenomyotic lesions, coexistence of gynecologic malignancy, concurrent pregnancy, and incomplete clinical follow-up. After applying these criteria, 78 patients were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 MRI Acquisition\u003c/h2\u003e \u003cp\u003eMRI examinations were performed using a 3.0-T scanner (MAGNETOM Vida and MAGNETOM Skyra; Siemens Healthineers, Erlangen, Germany) or a 1.5-T scanner (MAGNETOM Avanto; Siemens Healthineers) equipped with a phased-array body coil. The imaging protocol included axial T1-weighted images acquired with or without fat suppression, axial and sagittal T2-weighted fast spin-echo images, axial DWI with b-values of 50 and 1000 s/mm\u0026sup2;, and coronal T2-weighted fast spin-echo images or half-Fourier acquisition of single-shot turbo spin-echo (HASTE) sequences. ADC maps were automatically generated on the operating console. All transverse images were acquired with a section thickness of 3\u0026ndash;6 mm and an inter-slice gap of 0\u0026ndash;2.4 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Diagnostic Criteria and Classification\u003c/h2\u003e \u003cp\u003eThe MRI diagnosis of adenomyosis was based on established criteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]: junctional zone thickness\u0026thinsp;\u0026gt;\u0026thinsp;12 mm, difference between the thickest and thinnest junctional zones\u0026thinsp;\u0026gt;\u0026thinsp;5 mm, asymmetry of the anterior and posterior myometrium, junctional zone-to-myometrium ratio\u0026thinsp;\u0026gt;\u0026thinsp;40%, and/or intramyometrial endometrial cysts. Adenomyosis subtypes were classified according to the modified Kishi criteria [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] as intrinsic, extrinsic, intramural, or penetrating. Lesion distribution was categorized as focal or diffuse based on a 25% involvement threshold [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The lesion location was recorded as anterior, posterior, lateral, fundal, or unclassifiable (in cases with multiple or diffuse involvements). The presence of coexisting conditions (ovarian endometriotic cysts, deep endometriosis, and uterine myomas) was also documented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Clinical Evaluation\u003c/h2\u003e \u003cp\u003eClinical variables extracted from the medical records included age at menarche, menstrual cycle characteristics, history of infertility, and history of gynecologic surgery. The severity of dysmenorrhea was assessed using a modified Andersch\u0026ndash;Milsom scale [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Menorrhagia or heavy menstrual bleeding was recorded and defined clinically as excessive menstrual bleeding affecting the quality of life, in accordance with the Federation of Gynecology and Obstetrics (FIGO) criteria [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Irregular menstrual bleeding was assessed using the FIGO abnormal uterine bleeding (AUB) System [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Because the FIGO classification does not provide an integrated severity grading system for irregular bleeding patterns, the overall severity was categorized as none, mild, or severe based on the combination of these parameters and their impact on the quality of life based on the FIGO heavy menstrual bleeding criteria [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Serum hemoglobin and CA125 levels were also recorded. Clinical efficacy was primarily assessed 3\u0026ndash;6 months after the initiation of therapy, based on improvements in dysmenorrhea, menstrual blood loss, and/or hemoglobin levels. Treatment effectiveness was defined as sustained symptom improvement with continuation of treatment. Treatment failure included inadequate symptom relief or discontinuation owing to adverse effects. Serious, unpredictable bleeding was defined according to the previously reported criteria [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Imaging Analysis\u003c/h2\u003e \u003cp\u003eAll imaging analyses were performed on a picture archiving and communication system workstation by two radiologists with 10 and 15 years of experience in imaging diagnosis, who were blinded to the clinical information. Discrepancies in the qualitative imaging findings were resolved by consensus.\u003c/p\u003e \u003cp\u003eUterine morphological parameters (maximum myometrial wall thickness, uterine body length excluding the cervix, and anteroposterior diameter) were measured on sagittal T2-weighted images, and uterine volume was calculated using the ellipsoid formula (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eQuantitative analysis involved the measurement of signal intensities on T2-weighted images, DWI, and ADC maps using standardized regions of interest (ROIs). The signal intensity ratios were calculated relative to the endometrium, outer myometrium, and gluteus maximus muscles according to previous reports [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Circular ROIs of \u0026ge;\u0026thinsp;100 mm\u0026sup2; were manually placed on adenomyotic lesions at the section showing optimal lesion visualization, avoiding adjacent tissues and maintaining approximately 1\u0026ndash;2 mm from lesion margins to minimize partial volume effects [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Each ROI measurement was repeated three times, and the mean value was used for the analysis. A second ROI (\u0026ge;\u0026thinsp;100 mm\u0026sup2;) was placed on the gluteus maximus muscle on the same slice. The third and fourth ROIs were placed on the normal endometrium and outer myometrium, respectively, with the largest possible area on the same or adjacent slice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eROIs were copied to identical locations across the T2-weighted images, DWI, and ADC maps using a copy function. Manual adjustments were made when the ROIs were misaligned in the endometrium or the normal myometrium.\u003c/p\u003e \u003cp\u003eThe signal intensity ratios were calculated as follows: SIR\u003csub\u003eglu\u003c/sub\u003e = SI\u003csub\u003eadenomyosis\u003c/sub\u003e / SI\u003csub\u003egluteal muscle\u003c/sub\u003e; SIR\u003csub\u003eendo\u003c/sub\u003e = SI\u003csub\u003eadenomyosis\u003c/sub\u003e/ SI\u003csub\u003eendometrium\u003c/sub\u003e; and SIR\u003csub\u003emyo\u003c/sub\u003e = SI\u003csub\u003eadenomyosis\u003c/sub\u003e / SI\u003csub\u003eouter myometrium\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using the R software (version 4.1.3; R Foundation for Statistical Computing, Vienna, Austria). Data normality was assessed using the Kolmogorov\u0026ndash;Smirnov test. Continuous variables were compared using the Mann\u0026ndash;Whitney U test, and categorical variables were analyzed using the chi-square test or Fisher's exact test, as appropriate. Interobserver agreement for quantitative MRI parameters was evaluated using the intraclass correlation coefficient (ICC). Parameters with an ICC greater than 0.75 were considered to have good reproducibility, and the mean values between readers were used for subsequent analyses. Receiver operating characteristic (ROC) curve analysis was performed for the selected quantitative parameters, with optimal cut-off values determined using the Youden index. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Based on these optimal cut-off values, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated for quantitative parameters with area under the curve (AUC)\u0026thinsp;\u0026ge;\u0026thinsp;0.7.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 Patient Demographics and Clinical Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeventy-eight patients with MRI-diagnosed adenomyosis, who underwent hormonal therapy, were included in the final analysis (Figure 1). The patient demographics, baseline clinical characteristics, and qualitative MRI findings are summarized in Online Resource 1 and Tables 1\u0026ndash;3. Of the 78 patients, 32 received GnRH agonist or antagonist therapy and 46 received DNG. In the GnRH cohort, 31 patients achieved effective treatment. Because only one patient in the GnRH cohort did not achieve treatment effectiveness, a formal comparison between the effective and ineffective groups within this cohort was not feasible; subsequent analyses of predictive MRI parameters were therefore focused on the DNG-treated cohort. Within the DNG cohort, 30 patients achieved treatment effectiveness, and 16 did not.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1.1 Comparison Between GnRH and DNG Groups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients treated with GnRH agonists or antagonists (median, 46 years [range, 31\u0026ndash;54 years]) were slightly older than those treated with DNG (median, 43 years [range, 22\u0026ndash;52 years]) (P = 0.0018), and the frequency of previous hormonal therapy was higher in the GnRH group (21.9% vs. 4.4%, P = 0.028). Similarly, the frequency of adverse effects other than irregular bleeding was higher in the GnRH group (28.1% vs. 6.5%, P = 0.022). Baseline demographic characteristics, including age at menarche, menstrual cycle length, duration of menstruation, prevalence of infertility, and history of gynecological surgery, did not differ significantly between the groups.\u003c/p\u003e\n\u003cp\u003ePretreatment menorrhagia was significantly more frequent in the GnRH group than in the DNG group (84.4% vs. 60.9%, P = 0.042), whereas pretreatment serum hemoglobin levels did not differ significantly (11.2 g/dL [5.2\u0026ndash;14.0] vs. 11.9 g/dL [6.4\u0026ndash;14.1], P = 0.068). Following treatment, improvement in the modified Andersch\u0026ndash;Milsom dysmenorrhea scale score was greater in the GnRH group (P = 0.006). Irregular bleeding during treatment occurred more frequently in patients receiving DNG (21.8% vs. 78.3%, P \u0026lt; 0.001), whereas unpredictable bleeding events were uncommon in both groups.\u003c/p\u003e\n\u003cp\u003eRegarding MRI-based adenomyosis subtypes, penetrating adenomyosis was more frequently observed in the GnRH group (62.5% vs. 34.8%), whereas extrinsic adenomyosis was more common in the DNG group (28.1% vs. 47.8%); however, these differences were not statistically significant (P = 0.087). Coexisting intramural myomas \u0026ge;4 cm in diameter were more frequently observed in the GnRH group (31.3% vs. 4.3%, P = 0.047), consistent with the larger uterine size observed in this group (Online Resource 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1.2 Comparison Between DNG-Effective and\u0026nbsp;Ine\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003effective Groups\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the DNG-treated cohort, no significant differences were observed between the effective (n = 30) and ineffective (n = 16) groups with respect to age, age at menarche, menstrual cycle length, menstruation duration, infertility, history of gynecological surgery, or history of hormonal therapy (Table 1). Irregular bleeding during treatment was more frequent in the ineffective treatment group (66.7% vs. 100%; P = 0.009).\u003c/p\u003e\n\u003cp\u003eRegarding MRI-based adenomyosis subtypes, extrinsic and penetrating subtypes were predominant in the DNG cohort, accounting for 47.8% and 34.8% of the patients, respectively. No statistically significant differences in subtype distribution were observed between the effective and ineffective groups. However, ineffective treatment showed a trend toward a higher proportion of diffuse disease involvement (46.7% vs. 75.0%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Quantitative MRI Morphologic Measurements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInterobserver agreement for uterine morphologic parameters demonstrated good reliability for all parameters, with ICC exceeding 0.75 (Online Resource 2 and Table 2). Patients receiving GnRH therapy exhibited significantly larger uterine dimensions than those receiving DNG therapy, including greater maximum myometrial thickness, uterine body length, anteroposterior diameter, and uterine body volume (all P \u0026lt; 0.05). In contrast, no significant differences in the uterine size indices were observed between the DNG-effective and DNG-ineffective groups (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 Quantitative Signal Intensity Analysis and ADC Measurements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interobserver agreement for the quantitative MRI parameters was good to excellent. Pretreatment signal intensity ratios on T2-weighted imaging and DWI did not differ significantly between the GnRH agonist and DNG groups or within the DNG-treated cohort. The quantitative MRI findings are summarized in Online Resource 3 and Table 3.\u003c/p\u003e\n\u003cp\u003eWhen comparing DNG-effective and\u0026nbsp;ineffective groups, significant differences were observed in absolute ADC values (1.03 [0.62\u0026ndash;1.52] \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s vs. 0.89 [0.70\u0026ndash;1.59] \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, P = 0.036) and ADC signal intensity ratio relative to the endometrium (ADC SIR\u003csub\u003eendo\u003c/sub\u003e: 0.92 [0.58\u0026ndash;1.72] vs. 0.85 [0.38\u0026ndash;0.94], P = 0.034). ROC analysis demonstrated that both the mean ADC value and mean ADC SIR\u003csub\u003eendo\u003c/sub\u003e provided moderate discrimination for predicting treatment effectiveness, with an area under the curve of 0.70 for each parameter (Table 4). Using the Youden index, the optimal cut-off value for ADC was 0.951 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, yielding a sensitivity of 70%, specificity of 75%, accuracy of 71.7%, a PPV of 0.84, and NPV of 0.57. The optimal cut-off value for ADC SIR\u003csub\u003eendo\u003c/sub\u003e was 0.952, yielding a sensitivity of 40%, specificity of 100%, accuracy of 60%, PPV of 1.00, and NPV of 0.46 (Table 4).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study examined the relationship between pretreatment MRI features and hormonal therapy response in patients with adenomyosis, with a particular focus on a DNG-treated cohort. Three principal findings were identified in this study. First, the adenomyosis subtype according to the modified Kishi classification and distribution pattern (diffuse vs. focal) did not differ significantly between the DNG-effective and ineffective groups. Second, the pretreatment uterine morphological indices were not associated with DNG effectiveness. Third, both the mean ADC and ADC SIR\u003csub\u003eendo\u003c/sub\u003e showed moderate predictive performance for DNG effectiveness (AUC\u0026thinsp;=\u0026thinsp;0.70 each). Clinically, the most relevant finding was the association between pretreatment ADC values and DNG effectiveness; the optimal cut-off value for ADC was 0.951 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s (sensitivity, 70%; specificity, 75%) and 0.952 for ADC SIR\u003csub\u003eendo\u003c/sub\u003e (sensitivity, 40%; specificity, 100%).\u003c/p\u003e \u003cp\u003eIn our cohort, the MRI-based intrinsic/extrinsic subtype classification was not associated with DNG effectiveness, whereas lower ADC values were observed in the ineffective group. This discrepancy suggests that ADC may capture microstructural features beyond the gross subtype classification. Histologically, adenomyosis is characterized by infiltration of basalis-derived endometrial glands and stroma into the myometrium, accompanied by reactive hypertrophic and hyperplastic changes in the surrounding myometrium [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Intrinsic and extrinsic adenomyosis may also differ in stromal architecture, progesterone receptor expression, and fibrosis patterns (intrinsic, filamentous fibrosis; extrinsic, dense fibrosis) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Although a direct histologic\u0026ndash;ADC correlation has not yet been systematically established and the specific microstructural determinants of diffusion properties remain incompletely understood, differences in glandular proliferation, stromal composition, and fibrosis may contribute to variations in tissue microstructure influencing diffusion characteristics on MRI. DWI studies have demonstrated heterogeneous signal intensity and ADC values in adenomyosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In general, high cellularity and reduced extracellular space are associated with restricted diffusion, whereas edema, cystic changes, and stromal expansion may increase the ADC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Yajima et al. reported significantly higher ADC values in high-intensity adenomyosis, largely attributable to T2 shine-through-related edema, congestion, or decidual change [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, diffusion metrics likely reflect both microstructural organization and tissue water content rather than glandular density alone. Although physiological variations in junctional zone thickness and ADC have been reported in healthy women during the menstrual cycle [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Kido et al. found no association between the menstrual cycle phase or hormonal status and a low-signal-intensity layer at the endometrial\u0026ndash;myometrial junction on ADC maps in adenomyosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], suggesting that certain endometrial\u0026ndash;myometrial junction diffusion features may represent relatively stable structural characteristics. Nakai et al. described a proliferative (\"fish-in-a-net\") variant characterized by high ADC values and a favorable response to hormonal therapy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Altogether, these observations support the hypothesis that lesions with relatively abundant glandular or stromal components are more susceptible to progestin-based suppression.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that intrinsic adenomyosis is less responsive to systemic progestins than extrinsic disease in a large MRI-based cohort [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], whereas intrinsic localization was reported to be favorable for LNG-IUS response, and extrinsic or advanced disease predicted resistance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, the MRI-based subtype classification did not predict DNG effectiveness in our cohort. These conflicting findings suggest that treatment response may not be determined solely by morphological differences in lesion localization or distribution, but may also be influenced by the underlying histologic heterogeneity. DNG, acting via progesterone receptor activation, induces decidualization and subsequent atrophy of ectopic endometrial tissue, while suppressing proliferation and local inflammatory activity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Receptor-level heterogeneity may further contribute to treatment variability, as intrinsic adenomyosis exhibits reduced progesterone receptor expression relative to estrogen receptors in both glandular and stromal components, compared with extrinsic disease [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Together, these findings suggest that anatomical classification alone may not fully explain the therapeutic heterogeneity.\u003c/p\u003e \u003cp\u003eTo our knowledge, quantitative ADC thresholds predicting response to DNG have not been previously established. Therefore, the high-specificity cut-off for ADC SIR\u003csub\u003eendo\u003c/sub\u003e (100% specificity) may be useful for identifying patients at a high risk of non-response.\u003c/p\u003e \u003cp\u003eIn Japan, DNG is contraindicated in patients with marked uterine enlargement (\u0026gt;\u0026thinsp;10 cm) or severe anemia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Furthermore, patients with intrinsic subtype adenomyosis are at an increased risk of DNG-related serious unpredictable bleeding [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; in such cases, alternative treatments, including GnRH agonists, may warrant consideration [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the dataset (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e), morphological parameters were significantly larger in the GnRH-treated group than in the DNG-treated group, reflecting the clinical selection patterns in which GnRH is preferentially used for patients with a greater disease burden. However, within the DNG-treated cohort, the uterine size indices and morphological subtypes did not differ between the effective and ineffective groups. Uterine size and subtype may influence treatment selection; however, they are not predictors of treatment response to DNG. In contrast, quantitative imaging biomarkers, such as ADC values, may provide additional predictive information regarding therapeutic effectiveness beyond morphological assessment alone.\u003c/p\u003e \u003cp\u003eThis study had several important limitations. First, the single-center retrospective design limits the generalizability and introduces a potential selection bias owing to clinician-driven treatment allocation, particularly given the greater uterine size and bleeding severity in the GnRH group. Second, the sample size was moderate, especially in the subgroup analyses, with limited statistical power, and precluded multivariate modeling. Third, MRI acquisition across different scanner platforms may have introduced measurement variability, and retrospectively placed ROIs, although blinded to the outcomes, may have introduced subtle bias. Fourth, follow-up was restricted to 3\u0026ndash;6 months, and the lack of histopathological correlation limited the mechanistic interpretation of the ADC findings. Despite these limitations, this study provides preliminary quantitative evidence that ADC parameters may differentiate DNG responders from non-responders, and supports future prospective multicenter validation.\u003c/p\u003e \u003cp\u003eIn conclusion, quantitative ADC parameters were associated with DNG effectiveness, whereas conventional morphological features, including the subtype and uterine size, were not predictive in the DNG-treated cohort. These findings indicate that diffusion-weighted MRI may be useful for treatment stratification, although prospective multicenter validation is warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eKazuhiko Morikawa, Akira Baba, Shun Kusada, Satoshi Matsushima and Hiroya Ojiri contributed to conceptualization, manuscript writing, and editing. Yohei Ohki, Megumi Shiraishi, Yoshitake Miyamoto, Aya Igarashi, Yumari Kusano and Ayako Kawabata contributed to collecting and compiling patient data. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank Editage (www.editage.com) for English language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHarada T, Taniguchi F, Guo SW, Choi YM, Biberoglu KO, Tsai SS et al (2023) The Asian Society of Endometriosis and adenomyosis guidelines for managing adenomyosis. Reprod Med Biol 22:e12535. https://doi.org/10.1002/rmb2.12535\u003c/li\u003e\n \u003cli\u003eLin CW, Ou HT, Wu MH, Yen CF, Taiwan Endometriosis Society Adenomyosis Consensus Group (2025) Expert consensus on the management of adenomyosis: A modified Delphi method approach by the Taiwan endometriosis society. Gynecol Minim Invasive Ther 14:24-32. https://doi.org/10.4103/gmit.GMIT-D-24-00055\u003c/li\u003e\n \u003cli\u003eDason ES, Maxim M, Sanders A, Papillon-Smith J, Ng D, Chan C, Sobel M (2023) Guideline No. 437: diagnosis and management of adenomyosis. J Obstet Gynaecol Can 45:417-429.e1. https://doi.org/10.1016/j.jogc.2023.04.008\u003c/li\u003e\n \u003cli\u003eMatsubara S, Kawaguchi R, Akinishi M, Nagayasu M, Iwai K, Niiro E, Yamada Y, Tanase Y, Kobayashi H (2019) Subtype I (intrinsic) adenomyosis is an independent risk factor for dienogest-related serious unpredictable bleeding in patients with symptomatic adenomyosis. Sci Rep 9:17654. https://doi.org/10.1038/s41598-019-54096-z\u003c/li\u003e\n \u003cli\u003eHan X, Gao X, Wang F, Shang C, Liu Z, Guo H (2023) Heterogeneity of clinical symptoms and therapeutic strategies for different subtypes of adenomyosis: An initial single-center study in China. Int J Gynaecol Obstet 161:775-783. https://doi.org/10.1002/ijgo.14650\u003c/li\u003e\n \u003cli\u003eChung YJ, Rha SE, Kim MR, Shin YR (2023) Correlation between MRI features of adenomyosis and clinical presentation. Diagnostics (Basel) 13:2749. https://doi.org/10.3390/diagnostics13172749\u003c/li\u003e\n \u003cli\u003eYajima R, Kido A, Kurata Y, Fujimoto K, Nakao KK, Kuwahara R et al (2021) Diffusion-weighted imaging of uterine adenomyosis: correlation with clinical backgrounds and comparison with malignant uterine tumors. J Obstet Gynaecol Res 47:949-960. https://doi.org/10.1111/jog.14621\u003c/li\u003e\n \u003cli\u003eYang Q, Zhang LH, Su J, Liu J (2011) The utility of diffusion-weighted MR imaging in differentiation of uterine adenomyosis and leiomyoma. Eur J Radiol 79:e47-e51. https://doi.org/10.1016/j.ejrad.2011.03.026\u003c/li\u003e\n \u003cli\u003eJung DC, Kim MD, Oh YT, Won JY, Lee DY (2012) Prediction of early response to uterine arterial embolisation of adenomyosis: value of T2 signal intensity ratio of adenomyosis. Eur Radiol 22:2044-2049. https://doi.org/10.1007/s00330-012-2436-z\u003c/li\u003e\n \u003cli\u003eKishi Y, Suginami H, Kuramori R, Yabuta M, Suginami R, Taniguchi F (2012) Four subtypes of adenomyosis assessed by magnetic resonance imaging and their specification. Am J Obstet Gynecol 207:114.e1-114.e7. https://doi.org/10.1016/j.ajog.2012.06.027\u003c/li\u003e\n \u003cli\u003eMoawad G, Fruscalzo A, Youssef Y, Kheil M, Tawil T, Nehme J et al (2023) Adenomyosis: an updated review on diagnosis and classification. J Clin Med 12:4828. https://doi.org/10.3390/jcm12144828\u003c/li\u003e\n \u003cli\u003eHarada T, Momoeda M, Taketani Y, Hoshiai H, Terakawa N (2008) Low-dose oral contraceptive pill for dysmenorrhea associated with endometriosis: a placebo-controlled, double-blind, randomized trial. Fertil Steril 90:1583-1588. https://doi.org/10.1016/j.fertnstert.2007.08.051\u003c/li\u003e\n \u003cli\u003eMunro MG, Critchley HOD, Fraser IS, FIGO Menstrual Disorders Committee (2018) The two FIGO systems for normal and abnormal uterine bleeding symptoms and classification of causes of abnormal uterine bleeding in the reproductive years: 2018 revisions. Int J Gynaecol Obstet 143:393-408. https://doi.org/10.1002/ijgo.12666\u003c/li\u003e\n \u003cli\u003eVora Z, Manchanda S, Sharma R, Das CJ, Hari S, Mathur S, Kumar S, Kachhawa G, Khan MA (2021) Normalized apparent diffusion coefficient: a novel paradigm for characterization of endometrial and subendometrial lesions. Br J Radiol 94:20201069. https://doi.org/10.1259/bjr.20201069\u003c/li\u003e\n \u003cli\u003eKurban LAS, Metwally H, Abdullah M, Kerban A, Oulhaj A, Alkoteesh JA (2021) Uterine artery embolization of uterine leiomyomas: predictive MRI features of volumetric response. AJR Am J Roentgenol 216:967-974. https://doi.org/10.2214/AJR.20.22906\u003c/li\u003e\n \u003cli\u003eValletta R, Corato V, Lombardo F, Avesani G, Negri G, Steinkasserer M, Tagliaferri T, Bonatti M (2024) Leiomyoma or sarcoma? MRI performance in the differential diagnosis of sonographically suspicious uterine masses. Eur J Radiol 170:111217. https://doi.org/10.1016/j.ejrad.2023.111217\u003c/li\u003e\n \u003cli\u003eKarakas O, Karakas E, Dogan F, Kilicaslan N, Camuzcuoglu A, Incebiyik A, Camuzcuoglu H (2015) Diffusion-weighted MRI in the differential diagnosis of uterine endometrial cavity tumors. Wien Klin Wochenschr 127:266-273. https://doi.org/10.1007/s00508-015-0709-7\u003c/li\u003e\n \u003cli\u003eKhan KN, Fujishita A, Kitajima M, Masuzaki H, Nakashima M, Kitawaki J (2016) Biological differences between functionalis and basalis endometria in women with and without adenomyosis. Eur J Obstet Gynecol Reprod Biol 203:49-55. https://doi.org/10.1016/j.ejogrb.2016.05.012\u003c/li\u003e\n \u003cli\u003eKhan KN, Fujishita A, Koshiba A, Kuroboshi H, Mori T, Ogi H, Itoh K, Nakashima M, Kitawaki J (2019) Biological differences between intrinsic and extrinsic adenomyosis with coexisting deep infiltrating endometriosis. Reprod Biomed Online 39:343-353. https://doi.org/10.1016/j.rbmo.2019.03.210\u003c/li\u003e\n \u003cli\u003eHe YL, Ding N, Li Y, Li Z, Xiang Y, Jin ZY, Xue HD (2016) Cyclic changes of the junctional zone on 3 T MRI images in young and middle-aged females during the menstrual cycle. Clin Radiol 71:341-348. https://doi.org/10.1016/j.crad.2015.12.005\u003c/li\u003e\n \u003cli\u003eKido A, Fujimoto K, Matsubara N, Kataoka M, Konishi I, Togashi K (2016) A layer of decreased apparent diffusion coefficient at the endometrial-myometrial junction in uterine adenomyosis. Magn Reson Med Sci 15:220-226. https://doi.org/10.2463/mrms.mp.2015-0084\u003c/li\u003e\n \u003cli\u003eNakai Y, Maeda E, Kanda T, Ikemura M, Ushiku T, Sasajima Y, Isshiki S, Abe O (2020) Uterine adenomyosis with extensive glandular proliferation: case series of a rare imaging variant. Diagn Interv Radiol 26:153-159. https://doi.org/10.5152/dir.2019.19252\u003c/li\u003e\n \u003cli\u003eHiratsuka D, Matsuo M, Ishizawa C, Fukui Y, Hiraoka T, Aikawa S, Izumi G, Harada M, Wada-Hiraike O, Osuga Y, Hirota Y (2025) Prognostic factors of progesterone resistance in symptomatic adenomyosis: impact of lesion localization on treatment outcome of levonorgestrel intrauterine system. BMC Womens Health 25:286. https://doi.org/10.1186/s12905-025-03817-w\u003c/li\u003e\n \u003cli\u003eYamanaka A, Kimura F, Kishi Y, Takahashi K, Suginami H, Shimizu Y, Murakami T (2014) Progesterone and synthetic progestin, dienogest, induce apoptosis of human primary cultures of adenomyotic stromal cells. Eur J Obstet Gynecol Reprod Biol 179:170-174. https://doi.org/10.1016/j.ejogrb.2014.05.031\u003c/li\u003e\n \u003cli\u003ePiriyev E, Schiermeier S, R\u0026ouml;mer T (2025) Hormonal treatment of endometriosis: a narrative review. Pharmaceuticals (Basel) 18:588. https://doi.org/10.3390/ph18040588\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Demographic, clinical, and radiological characteristics of the DNG-treated group.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 47.1366%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDNG (n=46)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffective (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneffective (n=16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eAge (years) [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e43 [22-52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e42 [35-50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eAge at menarche (years) [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e13 [10-15] (n=19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e14 [9-16] (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eMenstrual cycle length (days) [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e28 [25-30] (n=20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e29 [26-30] (n=10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eDuration of menstruation (days) [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e6 [4-10] (n=19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e6.5 [4-7] (n=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eInfertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e7 (7/30, 23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e4 (4/16, 25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eHistory of gynecologic surgery or disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e9 (9/30, 30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e5 (5/16, 31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eHistory of hormonal therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e2 (2/30, 6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003e\u0026ge;1-month drug-free interval before MRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e1 (1/2, 50%) (n=2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e－\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e－\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eModified Andersch\u0026ndash;Milsom scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePre-treatment [Pts count of each grade 0/1/2/3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e2 [1/5/19/5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e2 [0/3/11/2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePost-treatment [Pts count of grade 0/1/2/3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e1 [11/19/0/0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e2 [0/5/9/2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eMenorrhagia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePre-treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e16 (16/30, 53.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e12 (12/16, 75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePost-treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e10 (10/16, 62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003eP\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eSerum hemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePre-treatment [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e11.8 [9.3-14.1] (n=25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e12.0 [6.4-13.8] (n=11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePost-treatment [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e12.9 [10.3-14.5] (n=23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e12.0 [6.8-13.6] (n=13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.0128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eSerum CA125 (U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePre-treatment [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e45.0 [11-253] (n=21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e65.8 [25-1083] (n=10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePost-treatment [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e42 [13-82] (n=9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e74 [15-149] (n=6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eAdverse effects except irregular bleeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e2 (2/30, 6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e1 (1/16, 6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eIrregular bleeding during treatment [Pts count of mild/severe bleeding]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e20 (20/30, 66.7%) [19/1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e16 (16/16, 100%) [12/4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.0088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eUnpredictable bleeding during treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e1 (1/30, 3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e2 (2/16, 12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eTreatment period (days) [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e193.5 [35-350]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e141.5 [22\u0026ndash;686]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eInitial hormonal therapy result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003esuccess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e28 (28/30, 93.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003efailure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e2 (2/30, 6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e16 (16/16, 100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eLocation 1 subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eIntrinsic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e5 (5/30, 16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e2 (2/16, 12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eExtrinsic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e15 (15/30, 50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e7 (7/16, 43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eIntramural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e1 (1/30, 3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePenetrating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e9 (9/30, 30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e7 (7/16, 43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eLocation 2 distribution pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eDiffuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e14 (14/30, 46.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e12 (12/16, 75.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eFocal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e16 (16/30, 53.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e4 (4/16, 25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eLocation 3 main distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eAnterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e4 (4/30, 13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e3 (3/16, 16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003ePosterior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e20 (20/30, 66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e10 (10/16, 62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eLateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e1 (1/30, 3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eFundus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e4 (4/30, 13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e2 (2/16, 12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eunclassifiable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e1 (1/30, 3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e1 (1/16, 6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eOvarian endometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e21 (21/30, 70.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e13 (13/16, 81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eDeep endometriosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e19 (19/30, 63.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e9 (9/16, 56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eUterine myoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e13 (13/30, 43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e8 (8/16, 50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eSubendometrial myoma \u0026gt;2cm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e1 (1/30, 3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e1 (1/16, 6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eIntramural myoma \u0026ge;4 cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e2 (2/30, 6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eShrinking in endometriotic cyst after treatment [enlarged/unchanged/reduced]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e[0/17/4] (n=21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e[4/4/4] (n=12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.00446\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 41.7034%;\"\u003e\n \u003cp\u003eShrinking in leiomyoma size after treatment [enlarged/unchanged/reduced]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.9633%;\"\u003e\n \u003cp\u003e[2/9/3] (n=14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.1733%;\"\u003e\n \u003cp\u003e[0/8/1] (n=9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.1601%;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as numbers (percentage) or median (range), as appropriate. Continuous variables were compared using the Mann\u0026ndash;Whitney U test, and categorical variables were analyzed using the chi-squared test or Fisher\u0026apos;s exact test. Treatment failure includes discontinuation or modification due to adverse effects. DNG, dienogest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Comparison of quantitative uterine morphologic parameters in the DNG-treated group.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"699\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.1429%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.2857%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffective (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5714%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneffective (n=16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8571%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2857%;\"\u003e\n \u003cp\u003emaximum wall thickness on sagittal (mm)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emaximum diameter of uterine body (mm)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003emaximum AP diameter (mm)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003euterine body volume (cm3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.1429%;\"\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.2857%;\"\u003e\n \u003cp\u003e33 [14-95]\u003c/p\u003e\n \u003cp\u003e33.5 [16-92]\u003c/p\u003e\n \u003cp\u003e33.3 [15-93.5]\u003c/p\u003e\n \u003cp\u003e65 [41-120]\u003c/p\u003e\n \u003cp\u003e64 [35-117]\u003c/p\u003e\n \u003cp\u003e64.8 [41.5-118.5]\u003c/p\u003e\n \u003cp\u003e54.5 [32-120]\u003c/p\u003e\n \u003cp\u003e55 [38-105]\u003c/p\u003e\n \u003cp\u003e54.3 [40-112.5]\u003c/p\u003e\n \u003cp\u003e115.5 [38-554]\u003c/p\u003e\n \u003cp\u003e111.5 [32-494]\u003c/p\u003e\n \u003cp\u003e112 [37-489.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.5714%;\"\u003e\n \u003cp\u003e37.5 [19-66]\u003c/p\u003e\n \u003cp\u003e37.5 [20-67]\u003c/p\u003e\n \u003cp\u003e37.5 [19.5 -66.5]\u003c/p\u003e\n \u003cp\u003e70 [41-104]\u003c/p\u003e\n \u003cp\u003e69.5 [47-103]\u003c/p\u003e\n \u003cp\u003e69.8 [44-103.5]\u003c/p\u003e\n \u003cp\u003e62 [42-87]\u003c/p\u003e\n \u003cp\u003e60 [40-87]\u003c/p\u003e\n \u003cp\u003e61.5 [41.5-87]\u003c/p\u003e\n \u003cp\u003e141 [38-460]\u003c/p\u003e\n \u003cp\u003e138 [42-470]\u003c/p\u003e\n \u003cp\u003e137.5 [40-465]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8571%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8571%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Quantitative MRI parameters are expressed as median (range). Interobserver agreement was assessed using the intraclass correlation coefficient. AP, anteroposterior; DNG, dienogest; ICC, intraclass correlation coefficient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Comparison of pretreatment signal intensity values and signal intensity ratios in the DNG-treated group.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6783%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7832%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1189%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffective (n=30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6993%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIneffective (n=16)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.95105%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7692%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6783%;\"\u003e\n \u003cp\u003eT2WI intensity value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eT2WI SIR\u003csub\u003eendo\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eT2WI SIR\u003csub\u003emyo\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eT2WI SIR\u003csub\u003eglu\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDWI intensity value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDWI SIR\u003csub\u003eendo\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDWI SIR\u003csub\u003emyo\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDWI SIR\u003csub\u003eglu\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eADC value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eADC SIR\u003csub\u003eendo\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eADC SIR\u003csub\u003emyo\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eADC SIR\u003csub\u003eglu\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.7832%;\"\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003cp\u003eReader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean Reader 1\u003c/p\u003e\n \u003cp\u003eReader 2\u003c/p\u003e\n \u003cp\u003eReaders mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1189%;\"\u003e\n \u003cp\u003e151.5 [38-476]\u003c/p\u003e\n \u003cp\u003e152 [56-597]\u003c/p\u003e\n \u003cp\u003e151 [50-536.5]\u003c/p\u003e\n \u003cp\u003e0.39 [0.12-1.45]\u003c/p\u003e\n \u003cp\u003e0.43 [0.24-0.90]\u003c/p\u003e\n \u003cp\u003e0.60 [0.22-2.08]\u003c/p\u003e\n \u003cp\u003e0.64 [0.36-1.77]\u003c/p\u003e\n \u003cp\u003e1.07 [0.29-2.82]\u003c/p\u003e\n \u003cp\u003e1.00 [0.28-2.66]\u003c/p\u003e\n \u003cp\u003e28 [13-230]\u003c/p\u003e\n \u003cp\u003e31.5 [14-225]\u003c/p\u003e\n \u003cp\u003e29 [13.5-227.5]\u003c/p\u003e\n \u003cp\u003e0.51 [0.18-0.78]\u003c/p\u003e\n \u003cp\u003e0.54 [0.25-0.73]\u003c/p\u003e\n \u003cp\u003e1.00 [0.42-1.69]\u003c/p\u003e\n \u003cp\u003e1.05 [0.65-1.61]\u003c/p\u003e\n \u003cp\u003e1.66 [0.94-2.45]\u003c/p\u003e\n \u003cp\u003e1.77 [0.89-2.55]\u003c/p\u003e\n \u003cp\u003e1.74 [0.91-2.50]\u003c/p\u003e\n \u003cp\u003e1.02 [0.59-1.47]\u003c/p\u003e\n \u003cp\u003e1.03 [0.65-1.56]\u003c/p\u003e\n \u003cp\u003e1.03 [0.62-1.52]\u003c/p\u003e\n \u003cp\u003e0.90 [0.56-1.61]\u003c/p\u003e\n \u003cp\u003e0.90 [0.57-1.83]\u003c/p\u003e\n \u003cp\u003e0.92 [0.58-1.72]\u003c/p\u003e\n \u003cp\u003e0.74 [0.53-1.19]\u003c/p\u003e\n \u003cp\u003e0.80 [0.54-1.52]\u003c/p\u003e\n \u003cp\u003e0.78 [0.55-1.36]\u003c/p\u003e\n \u003cp\u003e0.85 [0.50-3.32]\u003c/p\u003e\n \u003cp\u003e0.88 [0.55-1.63]\u003c/p\u003e\n \u003cp\u003e0.86 [0.53-2.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6993%;\"\u003e\n \u003cp\u003e154.5 [70-261]\u003c/p\u003e\n \u003cp\u003e155 [74-275]\u003c/p\u003e\n \u003cp\u003e154.5 [72-265.5]\u003c/p\u003e\n \u003cp\u003e0.42 [0.18-0.96]\u003c/p\u003e\n \u003cp\u003e0.42 [0.20-1.03]\u003c/p\u003e\n \u003cp\u003e0.67 [0.39-1.29]\u003c/p\u003e\n \u003cp\u003e0.73 [0.42-1.62]\u003c/p\u003e\n \u003cp\u003e1.25 [0.61-1.72]\u003c/p\u003e\n \u003cp\u003e1.38 [0.74-2.08]\u003c/p\u003e\n \u003cp\u003e30 [11-119]\u003c/p\u003e\n \u003cp\u003e26.5 [11-128]\u003c/p\u003e\n \u003cp\u003e28.8 [11-123.5]\u003c/p\u003e\n \u003cp\u003e0.55 [0.29-0.77]\u003c/p\u003e\n \u003cp\u003e0.56 [0.29-0.71]\u003c/p\u003e\n \u003cp\u003e1.03 [0.69-1.55]\u003c/p\u003e\n \u003cp\u003e1.04 [0.55-1.55]\u003c/p\u003e\n \u003cp\u003e1.62 [0.95-2.81]\u003c/p\u003e\n \u003cp\u003e1.68 [0.95-3.11]\u003c/p\u003e\n \u003cp\u003e1.62 [0.95-2.96]\u003c/p\u003e\n \u003cp\u003e0.88 [0.70-1.56]\u003c/p\u003e\n \u003cp\u003e0.90 [0.70-1.62]\u003c/p\u003e\n \u003cp\u003e0.89 [0.70-1.59]\u003c/p\u003e\n \u003cp\u003e0.79 [0.37-1.12]\u003c/p\u003e\n \u003cp\u003e0.76 [0.40-1.05]\u003c/p\u003e\n \u003cp\u003e0.85 [0.38-0.94]\u003c/p\u003e\n \u003cp\u003e0.72 [0.47-1.27]\u003c/p\u003e\n \u003cp\u003e0.68 [0.54-1.05]\u003c/p\u003e\n \u003cp\u003e0.71 [0.52-1.15]\u003c/p\u003e\n \u003cp\u003e0.81 [0.63-2.06]\u003c/p\u003e\n \u003cp\u003e0.84 [0.62-1.02]\u003c/p\u003e\n \u003cp\u003e0.82 [0.63-1.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.95105%;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003cp\u003e0.0362\u003c/p\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.7692%;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eQuantitative parameters were analyzed using the Mann\u0026ndash;Whitney U test. Interobserver agreement was evaluated using the intraclass correlation coefficient. ADC, apparent diffusion coefficient; DNG, dienogest; DWI, diffusion-weighted imaging; endo, endometrium; glu, gluteal muscle; myo, myometrium; SIR, signal intensity ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Diagnostic performance of quantitative MRI parameters for predicting treatment effectiveness in the DNG-treated group.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"784\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9592%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9898%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1173%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut off value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.41837%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.9592%;\"\u003e\n \u003cp\u003eADC value\u0026nbsp;(Readers mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9898%;\"\u003e\n \u003cp\u003e71.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1173%;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.41837%;\"\u003e\n \u003cp\u003e0.0362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.9592%;\"\u003e\n \u003cp\u003eADC SIR\u003csub\u003eendo\u003c/sub\u003e (Readers mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2653%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9898%;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.9949%;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.1173%;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.41837%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDiagnostic performance metrics were derived from ROC curve analysis. Optimal cut-off values were determined using the Youden index. Sensitivity, specificity, accuracy, PPV, and NPV were calculated based on these cut-off values. ADC, apparent diffusion coefficient; AUC, area under the curve; DNG, dienogest; endo, endometrium; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic; SIR, signal intensity ratio.\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\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":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adenomyosis, Diffusion-weighted imaging, Apparent diffusion coefficient, Dienogest, Progestins, Treatment outcome","lastPublishedDoi":"10.21203/rs.3.rs-9201156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9201156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo evaluate whether pretreatment magnetic resonance imaging (MRI) parameters can predict response to hormonal therapy in patients with adenomyosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included 78 patients with MRI-diagnosed adenomyosis who underwent pelvic MRI before hormonal therapy between October 2018 and July 2025. Quantitative MRI parameters included T2 signal intensity ratios, diffusion-weighted imaging (DWI) signal intensity ratios, normalized apparent diffusion coefficient (ADC), and uterine morphological parameters. Adenomyosis subtypes were classified according to the modified Kishi criteria. Clinical response was evaluated 3\u0026ndash;6 months after treatment initiation based on improvements in dysmenorrhea and/or hemoglobin levels.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 78 patients, 32 received gonadotropin-releasing hormone (GnRH) agonist or antagonist therapy, and 46 received dienogest (DNG). In the GnRH cohort, 31 of the 32 patients achieved treatment effectiveness. In the DNG cohort, 30 patients achieved treatment effectiveness, and 16 did not. MRI-based adenomyosis subtype, lesion distribution, and uterine morphological parameters were not significantly associated with treatment effectiveness in DNG-treated patients. However, absolute ADC values were significantly higher in the effective group (1.03 vs. 0.89 \u0026times;10⁻\u0026sup3; mm\u0026sup2;/s, P\u0026thinsp;=\u0026thinsp;0.036), as was the ADC signal intensity ratio relative to the endometrium (ADC signal intensity ratio [SIR\u003csub\u003eendo\u003c/sub\u003e]: 0.92 vs. 0.85, P\u0026thinsp;=\u0026thinsp;0.034). Receiver operating characteristic curve analysis demonstrated moderate discrimination between both parameters (area under the curve\u0026thinsp;=\u0026thinsp;0.70). Optimal cut-off values were 0.951 \u0026times; 10⁻\u0026sup3; mm\u0026sup2;/s for ADC and 0.952 for ADC SIR\u003csub\u003eendo\u003c/sub\u003e.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eQuantitative diffusion MRI parameters were associated with DNG treatment effectiveness, whereas conventional morphological features were not. Diffusion-weighted MRI may provide complementary imaging biomarkers for adenomyosis stratification.\u003c/p\u003e","manuscriptTitle":"Pretreatment MRI Parameters as Predictive Biomarkers for Hormonal Therapy Response in Adenomyosis: A Comprehensive Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 06:43:52","doi":"10.21203/rs.3.rs-9201156/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T17:57:10+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"75040323567703837281266887454642108567","date":"2026-03-30T16:18:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88064224432968211940958596508045176731","date":"2026-03-30T14:57:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T15:33:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163070450275318930185243599196739634220","date":"2026-03-28T15:06:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-28T11:37:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T07:06:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T07:06:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2026-03-23T13:12:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"48df9bf9-f97e-4bc7-a593-adae40c03326","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T15:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 06:43:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9201156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9201156","identity":"rs-9201156","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}