{"paper_id":"dd8c8005-0874-4f39-a6ee-24d63ecb3d80","body_text":"ARTICLE IN PRESS\nArticle in Press\nDevelopment and validation of a clinical \nprediction model for the success of focused \nultrasound ablation system for the treatment of \nadenomyosis\nScientific Reports\nReceived: 5 October 2025\nAccepted: 8 April 2026\nCite this article as: Cui L., Zhang G., \nSang C. et al. Development and \nvalidation of a clinical prediction model \nfor the success of focused ultrasound \nablation system for the treatment of \nadenomyosis. Sci Rep (2026). https://doi.\norg/10.1038/s41598-026-48587-z\nLimei Cui, Gang Zhang, Changmei Sang, Zhiyan Wu, Ruoqing Li & Shuping Zhao\nWe are providing an unedited version of this manuscript to give early access to its \nfindings. Before final publication, the manuscript will undergo further editing. Please \nnote there may be errors present which affect the content, and all legal disclaimers \napply.\nIf this paper is publishing under a Transparent Peer Review model then Peer \nReview reports will publish with the final article.\nhttps://doi.org/10.1038/s41598-026-48587-z\n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, \nadaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, \nprovide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included \nin the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\n\nDevelopment and Validation of a Clinical Prediction Model for the \nSuccess of Focused Ultrasound Ablation System for the Treatment \nof Adenomyosis\nLimei Cui 1,2, Gang Zhang 3, Changmei Sang 1, Zhiyan Wu 2, Ruoqing Li 4, \nShuping Zhao 1\n1 Department of Gynecology, Qingdao Women and Children’s Hospital, \nShandong University, Qingdao 266071, Shandong, China\n2 Department of Gynecology, Qingzhou People’s Hospital, WeiFang \n262500, Shandong, China\n3 Department of Thoracic Surgery, Qingzhou People’s Hospital, WeiFang \n262500, Shandong, China\n4 Medical College of Qingdao University, Qingdao 266071, Shandong, \nChina\nCorresponding author\nShuping Zhao\nDepartment of Gynecology, Qingdao Women and Children’s Hospital, \nShandong University, Qingdao 266071, Shandong, China\nE-mail: zhaosp66@126.com\nTel: +86-13325027766\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nRunning title: Predictive model for the success of FUAS.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nAbstract\nThis study aimed to develop and validate a clinical prediction model for \nthe success of the focused ultrasound ablation system (FUAS) in treating \nadenomyosis. A retrospective analysis was conducted on 250 patients from \nQingdao Women and Children’s Hospital (2019-2022). Patients were \ncategorized into success (n=108) or failure (n=142) groups based on a \npost-treatment lesion ablation rate greater than 80%. The dataset was split \ninto training (70%) and validation (30%) sets. The multivariable analysis \nidentified age (OR = 1.10, P = 0.014), depth of adenomyosis (OR = 1.03, \nP = 0.036), and uterine body (OR = 0.25, P = 0.027) as independent \npredictors of FUAS efficacy, which were used to build the model. In the \ntraining set, the model achieved an AUC of 0.74 for efficacy prediction. \nThe Bootstrap ROC indicates an AUC mean of 0.751 with a standard \ndeviation of 0.036. In conclusion, a model based on age, depth of \nademyosis, and uterine body lesions treatment dose accurately predicts \nFUAS success for adenomyosis. This tool can aid clinical decision-making \nand promote personalized treatment.\nKeywords: Adenomyosis; Focused Ultrasound Ablation System; Predictive \nmodelling; Estrogen; Therapeutic dose\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nIntroduction\nAdenomyosis is a prevalent gynecological condition characterized by the \nectopic presence of endometrial tissue within the myometrium, leading to \nsevere dysmenorrhea, heavy menstrual bleeding, and chronic pelvic \npain.[1] Adenomyosis affects approximately 20-35% of women of \nreproductive age, but the condition remains underdiagnosed.[2, 3] The \nsignificant impact of adenomyosis on women’s quality of life, fertility, and \nmental health.[4, 5] Current interventions, including hormonal therapy \nand conservative surgery, frequently fail to provide long-term relief or are \nassociated with significant side effects and recurrence of symptoms.[6] \nConsequently, there is a pressing need for innovative treatment \napproaches that promise more targeted and effective disease management.\nA focused ultrasound ablation system (FUAS) represents a significant \nadvancement in the non-invasive treatment of adenomyosis,[7] generating \nlocalized heat to destroy the ectopic endometrial tissue embedded within \nthe myometrium without damaging the surrounding uterine tissue.[8] The \nprecision of FUAS allows for targeted treatment, minimizing the risk of \ncomplications associated with more invasive surgical methods.[9, 10] The \noutpatient nature of the procedure, combined with its rapid recovery time, \npositions FUAS as a preferable option for patients seeking less disruptive \ntreatment alternatives. Despite these advantages, the effectiveness of \nFUAS can vary significantly among individuals, underscoring the need for \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\npredictive models to optimize patient selection and enhance outcomes. \nPrevious research on FUAS ablation for adenomyosis has provided \nvaluable insights into its potential benefits;[11-14] however, these studies \nhave also exposed significant gaps in our understanding and application of \nthis technology.[15] \nThe primary hypothesis is that a well-constructed clinical prediction model \ncan forecast the success of FUAS ablation in treating adenomyosis based \non predefined clinical and demographic parameters. This hypothesis is \nbased on the observation that certain patient-specific factors, including \nthe extent of uterine involvement, age, reproductive history, and estrogen \nlevels, may influence the effectiveness of FUAS ablation.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nMethods\nStudy Design and Patients\nThis retrospective study included adult female patients diagnosed with \nadenomyosis from the authors’ hospital between 2019 and 2022. The \ninclusion criteria were 1) female patients aged 18-60 years, 2) diagnosed \nwith adenomyosis from 2019 to 2022 at the authors’ hospital,[16, 17] 3) \nunderwent FUAS ablation, and 4) available estrogen levels measured on \ncycle day 2-4 of the follicular phase. The exclusion criteria were 1) history \nof partial hysterectomy for adenomyosis or history of FUAS before the \nstudy period, 2) presence of specific comorbid conditions, or 3) pregnancy.\nThe dataset was split into training (70%) and validation (30%) sets for \nmodel development and validation.\nEthical Review and Patient Consent\nThe study protocol has been reviewed and approved by the institutional \nreview board (IRB) at the author’s Hospital. All patients provided clinical \nconsent to the FUAS procedure. The ethics committee waived the \nrequirement for individual informed consent for research purposes, given \nthe study’s retrospective nature.\nData Collection and Definitions\nIn this study, comprehensive data were collected from the patient charts. \nThe variables included basic demographic details, such as age, body mass \nindex (BMI), hyperprolactinemia, estrogen levels, CA125, and CA199. \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nAnatomical and morphological measurements were meticulously noted, \nincluding uterine and lesion volumes, thickness of the rectus abdominis \nand subcutaneous fat, and the depth of adenomyosis lesions from the \nsacrococcygeal junction. Detailed treatment parameters, including \nmaximum, minimum, and average power, treatment and irradiation times, \ntreatment intensity, dose, and volume, were also recorded. Reproductive \nhistory was detailed through variables such as parity, symptoms of urinary \nfrequency and constipation, presence of anemia, fertility requirements, \nhistory of uterine surgery, reproductive tract anomalies, and family history \nof adenomyosis or endometriosis. Further, uterine position, scores for \nheavy menstrual bleeding and dysmenorrhea, and a diagnostic \nclassification system were assessed. Uterine and lesion volumes were \ncalculated as V=0.5233×a×b×c based on the three-axis measurements \nfrom ultrasound or magnetic resonance imaging (MRI) images. Lesion \nposition was categorized as (1) uterine floor (lower segment of the uterus \nnear the cervix on the right side, including the uterine floor, posterior wall, \nanterior and posterior walls of the uterine floor, anterior and posterior \nwalls of the uterine floor, anterior and posterior walls of the uterine floor, \nanterior and posterior walls of the uterine floor, right side of the uterine \nfloor, uterine floor, posterior wall, and uterine floor), (2) uterine body \n(posterior wall, posterior wall, anterior wall, right anterior wall, left \nposterior wall, right posterior wall, left anterior wall, right anterior wall, \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nright anterior wall, right posterior wall, left anterior wall, right anterior \nwall, right posterior wall, left anterior wall, posterior wall), and (3) diffuse. \nUterine adenomyosis ablation ratio = (volume of non-perfused uterine \nlesion after treatment/volume of uterine adenomyosis lesion before \ntreatment) × 100%. The success rate of FUAS in treating adenomyosis was \nassessed as an 80% reduction in lesion volume. The selection of the 80% \nnon-perfused volume ratio (NPVR) threshold as the definition of sufficient \nablation is grounded in a well-established body of literature rather than a \nsingle arbitrary cutoff. Indeed, the 80% NPVR threshold has been \nprogressively validated through an evolving series of studies. Initially, an \nNPVR >60% was used as the efficacy endpoint during early FUAS training \nprograms, as achieving this level yielded re-intervention rates comparable \nto those of myomectomy. Subsequently, as technical proficiency improved, \nthe threshold for operational training standards was raised to 70%. Park \net al. then reported that achieving an immediate NPVR of at least 80% \nduring FUAS of uterine fibroids was safe and associated with significantly \ngreater tumor volume shrinkage (43% reduction) compared with cases \nachieving an NPVR <80% (20% reduction). On this basis, multiple \ninvestigators have adopted NPVR >80% as the benchmark for sufficient \nablation.[18-21] The energy doses were taken from the ultrasound system \ndata. When analyzing the dose as a continuous variable, using an ablation \nrate >80% as the gold standard, and running a ROC curve analysis, it was \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nfound that 240 kJ corresponds to the maximum Youden index. Therefore, \nthe treatment dose was dichotomized based on 240 kJ. Routine follow-ups \nwere at 24 h and 1, 3, and 6 months after the procedure. Two physicians \nwith associate senior professional titles or above in the imaging \ndepartment independently performed the measurements. Thirty cases \nwere randomly selected and independently measured by two senior \nphysicians/technicians in a double-blind manner. ICC (continuous variable) \nor kappa (categorical variable) was calculated. If ICC were >0.75 or kappa \nwere >0.60, good consistency could be considered.\nStatistical Analysis\nThis study encompassed a cohort of 250 patients, comprising 108 events \n(indicative of treatment success) and 142 non-events (representative of \ntreatment failure). The adequacy of sample size for the development of the \npredictive model utilizing logistic regression was assessed based on the \nevents-per-variable (EPV) principle. In accordance with widely accepted \nguidelines (EPV ≥10), the available number of events (n=108) permitted \nthe inclusion of up to approximately 10 predictors in the multivariable \nmodel, thereby indicating that the present sample size was adequate for \nreliable model estimation. Furthermore, a post hoc power analysis was \nperformed for the logistic regression. Assuming a two-sided alpha level of \n0.05, a sample size of 250, and an event rate of 43.2%, the study \ndemonstrated more than 80% statistical power to identify an odds ratio of \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\napproximately 1.8 or greater for the key predictors. \nFor continuous data, the mean method was adopted for the interpolation \nof missing data. For categorical variables, the mode interpolation method \nis used for interpolation. Descriptive statistics were applied to baseline \nvariables, summarizing normally distributed variables using means ± SD \nand non-normally distributed variables with medians and interquartile \nranges. Categorical variables were reported as frequencies and \npercentages, with comparisons made via t-tests, Mann-Whitney U tests, or \nchi-squared tests as appropriate. For model development and validation, \nthe sample was divided into a training set (70%) and a test set (30%). The \nsplit was performed in R 4.4.1 using a seed (12345). Logistic regression \nwas utilized to develop the model on the training set and validate it on the \ntest set, with groups formed based on treatment outcomes to facilitate \nunivariate analysis. Univariate logistic regression identified potential \npredictors; variables with p-values <0.2 advanced to multivariable logistic \nregression to adjust for confounders and identify independent predictors \n(stepwise method). The variance inflation factor (VIF) was used to assess \nmulticollinearity among continuous variables, leading to the exclusion or \ncombination of variables with strong multicollinearity, i.e., with a VIF >10. \nThe bidirectional stepwise regression method was used to include \nvariables in the model, and the variables that contributed significantly to \nthe model were included. The model was subsequently revised to \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nincorporate only the non-multicollinear variables. The discriminative \nability of the nomogram was evaluated by the area under the receiver \noperating characteristic curve (AUC) in both the training and validation \nsets. To assess the model's stability and correct for optimism due to the \nlimited training sample size, internal validation was performed using 1000 \nbootstrap resamples on the training set. This process generated the \noptimism-corrected AUC and its 95% confidence interval, as well as the \nbootstrap-corrected calibration slope. All analyses were performed using \nR 4.4.1.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nResults\nPredictive value of clinical parameters\nA total of 250 patients with adenomyosis, aged 27-55 years (median age, \n42 years), were included in this study, comprising 175 (70%) in the \ntraining set and 75 (30%) in the validation set. They were divided into two \ngroups based on FUAS ablation rate: the failure group (ablation rate < \n80%, 142 patients, 56.8%) and the success group (ablation rate ≥ 80%, \n108 patients, 43.2%). \nUnivariate analysis revealed that age (P = 0.014) and lesion volume (P = \n0.029) were statistically significantly different between the FUAS efficacy \nsubgroups. Notably, the median age of patients in the success group (41 \nyears) was significantly lower than that of the failure group (43.5 years). \nThe volume of lesions in the success group (88.29 cm 3) was significantly \nsmaller than in the failure group (107.48 cm3). Table 1 summarizes all the \nbaseline characteristics of the 250 study subjects.\nEstablishment and Validation of Clinical Models\nVariables that were statistically significant in the above univariate \nanalyses were further included in both univariate and multivariable \nlogistic regression analyses to predict the success of FUAS in treating \nadenomyosis. These variables included age, lesion volume, estrogen level, \nmean power, and treatment dose.\nIn the univariate logistic regression analyses, age (OR = 1.08, P = 0.004), \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\none birth (OR = 3.01, P = 0.045), and uterine body (OR = 0.33, P = 0.032) \nwere predictive of FUAS efficacy in patients with adenomyosis (Table 2). \nAll VIFs were <10, meaning that no variables were excluded on the basis \nof multicollinearity (Supplementary Table S1). In the multivariable \nlogistic regression analysis, age (OR = 1.10, P = 0.014), depth of \nadenomyosis lesions (OR = 1.03, P = 0.036), and uterine body lesions (OR \n= 0.25, P = 0.027) demonstrated independent predictive value for FUAS \nefficacy in adenomyosis (Table 2). \nA nomogram was constructed based on the independent predictors in the \nmultivariable logistic regression analysis. The nomogram model predicted \nFUAS efficacy in patients with adenomyosis, with AUCs of 0.74 in the \ntraining set and 0.60 in the validation set (Figure 1). The \"Bootstrap ROC \nCurve\" indicates an AUC mean of 0.751 with a standard deviation of 0.036, \ndemonstrating the model’s predictive performance and its variability \nacross bootstrap samples (Figure 2). The nomogram is shown in Figure \n3, and the intercept values are shown in Supplementary Table S2. The \nmodel shows generally good bootstrap calibration in the training set, with \npredicted probabilities close to observed outcomes overall, but a tendency \nto slightly underestimate risk at low predicted probabilities and \noverestimate risk at high predicted probabilities. In the validation set, the \nmodel is fairly well calibrated overall but tends to underestimate risk at \nlower predicted probabilities and increasingly overestimate risk at higher \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\npredicted probabilities (Supplementary Figure S1). \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nDiscussion\nAdenomyosis is a common clinical gynecological disease with an overall \nprevalence of approximately 0.79% worldwide.[22] Despite its high \nprevalence and its correlation with the pathogenesis of endometriosis, the \npathogenesis and pathophysiological processes of adenomyosis remain \npoorly understood, which, to some extent, hinders the development of \nmore effective treatment options.[23] Currently, treatments for \nadenomyosis include pharmacological treatments (painkillers, hormonal \ndrugs), interventional treatments (uterine artery embolization), and \nsurgical treatments (lesion excision, hysterectomy). However, these \nmethods have limitations in terms of efficacy and side effects. \nPharmacological treatments typically only alleviate symptoms, making it \nchallenging to cure the disease at its root.[24] Surgical treatments, \nalthough effective in removing the lesions, may have an impact on fertility, \nespecially for women with reproductive needs. Therefore, ensuring \ntreatment efficacy while preserving patient fertility is a major challenge in \nthe management of adenomyosis.\nThe emergence of FUAS technology has brought new hope for the \ntreatment of adenomyosis. FUAS utilizes the focusing properties of \nultrasound to accurately concentrate energy at the lesion site, causing \ncoagulative necrosis of the diseased tissue through thermal effects, \nthereby achieving therapeutic goals.[25] Its main advantages and features \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\ninclude: non-invasiveness, targeted delivery, significant efficacy, \nreproducibility, and individualized treatment. For patients with \nadenomyosis who do not wish to undergo surgery or who are not suitable \nfor surgery, FUAS is an ideal alternative treatment option, especially in \npreserving fertility. FUAS treatment can significantly improve the \nsymptoms of patients with adenomyosis and have a positive impact on their \nfertility.[13] Compared with drug therapy alone, FUAS combined with \ndrug therapy can solve the problem at the root, reduce the possibility of \nrecurrence to a greater extent, and provide patients with more durable \nefficacy. Dai et al.[26] explored the value of FUAS in combination with \nvarious drugs for the treatment of adenomyosis and found that FUAS, \ncombined with dienogest or GnRH-a, was effective in reducing pain and \nanemia and had a low risk of recurrence. The development of FUAS \ntechnology has also contributed to improvements in symptoms in patients \nwith adenomyosis. The development of FUAS technology also advances \nprecision medicine for adenomyosis, enabling patients to receive more \npersonalized treatment plans through more accurate diagnosis and \ntreatment.\nAdenomyosis is characterized by significant heterogeneity among \nindividuals.[27] Yildiz et al.[28] found a high degree of heterogeneity of \nfibroblast-like cells in adenomyosis. Therefore, the clinical presentation, \ndisease severity, and response to treatment may vary significantly among \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\npatients with adenomyosis. The traditional ‘one-size-fits-all’ treatment \nmodel often fails to meet the diverse needs of all patients. In this study, we \nleveraged clinical information to develop an individualized risk assessment \nand efficacy prediction model that guides clinicians in selecting the most \nsuitable treatment options. We constructed a prediction model that \nincluded three parameters (age, uterine body lesions, and depth of \nadenomyosis) based on the results of the multivariable logistic regression \nanalysis. The model predicted FUAS efficacy in patients with adenomyosis, \nwith AUCs of 0.74 and 0.60 in the training and validation sets, \nrespectively. A combined non‑contrast MRI radiomics + clinical model in \n130 adenomyosis patients predicted high vs low ablation rate (NPVR >50% \nvs <50%); the radiomics and combined models outperformed a purely \nclinical-imaging model and showed superior net benefit on DCA.[29] A \n2025 study developed a joint T2WI‑FS radiomics + clinical model (decision \ntree/random forest) to predict energy efficiency factor (EEF) requirements \nfor FUAS treatment of adenomyosis, enabling preoperative estimation of \nenergy dose and procedural difficulty, with good performance in both \ntraining and test cohorts.[30]\nAge influences both clinical success and the durability of symptom relief \nafter FUAS for adenomyosis, but its impact depends on the outcome of \ninterest (symptom control vs. fertility) and interacts with treatment \nparameters, such as the non‑perfused volume ratio. There is evidence that \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nolder age is associated with better clinical outcomes, supporting the \npresent study. In a retrospective cohort of 230 women treated with \nultrasound‑guided FUAS, age >40 years was independently associated \nwith higher long‑term clinical success (89.0% vs 78.6% in those <40 years; \nOR for younger age: 0.342, 95% CI 0.143-0.819).[31] The same study \nreported that older age and a higher non‑perfused volume (NPV) ratio \n(extent of ablation) were the main predictors of durable symptom relief; \nhigher BMI and lower acoustic power increased the risk of recurrence. The \nauthors hypothesized that younger women have higher estrogen levels and \nmore aggressive lesion biology, and often receive more conservative \nablation to preserve fertility, resulting in lower NPV ratios and reduced \nclinical durability.[31] In practice, this means that middle‑aged, \nperimenopausal women in whom a more extensive ablation is acceptable \nmay experience more sustained symptom control after focused ultrasound. \nA 2024 review of high‑intensity focused ultrasound for adenomyosis \nreported that younger age, lower BMI, internal (vs external) adenomyosis, \nhigher non‑perfused volume ratio, and shorter infertility duration were \nassociated with better reproductive outcomes following FUAS.[12] The \nreview explicitly suggests treating at an earlier age and optimizing body \nweight to improve post‑FUAS fertility prospects, and recommends earlier \nreferral to ART for patients with external adenomyosis. Thus, while older \nage may favor symptom durability, younger age appears advantageous for \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\npost‑treatment fertility, so “success” must be defined according to the \npatient’s primary goal. Observational data indicate that most FUAS \ncandidates with adenomyosis are in their late 30s to early 40s (mean ages \naround 38-40 years).[31, 32] In a study of factors influencing treatment \nchoice, age 31-40 years and a desire for fertility were among the key \nfactors associated with choosing FUAS over hysterectomy.[33] MR‑guided \nFUS series for adenomyosis similarly enroll predominantly premenopausal \nwomen in their 30s-40s, with about one‑third remaining symptomatic at 6 \nmonths, prompting interest in models that incorporate age and imaging \nfeatures to predict poor responders.[34] These patterns suggest that age \nnot only affects biological response but also shapes patient selection, \nexpectations (fertility vs symptom control), and acceptable treatment \naggressiveness. Higher estrogen milieu and more active myometrial \ninvasion in younger women may promote progression or recurrence of \nadenomyosis after a partial ablation, reducing long‑term symptom \ncontrol.[31] In older, perimenopausal women, lower hormonal stimulation \nand proximity to natural menopause may synergize with extensive ablation \nto yield more durable symptom improvement and lower re‑intervention \nrates.[12, 31] Conversely, in younger women with fertility desire, \noperators may deliberately limit ablation volume to preserve myometrial \nintegrity, trading off some long‑term symptom durability for reproductive \nsafety, which can appear as an age effect in outcome analyses.[12, 31] \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nThe depth of adenomyosis within the myometrium strongly influences the \ntechnical feasibility, ablation efficiency, and clinical success of FUAS. \nMRI‑based classifications distinguish internal (subtype I, junctional \nzone/inner myometrium), external (subtype II, outer myometrium), \nintramural (subtype III), and full‑thickness (subtype IV) adenomyosis, \nwhich reflect the extent and depth of myometrial penetration.[35] Internal \nlesions tend to lie closer to the endometrium and often more centrally, \nwhereas external and full‑thickness lesions extend toward the serosa, \nincreasing sonication path length and proximity to bowel, sacrum, or \nabdominal wall.[12, 35] For FUAS, “depth” effectively translates into \nsonication path length and relative position of the lesion between the \nendometrium and serosa. In a retrospective study of 238 internal and 167 \nexternal adenomyosis cases treated with FUAS, both groups showed \nsignificant improvement, but internal adenomyosis had a higher \nmenorrhagia relief rate at 18 months (86.2% vs 77.1%, p = 0.030), \nsuggesting a more favorable clinical response in lesions closer to the \njunctional zone.[36] A 2024 series classifying patients into internal (I), \nexternal (II), intramural (III), and full‑thickness (IV) adenomyosis showed \nthat full‑thickness lesions had significantly larger volumes and required \nlonger irradiation time, longer total procedure time, and higher total \nenergy input than internal or intramural lesions.[35] Full‑thickness \ndisease also carried higher rates of post‑procedural abdominal pain and \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nvaginal discharge from the treatment area than external disease, \nreflecting the greater tissue volume traversed and treated.[35] A 2024 \nnarrative review of FUAS in adenomyosis highlights shorter sonication \npaths, internal or focal adenomyosis, low vascularity, fewer hyperintense \nfoci on T2‑weighted MRI, and anterior location as key imaging predictors \nof favorable treatment outcomes.[12] Longer sonication paths (as in deep \nposterior or full‑thickness disease) increase energy loss and the risk of \nnear‑field heating, limiting the safe acoustic power and thereby reducing \nachievable NPVR, which is a major determinant of long‑term symptom \nrelief. Because internal/focal lesions usually have shorter paths and more \ncompact geometry, they typically allow higher NPVR with less energy and \nshorter treatment times, translating into better volume reduction and \nmore durable symptom control.[12, 37] Clinically, this means that deep, \nposterior, and full‑thickness disease often receives a more conservative \nablation than shallow, internal disease, which can lower success when \nsuccess is defined as high NPVR and sustained symptom relief.\nThe distribution of adenomyosis within the uterus also influences the \ntechnical feasibility, ablation efficiency, and clinical success of FUAS. In \nthe present study, uterine body adenomyosis showed lower FUAS efficacy \nthan fundal disease, which is consistent with reports that lesion location \nand sonication path markedly influence energy transmission and \nachievable non‑perfused volume. Lesions situated deeper within the \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nuterine body are often farther from the transducer and more likely to be \ntraversed by abdominal fat, bowel, or scar tissue, all of which can \nattenuate or reflect ultrasound waves, reduce focal temperature, and limit \neffective ablation. Recent reviews and radiomics‑based analyses similarly \nidentify greater lesion depth from the skin surface, posterior or deeply \nlocated adenomyosis, and more complex internal architecture as \nindependent predictors of reduced NPV ratio and symptom response after \nFUAS [12, 38, 39].\nThis study has several limitations that should be considered when \ninterpreting the findings. First, it used a single-center, retrospective \ndesign, which may introduce information bias and limit the generalizability \nof the results to other institutions and patient populations. The overall \nsample size was relatively small (n=250), and the distribution of cases \nacross predictor categories was uneven, which may reduce statistical \npower and increase the risk of overfitting in the prediction model. In \naddition, there was no external validation cohort; thus, the nomogram has \nnot yet been tested in independent populations, and its transportability to \nother clinical settings remains uncertain. Second, the endpoint used was \nprimarily anatomical (ablation rate), which may not fully capture clinical \nsuccess in terms of symptom relief, quality of life, or need for additional \ntreatments. Patients selected for FUAS may also differ systematically from \nthe broader population of individuals with adenomyosis (e.g., in terms of \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nsymptom severity, comorbidities, or treatment preferences), potentially \nintroducing selection bias and further limiting the model’s applicability to \nall patients with adenomyosis. Third, although the follow-up period \nallowed assessment of short-term outcomes after FUAS, long-term follow-\nup was not systematically performed, and follow-up examinations were \ninconsistent among patients. As a result, we were unable to evaluate the \ndurability of treatment response, long-term recurrence, or downstream \nclinical outcomes, and the model reflects only short-term anatomical \nefficacy rather than long-term clinical benefit. Finally, as with all \nretrospective observational studies, there is a risk of residual and \nunmeasured confounding; some relevant clinical, imaging, or procedural \nvariables may not have been recorded or may have been incompletely \ncaptured in the medical records, which could affect both model \ndevelopment and the observed associations. Prospective, multicenter \nstudies with larger, more diverse cohorts, standardized follow-up, \nclinically meaningful endpoints, and external validation are needed to \nconfirm and refine this prediction model and to support its broader clinical \napplication.\nIn conclusion, conventional clinical parameters, anatomical and \nmorphological indicators, and FUAS treatment parameters have the \npotential to predict FUAS outcomes in patients with adenomyosis. Age, \ndepth of adenomyosis, and uterine body lesions were significant predictors \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nof FUAS efficacy in patients with adenomyosis. The nomogram can \naccurately predict the success rate of adenomyosis after undergoing FUAS. \nIt provides an early, accurate, non-invasive, and comprehensive evaluation \ntool for assessing FUAS sensitivity in patients with adenomyosis and offers \nan objective basis for individualized treatment.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nList of abbreviations\nFUAS, Focused ultrasound ablation system\nIRB, Institutional Review Board\nVEGF, vascular endothelial growth factor\nER, estrogen receptor\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nStatements & Declarations\nEthics approval\nThe study protocol has been reviewed and approved by the Institutional \nReview Board (IRB) at Qingdao Women and Children’s Hospital (No. \nQFELL-YJ-2024-64). I confirm that all methods were performed in \naccordance with the relevant guidelines. All procedures were performed \nin accordance with the ethical standards set out in the 1964 Declaration \nof Helsinki and its subsequent amendments. All patients provided clinical \nconsent to the FUAS procedure. The ethics committee waived the \nrequirement for individual informed consent for research purposes, given \nthe study’s retrospective nature.\nConsent to publish\nNot applicable\nData availability statements\nAll data generated or analyzed during this study are included in this \npublished article.\nFunding\nThis research was funded by the Weifang City Youth Talent Lifting Project.\nCompeting Interests\nThe authors declare no relevant financial or non-financial interests.\nAuthor Contributions\nAll authors contributed to the conception and design of the study. Material \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\npreparation, data collection, and analysis were performed by Limei Cui, \nGang Zhang, Changmei Sang, Zhiyan Wu, Ruoqing Li, and Shuping Zhao. \nThe first draft of the manuscript was written by Limei Cui and Gang Zhang. \nAll authors commented on previous versions of the manuscript. All authors \nread and approved the final manuscript.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nReferences\n1. Vercellini P, Viganò P, Bandini V, Buggio L, Berlanda N, Somigliana \nE. Association of endometriosis and adenomyosis with pregnancy and \ninfertility. Fertility and sterility. 2023;119(5):727-40.\n2. Bulun SE, Yildiz S, Adli M, Chakravarti D, Parker JB, Milad M, et al. \nEndometriosis and adenomyosis: shared pathophysiology. Fertility and \nsterility. 2023;119(5):746-50.\n3. Moawad G, Kheil MH, Ayoubi JM, Klebanoff JS, Rahman S, Sharara \nFI. Adenomyosis and infertility. 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Adenomyosis and Its Possible Malignancy: A Review of the \nLiterature. Diagnostics (Basel, Switzerland). 2023;13(11).\n28. Yildiz S, Kinali M, Wei JJ, Milad M, Yin P, Adli M, et al. Adenomyosis: \nsingle-cell transcriptomic analysis reveals a paracrine mesenchymal-\nepithelial interaction involving the WNT/SFRP pathway. Fertility and \nsterility. 2023;119(5):869-82.\n29. Liu Z, Liu Z, Wan X, Wang Y, Huang X. Prediction of clinical outcome \nfor high-intensity focused ultrasound ablation of adenomyosis based on \nnon-enhanced MRI radiomics. International journal of hyperthermia : the \nofficial journal of European Society for Hyperthermic Oncology, North \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nAmerican Hyperthermia Group. 2025;42(1):2468766.\n30. Liu Z, Liu Z, Wang Y, Wan X, Huang X. Machine learning-based \npredictive analysis of energy efficiency factors necessary for the HIFU \ntreatment of adenomyosis. Frontiers in Physiology. 2025;Volume 16 - \n2025.\n31. Liu X, Wang W, Wang Y, Wang Y, Li Q, Tang J. Clinical Predictors of \nLong-term Success in Ultrasound-guided High-intensity Focused \nUltrasound Ablation Treatment for Adenomyosis: A Retrospective Study. \nMedicine. 2016;95(3):e2443.\n32. Feng Y, Hu L, Chen W, Zhang R, Wang X, Chen J. Safety of \nultrasound-guided high-intensity focused ultrasound ablation for diffuse \nadenomyosis: A retrospective cohort study. Ultrasonics Sonochemistry. \n2017;36:139-45.\n33. Zhong Q, Yang MJ, Hu Y, Jiang L, Yu JW, Chen JY, et al. Factors \ninfluencing treatment decisions in HIFU treatment of adenomyosis: A \nretrospective study. Frontiers in surgery. 2022;9:941368.\n34. Li Z, Zhang J, Song Y, Yin X, Chen A, Tang N, et al. Utilization of \nradiomics to predict long-term outcome of magnetic resonance-guided \nfocused ultrasound ablation therapy in adenomyosis. European radiology. \n2021;31(1):392-402.\n35. Liu R, Hao H, Yin Y, Chen L, Liu Y. Effect of High-Intensity Focused \nUltrasound on Different Types of Adenomyosis Based on Magnetic \nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nResonance Imaging Classification. Journal of ultrasound in medicine : \nofficial journal of the American Institute of Ultrasound in Medicine. \n2024;43(10):1947-55.\n36. Xu F, Lin Z, Wang Y, Gong C, He M, Guo Q, et al. Comparison of high-\nintensity focused ultrasound for the treatment of internal and external \nadenomyosis based on magnetic resonance imaging classification. \nInternational journal of hyperthermia : the official journal of European \nSociety for Hyperthermic Oncology, North American Hyperthermia \nGroup. 2023;40(1):2211268.\n37. Coy H, Tan N, Margolis D, Lu P, Kim G, Brown M, et al. Efficacy of \nMR-guided focused ultrasound ablation for localized adenomyosis in \ncomparison to leiomyoma. Journal of Therapeutic Ultrasound. \n2015;3(Suppl 1):O95.\n38. Athanasiou A, Fruscalzo A, Dedes I, Mueller MD, Londero AP, Marti \nC, et al. Advances in Adenomyosis Treatment: High-Intensity Focused \nUltrasound, Percutaneous Microwave Therapy, and Radiofrequency \nAblation. J Clin Med. 2024;13(19).\n39. Liu Z, Liu Z, Wan X, Wang Y, Huang X. Predicting high-intensity \nfocused ultrasound efficacy in adenomyosis treatment based on magnetic \nresonance (MR) radiomics and clinical-imaging features. Clinical \nRadiology. 2025;81:106778.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nTable 1. Characteristics of 250 patients with adenomyosis\nVariables Total (n = 250) Failure group (n = \n142)\nSuccess group (n = \n108)\nStatistic P\nAGE, M (Q₁, Q₃) 42.00 (37.25, \n46.00)\n41.00 (37.00, \n46.00)\n43.50 (39.00, \n46.25)\nZ=-2.46 0.014\nBMI, M (Q₁, Q₃) 24.03 (21.78, \n26.64)\n23.62 (21.88, \n26.70)\n24.24 (21.77, \n26.47)\nZ=-0.06 0.951\nCA125, M (Q₁, Q₃) 75.90 (40.60, \n132.53)\n77.85 (39.70, \n133.73)\n74.70 (42.02, \n121.10)\nZ=-0.39 0.696\nCA199, M (Q₁, Q₃) 21.77 (12.21, \n33.47)\n23.83 (11.96, \n35.89)\n19.64 (12.60, \n32.01)\nZ=-0.88 0.380\nUterine volume, M (Q₁, Q₃) 268921.52 \n(205903.38, \n381535.94)\n251776.38 \n(194607.16, \n368403.79)\n288630.83 \n(219955.55, \n420274.79)\nZ=-1.78 0.075\nLesion volume, M (Q₁, Q₃) 94812.02 \n(67278.06, \n148496.84)\n88291.18 \n(60539.01, \n141621.86)\n107477.45 \n(75640.92, \n160006.17)\nZ=-2.19 0.029\nRectus abdominis thickness, M (Q₁, \nQ₃)\n9.00 (7.00, 11.00) 9.00 (7.00, 11.00) 9.00 (7.00, 10.00) Z=-1.16 0.244\nSubcutaneous fat thickness, M (Q₁, \nQ₃)\n16.00 (12.00, \n21.75)\n18.00 (13.00, \n22.00)\n15.00 (11.00, \n21.00)\nZ=-1.65 0.099\nDepth of adenomyosis lesions, M (Q₁, \nQ₃)\n14.00 (8.00, 24.00) 13.00 (8.00, 23.75) 14.50 (8.00, 24.75) Z=-0.41 0.684\nAverage power, M (Q₁, Q₃) 400.00 (398.00, \n400.00)\n400.00 (394.00, \n400.00)\n400.00 (399.00, \n400.00)\nZ=-1.94 0.053\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nVariables Total (n = 250) Failure group (n = \n142)\nSuccess group (n = \n108)\nStatistic P\nTreat Time, M (Q₁, Q₃) 73.00 (55.00, \n97.75)\n71.00 (55.25, \n100.25)\n74.50 (54.75, \n96.00)\nZ=-0.03 0.977\nIrradiation time, M (Q₁, Q₃) 600.00 (401.00, \n859.50)\n600.00 (416.75, \n889.50)\n611.50 (387.75, \n836.75)\nZ=-0.11 0.910\nTreat Intensity, M (Q₁, Q₃) 480.00 (421.00, \n559.75)\n474.50 (411.75, \n550.00)\n491.50 (425.50, \n570.75)\nZ=-0.89 0.372\nTreat volume, M (Q₁, Q₃) 2.90 (2.30, 3.80) 2.80 (2.20, 3.88) 3.10 (2.30, 3.80) Z=-0.82 0.410\nHigh estrogen level, n (%) χ²=0.32 0.570\n  0 176 (70.40) 102 (71.83) 74 (68.52)\n  1 74 (29.60) 40 (28.17) 34 (31.48)\nTreatment dose, n (%) χ²=0.02 0.885\n<240 124 (49.60) 71 (50.00) 53 (49.07)\n≥240 126 (50.40) 71 (50.00) 55 (50.93)\nMaximum power, n (%) χ²=1.70 0.192\n  350 12 (4.80) 9 (6.34) 3 (2.78)\n  400 238 (95.20) 133 (93.66) 105 (97.22)\nMinimum power, n (%) - 0.635\n  300 4 (1.60) 2 (1.41) 2 (1.85)\n  350 91 (36.40) 55 (38.73) 36 (33.33)\n  400 155 (62.00) 85 (59.86) 70 (64.81)\nNumber of births, n (%) - 0.402\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nVariables Total (n = 250) Failure group (n = \n142)\nSuccess group (n = \n108)\nStatistic P\n  0 35 (14.00) 24 (16.90) 11 (10.19)\n  1 140 (56.00) 74 (52.11) 66 (61.11)\n  2 68 (27.20) 40 (28.17) 28 (25.93)\n  3 6 (2.40) 3 (2.11) 3 (2.78)\n  4 1 (0.40) 1 (0.70) 0 (0.00)\nUrinary frequency and constipation, \nn (%)\n- >0.999\n  no 248 (99.20) 141 (99.30) 107 (99.07)\n  yes 2 (0.80) 1 (0.70) 1 (0.93)\nAnemia, n (%) χ²=2.93 0.087\n  no 122 (48.80) 76 (53.52) 46 (42.59)\n  yes 128 (51.20) 66 (46.48) 62 (57.41)\nFertility requirements, n (%) χ²=1.71 0.191\n  no 184 (73.60) 100 (70.42) 84 (77.78)\n  yes 66 (26.40) 42 (29.58) 24 (22.22)\nHistory of uterine surgery, n (%) χ²=1.09 0.296\n  no 167 (66.80) 91 (64.08) 76 (70.37)\n  yes 83 (33.20) 51 (35.92) 32 (29.63)\nHistory of reproductive tract \nanomalies causing obstruction, n (%)\n- >0.999\n  no 248 (99.20) 141 (99.30) 107 (99.07)\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nVariables Total (n = 250) Failure group (n = \n142)\nSuccess group (n = \n108)\nStatistic P\n  yes 2 (0.80) 1 (0.70) 1 (0.93)\nFamily history of adenomyosis or \nendometriosis, n (%)\nχ²=1.70 0.192\n  no 238 (95.20) 133 (93.66) 105 (97.22)\n  yes 12 (4.80) 9 (6.34) 3 (2.78)\nHyperprolactinemia, n (%) χ²=0.60 0.440\n  no 230 (92.00) 129 (90.85) 101 (93.52)\n  yes 20 (8.00) 13 (9.15) 7 (6.48)\nUterine position, n (%) - 0.248\nAnterior 193 (77.20) 105 (73.94) 88 (81.48)\nMid-position 56 (22.40) 36 (25.35) 20 (18.52)\nPosterior 1 (0.40) 1 (0.70) 0 (0.00)\nLesion position, n (%) - 0.485\n  Uterine floor 30 (12.00) 14 (9.86) 16 (14.81)\n  Uterine body 218 (87.20) 127 (89.44) 91 (84.26)\n  Diffuse 2 (0.80) 1 (0.70) 1 (0.93)\nPregnancy number, n (%) - 0.216*\n  0 14 (5.60) 10 (7.04) 4 (3.70)\n  1 41 (16.40) 27 (19.01) 14 (12.96)\n  2 47 (18.80) 24 (16.90) 23 (21.30)\n  3 62 (24.80) 32 (22.54) 30 (27.78)\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nVariables Total (n = 250) Failure group (n = \n142)\nSuccess group (n = \n108)\nStatistic P\n  4 46 (18.40) 26 (18.31) 20 (18.52)\n  5 23 (9.20) 10 (7.04) 13 (12.04)\n  6 8 (3.20) 7 (4.93) 1 (0.93)\n  7 6 (2.40) 4 (2.82) 2 (1.85)\n  8 2 (0.80) 2 (1.41) 0 (0.00)\n  9 1 (0.40) 0 (0.00) 1 (0.93)\nHeavy menstrual bleeding score, n \n(%)\n- 0.929\n  0 35 (14.00) 20 (14.08) 15 (13.89)\n  1 5 (2.00) 4 (2.82) 1 (0.93)\n  2 46 (18.40) 28 (19.72) 18 (16.67)\n  3 64 (25.60) 34 (23.94) 30 (27.78)\n  4 52 (20.80) 28 (19.72) 24 (22.22)\n  5 46 (18.40) 27 (19.01) 19 (17.59)\n  6 2 (0.80) 1 (0.70) 1 (0.93)\nDysmenorrhea score, n (%) χ²=10.71 0.296\n  0 12 (4.80) 5 (3.52) 7 (6.48)\n  1 20 (8.00) 12 (8.45) 8 (7.41)\n  2 1 (0.40) 0 (0.00) 1 (0.93)\n  3 20 (8.00) 13 (9.15) 7 (6.48)\n  4 17 (6.80) 5 (3.52) 12 (11.11)\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nVariables Total (n = 250) Failure group (n = \n142)\nSuccess group (n = \n108)\nStatistic P\n  5 22 (8.80) 15 (10.56) 7 (6.48)\n  6 46 (18.40) 27 (19.01) 19 (17.59)\n  7 24 (9.60) 15 (10.56) 9 (8.33)\n  8 65 (26.00) 35 (24.65) 30 (27.78)\n  9 23 (9.20) 15 (10.56) 8 (7.41)\nType, n (%) - 0.759\n  1 64 (25.60) 33 (23.24) 31 (28.70)\n  2 44 (17.60) 28 (19.72) 16 (14.81)\n  3 9 (3.60) 4 (2.82) 5 (4.63)\n  4 109 (43.60) 62 (43.66) 47 (43.52)\n  5 4 (1.60) 3 (2.11) 1 (0.93)\n  6 20 (8.00) 12 (8.45) 8 (7.41)\n Z: Mann-Whitney test, χ²: Chi-\nsquare test, -: Fisher exact, *: \nSimulated p-value\n M: Median, Q₁: 1st Quartile, Q₃: \n3st Quartile\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nTable 2. Univariate and multivariable Logistic regression analysis for predicting the efficacy of FUAS in \nadenomyosis patients in the training set\nUnivariate MultivariableVariables\nβ S.E Z P OR (95%CI) β S.E Z P OR (95%CI)\nAGE 0.08 0.03 2.89 0.0\n04\n1.08 (1.03 ~ 1.14) 0.10 0.04 2.47 0.0\n14\n1.10 (1.02 ~ \n1.19)\nBMI 0.02 0.04 0.42 0.67\n4\n1.02 (0.93 ~ 1.11)\nCA125 -0.00 0.00 -\n0.64\n0.51\n9\n1.00 (1.00 ~ 1.00)\nCA199 -0.00 0.01 -\n0.82\n0.41\n2\n1.00 (0.99 ~ 1.01)\nUterine volume -0.00 0.00 -\n0.43\n0.66\n6\n1.00 (1.00 ~ 1.00)\nLesion volume 0.00 0.00 0.56 0.57\n5\n1.00 (1.00 ~ 1.00)\nRectus abdominis thickness 0.01 0.05 0.12 0.90\n3\n1.01 (0.91 ~ 1.12)\nSubcutaneous fat thickness -0.02 0.02 -\n0.78\n0.43\n3\n0.98 (0.95 ~ 1.02)\nDepth of adenomyosis lesions 0.02 0.01 1.34 0.18\n0\n1.02 (0.99 ~ 1.04) 0.03 0.01 2.09 0.0\n36\n1.03 (1.01 ~ \n1.06)\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nUnivariate MultivariableVariables\nβ S.E Z P OR (95%CI) β S.E Z P OR (95%CI)\nMaximum power\n  350 1.00 (Reference) 1.00 (Reference)\n  400 1.79 1.08 1.66 0.09\n8\n5.99 (0.72 ~ 49.76) 2.19 1.36 1.61 0.10\n7\n8.95 (0.62 ~ \n128.96)\nMinimum power\n  300 1.00 (Reference)\n  350 -0.33 1.03 -\n0.32\n0.75\n3\n0.72 (0.10 ~ 5.47)\n  400 -0.17 1.02 -\n0.16\n0.87\n1\n0.85 (0.12 ~ 6.24)\nAverage power 0.02 0.01 1.85 0.06\n5\n1.02 (1.00 ~ 1.05) 0.00 0.02 0.04 0.96\n8\n1.00 (0.97 ~ \n1.03)\nTreat Time 0.00 0.00 0.81 0.41\n6\n1.00 (1.00 ~ 1.01)\nIrradiation time 0.00 0.00 0.29 0.76\n9\n1.00 (1.00 ~ 1.00)\nTreat Intensity 0.00 0.00 0.63 0.52\n7\n1.00 (1.00 ~ 1.00)\nTreat volume 0.03 0.12 0.29 0.77\n4\n1.04 (0.82 ~ 1.32)\nTreatment Dose\n<240 1.00 (Reference)\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nUnivariate MultivariableVariables\nβ S.E Z P OR (95%CI) β S.E Z P OR (95%CI)\n≥240 0.36 0.31 1.18 0.23\n8\n1.43 (0.79 ~ 2.61)\nNumber of births\n  0 1.00 (Reference) 1.00 (Reference)\n  1 1.10 0.55 2.00 0.0\n45\n3.01 (1.02 ~ 8.86) 1.35 0.71 1.91 0.05\n6\n3.86 (0.97 ~ \n15.41)\n  2 1.00 0.59 1.71 0.08\n7\n2.73 (0.86 ~ 8.59) 1.38 0.81 1.70 0.09\n0\n3.96 (0.81 ~ \n19.41)\n  3 1.16 1.12 1.04 0.30\n1\n3.20 (0.35 ~ 28.94) 0.91 1.35 0.67 0.50\n1\n2.48 (0.18 ~ \n34.73)\n  4 -\n13.4\n0\n882.7\n4\n-\n0.02\n0.98\n8\n0.00 (0.00 ~ Inf) -\n15.7\n3\n1455.\n40\n-\n0.01\n0.99\n1\n0.00 (0.00 ~ Inf)\nUrinary frequency and constipation\n  no 1.00 (Reference)\n  yes 14.8\n0\n882.7\n4\n0.02 0.98\n7\n2668356.59 (0.00 ~ \nInf)\nAnemia\n  no 1.00 (Reference) 1.00 (Reference)\n  yes 0.49 0.31 1.59 0.11\n2\n1.63 (0.89 ~ 2.99) 0.46 0.37 1.26 0.20\n7\n1.59 (0.77 ~ \n3.24)\nFertility requirements\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nUnivariate MultivariableVariables\nβ S.E Z P OR (95%CI) β S.E Z P OR (95%CI)\n  no 1.00 (Reference) 1.00 (Reference)\n  yes -0.48 0.37 -\n1.32\n0.18\n7\n0.62 (0.30 ~ 1.26) 1.13 0.60 1.89 0.05\n8\n3.10 (0.96 ~ \n10.01)\nHistory of uterine surgery\n  no 1.00 (Reference)\n  yes -0.19 0.32 -\n0.60\n0.55\n0\n0.82 (0.44 ~ 1.56)\nHistory of reproductive tract anomalies \ncausing obstruction\n  no 1.00 (Reference)\n  yes 14.8\n0\n882.7\n4\n0.02 0.98\n7\n2668356.59 (0.00 ~ \nInf)\nFamily history of adenomyosis or \nendometriosis\n  no 1.00 (Reference)\n  yes -0.49 0.88 -\n0.56\n0.57\n7\n0.61 (0.11 ~ 3.43)\nHyperprolactinemia\n  no 1.00 (Reference)\n  yes 0.09 0.54 0.17 0.86\n4\n1.10 (0.38 ~ 3.17)\nHigh estrogen level\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nUnivariate MultivariableVariables\nβ S.E Z P OR (95%CI) β S.E Z P OR (95%CI)\n  no 1.00 (Reference)\n  yes 0.03 0.33 0.09 0.92\n8\n1.03 (0.54 ~ 1.98)\nUterine position\nAnterior 1.00 (Reference) 1.00 (Reference)\nMid-position -0.60 0.36 -\n1.65\n0.10\n0\n0.55 (0.27 ~ 1.12) -0.18 0.44 -\n0.42\n0.67\n5\n0.83 (0.35 ~ \n1.96)\nPosterior -\n14.5\n0\n882.7\n4\n-\n0.02\n0.98\n7\n0.00 (0.00 ~ Inf) -\n15.1\n2\n1455.\n40\n-\n0.01\n0.99\n2\n0.00 (0.00 ~ Inf)\nLesion position\n  Uterine floor 1.00 (Reference) 1.00 (Reference)\n  Uterine body -1.11 0.52 -\n2.14\n0.0\n32\n0.33 (0.12 ~ 0.91) -1.39 0.63 -\n2.21\n0.0\n27\n0.25 (0.07 ~ \n0.85)\n  Diffuse -0.77 1.50 -\n0.52\n0.60\n6\n0.46 (0.02 ~ 8.69) -1.84 1.58 -\n1.16\n0.24\n7\n0.16 (0.01 ~ \n3.56)\nPregnancy number\n  0 1.00 (Reference)\n  1 -0.59 0.87 -\n0.68\n0.49\n9\n0.56 (0.10 ~ 3.05)\n  2 0.29 0.80 0.36 0.72\n0\n1.33 (0.28 ~ 6.44)\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nUnivariate MultivariableVariables\nβ S.E Z P OR (95%CI) β S.E Z P OR (95%CI)\n  3 0.69 0.79 0.88 0.38\n1\n2.00 (0.42 ~ 9.42)\n  4 0.45 0.80 0.56 0.57\n3\n1.57 (0.33 ~ 7.62)\n  5 0.76 0.89 0.86 0.39\n0\n2.14 (0.38 ~ 12.20)\n  6 -1.10 1.32 -\n0.83\n0.40\n4\n0.33 (0.03 ~ 4.40)\n  7 0.11 1.17 0.09 0.92\n8\n1.11 (0.11 ~ 10.99)\n  8 -\n14.0\n6\n882.7\n4\n-\n0.02\n0.98\n7\n0.00 (0.00 ~ Inf)\nHeavy menstrual bleeding score\n  0 1.00 (Reference)\n  1 -\n16.0\n6\n1199.\n77\n-\n0.01\n0.98\n9\n0.00 (0.00 ~ Inf)\n  2 0.13 0.55 0.24 0.81\n3\n1.14 (0.38 ~ 3.38)\n  3 0.55 0.51 1.08 0.28\n0\n1.74 (0.64 ~ 4.75)\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nUnivariate MultivariableVariables\nβ S.E Z P OR (95%CI) β S.E Z P OR (95%CI)\n  4 0.51 0.54 0.94 0.34\n7\n1.67 (0.57 ~ 4.84)\n  5 0.33 0.55 0.60 0.54\n9\n1.39 (0.47 ~ 4.06)\n  6 -\n16.0\n6\n2399.\n54\n-\n0.01\n0.99\n5\n0.00 (0.00 ~ Inf)\nDysmenorrhea score -0.09 0.06 -\n1.44\n0.15\n0\n0.92 (0.81 ~ 1.03) -0.05 0.07 -\n0.73\n0.46\n3\n0.95 (0.83 ~ \n1.09)\nClassification\n  1 1.00 (Reference) 1.00 (Reference)\n  2 -0.63 0.50 -\n1.26\n0.20\n6\n0.53 (0.20 ~ 1.41) -0.22 0.58 -\n0.38\n0.70\n6\n0.80 (0.26 ~ \n2.50)\n  3 -0.19 0.77 -\n0.25\n0.80\n5\n0.83 (0.18 ~ 3.75) -0.14 0.96 -\n0.14\n0.88\n6\n0.87 (0.13 ~ \n5.69)\n  4 -0.46 0.39 -\n1.18\n0.23\n9\n0.63 (0.30 ~ 1.35) -0.59 0.44 -\n1.34\n0.18\n0\n0.56 (0.24 ~ \n1.31)\n  5 -1.29 1.20 -\n1.08\n0.28\n1\n0.28 (0.03 ~ 2.87) -1.81 1.32 -\n1.37\n0.16\n9\n0.16 (0.01 ~ \n2.17)\n  6 -0.80 0.59 -\n1.34\n0.18\n0\n0.45 (0.14 ~ 1.45) -1.20 0.67 -\n1.81\n0.07\n0\n0.30 (0.08 ~ \n1.10)\nOR: Odds Ratio, CI: Confidence Interval\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nFIGURE LEGENDS\n \nFigure 1. ROC for training set and validation set.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nFigure 2. ROC for bootstrap in validation set.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS\n\nFigure 3. Nomogram. 1) Locate each predictor on its corresponding \naxis and mark the patient’s value. 2) For each predictor, draw a vertical \nline up to the “Points” axis to obtain the individual point score. 3) Sum \nthe points from all predictors to obtain the “Total points.” 4) Find the \n“Total points” value on the total-points axis and draw a vertical line \ndown to the outcome (e.g., risk or survival probability) scale. 5) Read \noff the predicted probability and interpret it in the context of clinical \njudgment and other relevant information.\nACCEPTED MANUSCRIPTARTICLE IN PRESS\nARTICLE IN PRESSARTICLE IN PRESS","source_license":"CC0","license_restricted":false}