{"paper_id":"933ba62e-46eb-45ee-b976-d369ad0cf608","body_text":"© 2026 Acta Medica International | Published by Parsvnath Publishing House \n \n134 \n \n                                                                                                     \n \n \nFunctional and Multi-parametric Imaging of Uterine Fibroids: Current \nAdvances and Clinical Applications \nManvi Dhingra1, Ashish Kumar Shukla2, Bhoomi Modi3, Anoushka Gupta1, Prithvi Chauhan1 \n1Junior Resident-1, Department of Radiodiagnosis, Santosh deemed to be University, Ghaziabad, Uttar Pradesh, India . 2Professor and HOD, Department \nof Radiodiagnosis, Santosh Deemed to be University, Ghaziabad, Uttar Pradesh, India. 3Associate Professor, Department of Radiodiagnosis, Santosh \nDeemed to be University, Ghaziabad, Uttar Pradesh, India  \n \n \nBackground: Uterine fibroids are the most common benign tumours  of the uterus and often require accurate diagnosis and individualised \nmanagement. However, traditional imaging techniques such as ultrasound and CT have limitations in detecting complex fibroid m orphology, \nassessing vascularity, and differentiating fibroi ds from other uterine pathologies, such as adenomyosis or malignancy. The objective is to \nevaluate and summarise the current advances in functional and multi -parametric imaging modalities for uterine fibroids, and highlight their \nclinical applications in d iagnosis, treatment planning, image -guided intervention, and follow -up. Material and Methods: A systematic \nliterature search was conducted across PubMed, Scopus, Embase, and Web of Science databases for English -language studies published \nbetween January 2015 and May 2025. Inclusion criteria comprised human studies evaluating functional imaging modalities, including diffusion-\nweighted imaging (DWI), dynamic contrast-enhanced MRI (DCE-MRI), elastography, contrast-enhanced ultrasound (CEUS), radiomics, and \nPET-MRI, in fibroid management. Case reports, reviews, and animal studies were excluded. A narrative synthesis of eligible studie s was \nperformed due to methodological heterogeneity.  Results: Recent studies demonstrate that multi -parametric imaging offers si gnificant \nadvantages in characterising fibroids, predicting response to minimally invasive therapies (e.g., UAE and MRgFUS), and monitoring treatment \noutcomes. Parameters such as T2 signal intensity, ADC values, vascular perfusion patterns, stiffness metri cs, and radiomic signatures provide \nquantitative, reproducible markers that improve diagnostic accuracy and guide personalised care. Conclusion: Functional and multi-parametric \nimaging has transformed fibroid management from a structural, symptom -based app roach to one guided by tissue -specific insights. Its \nintegration into clinical practice, supported by standardisation and prospective multicenter validation, holds the potential to improve patient \noutcomes and reduce unnecessary interventions. \nKeywords: Uterine fibroids; multiparametric imaging; functional MRI; CEUS; elastography; radiomics. \n \nReceived: 20 November 2025 Revised: 01 December 2025 Accepted: 15 December 2025 Published: 24 January 2026 \n \nINTRODUCTION  \nUterine fibroids, or leiomyomas, are the most common \nbenign tumours of the female reproductive tract, affecting \napproximately 70–80% of women by the age of 50, with a \nhigher prevalence among women of African ancestry and \nthose in their reproductive years. [1,2] While many fibroids \nremain asymptomatic, they can produce a range of clinical \nsymptoms such as menorrhagia, pelvic pressure, urinary \nfrequency, and infertility, significantly impairing the quality \nof life and leading to a substantial number of gynecol ogic \nconsultations and surgeries, particularly hysterectomy.[3] \nDespite their prevalence, the diagnostic and therapeutic \nmanagement of fibroids is often complicated by limitations \nof conventional imaging modalities. Two -dimensional \ntransabdominal an d transvaginal ultrasonography, though \ncommonly used as the first -line imaging tool, may fail to \naccurately determine the size, number, or anatomical location \nof fibroids in complex or multiple lesions. [4] Furthermore, \nultrasound has limited capability to characterise internal \nfibroid architecture or vascularity and often cannot reliably \ndistinguish fibroids from other uterine pathologies, such as \nadenomyosis or leiomyosarcoma. [5] These limitations may \nlead to misdiagnosis, suboptimal treatment selection, o r \nunnecessary surgical interventions. \nAdvances in imaging technology, particularly functional and \nmulti-parametric imaging, have significantly improved \ndiagnostic accuracy and therapeutic planning for uterine fibroids. \nTechniques such as magnetic resonance  imaging (MRI), \ndiffusion-weighted imaging (DWI), dynamic contrast -enhanced \nMRI (DCE -MRI), MR elastography, and contrast -enhanced \nultrasound (CEUS) provide a comprehensive assessment of \nfibroid characteristics, including tissue stiffness, perfusion \ndynamics, and cellularity. [6-9] These functional insights are \nparticularly beneficial in selecting patients for minimally \n \n \nAddress for correspondence: Dr. Ashish Kumar Shukla, \nProfessor and HOD, Department of Radiodiagnosis, Santosh Deemed to be \nUniversity, Ghaziabad, Uttar Pradesh, India. \nE‑mail: drashish07@rediffmail.com  \nDOI: \n10.21276/amit.2026.v13.i1.310 \n \nHow to cite this article:  Dhingra M, Shukla  AK, Modi  B, Gupta  A, Chauhan  P. \nFunctional and Multi-parametric Imaging of Uterine Fibroids: Current  Advances and \nClinical Applications. Acta Med Int. 2026;13(1):134-141. \n\nActa Medica International ¦ Volume 13 ¦ Issue 1 ¦ January-April 2026 \n \n135 \nDhingra M et al; Multiparametric Imaging of Uterine Fibroids \n \n \ninvasive or uterus -sparing treatments like myomectomy, \nuterine artery embolisation (UAE), or MR -guided focused \nultrasound surgery (MRgFUS).[10] \nThis review highlights recent advances in functional and \nmulti-parametric imaging of uterine fibroids, focusing on \ntheir clinical roles in diagnosis, treatment planning, image -\nguided therapy, and follow -up. It underscores how these \nmodalities support pe rsonalised care and improve clinical \ndecision-making. \n \n \nFigure 1: Uterine Fibroids \n \nConventional Imaging Modalities: Limitations \nAccurate assessment of uterine fibroids is essential for \noptimal management, and while traditional imaging \ntechniques are widely used, they have notable limitations. \nThis section outlines the limitations of the most commonly \nemployed conventional modalities: ultrasound, Doppler \ntechniques, and computed tomography (CT). \nUltrasound (USG) \nUltrasound is the most commonly used modality in the initial \nevaluation of uterine fibroids, especially for its accessibility, \naffordability, and non -invasive nature. Both transabdominal \nand transvaginal approaches are frequently employed. \nHowever, its diagnostic reliability is highly operato r-\ndependent and varies with patient body habitus, uterine \nposition, and bowel gas interference.[11] \n \n \nFigure 2: Different ultrasonic image features of uterine fibroids. \n(A) Fibroid is elliptical with high echogenicity; (B) Fibroid has \na round shape with low echogenicity; (C) Fibroid with five \nattenuation bands; (D) Fibroids with no attenuation band and \nlow echogenicity \n \nIn patients with multiple, submucosal, or deeply located \nintramural fibroids, ultrasound may not precisely determine \nthe number, size, or  anatomical relationship of the lesions. [12] \nIsoechoic fibroids, in particular, can blend with the normal \nmyometrium, making detection difficult. Additionally, it \nprovides limited insight into internal tissue characteristics or \nperfusion, thereby limiting its utility for pre -interventional \nplanning or differentiating from malignancy.[13] \n2D and 3D Doppler Ultrasound \nDoppler techniques, including colour, power, and 3D Doppler, \nare used adjunctively to assess fibroid vascularity. They may aid \nin differentiati ng highly vascular fibroids from degenerating \nfibroids or in identifying atypical perfusion suggestive of \nalternative pathology.[14] However, the vascular patterns assessed \nby Doppler are qualitative and often inconsistent, limiting their \nspecificity.[9] \nFurthermore, the interpretation of Doppler signals is subject to \ntechnical variability, and the absence of standardised perfusion \nthresholds reduces reproducibility. This hampers its ability to \nreliably guide therapy, especially when considering minimally \ninvasive options like uterine artery embolisation or MR -guided \nfocused ultrasound.[15] \n \n \nFigure 3: Uterine Fibroids Using Two -Dimensional and Three -\nDimensional Ultrasonography  \n \nComputed Tomography (CT) : CT is not a frontline imaging \nmodality for fibroid assessment due to its suboptimal soft -tissue \ncontrast and radiation exposure—factors of particular concern in \nwomen of reproductive age. [5] It is sometimes used in complex \npelvic evaluations or when MRI is contraindicated, but its \ncapability to delineate f ibroid composition, degeneration, or \nvascularity is limited. \nIn comparison to MRI, CT cannot distinguish between \nleiomyomas and other pelvic pathologies, such as adenomyosis, \novarian tumours, or uterine sarcomas, with adequate \nconfidence.[8] Therefore, its use in fibroid management remains \nlimited to select scenarios. \n \n \nFigure 4: CT of Uterine Fibroids \n\n\nActa Medica International ¦ Volume 13 ¦ Issue 1 ¦ January-April 2026 \n \n136 \nDhingra M et al; Multiparametric Imaging of Uterine Fibroids \n \n \nMultiparametric and Functional Imaging Modalities \nConventional imaging techniques, though widely used, are \nlimited in fully characterizing uterine fibroids.  Functional \nand multiparametric imaging modalities offer deeper tissue \ninsights—enabling refined diagnosis, subtype classification, \nand better therapeutic planning. The following advanced \nmodalities represent current innovations in fibroid imaging. \nMagnetic Resonance Imaging (MRI) \n• Principle & Technique:  MRI employs strong magnetic \nfields and radiofrequency pulses to generate high -\nresolution images. T1 - and T2 -weighted sequences are \nstandard. \n• Diagnostic Utility: Fibroids usually appear hypointense \non T2-weighted images and isointense or hypointense on \nT1-weighted images. A high T2 signal suggests increased \ncellularity or degeneration. MRI is superior to ultrasound \nfor evaluating the number, location, size, and ty pe of \nfibroids, as well as adjacent organ involvement.[6,11] \n \n \nFigure 5: MRIs show (A) uterine fibroid on the anterior wall of \nthe uterus (white arrow); (B) the uterine fibroid on fundus of \nuterus (white arrow); (C) the uterine fibroid on the posterior \nwall of the uterus (white arrow); and (D) the uterine fibroid on \ncervical area of the uterus (white arrow). \n \nClinical Application:  It is the preferred modality for \nsurgical mapping and assessing eligibility for uterus -sparing \nprocedures like uterine artery embolisation (UAE) and MR -\nguided focused ultrasound surgery (MRgFUS).[10] \nLimitations: Cost, availability, and contraindications such \nas implanted metallic devices limit its use, especially in \nresource-limited settings. \nDiffusion-Weighted Imaging (DWI) and ADC Mapping \nPrinciple & Technique: DWI assesses the Brownian motion \nof water molecules; ADC maps provide quantitative \ndiffusion values. \nDiagnostic Utility: DWI is valuable for assessing fibroid \ndegeneration. Cellular fibroids show restricted diffusion and \nlower ADC values. It also helps differentiate fibroids from \nmalignant lesions such as leiomyosarcoma.[16,17] \n \nFigure 6:  Magnet ic resonance imaging (MRI) types of uterine \nfibroid (UF). (A). Type I presents as a “dark” UF as seen on MRI \nT2-weighted imaging. (B). Type II has a mixed MRI bright and dark \nstructure. (C). Type III presents in MRI as a “bright” type of UF, \nusually not su itable for MRI -HIFU (high -intensity ultrasound) \ntreatment. \n \n \nFigure 7: DWI and ADC maps of uterine tumours. (a –c) \nleiomyosarcoma showing intermediate T2 signal with necrosis (a), \nhigh signal on b1000 DWI (b), and restricted diffusion with low \nADC (873 × 10⁻³ mm²/s) (c). Coexisting benign leiomyomas show low \nT2 and DWI signal with lower ADC (796 × 10⁻³ mm²/s). (d –f) A 67-\nyear-old woman with atypical leiomyoma showing heterogeneous \nlow T2 signal (d), no restriction on DWI (e), and high ADC (1661 × \n10⁻³ mm²/s) (f). Final diagnosis confirmed post-hysterectomy. \n \n• Limitations: There can be overlap in ADC values between \ncellular fibroids and malignancies, reducing specificity \nwithout correlation with other MRI parameters. \n\n\nActa Medica International ¦ Volume 13 ¦ Issue 1 ¦ January-April 2026 \n \n137 \nDhingra M et al; Multiparametric Imaging of Uterine Fibroids \n \n \nDynamic Contrast-Enhanced MRI (DCE-MRI) \n• Principle & Technique: DCE-MRI involves serial post-\ncontrast imaging to evaluate perfusion and enhancement \nkinetics. \n• Diagnostic Utility:  It enables vascular characterization \nof fibroids, assisting in pre -treatment planning and post -\ntreatment assessment. High perfusion indicates suitability \nfor MRgFUS, while low enhancement may suggest \ndegeneration.[18] \n• Limitations: Gadolinium-based contrast agents pose \nrisks in renal impairment. The technique requires high \ntemporal resolution and expert interpretation. \nMR Elastography \n• Principle & Technique:  This modality evaluates tissue \nstiffness by transmitting mechanical waves and capturing \ndisplacement with MRI. \n• Diagnostic Utility: MR elastography offers quantitative \nstiffness values in kilopascals. It can help in classify \nfibroids (e.g., cellular vs hyalinized) and guide therapy \nbased on stiffness.[19] \n \n \nFigure 8: MRI and MR elastography of uterine fibroids. \nColumns show axial T1 -, T2 -weighted images, and composite \nelastograms with shear stiffness colour maps (kPa). Rows \nillustrate representative cases with fibroids of minimal \nheterogeneity, substantial he terogeneity, and bright T2 signal \nintensity. \n \n• Limitations: Limited availability, longer scan times, and \ncost hinder widespread use. \nContrast-Enhanced Ultrasound (CEUS) \n• Principle & Technique:  CEUS uses microbubble \ncontrast agents to assess real-time perfusion. \n• Diagnostic Utility:  It allows evaluation of vascularity \nand real -time monitoring post -UAE or focused \nultrasound therapy. CEUS is useful where MRI is \ncontraindicated.[9] \n• Limitations: Short imaging window and lower \nresolution in obese patients reduce reliability. \nStandardization of parameters is still evolving. \nShear-Wave and Strain Elastography (US-based) \n• Principle & Technique:  These techniques assess tissue \nelasticity using ultrasound. Shear -wave elastography \nmeasures wave speed, while strain elastography measures \ntissue deformation under pressure. \n• Diagnostic Utility:  Fibroids exhibit higher stiffness than \nnormal myometrium or adenomyosis. Elastography aids in \ndifferentiating fibroids from other myometrial pathologies \nand predicting treatment response.[20] \n \n \nFigure 9: Contrast -Enhanced Ultrasound in the Assessment of \nUterine Fibroids \n \n \nFigure 10: ultrasound shear wave elastography in uterine fibroids \n \n• Limitations: Results are operator -dependent and limited by \nuterine position and depth. Reproducibility is a concern \nacross vendors and systems. \nPET-CT and SPECT in Uterine Fibroid Imaging \n• Principle & Technique:  Positron Emission Tomography –\nComputed Tomography (PET -CT) and Single Photon \nEmission Computed Tomography (SPECT) are functional \nnuclear imaging modalities that evaluate tissue metabolism \nand perfusion. PET -CT commonly uses 18F -\nfluorodeoxyglucose (FDG) to detect areas of increased \nglucose uptake, while SPECT uses radiotracers such as Tc -\n99m to assess vascular activity and cellular turnover. \n• Diagnostic Utility: While uterine fibroids typically show low \nFDG uptake, uterine sarcomas and malignant lesions exhib it \nsignificantly higher metabolic activity, aiding in \ndifferentiation between benign and malignant myometrial \nmasses. PET -CT may be particularly valuable in cases of \nrapidly enlarging fibroids, postmenopausal growth, or \nindeterminate MRI findings. SPECT, t hough less commonly \napplied, may offer insights into vascular behaviour, \nparticularly in atypical fibroids or tumours with necrotic \ncomponents.[21,22] \n\n\nActa Medica International ¦ Volume 13 ¦ Issue 1 ¦ January-April 2026 \n \n138 \nDhingra M et al; Multiparametric Imaging of Uterine Fibroids \n \n \n \nFigure 11: Uterine fibroid imaging. (a) Fused PET-CT showing \na hypermetabolic focus (arrowhead) in the uterine body, \ncorresponding to a hypoattenuating fibroid on CT. About 20% \nof fibroids display this FDG PET pattern. (b) Contrast -\nenhanced CT demonstrating a focal hypoattenuating fibroid \n(arrowhead) in the left uterine wall. \n• Limitations: These modalities are not used routinely due \nto high cost, radiation exposure, limited availability, and \nlack of standardized uptake thresholds for fibroid \nevaluation. Their role remains largely adjunctive, \nrecommended in selective or equivocal cases rather than \nfor routine fibroid assessment. \nRecent Advances in Functional and Multiparametric \nImaging of Uterine Fibroids \nArtificial Intelligence and Radiomics in Fibroid Imaging \nRecent developments in artificial intelligence (AI) and \nradiomics have significantly enhanced the diagnostic \ncapabilities of imaging in uterine fibroids. Radiomics \ninvolves extracting quantitative features from standard \nimaging modalities —particularly MRI and ultrasound —to \nanalyse tissue heterogeneity, vascularity, and growth patterns \nbeyond what the human eye can discern. In fibroid imaging, \nradiomic algorithms have demonstrated potential in \ndifferentiating fibroids from uterine sarcomas, predicting \ntreatment response, and monitoring recurrence post -\nintervention.[23] \nFor instance, studies employing machine learning on T2 -\nweighted MRI datasets have successfully achieved high \ndiagnostic accuracy in characterizing fibroid types, enabling \nmore precise selection for uterine artery embolization (UAE) \nor high -intensity focused ultrasound (HIFU) therapy. [24] \nThese models are also being trained to forecast volumetric \nregression following treatment, allowing clinicians to \ndevelop management plans more effectively. \n \n \nFigure 12: The pipeline of DL for medical image analysis \nincludes three stages, i.e., data collection, data annotation, and \nmodel training. The process of expert supervision and back \npropagation is introduced to update the parameters of DNNs, \nwhich enables it to train itself by continuously inputting large -\nscale data for machine learning. \n3D and Fusion Imaging \nThree-dimensional (3D) imaging and fusion techniques, which \nintegrate anatomical and functional datasets, have appeared as \npowerful tools for correct  fibroid mapping. 3D ultrasound \nprovides volumetric rendering, which is particularly valuable in \nassessing fibroid burden and guiding preoperative planning. \nFusion imaging, which overlays real -time ultrasound with MRI \ndatasets, is increasingly being used to guide targeted biopsies and \nablation procedures, especially for fibroids found near critical \nstructures.[25] \nThese technologies have been shown to improve the accuracy of \nfibroid volume estimation, an essential parameter in finding \neligibility for conser vative therapies and predicting fertility \noutcomes. \nFunctional Imaging in Guiding Interventions \nMultiparametric imaging is now central to planning and \nevaluating minimally invasive fibroid treatments. In the UAE, \nperfusion-weighted MRI and contrast -enhanced ultrasound are \nused to assess vascular supply and infarction zones, predicting \nprocedural success and long -term symptom relief. Similarly, \nMR-guided focused ultrasound surgery (MRgFUS) relies heavily \non T2 -weighted imaging, temperature -sensitive sequence s, and \ndynamic contrast-enhanced (DCE) MRI to ensure correct energy \ndelivery and real-time monitoring of tissue ablation.[26] \nMoreover, diffusion -weighted imaging (DWI) and apparent \ndiffusion coefficient (ADC) values are increasingly utilised to \ndetect ear ly post -treatment cellular changes, offering a non -\ninvasive marker of therapeutic efficacy. \nHybrid Imaging Techniques \nThe integration of structural and functional imaging in hybrid \nmodalities such as PET-MRI and SPECT-CT is showing promise \nin research and select clinical scenarios. Although not yet widely \nadopted in routine fibroid evaluation, these modalities have \nproven utility in cases with ambiguous findings, especially in \ndifferentiating fibroids from malignant or atypical uterine \nmasses. Ongoing trial s are exploring the value of PET -MRI in \ndetecting residual viable tissue after UAE and in assessing \nfibroid metabolism and vascular remodeling.[27] \nSuch hybrid approaches may pave the way for personalised \nimaging algorithms that combine molecular, structur al, and \nperfusion data for comprehensive evaluation. \nClinical Applications \nDiagnosis and Differential Diagnosis \nMulti-parametric MRI enhances diagnostic specificity, \ndistinguishing fibroids from adenomyosis or malignancy. T2 -\nweighted, DWI, and DCE sequences provide complementary \ndata on margins, diffusion, and perfusion, while CEUS offers \nrapid functional correlation.[12,28,29] \nTreatment Planning \nHigh-T2, well -perfused fibroids respond favourably to \nMRgFUS, whereas hypo -vascular lesions are less suitable for \nimmobilisation. Elastography quantifies stiffness, aiding energy-\ndose adjustment, while 3D imaging delineates the submucosal \nextension, which is critical for myomectomy planning. [10,30] \nMonitoring Therapeutic Response \nPost-treatment MRI and CEUS objectively evaluate outcomes. \nNon-enhancing regions on post-UAE MRI indicate necrosis and \nsymptom relief. Rising ADC va lues and reduced stiffness on \nelastography confirm successful therapy.[31,32] \n\n\nActa Medica International ¦ Volume 13 ¦ Issue 1 ¦ January-April 2026 \n \n139 \nDhingra M et al; Multiparametric Imaging of Uterine Fibroids \n \n \nReproductive Assessment \nImaging defines fibroid proximity to the endometrium, \nguiding fertility -sparing management. MRI and 3D US \naccurately assess cavity distortion. Preliminary  data suggest \nthat perfusion and stiffness metrics from CEUS and \nelastography may predict endometrial receptivity and \nimplantation potential.[33,34] \n \nMATERIALS AND METHODS \nA systematic literature search (PubMed, Scopus, Embase, \nWeb of Science) identified human studies from 2015 to 2025 \non functional and multi -parametric imaging in uterine \nfibroids. Inclusion: Original human studies on DWI, DCE -\nMRI, CEUS, elastography, and PET-MRI. \nExclusion: Case reports, reviews, animal or paediatric \nstudies. \nTwo reviewers independently screened and extracted data on \nstudy design, imaging parameters, and outcomes. Owing to \nheterogeneity in protocols, a narrative synthesis was \nperformed. \nRESULTS \nOf 395 records, 22 studies met the inclusion criteria: 9 \nprospective, 8 retrospective, and 5 cross -sectional. Modalities \nanalysed included MRI (T2, DWI, DCE), CEUS, elastography, \nradiomics, and PET-MRI. \n \nKey imaging biomarkers: \n• T2 Signal Intensity: A High T2 signal predicts a better \nUAE/MRgFUS response.[10,12] \n• ADC Values: Lower in fibroids; increase after therapy \ndenotes necrosis.[12,35] \n• Perfusion Metrics: Enhancement parameters differentiate \nviable from degenerated tissue.[10,36] \n• Elastography: Quantified stiffness correlates with pathology \nand therapy success.[37,38] \n• Radiomics: Texture-based T2 models achieve >90 % \naccuracy for HIFU response prediction.[24] \n• Hybrid Imaging:  PET-MRI detects atypical metabolic \npatterns suggesting malignancy.[39] \n \nTable 1: Summary of Key Studies \nStudy Name Study Design Imaging Modality / \nTechnique \nKey Findings Clinical Relevance \nFunaki K et al. \n(2007)[10] \nProspective cohort T2-weighted MRI \n(MRgFUS correlation) \nHigh T2 signal → lower MRgFUS \nefficacy; Type 1–2 fibroids \nresponded best (P < .01) \nT2 intensity guides \nMRgFUS treatment planning \nPongpunprut S et al. \n(2022)[20] \nCross-sectional Shear Wave Elastography SWE differentiated normal \nmyometrium vs adenomyosis and \nfibroids (AUC 0.80) \nSWE helps differentiate \nfibroid pathologies in \ninfertility \nCheng Y et al. \n(2024)[24] \nRetrospective radiomics \nstudy \nMRI T2WI radiomics for \nHIFU prediction \nRadiomics model predicted HIFU \nresponse (AUC 0.81) \nEnables pre-treatment \nprediction and personalized \ntherapy \nLi ZY et al. (2024)[34] Cross-sectional Shear Wave Elastography Endometrial stiffness correlated \nwith receptivity markers (AUC \n0.89) \nNon-invasive assessment of \nfertility potential \nChen XY et al. \n(2025)[38] \nProspective follow-up MRI (DWI/ADC post-\nUAE) \n3-day MRI ADC changes predicted \nUAE efficacy (P < .001) \nEarly MRI biomarkers \npredict treatment success \nYang L et al. \n(2025)[40] \nComparative cohort USgHIFU with Doppler \n(Alder grading) \nHigher vascular grade → lower \nHIFU success (P < .05) \nDoppler grading predicts \nablation outcome \n \n \nFigure 13: Forest plot summarizing effect estimates of included \nstudies evaluating diagnostic and predictive performance of \nfunctional imaging. \n \nFunctional imaging consistently improved diagnostic \naccuracy and predictive power, though heterogeneity in \nacquisition parameters and small sample sizes limited the pooled \nanalysis.[12,40] \n \nDISCUSSION \nFunctional and multi-parametric imaging has transformed fibroid \nevaluation by providing quantitative biomarkers beyond \nmorphology. T2 and perfusion MRI remain central for candidate \nselection in UAE and MRgFUS, while elastography provides \nmechanical charact erisation, useful for predicting response. \nCEUS offers an inexpensive bedside functional assessment. \nThe major barriers are small single -centre studies, differing \nimaging protocols, and a lack of standardised thresholds. Future \nwork should prioritise multi center validation, harmonised \nacquisition parameters, and outcome -based research integrating \nradiomics and clinical endpoints. AI -based tools show potential \nfor personalised therapy planning but require transparency and \nclinical validation before integrati on into routine \npractice.[9,24,37,40] \n\n\nActa Medica International ¦ Volume 13 ¦ Issue 1 ¦ January-April 2026 \n \n140 \nDhingra M et al; Multiparametric Imaging of Uterine Fibroids \n \n \nCONCLUSION \nMulti-parametric imaging has revolutionised the evaluation \nof uterine fibroids, extending assessment beyond the \nstructural domain into the functional and molecular domains. \nTechniques such as DWI, DCE -MRI, CEUS, and \nelastography provide detailed informatio n on cellularity, \nperfusion, and stiffness, supporting accurate diagnosis, \nindividualised therapy, and objective follow -up. Integration \nof AI and radiomics will further refine prediction models. \nStandardisation, cost -effective access, and multicenter \nvalidation are essential for widespread adoption. Functional \nimaging now stands as a cornerstone of modern, fertility -\npreserving, precision gynecologic care. \n \nFinancial support and sponsorship \nNil.  \n \nConflicts of interest  \nThere are no conflicts of interest. \n \nREFERENCES \n1. Stewart EA. Uterine fibroids. The Lancet. 2001 Jan \n27;357(9252):293-8. \n2. Baird DD, Dunson DB, Hill MC, Cousins D, Schectman JM. \nHigh cumulative incidence of uterine leiomyoma in black and \nwhite women: ultrasound evidence. American journal of \nobstetrics and gynecology. 2003 Jan 1;188(1):100-7. \n3. Kossaï M, Penault -Llorca F. Role of hormones in common \nbenign uterine lesions: endometrial polyps, leiomyomas, and \nadenomyosis. Hormonal Pathology of the Uterus. 2020 May \n14:37-58. \n4. Jayni D. Comparison of Transvaginal Ultrasound, Saline \nInfusion Sonohysterography Versus Diagnostic Hysteroscopy \nin Evaluation of Endometrial Cavity Pathology Amongst \nWomen With Abnormaluterine Bleeding in Nairobi. a \nProspective Cohort Study (Doctoral dissertation, UON). \n5. Fasih N, Prasad Shanbhogue AK, Macdonald DB, Fraser -Hill \nMA, Papadatos D, Kielar AZ, Doherty GP, Walsh C, McInnes \nM, Atri M. Leiomyomas beyond the uterus: unusual locations, \nrare manifestations. Radiographics. 2008 Nov;28(7):1931-48. \n6. Okolo S. Incidence, aetiology and epidemiology of uterine \nfibroids. Best practice & research Clinical obstetrics & \ngynaecology. 2008 Aug 1;22(4):571-88. \n7. Van den Bosch T, Dueholm M, Leone FP, Valentin L, \nRasmussen CK, Votino A, Van Schoubroeck D, Landolfo C, \nInstallé AJ, Guerriero S, Exacoustos C. Terms, definitions and \nmeasurements to describe sonographic features of myometrium \nand uterine masses: a consensus opinion from the \nMorphological Uterus Sonographic Assessment (MUSA) \ngroup. Ultrasound in Obstetrics & Gyn ecology. 2015 \nSep;46(3):284-98. \n8. Szklaruk J, Tamm EP, Choi H, Varavithya V. MR imaging of \ncommon and uncommon large pelvic masses. Radiographics. \n2003 Mar;23(2):403-24. \n9. Stoelinga B, Hehenkamp WJ, Brölmann HA, Huirne JA. Real‐\ntime elastography for assessment  of uterine disorders. \nUltrasound in obstetrics & gynecology. 2014 Feb;43(2):218-26. \n10. Funaki K, Fukunishi H, Funaki T, Sawada K, Kaji Y, Maruo T. \nMagnetic resonance -guided focused ultrasound surgery for \nuterine fibroids: relationship between the therapeutic effects and \nsignal intensity of preexisting T2-weighted magnetic resonance \nimages. American journal of obstetrics and gynecology. 2007 \nFeb 1;196(2):184-e1. \n11. Dueholm M. Transvaginal ultrasound for diagnosis of adenomyosis: \na review. Best Practice & Research  Clinical Obstetrics & \nGynaecology. 2006 Aug 1;20(4):569-82. \n12. Khan AT, Shehmar M, Gupta JK. Uterine fibroids: current \nperspectives. International journal of women's health. 2014 Jan \n29:95-114. \n13. Woźniak A, Woźniak S. Ultrasonography of uterine leiomyomas. \nMenopause Review/Przegląd Menopauzalny. 2017 Dec \n30;16(4):113-7. \n14. Ng EH, Ho PC. Doppler ultrasound examination of uterine arteries \non the day of oocyte retrieval in patients with uterine fibroids \nundergoing IVF. Human Reproduction. 2002 Mar 1;17(3):765-70. \n15. Worthington-Kirsch RL. Uterine Artery Embolization. Tumor \nAblation: Principles and Practice. 2008 Sep 8:412. \n16. Thomassin-Naggara I, Dechoux S, Bonneau C, Morel A, Rouzier R, \nCarette MF, Daraï E, Bazot M. How to differentiate benign from \nmalignant myometrial tum ours using MR imaging. European \nradiology. 2013 Aug;23(8):2306-14. \n17. Ueda H, Togashi K, Konishi I, Kataoka ML, Koyama T, Fujiwara \nT, Kobayashi H, Fujii S, Konishi J. Unusual appearances of uterine \nleiomyomas: MR imaging findings and their histopathologic \nbackgrounds. Radiographics. 1999 Oct;19(suppl_1):S131-45. \n18. Ananthakrishnan G, Macnaught G, Hinksman L, Gilmour H, Forbes \nKP, Moss JG. Diffusion -weighted imaging in uterine artery \nembolisation: do findings correlate with contrast enhancement and \nvolume reductio n?. The British Journal of Radiology. 2012 Nov \n1;85(1019):e1046-50. \n19. Mariappan YK, Glaser KJ, Ehman RL. Magnetic resonance \nelastography: a review. Clinical anatomy. 2010 Jul;23(5):497-511. \n20. Pongpunprut S, Panburana P, Wibulpolprasert P, Waiyaput W, \nSroyraya M, Chansoon T, Sophonsritsuk A. A comparison of shear \nwave elastography between normal myometrium, uterine fibroids, \nand adenomyosis: a cross -sectional study. International journal of \nfertility & sterility. 2022 Jan 17;16(1):49. \n21. Tamai K, Koyama T, Saga T, Morisawa N, Fujimoto K, Mikami Y, \nTogashi K. The utility of diffusion -weighted MR imaging for \ndifferentiating uterine sarcomas from benign leiomyomas. European \nradiology. 2008 Apr;18(4):723-30. \n22. Bang JI, Kang SY. Diagnostic performance of 18F -FDG PET or \nPET/CT in differential diagnosis of uterine leiomyomas and uterine \nsarcomas: systematic review and meta -analysis of the literature. \nClinical and Translational Imaging. 2022 Jun;10(3):301-9. \n23. Raffone A, Raimondo D, Neola D, Travaglino A, Giorgi M, Lazzeri \nL, De Laurentiis F, Carravetta C, Zupi E, Seracchioli R, Casadio P. \nDiagnostic accuracy of MRI in the differential diagnosis between \nuterine leiomyomas and sarcomas: A systematic review and meta‐\nanalysis. International Journal of Gynecology & Obstetrics. 2024 \nApr;165(1):22-33. \n24. Cheng Y, Yang L, Wang Y, Kuang L, Pan X, Chen L, Cao X, Xu Y. \nDevelopment and validation of a radiomics model based on T2 -\nweighted imaging for predicting the efficacy of high intensity \nfocused ultrasound ablation in uterine fibroids. Quantitative Imaging \nin Medicine and Surgery. 2024 Jan 22;14(2):1803. \n25. Theis M, Tonguc T, Savchenko O, Nowak S, Block W, Recker F, \nEssler M, Mustea A, Attenberger U, Marinova M, Sprinkart  AM. \nDeep learning enables automated MRI -based estimation of uterine \nvolume also in patients with uterine fibroids undergoing high -\nintensity focused ultrasound therapy. Insights into imaging. 2023 \nJan 5;14(1):1. \n26. Kalina I, Tóth A, Valcseva É, Kaposi PN, Ács N, Várbíró S, Bérczi \nV. Prognostic value of pre -embolisation MRI features of uterine \nfibroids in uterine artery embolisation. Clinical radiology. 2018 Dec \n1;73(12):1060-e1. \n27. Schwartz M, Gavane SC, Bou-Ayache J, Kolev V, Zakashansky K, \n\nActa Medica International ¦ Volume 13 ¦ Issue 1 ¦ January-April 2026 \n \n141 \nDhingra M et al; Multiparametric Imaging of Uterine Fibroids \n \n \nPrasad-Hayes M, Taoul i B, Chuang L, Kostakoglu L. \nFeasibility and diagnostic performance of hybrid PET/MRI \ncompared with PET/CT for gynecological malignancies: a \nprospective pilot study. Abdominal Radiology. 2018 Dec \n1;43(12):3462-7. \n28. Tellum T, Nygaard S, Lieng M. Noninvasive d iagnosis of \nadenomyosis: a structured review and meta -analysis of \ndiagnostic accuracy in imaging. Journal of minimally invasive \ngynecology. 2020 Feb 1;27(2):408-18. \n29. Pérez-Fidalgo JA, Ortega E, Ponce J, Redondo A, Sevilla I, \nValverde C, Isern Verdum J, De Á lava E, Galera López M, \nMarquina G, Sebio A. Uterine sarcomas: clinical practice \nguidelines for diagnosis, treatment, and follow -up, by Spanish \ngroup for research on sarcomas (GEIS). Therapeutic Advances \nin Medical Oncology. 2023 Mar;15:17588359231157645. \n30. Valenti G, Milone P, D’Amico S, Caldaci LM, Vitagliano A, \nSapia F, Fichera M. Use of pre -operative imaging for \nsymptomatic uterine myomas during pregnancy: a case report \nand a systematic literature review. Archives of Gynecology and \nObstetrics. 2019 Jan 14;299(1):13-33. \n31. Pelage JP, Guaou NG, Jha RC, Ascher SM, Spies JB. Uterine \nfibroid tumors: long -term MR imaging outcome after \nembolization. Radiology. 2004 Mar;230(3):803-9. \n32. Bengtsson J. Novel radiological approaches for diagnosis and \ntreatment of diseases i n the pelvic region. Applications in the \nuterus and the prostate. Lund University; 2024. \n33. De La Cruz MS, Buchanan EM. Uterine fibroids: diagnosis and \ntreatment. American family physician. 2017 Jan 15;95(2):100 -\n7. \n34. Li ZY, Cai L, Zhang ZJ, Zou HR, He M, Qin ML , Wang H. \nEvaluation of endometrial receptivity in women with unexplained \ninfertility by shear wave elastography. Insights into Imaging. 2024 \nMar 22;15(1):85. \n35. Rees CO, Nederend J, Mischi M, van Vliet HA, Schoot BC. \nObjective measures of adenomyosis on MRI and their diagnostic \naccuracy—a systematic review & meta‐analysis. Acta obstetricia et \ngynecologica Scandinavica. 2021 Aug;100(8):1377-91. \n36. Li Z, Zhang P, Shen H, Ding B, Wang H, Li J, Shen Y. Clinical \nvalue of contrast‐enhanced ultrasound for the different ial diagnosis \nof specific subtypes of uterine leiomyomas. Journal of Obstetrics \nand Gynaecology Research. 2021 Jan;47(1):311-9. \n37. Pongpunprut S, Panburana P, Wibulpolprasert P, Waiyaput W, \nSroyraya M, Chansoon T, Sophonsritsuk A. A comparison of shear \nwave elastography between normal myometrium, uterine fibroids, \nand adenomyosis: a cross -sectional study. International journal of \nfertility & sterility. 2022 Jan 17;16(1):49. \n38. Chen XY, Zhang MZ, Wang JK, Li B, Qin RQ, Zhang YB, Wan \nCY, Hu RC, Zhu JY, Zhou B. Post -uterine artery embolization: 3 -\nday MRI changes and their predictive value for therapeutic efficacy \nin symptomatic uterine fibroids. British Journal of Radiology. 2025 \nFeb;98(1166):220-8. \n39. Lakhani A, Khan SR, Bharwani N, Stewart V, Rockall AG, Khan S, \nBarwick TD. FDG PET/CT pitfalls in gynecologic and \ngenitourinary oncologic imaging. Radiographics. 2017 \nMar;37(2):577-94. \n40. Yang L, Liu J, Wu S, Han Y, Bai J, Shi Q. Comparison of high -\nintensity focused ultrasound ablation for uterine fibroids with \ndifferent bloo d flow grading based on Alder classification. \nInternational Journal of Hyperthermia. 2025 Dec 31;42(1):2519346.","source_license":"CC0","license_restricted":false}