Diffusion-Weighted Imaging for Differentiating Uterine Leiomyosarcoma From Degenerated Leiomyoma:

The utility of MRI for the preoperative differential diagnosis of uterine sarcoma and leiomyoma:a single-center study

Although uterine sarcoma is a rare disease, its prognosis is extremely poor;thus, it is important to differentiate it from uterine leiomyoma.　In this retrospective study, we examined the association between preoperative MRI findings and postoperative pathology results in 170 patients with uterine tumors who underwent preoperative MRI examination at Fukushima Red Cross Hospital.　In 4 cases of sarcoma / smooth muscle tumor of unknown malignant potential (STUMP), abnormal findings were found at a high frequency with T1-weighted imaging (T1WI) (75%), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast enhancement (CE) (100%).　In cases of ordinary leiomyoma, on the other hand, abnormal findings were less frequent.　The rates of high DWI signal intensity for degenerated and cellular leiomyoma were 31% and 64%, respectively, and the CE-positive rates were 31% and 57%, respectively.　Apparent Diffusion Coefficient (ADC) values appeared to be useful in differentiating degenerated leiomyoma from sarcoma.　The relatively characteristic findings of uterine sarcoma on MRI images may overlap with those of degenerated leiomyoma and cellular leiomyoma, making it difficult to diagnose sarcoma on imaging alone.　However, findings that distinguish sarcoma from ordinary, degenerated, and cellular leiomyoma cases are worthy of attention, to avoid overlooking sarcoma.

Utility of ADC Values for Differentiating Uterine Sarcomas From Leiomyomas: Systematic Review and Meta-Analysis

BACKGROUND. Uterine sarcomas are rare; however, they display imaging features that overlap those of leiomyomas. The potential for undetected uterine sarcomas is clinically relevant because minimally invasive treatment of leiomyomas may lead to cancer dissemination. ADC values have shown potential for differentiating benign from malignant uterine masses. OBJECTIVE. The purpose of this study was to perform a systematic review of the diagnostic performance of ADC values in differentiating uterine sarcomas from leiomyomas. EVIDENCE ACQUISITION. We searched three electronic databases (the MEDLINE, Embase, and Cochrane databases) for studies distinguishing uterine sarcomas from leiomyomas using MRI, including ADC values, with pathologic tissue confirmation or imaging follow-up used as the reference standard. Data extraction and QUADAS-2 quality assessment were performed. Sensitivity and specificity were pooled using hierarchical models, including bivariate and hierarchical summary ROC models. Metaregression was used to assess the impact of various factors on heterogeneity. EVIDENCE SYNTHESIS. Twenty-one studies met the study inclusion criteria. Pooled sensitivity and specificity were 89% (95% CI, 82-94%) and 86% (95% CI, 78-92%), respectively. The area under the summary ROC curve was 0.94 (95% CI, 0.92-0.96). The context of the ADC interpretation (i.e., used as a stand-alone assessment vs integrated as part of multiparametric MRI [mpMRI]) was the only factor found to account significantly for heterogeneity (p = .01). Higher specificity (95% [95% CI, 92-99%] vs 82% [95% CI, 75-89%]) and similar sensitivity (94% [95% CI, 89-99%] vs 88% [95% CI, 82-93%]) were observed when ADC was evaluated among mpMRI features rather than as a stand-alone ADC assessment. ADC cutoff values ranged from 0.87 to 1.29 × 10-3 mm2/s but were not associated with statistically different performance (p = .37). Pooled mean ADC values for sarcomas and leiomyomas were 0.904 × 10-3 mm2/s and 1.287 × 10-3 mm2/s, respectively. CONCLUSION. As part of mpMRI evaluation of uterine masses, a mass ADC value of less than 0.904 × 10-3 mm2/s may be a useful test-positive threshold for uterine sarcoma, consistent with the findings of a prior expert consensus statement. Institutional protocols may influence locally selected ADC values. CLINICAL IMPACT. Using ADC as part of mpMRI assessment improves detection of uterine sarcoma, which could influence candidate selection for minimally invasive treatments. TRIAL REGISTRATION. Prospective Register of Systematic Reviews CRD42024499383.

Risk assessment and prediction of occult uterine sarcoma in patients with presumed uterine fibroids before high-intensity focused ultrasound treatment

OBJECTIVE

To develop a diagnostic model for predicting occult uterine sarcoma in patients with presumed uterine fibroids.

MATERIALS AND METHODS

We retrospectively reviewed 41631 patients with presumed uterine fibroids who presented for HIFU treatment in 13 hospitals between November 2008 and October 2023. Of these patients, 27 with occult uterine sarcoma and 54 with uterine fibroids were enrolled. Univariate analysis and multivariate logistics regression analysis were used to determine the independent risk factors for the diagnosis of occult uterine sarcoma. A prediction model was constructed based on the coefficients of the risk factors.

RESULTS

The multivariate analysis revealed abnormal vaginal bleeding, ill-defined boundary of tumor, hyperintensity on T2WI, and central unenhanced areas as independent risk factors. A scoring system was created to assess for occult uterine sarcoma risk. The score for abnormal vaginal bleeding was 56. The score for ill-defined lesion boundary was 90. The scores for lesions with hypointensity, isointensity signal/heterogeneous signal intensity, and hyperintensity on T2WI were 0, 42, and 93, respectively. The scores for lesions without enhancement on the mass margin, uniform enhancement of tumor, and no enhancement in the center of tumor were 0, 20, and 100, respectively. Patients with a higher total score implied a higher likelihood of a diagnosis of occult uterine sarcoma than that of patients with a lower score. The established model showed good predictive efficacy.

CONCLUSIONS

Our results demonstrated that the diagnostic prediction model can be used to evaluate the risk of uterine sarcoma in patients with presumed uterine fibroids.

The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma

OBJECTIVE

Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources.

METHODS

The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas.

RESULTS

Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity.

CONCLUSION

Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta-analysis

BACKGROUND

Differential diagnosis between uterine leiomyomas and sarcomas is challenging. Magnetic resonance imaging (MRI) represents the second-line diagnostic method after ultrasound for the assessment of uterine masses.

OBJECTIVES

To assess the accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas.

SEARCH STRATEGY

A systematic review and meta-analysis was performed searching five electronic databases from their inception to June 2023.

SELECTION CRITERIA

All peer-reviewed observational or randomized clinical trials that reported an unbiased postoperative histologic diagnosis of uterine leiomyoma or uterine sarcoma, which also comprehended a preoperative MRI evaluation of the uterine mass.

DATA COLLECTION AND ANALYSIS

Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve on summary receiver operating characteristic of MRI in differentiating uterine leiomyomas and sarcomas were calculated as individual and pooled estimates, with 95% confidence intervals (CI).

RESULTS

Eight studies with 2495 women (2253 with uterine leiomyomas and 179 with uterine sarcomas), were included. MRI showed pooled sensitivity of 0.90 (95% CI 0.84-0.94), specificity of 0.96 (95% CI 0.96-0.97), positive likelihood ratio of 13.55 (95% CI 6.20-29.61), negative likelihood ratio of 0.08 (95% CI 0.02-0.32), diagnostic odds ratio of 175.13 (95% CI 46.53-659.09), and area under the curve of 0.9759.

CONCLUSIONS

MRI has a high diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas.

Neutrophil to lymphocyte ratio as an assessment tool to differentiate between uterine sarcoma and myoma: a systematic review and meta-analysis

BACKGROUND

This systematic review and meta-analysis aimed to determine the potential value of neutrophil to lymphocyte ratio (NLR) as an assessment tool in the clinical distinction between uterine sarcoma and uterine leiomyoma.

METHODS

We comprehensively searched Web of Science, Scopus, and PubMed for relevant papers published before March 19, 2023. The standardized mean difference (SMD) was provided, along with a 95% confidence interval (CI). The random-effects model was employed to derive pooled effects due to the high levels of heterogeneity. The Newcastle-Ottawa scale was used for the quality assessment. Our study was registered in PROSPERO (CRD42023478331).

RESULTS

Overall, seven articles were included in the analysis. A random-effect model revealed that patients with uterine sarcoma had higher NLR levels compared to those with uterine myoma (SMD = 0.60, 95% CI = 0.22-0.98; p = 0.002). In the subgroup analysis according to sample size, we found that patients with uterine sarcoma had elevated levels of NLR compared to those with uterine myoma in either large studies (SMD = 0.58, 95% CI = 0.04-1.13; P < 0.001) or small studies (SMD = 0.64, 95% CI = 0.33-0.96; P = 0.32). In the sensitivity analysis, we found that the final result was not significantly changed when single studies were removed, suggesting that the finding of this meta-analysis was stable. The pooled sensitivity of NLR was 0.68 (95% CI = 0.61-0.73), and the pooled specificity was 0.64 (95% CI = 0.59-0.69).

CONCLUSION

NLR might be utilized as an assessment tool in clinics to help clinicians differentiate between patients with uterine sarcoma and those with myoma.

Differentiating Uterine Sarcoma From Atypical Leiomyoma on Preoperative Magnetic Resonance Imaging Using Logistic Regression Classifier: Added Value of Diffusion-Weighted Imaging-Based Quantitative Parameters

OBJECTIVE

To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC).

RESULTS

Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10-3 mm²/s vs. 1.23 ± 0.25 10-3 mm²/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001).

CONCLUSION

The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.

High negative prediction for the Basel sarcoma score: Sonographic assessment of features suspicious of uterine sarcoma

INTRODUCTION

In the management of uterine myomas, laparoscopic surgery with morcellation enables a minimal invasive procedure. Cases of unsuspected uterine sarcoma dissemination have been reported and led to regulative restrictions. To help to distinguish preoperatively myomas from sarcomas, we assessed the value of six sonographic criteria (Basel Sarcoma Score, BSS) in a prospective outpatient cohort of consecutive patients with uterine masses.

MATERIAL AND METHODS

We prospectively evaluated all patients presenting with myoma-like masses planned for surgery with standardized ultrasound examination. BSS including the following criteria was investigated: rapid growth in past three months, high blood flow, atypical growth, irregular lining, central necrosis and oval solitary lesion. For each criterion, a score 0/1 was given. BSS (0-6) equals the sum of all given scores. Histological diagnosis was used as reference.

RESULTS

Among 545 patients, 522 had the final diagnosis of myoma, 16 had peritoneal masses with sarcomatous components (PMSC), and seven had other malignancies. Median BSS for PMSC was 2.5 (range: 0-4) vs 0 for myomas (range: 0-3). The most common sonographic criteria leading to a false positive score in myomas were rapid growth in past three months and high blood flow. For the detection of sarcomatous masses with BSS threshold of >1, sensitivity was 93.8%, specificity 97.9%, and positive predictive value (PPV) and negative predictive value (NPV) were 57.7% and 99.8%, respectively (AUC 0.95).

CONCLUSION

BSS can help distinguishing between myomas and sarcomatous masses, with high NPV. Caution is required when >1 criterion is present. As a simple tool, it could easily be integrated into routine myoma sonographic examination and help develop standardized assessment of uterine masses for better preoperative triage.

Development of an Algorithm to Differentiate Uterine Sarcoma from Fibroids Using MRI and LDH Levels

BACKGROUND

This study aimed to establish an evaluation method for detecting uterine sarcoma with 100% sensitivity using MRI and serum LDH levels.

METHODS

One evaluator reviewed the MRI images and LDH values of a total of 1801 cases, including 36 cases of uterine sarcoma and 1765 cases of uterine fibroids. The reproducibility of the algorithm was also examined by four evaluators with different imaging experience and abilities, using a test set of 61 cases, including 14 cases of uterine sarcoma.

RESULTS

From the MRI images and LDH values of 1801 cases of uterine sarcoma and uterine fibroids, we found that all sarcomas were included in the group with a high T2WI and either a high T1WI, an unclear margin, or high LDH values. In addition, when cases with DWI were examined, all sarcomas had high DWI. Among the 36 sarcoma cases, the group with positive findings for T2WI, T1WI, margins, and serum LDH levels all had a poor prognosis (p = 0.015). The reproducibility of the algorithm was examined by four evaluators and the sensitivity of sarcoma detection ranged from 71% to 93%.

CONCLUSION

We established an algorithm to distinguish uterine sarcoma if tumors in the myometrium with low T2WI and DWI are present.

Sonographic and Magnetic Resonance Characteristics of Gynecological Sarcoma

INTRODUCTION

Gynecological sarcomas are rare malignant tumors with an incidence of 1.5-3/100,000 and are 3-9% of all malignant uterine tumors. The preoperative differentiation between sarcoma and myoma becomes increasingly important with the development of minimally invasive treatments for myomas, as this means undertreatment for sarcoma. There are currently no reliable laboratory tests or imaging-characteristics to detect sarcomas. The objective of this article is to gain an overview of sarcoma US/MRI characteristics and assess their accuracy for preoperative diagnosis.

METHODS

A systematic literature review was performed and 12 studies on ultrasound and 21 studies on MRI were included.

RESULTS

For the ultrasound, these key features were gathered: solid tumor > 8 cm, unsharp borders, heterogeneous echogenicity, no acoustic shadowing, rich vascularization, and cystic changes within. For the MRI, these key features were gathered: irregular borders; heterogeneous; high signal on T2WI intensity; and hemorrhagic and necrotic changes, with central non-enhancement, hyperintensity on DWI, and low values for ADC.

CONCLUSIONS

These features are supported by the current literature. In retrospective analyses, the ultrasound did not show a sufficient accuracy for diagnosing sarcoma preoperatively and could also not differentiate between the different subtypes. The MRI showed mixed results: various studies achieved high sensitivities in their analysis, when combining multiple characteristics. Overall, these findings need further verification in prospective studies with larger study populations.

Methodological considerations on segmenting rhabdomyosarcoma with diffusion-weighted imaging-What can we do better?

PURPOSE

Diffusion-weighted MRI is a promising technique to monitor response to treatment in pediatric rhabdomyosarcoma. However, its validation in clinical practice remains challenging. This study aims to investigate how the tumor segmentation strategy can affect the apparent diffusion coefficient (ADC) measured in pediatric rhabdomyosarcoma.

MATERIALS AND METHODS

A literature review was performed in PubMed using search terms relating to MRI and sarcomas to identify commonly applied segmentation strategies. Seventy-six articles were included, and their presented segmentation methods were evaluated. Commonly reported segmentation strategies were then evaluated on diffusion-weighted imaging of five pediatric rhabdomyosarcoma patients to assess their impact on ADC.

RESULTS

We found that studies applied different segmentation strategies to define the shape of the region of interest (ROI)(outline 60%, circular ROI 27%), to define the segmentation volume (2D 44%, multislice 9%, 3D 21%), and to define the segmentation area (excludes edge 7%, excludes other region 19%, specific area 27%, whole tumor 48%). In addition, details of the segmentation strategy are often unreported. When implementing and comparing these strategies on in-house data, we found that excluding necrotic, cystic, and hemorrhagic areas from segmentations resulted in on average 5.6% lower mean ADC. Additionally, the slice location used in 2D segmentation methods could affect ADC by as much as 66%.

CONCLUSION

Diffusion-weighted MRI studies in pediatric sarcoma currently employ a variety of segmentation methods. Our study shows that different segmentation strategies can result in vastly different ADC measurements, highlighting the importance to further investigate and standardize segmentation.

The role of multiparametric MRI in differentiating uterine leiomyosarcoma from benign degenerative leiomyoma and leiomyoma variants: a retrospective analysis

AIM

To assess qualitative and quantitative magnetic resonance imaging (MRI) factors that can help distinguish leiomyosarcoma (LMS) from benign degenerative leiomyoma (BDL) and leiomyoma variants (LV) and assess the interobserver agreement for the proposed quantitative factors.

MATERIALS AND METHODS

Retrospective analysis of all histopathology proven cases of LV, BDL, and LMS with a preoperative MRI was performed. Twenty-seven cases were included (five LMS, three LV, and 19 BDL) with each case independently read by a pair of radiologists. Lesion size, margins, presence or absence of degeneration, necrosis, and haemorrhage were assessed on MRI along with quantitative factors such as mean T2-weighted (W) and T1W signal intensity, T1W signal heterogeneity, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) ratios as well as dynamic contrast enhancement (DCE) characteristics along with the presence or absence of lymphadenopathy and extra-uterine and peritoneal spread. Mean and standard deviation for quantitative variables and frequency with percentages for qualitative variables were assessed.

RESULTS

Infiltrative margins were seen exclusively in the LMS group (n=1), with the remaining LMS cases showing lobulate or rounded smooth margins similar to BDL or LV. A high T2W signal <25% was seen exclusively in the BDL group (n=8). The presence of concomitant necrosis and haemorrhage was seen exclusively in the LMS group (n=2). Quantitative MRI had good inter-reader correlation but was not significantly different between the LMS, BDL, and LV groups.

CONCLUSION

LMS, BDL, and LV may have overlapping features on multiparametric MRI making differentiation difficult.

Advances in the Preoperative Identification of Uterine Sarcoma

Simple Summary

As a lethal malignant tumor, uterine sarcomas lack specific diagnostic criteria due to their similar presentation with uterine fibroids, clinicians are prone to make the wrong diagnosis or adopt incorrect treatment methods, which leads to rapid tumor progression and increased metastatic propensity. In recent years, with the improvement of medical level and awareness of uterine sarcoma, more and more studies have proposed new methods for preoperative differentiation of uterine sarcoma and uterine fibroids. This review outlines the up-to-date knowledge about preoperative differentiation of uterine sarcoma and uterine fibroids, including laboratory tests, imaging examinations, radiomics and machine learning-related methods, preoperative biopsy, integrated model and other relevant emerging technologies, and provides recommendations for future research.

Abstract

Uterine sarcomas are rare malignant tumors of the uterus with a high degree of malignancy. Their clinical manifestations, imaging examination findings, and laboratory test results overlap with those of uterine fibroids. No reliable diagnostic criteria can distinguish uterine sarcomas from other uterine tumors, and the final diagnosis is usually only made after surgery based on histopathological evaluation. Conservative or minimally invasive treatment of patients with uterine sarcomas misdiagnosed preoperatively as uterine fibroids will shorten patient survival. Herein, we will summarize recent advances in the preoperative diagnosis of uterine sarcomas, including epidemiology and clinical manifestations, laboratory tests, imaging examinations, radiomics and machine learning-related methods, preoperative biopsy, integrated model and other relevant emerging technologies.

Neurofibromatosis from Head to Toe: What the Radiologist Needs to Know

Neurofibromatosis type 1 (NF1) and neurofibromatosis type 2 (NF2) are autosomal dominant inherited neurocutaneous disorders or phakomatoses secondary to mutations in the NF1 and NF2 tumor suppressor genes, respectively. Although they share a common name, NF1 and NF2 are distinct disorders with a wide range of multisystem manifestations that include benign and malignant tumors. Imaging plays an essential role in diagnosis, surveillance, and management of individuals with NF1 and NF2. Therefore, it is crucial for radiologists to be familiar with the imaging features of NF1 and NF2 to allow prompt diagnosis and appropriate management. Key manifestations of NF1 include café-au-lait macules, axillary or inguinal freckling, neurofibromas or plexiform neurofibromas, optic pathway gliomas, Lisch nodules, and osseous lesions such as sphenoid dysplasia, all of which are considered diagnostic features of NF1. Other manifestations include focal areas of signal intensity in the brain, low-grade gliomas, interstitial lung disease, various abdominopelvic neoplasms, scoliosis, and vascular dysplasia. The various NF1-associated abdominopelvic neoplasms can be categorized by their cellular origin: neurogenic neoplasms, interstitial cells of Cajal neoplasms, neuroendocrine neoplasms, and embryonal neoplasms. Malignant peripheral nerve sheath tumors and intracranial tumors are the leading contributors to mortality in NF1. Classic manifestations of NF2 include schwannomas, meningiomas, and ependymomas. However, NF2 may have shared cutaneous manifestations with NF1. Lifelong multidisciplinary management is critical for patients with either disease. The authors highlight the genetics and molecular pathogenesis, clinical and pathologic features, imaging manifestations, and multidisciplinary management and surveillance of NF1 and NF2. Online supplemental material is available for this article. ^©RSNA, 2022.

Uterine fibroid-like tumors: spectrum of MR imaging findings and their differential diagnosis

Uterine leiomyoma, also known as uterine fibroid, is the most common gynecological tumor, affecting almost 80% of women at some point during their lives. In the same time, other fibroid-like tumors have similar clinical presentations and about 0.5% of resected tumors of which were presumed benign fibroids in the preoperative diagnosis revealed as malignant sarcomas in the final histopathological examination. Amid the emergence of nonsurgical or minimally invasive procedures for symptomatic benign uterine fibroids, such as uterine artery embolization, high-intensity-focused ultrasound, or laparoscopic myomectomy, the preoperative diagnosis of uterine tumors through imaging becomes all the more relevant. Preoperative tissue sampling is challenging because of the variable location of the myometrial mass; thus, the preoperative evaluation of size and location is increasingly performed through magnetic resonance imaging. Features in images might also be useful for examining the full spectrum of such growths, from benign fibroids to neoplasms of uncertain behavior and malignant sarcomas. Benign fibroids include usual-type leiomyomas, myomas with degeneration, and mitotically active leiomyomas. Neoplasms of uncertain behavior include smooth muscle tumors of uncertain malignant potential, leiomyomas with bizarre nuclei, and cellular leiomyomas. Malignant sarcomas comprise leiomyosarcomas, endometrial stromal sarcomas, adenosarcomas, and carcinosarcomas. The purpose of this article is to review the spectrum of MRI findings of uterine fibroid-like tumors, from benign variants, uncertain behavior to malignant sarcomas, and update the advanced imaging modalities, including diffusion-weighted imaging, positron emission tomography/computed tomography, combining texture analysis and radiomics, to tackle this important issue.

Preoperative Differentiation of Uterine Leiomyomas and Leiomyosarcomas: Current Possibilities and Future Directions

The distinguishing of uterine leiomyosarcomas (ULMS) and uterine leiomyomas (ULM) before the operation and histopathological evaluation of tissue is one of the current challenges for clinicians and researchers. Recently, a few new and innovative methods have been developed. However, researchers are trying to create different scales analyzing available parameters and to combine them with imaging methods with the aim of ULMs and ULM preoperative differentiation ULMs and ULM. Moreover, it has been observed that the technology, meaning machine learning models and artificial intelligence (AI), is entering the world of medicine, including gynecology. Therefore, we can predict the diagnosis not only through symptoms, laboratory tests or imaging methods, but also, we can base it on AI. What is the best option to differentiate ULM and ULMS preoperatively? In our review, we focus on the possible methods to diagnose uterine lesions effectively, including clinical signs and symptoms, laboratory tests, imaging methods, molecular aspects, available scales, and AI. In addition, considering costs and availability, we list the most promising methods to be implemented and investigated on a larger scale.

MR Imaging of uterine sarcomas: a comprehensive review with radiologic-pathologic correlation

The aim of this article is to summarize the MRI features of each sarcoma subtype and to correlate them with its pathological findings. Literature review through PubMed/Medline database to identify relevant articles on uterine sarcomas, with a special emphasis on their MRI findings and pathological features. While several, more generalistic, MRI findings of a uterine tumour should raise suspicion for malignancy (including irregular contour, intra-tumoral necrosis/hemorrhage and low ADC values), some particular features may suggest their specific histological subtype such as the gross lymphovascular invasion associated with endometrial stromal sarcomas, the "bag of worms" appearance of the low-grade endometrial stromal sarcoma and the "lattice-like" aspect of adenosarcomas which results from the mixed composition of solid and multiseptated cystic components. Knowledge of the different histological uterine sarcoma subtypes, their specific MRI features and comprehension of their pathological background allows for a more confident diagnosis and may indicate the correct histological subtype.

New imaging modalities to distinguish rare uterine mesenchymal cancers from benign uterine lesions

PURPOSE OF REVIEW

Uterine sarcomas are rare and are often challenging to differentiate on imaging from benign mimics, such as leiomyoma. As functional MRI techniques have improved and new adjuncts, such as machine learning and texture analysis, are now being investigated, it is helpful to be aware of the current literature on imaging features that may sometimes allow for preoperative distinction.

RECENT FINDINGS

MRI, with both conventional and functional imaging, is the modality of choice for evaluating uterine mesenchymal tumors, especially in differentiating uterine leiomyosarcoma from leiomyoma through validated diagnostic algorithms. MRI is sometimes helpful in differentiating high-grade stromal sarcoma from low-grade stromal sarcoma or differentiating endometrial stromal sarcoma from endometrial carcinoma. However, imaging remains nonspecific for evaluating rarer neoplasms, such as uterine tumor resembling ovarian sex cord tumor or perivascular epithelioid cell tumor, primarily because of the small number and power of relevant studies.

SUMMARY

Through advances in MRI techniques and novel investigational imaging adjuncts, such as machine learning and texture analysis, imaging differentiation of malignant from benign uterine mesenchymal tumors has improved and could help reduce morbidity relating to misdiagnosis or diagnostic delays.

Reproducibility of MRI Features of Uterine Leiomyomas: A Study on Interobserver Agreement and Inter-Method Agreement With Surgery

PURPOSE

To evaluate interobserver agreement in the interpretation of different MRI features of uterine leiomyomas (UL) according to observers' experience, and to assess the inter-method reproducibility (MRI versus surgery) regarding the International Federation of Gynecology and Obstetrics (FIGO) classification.

METHODS

Retrospective study including UL patients who underwent MRI and surgical treatment. Four blinded observers (2 vs >10 years of experience) assessed UL regarding dimensions and volume; inner and outer mantles; FIGO classification; vascularization; degeneration; and diffusion-weighted imaging features. Uterine dimensions and volume were calculated. FIGO classification as ascertained by observers was compared to surgical findings. Intraclass correlation coefficient (ICC) estimates were used for interobserver comparison of numerical variables, and kappa statistic for categorical variables.

RESULTS

Thirty-five patients (26y-73y) with 61 UL were included in the interobserver analyses, and 31 patients (54 UL) had available data allowing retrospective surgical FIGO classification for assessment of inter-method reproducibility. Both groups of observers had good to excellent agreement in assessing UL (ICC = 0.980-0.994) and uterine volumes (ICC = 0.857-0.914), mantles measurement (ICC = 0.797-0.920), and apparent diffusion coefficient calculation (ICC = 0.787-0.883). There was substantial agreement for both groups regarding FIGO classification (κ = 0.645-0.767). Vascularization, degeneration and restricted diffusion had lower agreement, varying from reasonable to moderate. Inter-method agreement was reasonable (κ = 0.341-0.395).

CONCLUSIONS

Interobserver agreement of MRI for UL was higher for quantitative than qualitative features, with a little impact of observers' experience for most features. MRI agreement with surgery was reasonable. Further efforts should be taken to improve interobserver and inter-method reproducibility for MRI in this scenario.

Imaging of the Vagina: Spectrum of Disease with Emphasis on MRI Appearance

The vagina is a median fibromuscular structure of the female reproductive system that extends from the vulva inferiorly to the uterine cervix superiorly. As most vaginal lesions are detected at gynecologic examination, imaging performed for nongynecologic indications can frequently cause concomitant vaginal pathologic conditions to be overlooked. The vagina is often underevaluated at routinely performed pelvic transvaginal US because of a narrow scan area and probe positioning. MRI has progressively become the imaging method of choice for vaginal pathologic conditions, as it provides excellent soft-tissue detail with unparalleled delineation of the complex pelvic floor anatomy and helps establish a diagnosis for most vaginal diseases. It is important that radiologists use a focused approach toward understanding and correctly recognizing different vaginal entities that may otherwise go unnoticed. In this case-based review, the authors discuss the key imaging features of wide-ranging vaginal pathologic conditions, with emphasis on appearance at MRI. Knowledge of vaginal anatomy and embryology is helpful in evaluating congenital anomalies at imaging. Often seen incidentally, vaginal inflammation can cause diagnostic confusion. Because of its central location in the pelvis, the vagina can form fistulas to the urinary bladder, colon, rectum, or anus. Vaginal masses can be neoplastic and nonneoplastic and include a myriad of benign and malignant conditions, some of which have characteristic imaging features. Therapeutic and nontherapeutic vaginal foreign bodies include pessaries, vaginal mesh, and packing that can be seen with or without associated complications. Online supplemental material is available for this article. ^©RSNA, 2021.

Diagnostic Value of Preoperative CA125, LDH and HE4 for Leiomyosarcoma of the Female Reproductive System

Purpose

Leiomyosarcoma (LMS) is a rare and extremely aggressive malignancy that is derived from or shows evidence of differentiation toward smooth muscle. If LMS occurs in the female reproductive system, preoperative diagnosis can be difficult, as LMS is easily mistaken for a uterine leiomyoma, especially a degenerated uterine fibroid (DUF). Thus, we assessed the diagnostic value of the preoperative serum concentrations of cancer antigen 125 (CA125), lactate dehydrogenase (LDH) and human epididymis protein 4 (HE4) for differentiating LMS from DUF.

Patients and Methods

We enrolled patients with LMS or DUF who were receiving treatment in The First Affiliated Hospital of Chongqing Medical University between 2009 and 2020. If the preoperative serum concentrations of CA125, LDH and HE4 of our study participants had been tested, these data were analyzed. The preoperative serum concentrations of CA125, LDH and HE4 in participants with LMS (n = 37) were compared with those of participants with pathologically diagnosed DUF (n = 102), who served as the control group.

Results

The preoperative serum concentrations of CA125, LDH and HE4 of participants with LMS of the female reproductive system were significantly higher than those of participants with DUF (P = 0.009, P < 0.001, P = 0.001, respectively). The cut-off preoperative serum concentrations of CA125, LDH and HE4 were 30.85 U/mL, 186.50 U/L and 50.50 pmol/L, respectively. When these three parameters were used for an analysis of their combined diagnostic utility, the area under the curve (AUC) was 0.892, the sensitivity was 68.4% and the specificity was 95.1% (P < 0.001).

Conclusion

A combined analysis of the preoperative serum concentrations of CA125, LDH and HE4 could be a promising method for diagnostically differentiating LMS of the female reproductive system from DUF.

Diffusion-tensor imaging for differentiating uterine sarcoma from degenerative uterine fibroids

AIM

To explore the applicability of diffusion-tensor imaging (DTI) sequence quantitative parameters in differentiating uterine sarcoma (USr) from degenerative uterine fibroids (DUF).

MATERIALS AND METHODS

Fourteen cases of USr and 30 cases of DUF were analysed retrospectively. The diffusion-weighted imaging (DWI) and DTI images were analysed by two observers using Functool software on a ADW4.6 workstation. The images were post-processed to generate an apparent diffusion coefficient (ADC) map of DWI, ADC map of DTI (ADC^T map), and fractional anisotropy (FA) map. Three regions of interest (ROI) were selected from the ADC, ADC^T, and FA maps to obtain the ADC, ADC^T, and FA values. The receiver operating characteristic (ROC) curves of all parameters were used to analyse and compare the diagnostic value of USr and DUF.

RESULTS

The ADC value, ADC^T value, and FA value of USr (1.190 ± 0.262 × 10^-3mm²/s, 1.165 ± 0.270 × 10^-9mm²/s, 0.168 ± 0.063) were significantly lower compared to the values for DUF (1.525 ± 0.314 × 10^-3mm²/s, 1.650 ± 0.332 × 10^-9mm²/s, 0.254 ± 0.111; all p<0.001). The diagnostic threshold values for USr were: ADC ≤1.290 × 10^-3mm²/s, ADC^T ≤1.322 × 10^-9mm²/s and FA ≤0.192. The corresponding sensitivities and specificities were 78.6%/90%, 96.7%/92.9%, and 86.7%/85.7%, respectively. The areas under the curve (AUC) were 0.875, 0.974, and 0.831, respectively.

CONCLUSIONS

DTI quantitative parameters can be used to differentiate USr from DUF. The ADC^T value had the highest diagnostic efficacy.

Diagnostic Performance of MRI to Differentiate Uterine Leiomyosarcoma from Benign Leiomyoma: A Meta-Analysis

Purpose:

To perform a meta-analysis comparing the diagnostic performance of increased signal intensity on T1- and T2-weighted magnetic resonance images and apparent diffusion coefficient (ADC) values in differentiating uterine leiomyosarcoma (LMS) from benign leiomyoma (LM).

Methods:

A systematic literature search for original studies was performed using PubMed/MEDLINE, the Cochrane Library, Embase, and Web of Science. Data necessary for the meta-analysis was extracted from the selected articles and analyzed.

Results:

Eight studies with 795 patients met our predefined inclusion criteria and were included in the analysis. Increased signal on T1-weighted imaging had a pooled sensitivity of 56.8% (95% CI: 20%–87.4%) for LMS (n = 60) which was significantly higher than 7.6% (95% CI: 2.2%–22.7%) for LM (n = 1272) (p = 0.0094). Increased signal analysis on T2-weighted imaging had a pooled sensitivities of 93.2% and 93.2% (95% CI: 45.7%–99.6% and 42.9%–99.6%) for LMS (n = 90), which were not significantly different from the 54.5% and 53.9% (95% CI: 33.6%–74%, 32%–74%) for LM (n = 215) (p = 0.102 and 0.112). On ADC value analysis, LMS (n = 43) had a weighted mean and standard deviation of 0.896 ± 0.19 10^–3 mm²/s, 0.929 ± 0.182 10^–3 mm²/s, which were significantly lower from 1.258 ± 0.303 10^–3 mm²/s, 1.304 ± 0.303 10^–3 mm²/s for LM (n = 159) (p = < 0.0001, < 0.0001).

Conclusion:

Our meta-analysis demonstrated that high signal intensity on T1-weighted images and low ADC values can accurately differentiate LMS from LM. Although, LMS had a higher pooled sensitivity for T2-weighted increased signal intensity compared to LM, there was no statistical significance.

ACR Appropriateness Criteria® Abnormal Uterine Bleeding

This publication summarizes the relevant literature for the imaging of patients with symptoms of abnormal uterine bleeding, including initial imaging, follow-up imaging when the original ultrasound is inconclusive, and follow-up imaging when surveillance is appropriate. For patients with abnormal uterine bleeding, combined transabdominal and transvaginal ultrasound of the pelvis with Doppler is the most appropriate initial imaging study. If the uterus is incompletely visualized with the initial ultrasou2nd, MRI of the pelvis without and with contrast is the next appropriate imaging study, unless a polyp is suspected on the original ultrasound, then sonohysterography can be performed. If the patient continues to experience abnormal uterine bleeding, assessment with ultrasound of the pelvis, sonohysterography, and MRI of the pelvis without and with contrast would be appropriate. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

A Diagnostic Algorithm using Multi-parametric MRI to Differentiate Benign from Malignant Myometrial Tumors: Machine-Learning Method

This study aimed to develop a diagnostic algorithm for preoperative differentiating uterine sarcoma from leiomyoma through a supervised machine-learning method using multi-parametric MRI. A total of 65 participants with 105 myometrial tumors were included: 84 benign and 21 malignant lesions (belonged to 51 and 14 patients, respectively; based on their postoperative tissue diagnosis). Multi-parametric MRI including T1-, T2-, and diffusion-weighted (DW) sequences with ADC-map, contrast-enhanced images, as well as MR spectroscopy (MRS), was performed for each lesion. Thirteen singular MRI features were extracted from the mentioned sequences. Various combination sets of selective features were fed into a machine classifier (coarse decision-tree) to predict malignant or benign tumors. The accuracy metrics of either singular or combinational models were assessed. Eventually, two diagnostic algorithms, a simple decision-tree and a complex one were proposed using the most accurate models. Our final simple decision-tree obtained accuracy = 96.2%, sensitivity = 100% and specificity = 95%; while the complex tree yielded accuracy, sensitivity and specificity of 100%. To summarise, the complex diagnostic algorithm, compared to the simple one, can differentiate tumors with equal sensitivity, but a higher specificity and accuracy. However, it needs some further time-consuming modalities and difficult imaging calculations. Trading-off costs and benefits in appropriate situations must be determinative.

Differential Diagnosis of Uterine Leiomyoma and Uterine Sarcoma using Magnetic Resonance Images: A Literature Review

MRI plays an essential role in patients before treatment for uterine mesenchymal malignancies. Although MRI includes methods such as diffusion-weighted imaging and dynamic contrast-enhanced MRI, the differentiation between uterine myoma and sarcoma always becomes problematic. The present paper discusses important findings to ensure that sarcomas are not overlooked in magnetic resonance (MR) images, and we describe the update in the differentiation between uterine leiomyoma and sarcoma with recent reports.

How to differentiate uterine leiomyosarcoma from leiomyoma with imaging

Uterine leiomyomas, the most frequent benign myomatous tumors of the uterus, often cannot be distinguished from malignant uterine leiomyosarcomas using clinical criteria. Furthermore, imaging differentiation between both entities is frequently challenging due to their potential overlapping features. Because a suspected leiomyoma is often managed conservatively or with minimally invasive treatments, the misdiagnosis of leiomyosarcoma for a benign leiomyoma could potentially result in significant treatment delays, therefore increasing morbidity and mortality. In this review, we provide an overview of the differences between leiomyoma and leiomyosarcoma, mainly focusing on imaging characteristics, but also briefly touching upon their demographic, histopathological and clinical differences. The main indications and limitations of available cross-sectional imaging techniques are discussed, including ultrasound, computed tomography, magnetic resonance imaging (MRI) and positron emission tomography/computed tomography. A particular emphasis is placed on the review of specific MRI features that may allow distinction between leiomyomas and leiomyosarcomas according to the most recent evidence in the literature. The potential contribution of texture analysis is also discussed. In order to help guide-imaging diagnosis, we provide an MRI-based diagnostic algorithm which takes into account morphological and functional features, both individually and in combination, in an attempt to optimize radiologic differentiation of leiomyomas from leiomyosarcomas.

Current Status of Magnetic Resonance Imaging in Patients with Malignant Uterine Neoplasms: A Review

In this study, we summarize the clinical role of magnetic resonance imaging (MRI) in the diagnosis of patients with malignant uterine neoplasms, including leiomyosarcoma, endometrial stromal sarcoma, adenosarcoma, uterine carcinosarcoma, and endometrial cancer, with emphasis on the challenges and disadvantages. MRI plays an essential role in patients with uterine malignancy, for the purpose of tumor detection, primary staging, and treatment planning. MRI has advanced in scope beyond the visualization of the many aspects of anatomical structures, including diffusion-weighted imaging, dynamic contrast enhancement-MRI, and magnetic resonance spectroscopy. Emerging technologies coupled with the use of artificial intelligence in MRI are expected to lead to progressive improvement in case management of malignant uterine neoplasms.

Diagnostic value of combination serum assay of lactate dehydrogenase, D-dimer, and C-reactive protein for uterine leiomyosarcoma

AIM

Leiomyosarcoma is the most common type of uterine sarcoma. In some leiomyosarcoma cases, preoperative diagnosis might be difficult, and they might be treated as benign lesions. We evaluated diagnostic values of preoperative serum lactate dehydrogenase (LDH), D-dimer and C-reactive protein for differentiating leiomyosarcoma.

METHODS

From 2008 to 2013, leiomyosarcoma cases in three university hospitals were enrolled. Preoperative serum LDH, D-dimer and C-reactive protein were analyzed if tested. These markers of pathologically diagnosed leiomyoma cases presumed benign (group B) and presumed malignant (group PM) were compared with those of leiomyosarcoma cases (group S).

RESULTS

Groups S, PM and B had 36, 28 and 69 cases, respectively. Positive rates of LDH were 66.7%, 14.3% and 0% in groups S, PM and B, respectively. Positive rates of D-dimer and C-reactive protein were 83.3% and 64.5%, 17.9% and 10.7% and 5% and 2.9% in groups S, PM and B, respectively. Positive rates of all three markers were high in the order of leiomyosarcoma, atypical leiomyoma and typical leiomyoma. In group PM, 12 (63.2%) cases were negative for all three markers, whereas 1 (3.3%) case was negative in group S. No case was positive for all markers in group PM, whereas 41.2% leiomyosarcoma cases were positive for all markers. When all parameters were positive, specificity and positive predictive value were 100% in differentiating leiomyosarcoma from group PM.

CONCLUSION

Combination of LDH, D-dimer and C-reactive protein could be useful for distinguishing leiomyosarcoma from especially degenerated or atypical leiomyoma.

European Society of Urogenital Radiology (ESUR) Guidelines: MR Imaging of Leiomyomas

OBJECTIVE

The aim of the Female Pelvic Imaging Working Group of the European Society of Urogenital Radiology (ESUR) was to develop imaging guidelines for MR work-up in patients with known or suspected uterine leiomyomas.

METHODS

Guidelines for imaging uterine leiomyomas were defined based on a survey distributed to all members of the working group, an expert consensus meeting at European Congress of Radiology (ECR) 2017 and a critical review of the literature.

RESULTS

The 25 returned questionnaires as well as the expert consensus meeting have shown reasonable homogeneity of practice among institutions. Expert consensus and literature review lead to an optimized MRI protocol to image uterine leiomyomas. Recommendations include indications for imaging, patient preparation, MR protocols and reporting criteria. The incremental value of functional imaging (DWI, DCE) is highlighted and the role of MR angiography discussed.

CONCLUSIONS

MRI offers an outstanding and reproducible map of the size, site and distribution of leiomyomas. A standardised imaging protocol and method of reporting ensures that the salient features are recognised. These imaging guidelines are based on the current practice among expert radiologists in the field of female pelvic imaging and also incorporate essentials of the current published MR literature of uterine leiomyomas.

KEY POINTS

• MRI allows comprehensive mapping of size and distribution of leiomyomas. • Basic MRI comprise T2W and T1W sequences centered to the uterus. • Standardized reporting ensures pivotal information on leiomyomas, the uterus and differential diagnosis. • MRI aids in differentiation of leiomyomas from other benign and malignant entities, including leiomyosarcoma.

Utility of Clinical Parameters and Multiparametric MRI as Predictive Factors for Differentiating Uterine Sarcoma From Atypical Leiomyoma

OBJECTIVES

The objective of this study was to find clinical parameters and qualitative and quantitative magnetic resonance imaging (MRI) features for differentiating uterine sarcoma from atypical leiomyoma (ALM) preoperatively and to calculate predictive values for uterine sarcoma.

MATERIALS AND METHODS

Data from 60 patients with uterine sarcoma and 88 patients with ALM confirmed by surgery and pathology were collected. Clinical parameters, qualitative MRI features, diffusion-weighted imaging with apparent diffusion coefficient values, and quantitative parameters of dynamic contrast-enhanced MRI of these two tumor types were compared. Predictive values for uterine sarcoma were calculated using multivariable logistic regression.

RESULTS

Patient clinical manifestations, tumor locations, margins, T2-weighted imaging signals, mean apparent diffusion coefficient values, minimum apparent diffusion coefficient values, and time-signal intensity curves of solid tumor components were obvious significant parameters for distinguishing between uterine sarcoma and ALM (all P <.001). Abnormal vaginal bleeding, tumors located mainly in the uterine cavity, ill-defined tumor margins, and mean apparent diffusion coefficient values of <1.272 × 10^-3mm²/s were significant preoperative predictors of uterine sarcoma. When the overall scores of these four predictors were greater than or equal to 7 points, the sensitivity, the specificity, the accuracy, and the positive and negative predictive values were 88.9%, 99.9%, 95.7%, 97.0%, and 95.1%, respectively.

CONCLUSIONS

The use of clinical parameters and multiparametric MRI as predictive factors was beneficial for diagnosing uterine sarcoma preoperatively. These findings could be helpful for guiding treatment decisions.

Uterine Fibroids: Burden and Unmet Medical Need

Uterine fibroids affect a wide cross-section of the population, with prevalence, symptom severity, and overall disease burden generally higher among black women, likely due to both genetic and environmental factors. Potential symptoms of uterine fibroids include painful and excessive uterine bleeding, interference with everyday life and self-image, and impaired fertility. Because of the high estimated prevalence and costs associated with treatments, the direct and indirect costs of uterine fibroids are substantial for both the health care system and the individual patient. Special patient populations—such as black women, women seeking to retain fertility, and women with asymptomatic fibroids—have particular treatment needs that require a variety of diagnostic methods and treatment options. Despite the widespread occurrence of uterine fibroids and newer treatment options, little high-quality data are available to formulate evidence-based guidelines that address these unmet patient needs. Specific areas in need of attention include improving diagnostic techniques, increasing patient access to early treatment, and identifying best practices for this diverse patient population.