Differentiating uterine sarcoma from leiomyoma: BET1T2ER Check!

Metastatic malignant peripheral nerve sheath tumor of the uterus and cervix: Diagnostic Challenges, prognostic determinants and treatment

•MPNST is an uncommon sarcoma of the nerve sheath that is rarely found in the female reproductive tract.•Preoperative uterine mass imaging should include pelvic MRI and thorough evaluation of imaging by an expert pelvic MRI radiologist.•Metastatic MPNST has a poor prognosis and systemic treatment options lack efficacy.

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.

Validating the diagnostic accuracy of an MRI-based scoring system for differentiating benign uterine leiomyomas from leiomyosarcomas

OBJECTIVE

Uterine leiomyomas are the most common benign uterine tumors. They are difficult to distinguish from their malignant counterparts-smooth muscle tumors of unknown malignant potential (STUMP) and leiomyosarcoma. The purpose of this study is to propose and validate the diagnostic accuracy of the MRI-based Oman-Canada Scoring System of Myometrial Masses (OCSSMM) to differentiate uterine leiomyomas from STUMP/leiomyosarcomas.

METHODS

This is a retrospective study performed at two tertiary care centers. All patients with a pathology-proven uterine mass who underwent pre-operative pelvic MRI between January 2010 and January 2020 were included. Using a 1.5T MRI machine, sequences included were axial/coronal/sagittal T2 and T1 weighted imaging, axial diffusion weighted and apparent diffusion coefficient map, and axial or sagittal dynamic contrast-enhanced sequences. A scoring system was designed based on previously published worrisome MRI features for uterine leiomyosarcoma. Each feature was allocated a score from 0 to 2 according to the strength of association with malignancy. Subsequently, the MR images were blindly and independently reviewed by a fellowship-trained radiologist and a clinical fellow/senior resident. Each uterine mass was scored according to their imaging features. The scores were divided into five categories according to the sum of scores. Category III and above was considered positive for leiomyosarcoma/STUMP. Sensitivity, specificity, and positive and negative predictive values were calculated.

RESULTS

A total of 244 women were included (age range 20-74 years, mean 40). Of these, 218 patients had benign leiomyoma, 13 had STUMP, and 13 had leiomyosarcoma. The sensitivity and specificity of the scoring system were 92.3% and 64.7%, respectively. The negative predictive value was 98.6%. No leiomyosarcoma was missed using this scoring system. The presence of non-cystic T2 hyperintensity or diffusion restriction in a uterine mass were the most sensitive signs of a leiomyosarcoma/STUMP.

CONCLUSION

The proposed multi-parametric MRI scoring system may be useful in differentiating benign uterine leiomyomas from leiomyosarcomas/STUMP.

Rapidly progressing ascites in a pregnancy with a massive fibroid: A case report and review of pseudo-Meigs syndrome

Meigs syndrome is a classic triad of ascites, pleural effusions, and an ovarian fibroma with resolution following excision. Pseudo-Meigs syndrome presents similarly but is caused by a pelvic mass other than an ovarian fibroma, such as a fibroid. We present a case report of a 33-year-old gravida 2 para 0-0-1-0 woman with a massive, pedunculated fibroid who developed rapid onset of ascites and edema beginning at 5 weeks of gestation. Malignant, cardiac, renal, hepatic, and rheumatologic causes were ruled out. Her symptoms resolved following myomectomy and delivery via cesarean. Pseudo-Meigs syndrome was suspected. Pseudo-Meigs syndrome is a diagnosis of exclusion and requires surgical management for resolution. Pregnancy may be an inciting factor. Myomectomy may be done safely at the time of cesarean.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study

BACKGROUND

The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas.

METHODS

Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data.

RESULTS

A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85).

CONCLUSIONS

CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

Smooth muscle tumours of the uterus: MR imaging malignant predictive features-a 12-year analysis in a referral hospital in Portugal

PURPOSE

To evaluate the magnetic resonance imaging (MRI) features that may help distinguish leiomyosarcomas from atypical leiomyomas (those presenting hyperintensity on T2-W images equal or superior to 50% compared to the myometrium).

MATERIALS AND METHODS

The authors conducted a retrospective single-centre study that included a total of 57 women diagnosed with smooth muscle tumour of the uterus, who were evaluated with pelvic MRI, between January 2009 and March 2020. All cases had a histologically proven diagnosis (31 Atypical Leiomyomas-ALM; 26 Leiomyosarcomas-LMS). The MRI features evaluated in this study included: age at presentation, dimension, contours, intra-tumoral haemorrhagic areas, T2-WI heterogeneity, T2-WI dark areas, flow voids, cyst areas, necrosis, restriction on diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) values, signal intensity and heterogeneity after contrast administration in T1-WI, presence and location of unenhanced areas. The association between the MRI characteristics and the histological subtype was evaluated using Chi-Square and ANOVA tests.

RESULTS

The MRI parameters that showed a statistically significance correlation with malignant histology and thus most strongly associated with LMS were found to be: irregular contours (p < 0.001), intra-tumoral haemorrhagic areas (p = 0.028), T2-WI dark areas (p = 0.016), high signal intensity after contrast administration (p = 0.005), necrosis (p = 0.001), central location for unenhanced areas (p = 0.026), and ADC value lower than 0.88 × 10-3 mm2/s (p = 0.002).

CONCLUSION

With our work, we demonstrate the presence of seven MRI features that are statistically significant in differentiating between LMS and ALM.

Breast lump as the initial presentation of metastatic uterine leiomyosarcoma: a case report and comprehensive literature review

Uterine leiomyosarcoma (uLMS) is a rare but aggressive cancer with a high metastatic potential and an unfavorable prognosis. A 54-year-old woman with a history of uterine fibroids clinically presented with a painless, palpable left breast mass measuring 20 mm. A core biopsy of the breast mass demonstrated a cellular spindle cell neoplasm (a potentially malignant smooth muscle neoplasm; B4). A wide local breast-mass excision was performed, revealing grade-2 leiomyosarcoma. A re-review of the uterine fibroids revealed that the largest one (200 × 130 mm), initially diagnosed as symplastic leiomyoma, was morphologically identical to the breast lesion. Additional diagnostic work-up revealed multiple liver and pulmonary metastases with a suspected metastatic sclerotic lesion in the L3 projection. The patient was subsequently treated with chemotherapy protocol for metastatic uLMS. The latest follow-up in September 2023 confirmed stable disease. This case highlights the importance of considering unusual metastatic patterns when evaluating breast masses, particularly in patients with a history of non-specific uterine conditions. Comprehensive diagnostic work-up, including imaging and histopathologic examinations, is crucial for an accurate diagnosis of uLMS and appropriate treatment selection. Further studies are needed to better understand the underlying mechanisms and optimal management strategies for metastatic uLMS.

Leiomyoma or sarcoma? MRI performance in the differential diagnosis of sonographically suspicious uterine masses

PURPOSE

To assess the diagnostic performance of MRI in distinguishing between leiomyomas and malignant/potentially malignant mesenchymal neoplasms in patients with rapidly enlarging/sonographically suspicious uterine masses.

METHODS

IRB-approved retrospective study including 88 patients (51 ± 11 years) who underwent MRI for rapidly enlarging/sonographically suspicious uterine mass at our Institution between January 2016 and December 2021, followed by surgery or >12 months follow-up. Qualitative image analysis was independently performed by 2 radiologists and included lesion's margins (sharp/irregular), architecture (homogeneous/inhomogeneous), presence of endometrial infiltration (yes/no), necrotic areas (yes/no), hemorrhagic areas (yes/no), predominant signal intensity on T1-WI, T2-WI, CE T1-WI, DWI, and ADC map. The same radiologists performed quantitative image analysis in consensus, which included lesion's maximum diameter, lesion/myometrium signal intensity ratio on T2-WI and CE T1-weighted images, lesion/endometrium signal intensity ratio on DWI and ADC map and necrosis percentage. Lesions were classified as benign or malignant. Imaging findings were compared with pathology and/or follow-up.

RESULTS

After surgery (52/88 patients) or follow-up (36/88 patients, 33 ± 20 months), 83/88 (94.3%) lesions were classified as benign and 5/88 (5.7%) as malignant/potentially malignant. Presence of necrotic areas, high necrosis percentage, hyperintensity on DWI and high lesion/endometrium DWI signal intensity ratio were significantly associated with malignant/potentially malignant lesions (p = 0.027, 0.002, 0.008 and 0.015, respectively). The two readers identified malignant/potentially malignant lesions with 95.5% accuracy, 80.0% sensitivity, 96.4% specificity, 57.1 % PPV, 93.3% NPV.

CONCLUSION

MRI has high accuracy in identifying malignant/potentially malignant myometrial masses. In everyday practice, however, MRI positive predictive value is relatively low given the low pre-test malignancy probability.

Clinical features of uterine sarcomas presenting mainly with uterine masses: a retrospective study

BACKGROUND

Uterine sarcomas are uncommon mesenchymal tumors of the uterus. The clinical problem is that the features of uterine sarcomas can sometimes mimic uterine fibroids. This study aims to investigate the clinical characteristics of patients with uterine sarcomas who were preoperative presenting mainly with uterine masses.

METHODS

A retrospective analysis of patients who underwent gynecological surgery for uterine sarcomas at the Obstetrics & Gynecology Hospital of Fudan University, between January 2016 and December 2021.

RESULTS

Over the 5-year period, 277 patients were final diagnosed of uterine sarcomas. A total of 162 patients were preoperatively diagnosed as uterine fibroids for surgical treatment, the majority of whom were diagnosed of uterine leiomyosarcoma (uLMS) (49/162) and low-grade endometrial stromal sarcoma (LG-ESS) (100/162). Ninety people underwent total hysterectomy and bilateral salpingo-oophorectomy (TH + BSO), while 72 underwent myomectomy followed by supplemental TH + BSO. The group with direct hysterectomy had a higher average age than the group with prior myomectomy (47.20 ± 8.94 vs. 40.86 ± 5.88, p < 0.001). Among patients preoperatively diagnosed as uterine fibroids, patients with uLMS had a higher proportion of previous myomectomy (26.53% vs. 5.00%, p < 0.001), a larger uterine mass diameter on ultrasound (8.38 ± 3.39 cm vs. 6.41 ± 1.92 cm, p < 0.001), and richer hypervascularity (34.69% vs. 18%, p = 0.024) compared with LG-ESS.

CONCLUSIONS

Analysis of our data showed that a large proportion of uterine sarcomas, especially uLMS and LG-ESS, present mainly with uterine masses. Ultrasound features including a large uterine mass diameter and rich hypervascularity, and with a history of myomectomy may alert clinicians in suspicion of uLMS when compared with LG-ESS.

Uterine mesenchymal tumors: development and preliminary results of a magnetic resonance imaging (MRI) diagnostic algorithm

PURPOSE

The aim of our study is to propose a diagnostic algorithm to guide MRI findings interpretation and malignancy risk stratification of uterine mesenchymal masses with a multiparametric step-by-step approach.

METHODS

A non-interventional retrospective multicenter study was performed: Preoperative MRI of 54 uterine masses was retrospectively evaluated. Firstly, the performance of MRI with monoparametric and multiparametric approach was assessed. Reference standard for final diagnosis was surgical pathologic result (n = 53 patients) or at least 1-year MR imaging follow-up (n = 1 patient). Subsequently, a diagnostic algorithm was developed for MR interpretation, resulting in a Likert score from 1 to 5 predicting risk of malignancy of the uterine lesion. The accuracy and reproducibility of the MRI scoring system were then tested: 26 preoperative pelvic MRI were double-blind evaluated by a senior (SR) and junior radiologist (JR). Diagnostic performances and the agreement between the two readers with and without the application of the proposed algorithm were compared, using histological results as standard reference.

RESULTS

Multiparametric approach showed the best diagnostic performance in terms of accuracy (94.44%,) and specificity (97.56%). DWI was confirmed as the most sensible parameter with a relative high specificity: low ADC values (mean 0.66) significantly correlated to uterine sarcomas diagnosis (p < 0.01). Proposed algorithm allowed to improve both JR and SR performance (algorithm-aided accuracy 88.46% and 96%, respectively) and determined a significant increase in inter-observer agreement, helping even the less-experienced radiologist in this difficult differential diagnosis.

CONCLUSIONS

Uterine leiomyomas and sarcomas often show an overlap of clinical and imaging features. The application of a diagnostic algorithm can help radiologists to standardize their approach to a complex myometrial mass and to easily identify suspicious MRI features favoring malignancy.

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.

An update on uterine artery embolization for uterine leiomyomata and adenomyosis of the uterus

Uterine artery embolization (UAE) is an established technique to treat benign diseases of the uterus such as uterine leiomyomata (fibroids) and adenomyosis. This article reviews the use of UAE in these conditions and summarizes the evidence regarding safety and efficacy of the technique based on the current literature.

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.

MRI findings in-between leiomyoma and leiomyosarcoma: a Rad-Path correlation of degenerated leiomyomas and variants

Leiomyomas are the most common benign tumors of the uterus. On the opposite side, leiomyosarcomas are rare malignant uterine tumors that account for a significant proportion of uterine cancer deaths. Especially when large and degenerated, leiomyomas and leiomyoma variants can have overlapping imaging characteristics with those of leiomyosarcomas. Although not always possible, it is paramount to be able to differentiate between leiomyomas and leiomyosarcomas on imaging, as the therapeutic management can differ. This pictorial review aims to familiarize radiologists with imaging features of leiomyomas and various types of leiomyoma degeneration and variants, together with their pathology correlates.