Magnetic resonance and diffusion-weighted imaging in categorization of uterine sarcomas: correlation with pathological findings

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.

Clinical and multiparametric MRI features for differentiating uterine carcinosarcoma from endometrioid adenocarcinoma

INTRODUCTION

The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features.

METHODS

We retrospectively evaluated clinical and MRI findings in 17 patients with UCS and 34 patients with EAC proven by histologically. The following clinical and pathological features were evaluated: post- or pre-menopausal, clinical presentation, invasion depth, FIGO stage, lymphaticmetastasis. The following MRI features were evaluated: tumor dimension, cystic degeneration or necrosis, hemorrhage, signal intensity (SI) on T2-weighted images (T2WI), relative SI of lesion to myometrium on T2WI, T1WI, DWI, ADCmax, ADCmin, ADCmean (RSI-T2, RSI-T1, RSI-DWI, RSI-ADCmax, RSI-ADCmin, RSI-ADCmean), ADCmax, ADCmin, ADCmean, the maximum, minimum and mean relative enhancement (RE) of lesion to myometrium on the arterial and venous phases (REAmax, REAmin, REAmean, REVmax, REVmin, REVmean). Receiver operating characteristic (ROC) analysis and the area under the curve (AUC) were used to evaluate prediction ability.

RESULTS

The mean age of UCS was higher than EAC. UCS occurred more often in the postmenopausal patients. UCS and EAC did not significantly differ in depth of myometrial invasion, FIGO stage and lymphatic metastasis. The anterior-posterior and transverse dimensions were significantly larger in UCS than EAC. Cystic degeneration or necrosis and hemorrhage were more likely occurred in UCS. The SI of tumor on T2WI was more heterogeneous in UCS. The RSI-T2, ADCmax, ADCmean, RSI-ADCmax and RSI-ADCmean of UCS were significantly higher than EAC. The REAmax, REAmin, REAmean, REVmax, REVmin and REVmean of UCS were all higher than EAC. The AUCs were 0.72, 0.71, 0.86, 0.96, 0.89, 0.84, 0.73, 0.97, 0.88, 0.94, 0.91, 0.69 and 0.80 for the anterior-posterior dimension, transverse dimension, RSI-T2, ADCmax, ADCmean, RSI-ADCmax, RSI-ADCmean, REAmax, REAmin, REAmean, REVmax, REVmin and REVmean, respectively. The AUC was 0.997 of the combined of ADCmax, REAmax and REVmax. Our study showed that ADCmax threshold value of 789.05 (10-3mm2/s) can differentiate UCS from EAC with 100% sensitivity, 76.5% specificity, and 0.76 AUC, REAmax threshold value of 0.45 can differentiate UCS from EAC with 88.2% sensitivity, 100% specificity, and 0.88 AUC.

CONCLUSION

Multiparametric MRI features may be utilized as a biomarker to distinguish UCS from EAC.

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.

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.

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.

A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI

OBJECTIVE

This study aims to develop a machine learning model for prediction of malignancy in T2 hyperintense mesenchymal uterine tumors based on T2-weighted image (T2WI) features and clinical information.

METHODS

This retrospective study included 134 patients with T2 hyperintense uterine mesenchymal tumors (104 patients in training cohort and 30 in testing cohort). A total of 960 radiomics features were initially computed and extracted from each 3D segmented tumor depicting on T2WI. The support vector machine (SVM) classifier was applied to build computer-aided diagnosis (CAD) models by using selected clinical and radiomics features, respectively. Finally, an observer study was conducted by comparing with two radiologists to evaluate the diagnostic performance. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model.

RESULTS

Comparing with the T2WI-based radiomics model (AUC: 0.76 ± 0.09) and the clinical model (AUC: 0.79 ± 0.09), the combined model significantly improved the AUC value to 0.91 ± 0.05 (p < 0.05). The clinical-radiomics combined model yielded equivalent or higher performance than two radiologists (AUC: 0.78 vs. 0.91, p = 0.03; 0.90 vs.0.91, p = 0.13). There was a significant difference between the AUC values of two radiologists (p < 0.05).

CONCLUSIONS

It is feasible to predict malignancy risk of T2 hyperintense uterine mesenchymal tumors by combining clinical variables and T2WI-based radiomics features. Machine learning-based classification model may be useful to assist radiologists in decision-making.

KEY POINTS

• Radiomics approach has the potential to distinguish between benign and malignant mesenchymal uterine tumors. • T2WI-based radiomics analysis combined with clinical variables performed well in predicting malignancy risk of T2 hyperintense uterine mesenchymal tumors. • Machine learning-based classification model may be useful to assist radiologists in characterization of a T2 hyperintense uterine mesenchymal tumor.

The value of clinical parameters combined with magnetic resonance imaging (MRI) features for preoperatively distinguishing different subtypes of uterine sarcomas: An observational study (STROBE compliant)

To investigate clinical parameters combined with magnetic resonance imaging (MRI) features including apparent diffusion coefficient (ADC) values in preoperative identification of different subtypes of uterine sarcomas including uterine leiomyosarcoma (LMS), endometrial stromal sarcoma (ESS), and carcinosarcoma (CS).Data from 71 patients with uterine sarcoma confirmed by surgery and pathology were collected. The clinical characteristics, conventional MRI features, mean ADC values, minimum ADC values, and lesion-muscle ADC ratio (rADC) values were compared with different subtypes of uterine sarcomas.Age, clinical manifestation, tumor location, shape, and T1-weighted image (T1WI) signals were significantly different between CS and LMS or ESS (all P < .01). The presence of band sign was significantly higher in ESS than in LMS or CS (both P < .001). The cystic change or necrosis and enhancement could help to differentiate LMS from ESS or CS (both P < .02). Significant differences were observed in T2-weighted image (T2WI) signals of the solid components of LMS compared with CS (P < .001). There was a significant difference between ESS and CS in the rADC values (P = .004).Clinical parameters combined with MRI features could help narrowing preoperative diagnostic possibilities in distinguishing subtypes of uterine sarcomas. These findings may be beneficial in helping guide operative decisions.

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.

PREOPERATIVE MAGNETIC RESONANCE IMAGING DIAGNOSTIC FEATURES OF UTERINE LEIOMYOSARCOMAS: A SYSTEMATIC REVIEW

Objectives: There are no current established pathognomonic diagnostic features for uterine leiomyosarcomas in the pre- or perioperative setting. Recent inadvertent upstaging of this rare malignancy during laparoscopic morcellation of a presumed fibroid has prompted widespread debate among clinicians regarding the safety of current surgical techniques for management of fibroids. This study aims to conduct a systematic review investigating significant diagnostic features in magnetic resonance imaging (MRI) of uterine leiomyosarcomas.

Methods: A comprehensive database search was conducted guided by PRISMA recommendations for peer-reviewed publications to November 2017. Parameters available in MRI were compared for reliability and accuracy of diagnosis of leiomyosarcomas. A decision tree algorithm classifier model was constructed to investigate whether T1 and T2 MRI signal intensities are useful indicators.

Results: Nine eligible studies were identified for analysis. There appears to be a significant relationship between histopathological type and T1 and T2 intensity signals (p < .05). A decision tree model analyzing T1 and T2 signal intensity readings supports this trend, with a diagnostic specificity of 77.78 percent for uterine leiomyosarcomas. The apparent diffusion coefficient (ADC) values were not observed to have a significant relationship with tumor pathology (p = .18).

Conclusions: Various studies have investigated pre- and perioperative techniques in differentiating uterine leiomyosarcoma from benign fibroids. Given the rarity of the malignancy and lack of pathognomonic diagnostic parameters, there is difficulty in establishing definitive criteria. A decision tree model is proposed to aid diagnosis based on MRI signal intensities.

What MRI features suspect malignant pure mesenchymal uterine tumors rather than uterine leiomyoma with cystic degeneration?

Objective

To retrospectively assess conventional magnetic resonance imaging (MRI) features that differentiate malignant pure mesenchymal uterine tumors (MPMUT); endometrial stromal sarcoma (ESS) and leiomyosarcoma (LMS) from uterine leiomyoma with cystic degeneration (ULCD).

Methods

We retrospectively reviewed magnetic resonance (MR) images of 30 patients with ULCD, 18 with ESS, and 15 with LMS, to assess tumor location, margin, T2 signal intensity (SI), speckled appearance, and peripheral band using univariate and multivariate analyses.

Results

ULCD more frequently showed subserosal location (53.3%), well-defined margin (96.7%), and speckled appearance (90.0%) compared with ESS (0%, 33.3%, and 33.3%, respectively) or LMS (20.0%, 33.3%, and 60.0%, respectively). In quantitative T2 SI comparisons, the T2 SI ratio of the main solid tumor portion to gluteus maximus muscle differed significantly among the three groups, with ULCD showing a lower SI ratio (0.62) compared with ESS (2.44) and LMS (1.13). On multivariate analysis, an ill-defined margin (odds ratio [OR]=44.885; p=0.003) and high T2 SI (OR=4.396; p=0.046) were the significant MR differentiators.

Conclusion

An ill-defined tumor margin and high T2 SI ratio of the main solid tumor-to-gluteus maximus muscle were useful MRI features in the differentiation of MPMUT from ULCD.

Diffusion weighted imaging in gynecological malignancies - present and future

The management of gynaecological malignancies has undergone a significant change in recent years with our improved understanding of cancer biogenetics, development of new treatment regimens and enhanced screening. Due to the rapid blooming of newer methods and techniques in gynaecology, surgery and oncology the scope and the role of imaging has also widened. Functional imaging in the form of diffusion weighted imaging (DWI) has been recently found to be very useful in assessing various tumours. Its ability to identify changes in the molecular level has dramatically changed the diagnostic approach of radiologists which was solely based on morphological criteria. It can improve the diagnostic accuracy of conventional magnetic resonance imaging, lend a hand in assessing tumour response to treatment regimens and detect tumour recurrence with better spatial resolution, negative radiation and diagnostic accuracy compared to positron emission tomography scan. The ability to quantify the diffusion has also lead to potential prediction of tumour aggressiveness and grade which directly correlate with the patient prognosis and management. Hence, it has become imperative for a radiologist to understand the concepts of DWI and its present and evolving role. In this article we present a brief description of the basics of DWI followed by its role in evaluation of female gynaecological malignancies.

Uterine sarcoma vs adenocarcinoma: can MRI distinguish between them?

OBJECTIVE

To analyze the MRI characteristics of uterine sarcomas (mainly carcinosarcomas) and to compare them with those of adenocarcinomas to define the findings that would be useful for the differential diagnosis.

MATERIALS AND METHODS

We retrospectively reviewed the MRI studies of 13 patients with histologically diagnosed uterine sarcoma. We analyzed tumor size, signal in T2-weighted, unenhanced and gadolinium-enhanced T1-weighted, and diffusion-weighted sequences. We compared the data obtained with those of another series of 30 consecutive cases of adenocarcinomas studied with MRI.

RESULTS

The sarcomas (> 9cm in 77% of cases) were considerably larger than the adenocarcinomas (p<0.001). There were no differences in FIGO staging by MRI or surgery: both tumor types were diagnosed in early stages. The signal intensity in T2-weighted images differed significantly between the two tumor types: all the sarcomas were heterogeneous and predominantly hyperintense with respect to the myometrium in T2-weighted sequences (p<0.001). In postcontrast studies, all the sarcomas showed enhancement greater than or equal to the myometrium; this finding was significantly different from the adenocarcinomas (p<0.001). In diffusion-weighted sequences, we found no significant differences in ADC values in the areas with greatest restriction, but the ADC map was more heterogeneous in the sarcomas.

CONCLUSION

Uterine sarcomas do not have specific characteristics on MRI, but some findings can indicate the diagnosis. In our study, we found significant differences between sarcomas and adenocarcinomas. Sarcomas were larger, had more hyperintense and heterogeneous signal intensity in T2-weighted sequences, and enhanced more than or at least as much as the myometrium.