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

Clinicopathological characteristics and prognosis of uterine sarcoma: a 10-year retrospective single-center study in China

BACKGROUND

Uterine sarcoma is a rare and heterogeneous gynecological malignancy characterized by aggressive progression and poor prognosis. The current study aimed to investigate the relationship between clinicopathological characteristics and the prognosis of uterine sarcoma in Chinese patients.

METHODS

In this single-center retrospective study, we reviewed the medical records of 75 patients with histologically verified uterine sarcoma treated at the First Affiliated Hospital of Xi'an Jiaotong University between 2011 and 2020. Information on clinical characteristics, treatments, pathology and survival was collected. Progression-free survival (PFS) and overall survival (OS) were visualized in Kaplan-Meier curves. Prognostic factors were identified using the log-rank test for univariate analysis and Cox-proportional hazards regression models for multivariate analysis.

RESULTS

The histopathological types included 36 endometrial stromal sarcomas (ESS,48%), 33 leiomyosarcomas (LMS,44%) and 6 adenosarcomas (8%). The mean age at diagnosis was 50.2 ± 10.7 years. Stage I and low-grade accounted for the majority. There were 26 recurrences and 25 deaths at the last follow-up. The mean PFS and OS were 89.41 (95% CI: 76.07-102.75) and 94.03 (95% CI: 81.67-106.38) months, respectively. Univariate analysis showed that > 50 years, post-menopause, advanced stage, ≥ 1/2 myometrial invasion, lymphovascular space invasion and high grade were associated with shorter survival (P < 0.05). Color Doppler flow imaging positive signals were associated with shorter PFS in the LMS group (P = 0.046). The ESS group had longer PFS than that of the LMS group (99.56 vs. 76.05 months, P = 0.043). The multivariate analysis showed that post-menopause and advanced stage were independent risk factors of both PFS and OS in the total cohort and LMS group. In the ESS group, diagnosis age > 50 years and high-grade were independent risk factors of PFS, while high-grade and lymphovascular space invasion were independent risk factors of OS.

CONCLUSION

In Chinese patients with uterine sarcoma, post-menopause and advanced stage were associated with a significantly poorer prognosis. The prognosis of ESS was better than that of LMS. Color Doppler flow imaging positive signals of the tumor helped to identify LMS, which needs to be further tested in a larger sample in the future.

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.

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.

Contained Power Morcellation in Laparoscopic Uterine Myoma Surgeries: A Brief Review

Uterine fibromas are the most common benign uterine tumors. Although the majority of leiomyomas remain asymptomatic, they can cause serious clinical problems, including abnormal uterine bleeding, pelvic pain, and infertility, which require effective gynecological intervention. Depending on the symptoms as well as patients' preferences, various treatment options are available, such as medical therapy, non-invasive procedures, and surgical methods. Regardless of the extent of the surgery, the preferred option is the laparoscopic approach. To reduce the risk of spreading occult malignancy and myometrial cells associated with fragmentation of the specimen before its removal from the peritoneal cavity, special systems for laparoscopic contained morcellation have been developed. The aim of this review is to present the state-of-the-art contained morcellation. Different types of available retrieval bags are demonstrated. The advantages and difficulties associated with contained morcellation are described. The impact of retrieval bag usage on the course of surgery, as well as the effects of the learning curve, are discussed. The role of contained morcellation in the overall strategy to optimize patient safety is highlighted.

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.

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.

Leiomyoma with Bizarre Nuclei: A Current Update

Leiomyoma with bizarre nuclei (LBN), also known as symplastic leiomyoma, is a histological subtype of benign leiomyoma with bizarre cells and nuclear atypia. Differentiating LBN from other benign leiomyoma subtypes, uterine smooth muscle tumors of uncertain malignant potential (STUMP), or leiomyosarcoma (LMS) can be diagnostically challenging owing to overlapping features in clinical presentation and pathologic morphological analysis. The difficulty of distinguishing LBN from other lesions, especially from LMS, and the potential of LBN for subsequent malignant transformation make LBN an important topic of research. Herein, we review the definition, diagnosis, treatment, and prognosis of LBN. Histopathological examination is essential for distinguishing LBN from other diseases. Pathology sampling and morphological examination remain the key to diagnosis. The newly established ancillary immunohistochemical (IHC) and molecular genetic analysis can be useful tools for differential diagnosis. Furthermore, serum biomarkers and imaging examination may also be useful diagnostic tools. Attention should be paid to the differentiation between LBN and LMS because morphological diagnosis may still be challenging in some cases. Some IHC markers of LBN have been identified, which may be helpful for differential diagnosis. Furthermore, the use of IHC panels as diagnostic markers may be advocated. Molecular genetic studies suggest that some genes can aid with the differential diagnosis between LBN and LMS. However, increasing evidence support the idea that LBN and LMS are molecularly related, indicating that LBN may represent a potentially malignant stage of precancerous progression. At present, conservative treatment is recommended for primary LBN, especially for patients desiring to retain fertility, but close follow-up with imaging examinations is required.

Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas

OBJECTIVES

To explore the feasibility and effectiveness of machine learning (ML) based on multiparametric magnetic resonance imaging (mp-MRI) features extracted from transfer learning combined with clinical parameters to differentiate uterine sarcomas from atypical leiomyomas (ALMs).

METHODS

The data of 86 uterine sarcomas between July 2011 and December 2019 and 86 ALMs between June 2013 and June 2017 were retrospectively reviewed. We extracted deep-learning features and radiomics features from T2-weighted imaging (T2WI) and diffusion weighted imaging (DWI). The two feature extraction methods, transfer learning and radiomics, were compared. Random forest was adopted as the classifier. T2WI features, DWI features, combined T2WI and DWI (mp-MRI) features, and combined clinical parameters and mp-MRI features were applied to establish T2, DWI, T2-DWI, and complex multiparameter (mp) models, respectively. Predictive performance was assessed with the area under the receiver operating characteristic curve (AUC).

RESULTS

In the test set, the T2, DWI, T2-DWI and complex mp models based on transfer learning (AUCs range from 0.76 to 0.81, 0.80 to 0.88, 0.85 to 0.92, and 0.94 to 0.96, respectively) outperformed the models based on radiomics (AUCs of 0.73, 0.76, 0.79, and 0.92, respectively). Moreover, the complex mp model showed the best prediction performance, with the Resnet50-complex mp model achieving the highest AUC (0.96) and accuracy (0.87).

CONCLUSIONS

Transfer learning is feasible and superior to radiomics in the differential diagnosis of uterine sarcomas and ALMs in our dataset. ML models based on deep learning features of nonenhanced mp-MRI and clinical parameters can achieve good diagnostic efficacy.

KEY POINTS

• The ML model combining nonenhanced mp-MRI features and clinical parameters can distinguish uterine sarcomas from ALMs. • Transfer learning can be applied to differentiate uterine sarcomas from ALMs and outperform radiomics. • The most accurate prediction model was Resnet50-based transfer learning, built with the deep-learning features of mp-MRI and clinical parameters.

Preliminary Application of Magnetization Transfer Imaging in the Study of Normal Uterus and Uterine Lesions

Purpose

The aim of this study is to evaluate the utility of magnetization transfer (MT) imaging in the study of normal uterus and common uterine lesions.

Methods

This prospective study enrolled 160 consecutive patients with suspected uterine lesions. MT ratio (MTR) map was obtained by pelvic MT imaging on a 3.0T MRI scanner. Patients confirmed by pathology were divided into microscopic lesion group and lesion group, according to whether the maximum diameter of the lesion was less than 5 mm. After evaluating and eliminating patients with poor image quality by a three-point Likert scale, MTR values of lesions and normal endometrium, myometrium, and cervix were independently measured on the MTR map by two radiologists. Inter-reader agreement was evaluated. MTR values were compared among different uterine lesions and normal uterine structures using the Mann–Whitney U test with Bonferroni correction. Receiver operating characteristic curve was performed. The correlations between age and MTR values were explored by Pearson correlation analyses.

Results

A total of 96 patients with 121 uterine lesions in the lesion group and 41 patients in the microscopic lesion group were measured. The MTR values among normal endometrium, myometrium, and cervix were statistical significant differences (P < 0.05). There were significant differences between endometrial cancer and normal endometrium and between cervical cancer and normal cervix (both P ≤ 0.001). Area under the curve (AUC) for diagnosing endometrial and cervical cancer were 0.73 and 0.86. Myometrial lesions had significantly higher MTR values than endometrial lesions and cervical cancer (both P < 0.001), and the AUC for differentiating myometrial lesions from them were 0.89 and 0.94. MTR values of endometrial cancer were significantly higher than those of cervical cancer (P = 0.02). There was a critical correlation between age and MTR values in endometrial cancer (r = 0.81, P = 0.04).

Conclusions

MTR values showed significant differences among normal uterine structures. It was valuable for diagnosing and differentiating uterine cancer. MTR values could differentiate myometrial lesions from endometrial or cervical lesions.

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.

Utility of magnetic resonance imaging for differentiating malignant mesenchymal tumors of the uterus from T2-weighted hyperintense leiomyomas

PURPOSE

To generate a new discrimination method to distinguish between malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma based on magnetic resonance imaging findings and clinical features.

MATERIALS AND METHODS

Data from 32 tumors of 32 patients with malignant mesenchymal tumors of the uterus and from 34 tumors of 30 patients with T2-weighted hyperintense leiomyoma were analyzed. Clinical parameters, qualitative magnetic resonance imaging features, including computed diffusion-weighted imaging, and quantitative characteristics of magnetic resonance imaging of these two tumor types were compared. Predictive values for malignant mesenchymal tumors of the uterus were calculated using variant discriminant analysis.

RESULTS

The T1 bright area on qualitative assessment and mean apparent diffusion coefficient value on quantitative assessment yielded the most independent magnetic resonance imaging differentiators of malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma. The classification accuracy of the variant discriminant analysis based on three selected findings, i.e., a T1 bright area, computed diffusion-weighted imaging with a b-value of 2000s/mm² (cDWI₂₀₀₀), and T2-hypointense bands, was 84.8% (56/66), indicating high accuracy.

CONCLUSIONS

Variant discriminant analysis using the T1 bright area, cDWI₂₀₀₀, and T2-hypointense bands yielded high accuracy for differentiating between malignant mesenchymal tumors of the uterus and T2-weighted hyperintense leiomyoma.

Differentiating uterine sarcoma from leiomyoma: BET1T2ER Check!

Although rare, uterine sarcoma is a diagnosis that no one wants to miss. Often benign leiomyomas (fibroids) and uterine sarcomas can be differentiated due to the typical low T2 signal intensity contents and well-defined appearances of benign leiomyomas compared to the suspicious appearances of sarcomas presenting as large uterine masses with irregular outlines and intermediate T2 signal intensity together with possible features of secondary spread. The problem is when these benign lesions are atypical causing suspicious imaging features. This article provides a review of the current literature on imaging features of atypical fibroids and uterine sarcomas with an aide-memoire BET¹T²ER Check! to help identify key features more suggestive of a uterine sarcoma.

Diagnostic interpretation of non-contrast qualitative MR imaging features for characterisation of uterine leiomyosarcoma

OBJECTIVE

To assess the value of non-contrast MRI features for characterisation of uterine leiomyosarcoma (LMS) and differentiation from atypical benign leiomyomas.

METHODS

This study included 57 atypical leiomyomas and 16 LMS which were referred pre-operatively for management review to the specialist gynaeoncology multidisciplinary team meeting. Non-contrast MRIs were retrospectively reviewed by five independent readers (three senior, two junior) and a 5-level Likert score (1-low/5-high) was assigned to each mass for likelihood of LMS. Evaluation of qualitative and quantitative MRI features was done using uni- and multivariable regression analysis. Inter-reader reliability for the assessment of MRI features was calculated by using Cohen's κ values.

RESULTS

In the univariate analysis, interruption of the endometrial interface and irregular tumour shape had the highest odds ratios (ORs) (64.00, p < 0.001 and 12.00, p = 0.002, respectively) for prediction of LMS. Likert score of the mass was significant in prediction (OR, 3.14; p < 0.001) with excellent reliability between readers (ICC 0.86; 95% CI, 0.76-0.92). The post-menopausal status, interruption of endometrial interface and thickened endometrial stripe were the most predictive independent variables in multivariable estimation of the risk of leiomyosarcoma with an accuracy of 0.88 (95%CI, 0.78-0.94).

CONCLUSION

At any level of expertise as a radiologist reader, the loss of the normal endometrial stripe (either thickened or not seen) in a post-menopausal patient with a myometrial mass was highly likely to be LMS.

ADVANCES IN KNOWLEDGE

This study demonstrates the potential utility of non-contrast MRI features in characterisation of LMS over atypical leiomyomas, and therefore influence on optimal management of these cases.

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.

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.

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.

Preliminary utilization of radiomics in differentiating uterine sarcoma from atypical leiomyoma: Comparison on diagnostic efficacy of MRI features and radiomic features

OBJECTIVES

To explore whether MRI and radiomic features can differentiate uterine sarcoma from atypical leiomyoma. And to compare diagnostic performance of radiomic model with radiologists.

METHODS

78 patients (29 sarcomas, 49 leiomyomas) imaged with pelvic MRI prior to surgery were included in this retrospective study. Certain clinical and MRI features were evaluated for one lesion per patient. Radiological diagnosis was made based on MRI features. A radiomic model using automated texture analysis based on ADC maps was built to predict pathological results. The association between MRI features and pathological results was determined by multivariable logistic regression after controlling for other variables in univariate analyses with P <  0.05. The diagnostic efficacy of radiologists and radiomic model were compared by area under the receiver-operating characteristic curve (AUC), sensitivity, specificity and accuracy.

RESULTS

In univariate analyses, patient's age, menopausal state, intratumor hemorrhage, tumor margin and uterine endometrial cavity were associated with pathological results, P <  0.05. Patient's age, tumor margin and uterine endometrial cavity remained significant in a multivariable model, P <  0.05. Diagnosis efficacy of radiologists based on MRI reached an AUC of 0.752, sensitivity of 58.6%, specificity of 91.8%, and accuracy of 79.5%. The optimal radiomic model reached an AUC of 0.830, sensitivity of 76.0%, average specificity of 73.2%, and accuracy of 73.9%.

CONCLUSIONS

Ill-defined tumor margin and interrupted uterine endometrial cavity of older women were predictors of uterine sarcoma. Radiomic analysis was feasible. Optimal radiomic model showed comparable diagnostic efficacy with experienced radiologists.

The Usefulness of Immunohistochemistry in the Differential Diagnosis of Lesions Originating from the Myometrium

Uterine leiomyomas (LMs), currently the most common gynecological complaint around the world, are a serious medical, social and economic problem. Accurate diagnosis is the necessary prerequisite of the diagnostic-therapeutic process. Statistically, mistakes may occur more often in case of disease entities with high prevalence rates. Histopathology, based on increasingly advanced immunohistochemistry methods, is routinely used in the diagnosis of neoplastic diseases. Markers of the highest sensitivity and specificity profiles are used in the process. As far as LMs are concerned, the crux of the matter is to identify patients with seemingly benign lesions which turn out to be suspicious (e.g., atypical LM) or malignant (e.g., leiomyosarcoma (LMS)), which is not uncommon. In this study, we present the current state of knowledge about the use of immunohistochemical markers in the differential diagnosis of LM, atypical LM, smooth muscle tumors of uncertain malignant potential (STUMP), and LMS, as well as their clinical predictive value.