Soft tissue masses with “cyst-like” appearance on MR imaging: distinction of benign and malignant lesions

Acro-osteolysis: imaging, differential diagnosis, and disposition review

Acro-osteolysis is the osseous destruction of the hand or foot distal phalanges. The categories of the disease include terminal tuft, midshaft, or mixed types. Recognition of acro-osteolysis is straightforward on radiographs, but providing an accurate differential diagnosis and appropriately recommending advanced imaging or invasive tissue diagnosis can be more elusive. A radiologist's ability to provide advanced assessment can greatly aid clinicians in expedient diagnosis and management of the array of diseases presenting with acro-osteolysis.

Texture analysis of magnetic resonance image to differentiate benign from malignant myxoid soft tissue tumors: A retrospective comparative study

It is important to differentiate between benign and malignant myxoid tumors to establish the treatment plan, determine the optimal surgical extent, and plan postoperative surveillance, but differentiation may be complicated by imaging-feature overlap. Texture analysis is used for quantitative assessment of imaging characteristics based on mathematically calculated pixel heterogeneity and has been applied to the discrimination of benign from malignant soft tissue tumors (STTs). In this study, we aimed to assess the diagnostic value of the texture features of conventional magnetic resonance images for the differentiation of benign from malignant myxoid STTs. Magnetic resonance images of 39 patients with histologically confirmed myxoid STTs of the extremities were analyzed. Qualitative features were assessed and compared between the benign and malignant groups. Texture analysis was performed, and texture features were selected based on univariate analysis and Fisher’s coefficient. The diagnostic value of the texture features was assessed using receiver operating curve analysis. T1 heterogeneity showed a statistically significant difference between benign and malignant myxoid STTs, with substantial inter-reader reliability. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of T1 heterogeneity were 55.6%, 83.3%, 88.2%, 45.5%, and 64.1%, respectively. Among the texture features, T2w-WavEnLL_s-3 showed good diagnostic performance, and T2w-WavEnLL_s-4 and GeoW4 showed fair diagnostic performance. The logistic regression model including T1 heterogeneity and T2_WavEnLL_s-4 showed good diagnostic performance. However, there was no statistically significant difference between the overall qualitative assessment by a radiologist and the predictor model. Geometry-based and wavelet-derived texture features from T2-weighted images were significantly different between benign and malignant myxoid STTs. However, the texture features had a limited additive value in differentiating benign from malignant myxoid STTs.

Radiomics of Musculoskeletal Sarcomas: A Narrative Review

Bone and soft-tissue primary malignant tumors or sarcomas are a large, diverse group of mesenchymal-derived malignancies. They represent a model for intra- and intertumoral heterogeneities, making them particularly suitable for radiomics analyses. Radiomic features offer information on cancer phenotype as well as the tumor microenvironment which, combined with other pertinent data such as genomics and proteomics and correlated with outcomes data, can produce accurate, robust, evidence-based, clinical-decision support systems. Our purpose in this narrative review is to offer an overview of radiomics studies dealing with Magnetic Resonance Imaging (MRI)-based radiomics models of bone and soft-tissue sarcomas that could help distinguish different histotypes, low-grade from high-grade sarcomas, predict response to multimodality therapy, and thus better tailor patients’ treatments and finally improve their survivals. Although showing promising results, interobserver segmentation variability, feature reproducibility, and model validation are three main challenges of radiomics that need to be addressed in order to translate radiomics studies to clinical applications. These efforts, together with a better knowledge and application of the “Radiomics Quality Score” and Image Biomarker Standardization Initiative reporting guidelines, could improve the quality of sarcoma radiomics studies and facilitate radiomics towards clinical translation.

Magnetic resonance imaging of trunk and extremity myxoid liposarcoma: diagnosis, staging, and response to treatment

Myxoid liposarcoma (MLS) accounts for approximately 30% of all liposarcomas. The majority are intermediate-grade tumours, but the presence of >5% round cell component renders it a high-grade sarcoma with subsequent poorer outcome. MLS most commonly arises in the lower extremities, has a predilection for extra-pulmonary sites of metastatic disease, and is recognized to be radiosensitive. The purpose of the current article is to review the role of MRI in the management of MLS, including the characteristic features of the primary tumour, features which help to identify a round cell component and thus determine prognosis, the role of whole-body MRI for evaluation of extra-pulmonary metastatic disease, and the utility of MRI for assessing treatment response. The MRI differential diagnosis of MLS is also considered.

Small Subcutaneous Soft Tissue Tumors (<5 cm) Can Be Sarcomas and Contrast-Enhanced Ultrasound (CEUS) Is Useful to Identify Potentially Malignant Masses

Subcutaneous masses smaller than 5 cm can be malignant, in contrast with the international guidelines. Ultrasound (US) and magnetic resonance imaging (MRI) are useful to distinguish a potentially malignant mass from the numerous benign soft tissue (ST) lesions. Contrast-enhanced ultrasound (CEUS) was applied in ST tumors, without distinguishing the subcutaneous from the deep lesions. We evaluated CEUS and MRI accuracy in comparison to histology in differentiating malignant from nonmalignant superficial ST masses, 50% smaller than 5 cm. Sensitivity, specificity, and positive and negative predictive values (PPV, NPV) with their 95% confidence intervals (CI) were calculated. Of malignant cases, 44.4% measured ≤5 cm. At univariate analysis, no statistically significant differences emerged between benign and malignant tumors in relation with clinical characteristics, except for relationship with the deep fascia (p = 0.048). MRI accuracy: sensitivity 52.8% (CI 37.0, 68.0), specificity 74.1% (CI 55.3, 86.8), PPV 73.1% (CI 53.9, 86.3), and NPV 54.1% (CI 38.4, 69.0). CEUS accuracy: sensitivity 75% (CI 58.9, 86.3), specificity 37% (CI 21.5, 55.8), PPV 61.4% (CI 46.6, 74.3), and NPV 52.6% (CI 31.7, 72.7). CEUS showed a sensitivity higher than MRI, whereas PPV and NPV were comparable. Also, masses measuring less than 5 cm can be malignant and referral criteria for centralization could be revised.

Primary Site Identification of Soft-Tissue Mass: Things to Know in MRI Assessment

The spectrum of soft-tissue mass is varied, including neoplastic and nonneoplastic/inflammatory lesions. However, soft-tissue tumors have similar imaging findings and, therefore, the diagnosis of soft-tissue mass is challenging. Although careful assessment of the internal characteristics on imaging can often narrow the differential diagnoses, the differential diagnosis may be out of the question if identification of the soft-tissue mass origin is missed. The purpose of this article is to review the imaging findings and the essential anatomy to identify the primary site of the soft-tissue mass, and discuss the associated potential pitfalls. In order not to fall into a pitfall, recognition of characteristic imaging findings indicating the origin of the soft-tissue mass and anatomical knowledge of the normal tissue distribution are necessary. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.

Characterization of small, deeply located soft-tissue tumors: Conventional magnetic resonance imaging features and apparent diffusion coefficient for differentiation between non-malignancy and malignancy

OBJECTIVES

To compare magnetic resonance imaging (MRI) parameters of small, deeply located non-malignant and malignant soft-tissue tumors (STTs).

METHODS

Between May 2011 and December 2017, 95 MRIs in 95 patients with pathologically proven STTs of small size (<5 cm) and deep location (66 non-malignant and 29 malignant) were identified. For qualitative parameters, consensus reading was performed by three radiologists for presence of necrosis, infiltration, lobulation, and the tail sign. Apparent diffusion coefficient (ADC) was analyzed by two other radiologists independently. Univariable and multivariable analyses were performed to determine the diagnostic performances of MRI parameters in differentiating non-malignancy and malignancy, and for non-myxoid, non-hemosiderin STTs and myxoid STTs as subgroups. Interobserver agreement for ADC measurement was calculated with the intraclass correlation coefficient.

RESULTS

Interobserver agreement on ADC measurement was almost perfect. On univariable analysis, the malignant group showed a significantly larger size, lower ADC, and higher incidence of all qualitative MRI parameters for all STTs. Size (p = 0.012, odds ratio [OR] 2.57), ADC (p = 0.041, OR 3.85), and the tail sign (p = 0.009, OR 6.47) were independently significant on multivariable analysis. For non-myxoid, non-hemosiderin STTs, age, size, ADC, frequency of infiltration, lobulation, and the tail sign showed significant differences between non-malignancy and malignancy on univariable analysis. Only ADC (p = 0.032, OR 142.86) retained its independence on multivariable analysis. For myxoid STTs, only size and tail sign were significant on univariable analysis without independent significance.

CONCLUSIONS

Size, ADC, and incidence of qualitative MRI parameters were significantly different between small, deeply located non-malignant and malignant STTs. Only ADC was independently significant for both overall analysis and the non-myxoid, non-hemosiderin subgroup.

Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study

BACKGROUND

Preoperative differentiation between malignant and benign soft-tissue masses is important for treatment decisions.

PURPOSE/HYPOTHESIS

To construct/validate a radiomics-based machine method for differentiation between malignant and benign soft-tissue masses.

STUDY TYPE

Retrospective.

POPULATION

In all, 206 cases.

FIELD STRENGTH/SEQUENCE

The T₁ sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352-550/2.75-19 msec. The T₂ sequence was acquired with the following parameters: TR/TE, 700-6370/40-120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort.

ASSESSMENT

Twelve machine-learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively).

STATISTICAL TESTS

1) Demographic characteristics: a one-way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ² test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) values.

RESULTS

The LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2.

DATA CONCLUSION

A machine-learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft-tissue masses.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2 J. Magn. Reson. Imaging 2020;52:873-882.

Development and Validation of Nomograms for Malignancy Prediction in Soft Tissue Tumors Using Magnetic Resonance Imaging Measurements

The objective of this study was to develop, validate, and compare nomograms for malignancy prediction in soft tissue tumors (STTs) using conventional and diffusion-weighted magnetic resonance imaging (MRI) measurements. Between May 2011 and December 2016, 239 MRI examinations from 236 patients with pathologically proven STTs were included retrospectively and assigned randomly to training (n = 100) and validation (n = 139) cohorts. MRI of each lesion was reviewed to assess conventional and diffusion-weighted imaging (DWI) measurements. Multivariate nomograms based on logistic regression analyses were built using conventional measurements with and without DWI measurements. Predictive accuracy was measured using the concordance index (C-index) and calibration plots. Statistical differences between the C-indexes of the two models were analyzed. Models were validated by leave-one-out cross-validation and by using a validation cohort. The mean lesion size, presence of infiltration, edema, and the absence of the split fat sign were significant and independent predictors of malignancy and included in the conventional model. In addition to these measurements, the mean and minimum apparent diffusion coefficient values were included in the DWI model. The DWI model exhibited significantly higher diagnostic performance only in the validation cohort (training cohort, 0.899 vs. 0.886, P = 0.284; validation cohort, 0.791 vs. 0.757, P = 0.020). Calibration plots showed fair agreements between the nomogram predictions and actual observations in both cohorts. In conclusion, nomograms using MRI features as variables can be utilized to predict the malignancy probability in patients with STTs. There was no definite gain in diagnostic accuracy when additional DWI features were used.

Scapulothoracic bursitis and snapping scapula syndrome: a critical review of current evidence

BACKGROUND

Symptomatic scapulothoracic disorders, such as painful scapular crepitus and/or bursitis, are uncommon; however, they can produce significant pain and disability in many patients.

PURPOSE

To review the current knowledge pertaining to snapping scapula syndrome and to identify areas of further research that may be helpful to improve clinical outcomes and patient satisfaction.

STUDY DESIGN

Systematic review.

METHODS

We performed a preliminary search of the PubMed and Embase databases using the search terms "snapping scapula," "scapulothoracic bursitis," "partial scapulectomy," and "superomedial angle resection" in September 2013. All nonreview articles related to the topic of snapping scapula syndrome were included.

RESULTS

The search identified a total of 167 unique articles, 81 of which were relevant to the topic of snapping scapula syndrome. There were 36 case series of fewer than 10 patients, 16 technique papers, 11 imaging studies, 9 anatomic studies, and 9 level IV outcomes studies. The level of evidence obtained from this literature search was inadequate to perform a formal systematic review or meta-analysis. Therefore, a critical review of current evidence is presented.

CONCLUSION

Snapping scapula syndrome, a likely underdiagnosed condition, can produce significant shoulder dysfunction in many patients. Because the precise origin is typically unknown, specific treatments that are effective for some patients may not be effective for others. Nevertheless, bursectomy with or without partial scapulectomy is currently the most effective primary method of treatment in patients who fail nonoperative therapy. However, many patients experience continued shoulder disability even after surgical intervention. Future studies should focus on identifying the modifiable factors associated with poor outcomes after operative and nonoperative management for snapping scapula syndrome in an effort to improve clinical outcomes and patient satisfaction.

Nuck canal endometriosis: MR imaging findings and clinical features

OBJECTIVE

The purpose of this study was to describe the MR imaging findings of Nuck canal endometriosis (NCE).

MATERIALS AND METHODS

In a 10-year period, 486 out of 612 patients, with laparoscopically and/or surgically proven diagnosis of pelvic endometriosis, underwent MR imaging examination. The examinations were reviewed by two urogenital experienced radiologists working in consensus. Data analysis included: lesions location, size, morphological and signal intensity pattern, involvement of the adjacent muscles, and tendons.

RESULTS

In 372 out of 486 patients an MRI diagnosis of endometriosis was made. NCE was found in eight patients. All the lesions were located on the right side. The mean size of the lesions was 2.5 cm (range 1.5-4.5 cm). Two patterns of NCE were found: type 1, prevalently cystic (n = 2); and type 2, prevalently solid with small scattered cysts within lesion (n = 6). In all the patients, hemorrhagic hyperintense cysts could be seen on T1-weighted images. In four patients, the lesions involved the inguinal canal, and in another four patients, the lesions were only outside the inguinal canal. Involvement of the abdominis rectus muscle was seen in two patients, and of the adductor common tendon in two patients.

CONCLUSION

MR imaging permits the diagnosis of NCE as well as the evaluation of exact extension of the disease.

Soft-tissue tumors and tumorlike lesions: a systematic imaging approach

Soft-tissue lesions are frequently encountered by radiologists in everyday clinical practice. Characterization of these soft-tissue lesions remains problematic, despite advances in imaging. By systematically using clinical history, lesion location, mineralization on radiographs, and signal intensity characteristics on magnetic resonance images, one can (a) determine the diagnosis for the subset of determinate lesions that have characteristic clinical and imaging features and (b) narrow the differential diagnosis for lesions that demonstrate indeterminate characteristics. If a lesion cannot be characterized as a benign entity, the lesion should be reported as indeterminate, and the patient should undergo biopsy to exclude malignancy.