Malignant Soft Tissue Masses Research Articles

Characteristics and Role of Apparent Diffusion Coefficient (ADC) Value in Musculoskeletal Soft Tissue Tumor

Introduction: Musculoskeletal soft tissue tumors (STTs) consist of a group of malignant and benign tumors that present challenges in diagnosis and therapy. Adding diffusion-weighted imaging and apparent diffusion coefficient (DWI/ADC) ​​provides tissue characteristics that are different from those obtained by conventional MR techniques. Quantitative examination of the diffusion of water molecules in tissue is expressed as ADC value, which is expected to be more helpful in differentiating malignant and benign soft tissue masses. This study was carried out to determine the characteristics and role of ADC values ​​in musculoskeletal STTs. Methods: This research was conducted retrospectively. Analysis was performed using MRI results of patients with musculoskeletal STTs who met the inclusion and exclusion criteria. Three regions of interest (ROI) were assessed in the tumor area with the strongest contrast enhancement for DWI mapping, ADC, and ROI ADC value placement. An Independent T-test test was performed to determine the mean difference of ADC in each tumor category. Receiver Operating Characteristics (ROC) were generated to find the optimal cut-off value. Then, a diagnostic test was carried out to determine the sensitivity, specificity, PPV, and NPV of the ADC value. Results: There were 41 research subjects with musculoskeletal STTs with male predominance, in which 75.61% of cases were malignant. DWI/ADC showed that 78.05% of patients with musculoskeletal soft tissue tumors had restricted diffusion areas. In this study, the ADC cutoff value to differentiate benign and malignant musculoskeletal STT is 0,84 x 10-3mm2/s with 77.4% sensitivity and 90% specificity, 96% positive predictive value and 56.3% negative predictive value. Conclusion: In differentiating malignant and benign musculoskeletal STTs, the ADC value provides fair sensitivity, very good specificity, good positive predictive value, and low negative predictive value.

Performance of Sonoelastography for predicting malignancy in soft tissue

BackgroundSeparating benign from malignant soft-tissue masses often requires a biopsy. The objective of this study was to assess whether shear-wave elastography (SWE) helped to separate benign from malignant soft-tissue masses.MethodsIn 2015–2016, we prospectively included patients with soft-tissue masses deemed by our multidisciplinary sarcoma board to require a diagnostic biopsy. All patients underwent ultrasonography (US) followed by SWE to measure elasticity. We compared benign and malignant tumors, overall and after separating tumors with vs. without a fatty component. The biopsy findings, and surgical-specimen histology when available, served as the reference standard.ResultsWe included 136 patients, 99 with non-fatty and 37 with fatty soft-tissue masses. Mean elasticity and tumor-to-fat elasticity ratio (T/F) values were significantly lower for the benign than the malignant soft-tissue masses in the overall cohort (30.9 vs. 50.0 kilopascals (kPa), P = 0.03; and 2.55 vs. 4.30, P = 0.046) and in the non-fatty subgroup (37.8 ± 31.9 vs. 58.9 ± 39.1 kPa, P = 0.049 and 2.89 ± 5.25 vs. 5.07 ± 5.41, P = 0.046). Data for fatty tumors were non relevant due to lack of conclusive results. By receiver operating characteristics curve analysis, a T/F cutoff of 3.5 had 46% sensitivity and 84% specificity for separating benign and malignant soft-tissue masses.ConclusionsSWE had good specificity and poor sensitivity for separating benign from malignant soft-tissue masses.

Diagnostic Performance of US and MRI in Predicting Malignancy of Soft Tissue Masses: Using a Scoring System.

ObjectiveTo assess the diagnostic performance of US and MRI in predicting malignancy of soft tissue masses by using a scoring system.MethodsA total of 120 cases of pathologically confirmed soft tissue masses (71 cases of malignant lesions and 49 cases of benign lesions) were enrolled. All patients underwent ultrasound and MRI examination prior to biopsy or surgical excision. A scoring system based on the parameters of conventional US and MRI to distinguish malignant and benign masses was established by the regression model. The receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of US and MRI.ResultsMultivariate analysis showed that margin, maximum diameter, and vascular density were independent predictors for malignancy found by US, while maximum diameter, margin, and affected peripheral soft tissue were independent predictors for malignancy found by MRI. The mean scores of the benign and malignant groups were 2.8 ± 1.6, 5.1 ± 1.1 on US and 1.3 ± 1.2, 3.5 ± 0.9 on MRI. Based on the cut-off score of 3.5 and 2.5 calculated by ROC analysis, US and MRI had 92% and 87% sensitivity, 72% and 76% specificity, 86% and 89% accuracy, respectively. The combination of these two modalities achieved the sensitivity of 91%, specificity of 82%, and accuracy of 93%.ConclusionsBoth US and MRI can provide valuable information about the differential diagnosis between benign and malignant soft tissue masses. The combination of the two imaging-based scoring systems can increase the diagnostic performance, especially in specificity.

Role of Diffusion Weighted MRI in Characterization of Musculoskeletal Soft Tissue Tumors

Abstract Background: Magnetic Resonance Imaging is the method of choice for the diagnostic work-up of soft tissue tumors. It is the modality of choice to evaluate such masses, because of its excellent soft tissue contrast. Aim of Study: To assess the efficacy of Diffusion weighted MRI in characterization of musculoskeletal soft tissue tumors. Patients and Methods: The current study enrolled 20 patients (11 female and 9 male) with musculoskeletal STTs with mean age was 41.08±11.54 year. The most frequent affected site with the swelling was the thigh (48%) followed by the arm (20%). Four (16%) patients suffered from leg swelling while three (12%) patients had shoulder swelling. Our results revealed that malignant musculoskeletal soft tissue masses had significantly lower ADC value in comparison to those with benign masses (0.70±0.09 vs. 1.58±0.52x10-3m2/s; p < 0.001). Results: Patients with malignant lesions had significantly lower ADC value in comparison to those with benign lesions (0.70±0.09 vs. 1.58±0.52 (103mm2/s). Myxoma had the highest ADC value that was 2.44 (103mm2/s) while sarcoma had lowest value that was 0.54±0.03 (103mm2/s). It was noticed that MRI had 100% sensitivity and 81.8% specificity for diagnosis of malignant musculoskeletal soft tissue masses with overall accuracy was 92% and area under curve was 0.91 at cut off value was <1.14 (103mm2/s). Based on final diag-nosis; MRI was successful in diagnosis of all cases with malignant musculoskeletal soft tissue masses but as the same time, MRI falsely diagnosed two cases with benign nature to be malignant. Those two cases were finally diagnosed to be lipoma. Conclusion: Based on the current study, DWI-MRI con-sidered the method of choice for the characterization of muscul oskel etal STTs; however, DW-MRI with ADC mapping is a rapidly, valuable, non-invasive, non-contrast tool for reliably differentiating between benign and malignant STTs. It's recommended to perform such study in large sample of patients and in more centers to confirm the great values of DWI-MRI in evaluation of such lesions.

Ultrasound of Soft Tissue Masses: Part II

In part I of this two-part series, we discussed the utility of ultrasound (US) for evaluation of soft tissue masses. Optimal scanning techniques were addressed. Multiple benign masses were highlighted in the first article, with particular attention to the features that were characteristic for a specific begin diagnosis. This article addresses more benign masses. Additionally, features of malignant soft tissue masses are reviewed and these findings reinforced with a discussion of several malignant diagnoses.

Ultrasound of Soft Tissue Masses: Part I

Ultrasound is an important initial imaging study for evaluation of soft tissue masses. Many benign masses have characteristic locations and imaging findings, which indicate their diagnosis. Likewise, malignant masses frequently have imaging features, which indicate a suspicious finding. It is important for radiologists to be able to differentiate benign from malignant soft tissue masses, and in turn, to recommend the appropriate next step for suspicious masses. This article focuses on multiple different benign masses and their imaging findings.

Soft tissue masses of the epitrochlear region.

The epitrochlear lymph nodes (ELN) are rarely examined clinically and are difficult to identify radiologically in healthy patients. They are, therefore, generally under appreciated as a source of significant pathology. Despite this, enlargement of an ELN is almost always secondary to a pathological process, the differential for which is relatively narrow. The following pictorial review illustrates the spectrum of infectious, inflammatory and malignant conditions affecting the ELN, some of which are quite specific to this location. We also emphasise the importance of distinguishing enlarged ELNs from benign and malignant non-nodal soft tissue masses, which can have very similar clinical presentation and imaging appearances.

The diagnostic accuracy of intravoxel incoherent motion and diffusion kurtosis imaging in the differentiation of malignant and benign soft-tissue masses: which is better?

It is difficult for conventional magnetic resonance imaging (MRI) to distinguish benign soft-tissue masses (STMs) from malignant masses. To quantitatively compare the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in STMs. The data from 58 patients with STMs were retrospectively analyzed. The GE Discovery 3.0-T MRI scanner was used to acquire conventional MRI sequences, IVIM, and DKI images. The chi-square test, independent sample t-test, and Mann-Whitney U tests were used to compare the differences between conventional MRI features, IVIM, and DKI parameters (Dslow, Dfast, f, mean kurtosis [MK], and mean diffusivity [MD]) between the benign and malignant groups. Receiver-operating characteristic (ROC) curve analysis was also performed. Tumor size and depth are statistically different in STTs. Dslow, MK, and MD values in the malignant groups are significantly lower than the benign groups (P < 0.05). However, Dfast and f values are not statistically different between the two groups. The area under the curve (AUC) of Dslow value (0.859) is higher than MD (0.765) and MK (0.676) values for identifying benign and malignant STMs. The Dslow value showed the best specificity (82.93%). The sensitivity and specificity of IVIM and DKI parameters are higher than that of conventional MRI sequences. IVIM and DKI can be used to distinguish between benign and malignant STMs, with Dslow as the most meaningful parameter.

Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors.

Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning. Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches. Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84), respectively. Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis. • Predictive models constructed from MRI-based radiomics data and machine learning-augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively. • Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84) for Adaboost and RF, respectively. • Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows.

Artificial Intelligence for Classification of Soft-Tissue Masses at US.

To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses. In this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years ± 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision (n = 227) or masses that demonstrated imaging characteristics of lipoma, benign peripheral nerve sheath tumor, and vascular malformation (n = 192) were included. Images in patients with a histologic diagnosis (n = 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions. Twenty percent of cases were withheld as a test dataset, and the remaining cases were used to train the model with a 75%-25% training-validation split and fourfold cross-validation. Performance of the model was compared with retrospective interpretation of the same dataset by two experienced musculoskeletal radiologists, blinded to clinical history. A second group of US images from 275 of the 419 patients containing the three common benign masses was used to train and evaluate a separate model to differentiate between the masses. The models were trained on the Keras machine learning platform (version 2.3.1), with a modified pretrained VGG16 network. Performance metrics of the model and of the radiologists were compared by using the McNemar test, and 95% CIs for performance metrics were estimated by using the Clopper-Pearson method (accuracy, recall, specificity, and precision) and the DeLong method (area under the receiver operating characteristic curve). The model trained to classify malignant and benign masses demonstrated an accuracy of 79% (95% CI: 68, 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.84, 0.98), matching the performance of two expert readers. Performance of the model distinguishing three benign masses was lower, with an accuracy of 71% (95% CI: 61, 80) on the test data. The trained CNN was capable of differentiating between benign and malignant soft-tissue masses depicted on US images, with performance matching that of two experienced musculoskeletal radiologists.© RSNA, 2020.

Accuracy of ultrasound in the characterisation of deep soft tissue masses: a prospective study.

To investigate the accuracy of ultrasound in characterising the type of mass and likelihood of malignancy in deep soft tissue masses. Five hundred seventy-nine deep soft tissue masses were prospectively studied by ultrasound. Masses (n = 137) with prior MRI or CT were not included. Following ultrasound examination, the likely nature of the mass as well as the confidence of the reporting radiologist ('fully confident' versus 'not fully confident') about the ultrasound diagnosis was recorded. Clinical and ultrasound diagnoses were compared with the histological diagnosis which was available in 134 (23%) of the 579 masses. Compared with histology, clinical and ultrasound accuracy for characterising the type of mass were 47% and 88% respectively when all differential diagnoses were considered. The radiologist was fully confident regarding the type of 436 (75%) of 579 masses and, in this setting, for those cases that could be compared with histology, diagnostic accuracy was 96%. For the remaining masses, where the radiologist was not fully confident, accuracy compared with histology was 58% for the first differential diagnosis and 80% for all differential diagnoses. For identifying malignancy, sensitivity, specificity, and positive and negative predictive value of ultrasound were 97%, 58%, 67%, and 99% respectively. Ultrasound alone was considered sufficient for diagnostic workup in over half of all deep soft tissue masses. Ultrasound is useful at characterising and recognising malignancy in deep soft tissue masses. Provided local practice patterns are favourable, ultrasound may be considered a first-line investigation in the diagnostic workup of deep soft tissue masses. • In three-quarters of cases, one can be fully confident about characterising the nature of deep soft tissue masses on ultrasound and, for those fully confident cases that could be compared with histology, the diagnostic accuracy of ultrasound was 96%. • Ultrasound can correctly recognise nearly all malignant deep soft tissue masses but some benign masses will also be considered possibly malignant. • Ultrasound alone was considered sufficient for imaging workup in over half of deep soft tissue masses.

Accuracy of ultrasound in the characterization of superficial soft tissue tumors: a prospective study.

To prospectively evaluate the accuracy of ultrasound in defining the specific nature of superficial soft tissue masses as well as determining malignancy. Eight hundred twenty-three superficial soft tissue masses were prospectively evaluated with ultrasound by one of five experienced musculoskeletal radiologists. The radiologist at the time of examination provided one to three specific differential diagnoses and the perceived level of confidence with regard to each diagnosis. Clinical and ultrasound diagnoses were compared with the histological diagnosis to determine accuracy. Tumor malignancy was determined by histology or clinical/imaging follow-up. Histological correlation was present for 219 (26.6%) of the 823 masses. Compared with histology, the accuracy of clinical and ultrasound examination for determining specific tumor type was 25.6% and 81.2% respectively considering all differential diagnoses provided. Radiologists were "fully confident" with the ultrasound diagnosis in 585 (71.1%) of 823 masses overall. In this setting, when compared with histology, the diagnostic accuracy of ultrasound was 95.5%. When the radiologist was "not fully confident," accuracy was 41.3% for the first differential diagnosis and 60.9% for all differential diagnoses. Diagnostic accuracy improved with increasing radiologist experience. Sensitivity, specificity, positive predictive value, and negative predictive value of ultrasound for identifying malignant tumor were 93.3%, 97.9%, 45.2%, and 99.9% respectively. One can be "fully confident" at characterizing over two-thirds of superficial soft tissue masses based on ultrasound appearances and, in this setting, diagnostic accuracy is very high. Ultrasound examination is also highly accurate at discriminating benign from malignant superficial soft tissue masses.

Is MRI diffusion-weighted imaging a reliable tool for the diagnosis and post-therapeutic follow-up of extremity soft tissue neoplasms?

Purpose:The aim of this study was to evaluate the benefit of using quantitative diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping in the initial diagnosis and post-therapeutic follow-up of extremity soft tissue masses.Patients and Methods:This study included 90 patients with extremity soft tissue masses. The DWI was obtained with 3 b values, including 0, 400, and 800 s/mm2. Calculation of the ADC value of the lesion was done by placing the region of interest (ROI) to include the largest area of the lesion. ADC values were compared with the histopathology. Eighteen patients had posttherapeutic magnetic resonance imaging (MRI).Results:Benign masses, fibromatosis, and malignant soft tissue masses had mean ADC values of 1.18 ± 1.0191 × 10−3 mm2/s; 1.31 ± 0.245 × 10−3 mm2/sec; and 1.3 ± 0.7 × 10−3 mm2/s, respectively. Myxomatous malignant masses had an ADC value of 2.6 ± 0.55 × 10−3 mm2/s, while nonmyxomatous malignant masses had an ADC value of 1.1 ± 0.8 × 10−3 mm2/s. ADC cutoff value between benign and non-benign (including malignant and locally aggressive masses) was 0.6 × 10−3 mm2/sec with 98.3% sensitivity and 50% specificity (P = 0.5123). The statistical difference between malignant soft tissue masses (mean ADC 1.309 ± 0.723 × 10−3 mm2/s) and fibromatosis masses (mean ADC value 1.31 ± 0.245 × 10−3 mm2/s) using a comparative T-test proved to be of poor significance level (P value ~ 0.9757). Nine patients with soft tissue sarcomas (STSs) had pre and post-therapeutic MRI examinations showing a mean increase of the recorded ADC values by about 0.28 × 10−3 mm2/s in the post-therapy study as compared with the recorded initial pretreatment values. Analysis of the post-therapy follow-up studies of fibromatosis showed that lesions with favorable response to chemotherapy or radiotherapy (8/12) exhibited significantly lower ADC values than those showing progressive disease course.Conclusion:DWI with ADC mapping of extremity soft tissue tumors are so complicated that they alone may not be of much value in differentiating between benign and malignant tumors; however, it can be used as a tool for monitoring response to treatment.

Combined classical spindle cell/pleomorphic lipoma spectrum imaging and clinical data.

Compile the largest study to date on the imaging and clinical features of the classic spindle cell/pleomorphic lipoma spectrum and suggest this diagnosis be included in the differential for benign and malignant macroscopic fat-containing soft tissue masses regardless of the mass location or patient demographics. An institutional search was performed to identify all available classic-type spindle cell/pleomorphic lipomas with available demographic and imaging data. Images and reports were analyzed by one MSK-trained radiologist and radiographic, anatomic and clinical data were recorded. Additionally, a literature search was performed to identify studies describing the spindle cell lipoma spectrum imaging features and were combined with institutional data. Forty-two institutional cases were identified, 37 of which had MRIs performed among which 21 had images available (T1- and T2-weighted pulse sequences) for review while the remainder had outside reports detailing the mass imaging features. There was a mean age of 57 with 79% of cases occurring in males. Contrary to prior reports, 57% of masses were subcutaneous, and the neck and back region accounted for 26% of cases. When the institutional cases were combined with available data in the literature, there was a new sample size of 91 masses, 74 of which had MRI and/or CT data. Eighty-seven percent of masses were heterogeneous, 51% were composed of less than 75% fat, 65% were in the back, neck or shoulder region, 27% of masses were deep and 91% demonstrated enhancement. Eighty-two percent of patients were males with a mean age of 58 at excision. Imaging features, patient demographics and tumor location alone are not enough to differentiate tumors of the spindle cell lipoma spectrum from other macroscopic fat-containing benign and malignant tumors, and these entities should be included in the same imaging differential diagnosis.

Diagnostic value of strain elastography for differentiating benign and malignant soft tissue masses.

The aim of the current study was to investigate the importance of strain elastography (SE) in the differential diagnosis of benign and malignant soft tissue masses. SE was adopted to examine 61 patients with superficial masses, classify their elastic scores and assess their strain ratios (SRs) between the masses and the surrounding structures. Significantly increased SR values and elastic scores were observed in the malignant masses compared with the benign masses (5.42±3.47 vs. 1.80±2.10, P<0.001; 3.13±0.34 vs. 2.03±0.99, P<0.001). Area under receiver operating characteristic curve values of the SRs and elastic scores were 0.87 (P<0.001) and 0.805 (P=0.001), respectively. With an SR of >2.295 as the optimal threshold value, the sensitivity, specificity and positive and negative predictive values for diagnosing a malignant mass were 93.8, 80.5, 65.2 and 97.1%, respectively; whilst using an elastic score of ≥3 as the optimal threshold value, the sensitivity, specificity and positive and negative predictive values for diagnosing a malignant mass were 100, 51.6, 51.6 and 100%, respectively. SR values and elastic scores were significantly different between the malignant and benign soft tissue masses. Therefore, SE may be used to effectively differentiate between malignant and benign soft tissue masses.

Diagnostic Evaluation of Upper Extremity Masses and Tumors.

The appropriate evaluation of hand and upper extremity masses is an important aspect of the care of orthopedic patients. Although most of these masses are benign, the orthopedic surgeon must have a high index of suspicion when assessing them because early diagnosis and treatment of aggressive or malignant masses may have a great effect on patient outcomes. This article provides an overview of benign and malignant osseous and soft tissue masses that orthopedic surgeons may encounter and a detailed algorithm for evaluating these masses. [Orthopedics. 2017; 40(5):e758-e764.].

Evaluation of Sonoelastography in Differentiating Benign and Malignant Soft Tissue Masses

To access the value of strain elastography in differentiating benign and malignant soft tissue masses. Soft tissue masses of 100 patients were examined by strain elastography. Elastogram scores(ES) and Strain ratios(SR) were obtained and compared between benign and malignant masses. Sixteen patients were lost to follow-up. Of remained 84 patients, 22 were malignant and 62 were benign. Significantly higher ESs and SRs were observed in the malignant masses than the benign masses (3.32±0.43 vs. 1.73±0.93, P<0.001; 5.84±3.38 vs. 1.92±2.33, P<0.001), AOC area under ; the curve of ES and SR were 0.884 (p<0.001) and 0.915 (P<0.001) respectively. With an ES ≥3 as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value for diagnosing a malignant mass were 100%、71.4%、75.9% and 100% respectively. With a SR value ≥ 2.78 as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value for diagnosing a malignant mass were 88.9%, 76.2%, 61.5%, and 94.1%, respectively. Both SR and ES were significantly different between the malignant and benign soft tissue masses; so strain elastography can be adopted as a high application value to differentiate the malignant and benign soft tissue masses,and it is a new technology which is worth promoting.

Characterization of indeterminate soft tissue masses referred for biopsy: What is the added value of contrast imaging at 3.0 tesla?

To assess the added value of contrast-enhanced (CE) MR sequences (static CE-MR sequences, dynamic CE-MR sequences) to noncontrast enhanced MR sequences (non-CE-MR sequences) including T1, fluid-sensitive, and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping for characterizing "indeterminate" soft tissue masses (STMs) as benign or malignant. Thirty-nine patients with indeterminate STMs (27 benign, 12 malignant) underwent 3 Tesla MRI with conventional non-CE-MR sequences (T1-weighted, fluid-sensitive), DWI (b-values 50, 400, 800, ADC mapping), dynamic CE-MR sequences (7-s time resolution), and static CE-MR sequences. Two readers independently reviewed imaging in four sessions (conventional non-CE-MR sequences alone, conventional+DWI/ADC, conventional+DWI/ADC+static CE-MR sequences, conventional+DWI/ADC+static CE-MR sequences dynamic CE-MR sequences). Readers recorded the potential of malignancy at each session; reader diagnostic performance (receiver operating characteristics analysis) and inter-observer variability (weighted kappa [k]) were determined. Diagnostic performance for distinguishing benign and malignant STMs was highest with the addition of dynamic CE-MR sequences (reader 1, area under the curve [AUC] 0.91; reader 2, AUC 0.88). The diagnostic performance of static CE-MR sequences (reader 1, AUC 0.86; reader 2, AUC 0.84) was not superior to non-CE-MR sequences with DWI (reader 1, AUC 0.88; reader 2, AUC 0.8). Interobserver agreement was: k = 0.82 (static CE-MRI), k = 0.79 (dynamic CE-MRI), k = 0.53 (non-CE-MR sequences without DWI), and k = 0.63 (with DWI). Non-CE-MR sequences offer similar diagnostic performance to imaging with the addition of static CE-MR sequences, but their interobserver reliability is lower. The addition of dynamic CE-MR sequences offers the higher diagnostic performance for distinguishing benign and malignant indeterminate STMs. 3 J. Magn. Reson. Imaging 2017;45:390-400.

The value of energy spectral CT in the differential diagnosis between benign and malignant soft tissue masses of the musculoskeletal system

ObjectiveTo explore the value of energy spectral CT in the differential diagnosis between benign and malignant tumor of the musculoskeletal system. MethodsEnergy spectral CT scan was performed on 100 patients with soft tissue mass caused by musculoskeletal tumors found by MRI. Solid areas with homogenous density were chosen as region of interests (ROI), avoiding necrosis, hemorrhage and calcification region. Select the optimal keV on single energy images, and then the keV–CT curve was automatically generated. All 100 cases of tumors proved by histological examination were divided into four groups, 38 cases were in benign group, 10 cases in borderline group, 49 cases in malignant group, and 3 cases of lipoma (that were analyzed separately since its curve was arc shaped, significantly different from other curves). The formula used to calculate the slope of spectral curve was as follows: slope=(Hu40 keV−Hu80keV)/40. As the slope was steep within the range of 40–80keV based on preliminary observations, 40keV and 80keV were used as the reference points to calculate the slope value of the energy spectral curve. Kruskal–Wallis rank sum test was applied for statistical analysis, and P<0.05 was considered to indicate a statistically significant difference. ResultsThe spectral curve of benign group was gradually falling type with a mean slope of 0.75±0.30, that of malignant group was sharply falling type with a mean slope of 1.64±1.00, and that of borderline group was a falling type between the above two groups with a mean slope of 1.34±0.45. The differences of slopes between benign and malignant group, benign and borderline group were of statistical significance (P<0.05) respectively. The spectral curves of 3 cases of lipoma showed arc shaped rising type with a mean slope of −2.00. ConclusionSpectral curve is useful in the differential diagnosis of benign and malignant tumor of the musculoskeletal system. Arc shaped curve is a specific sign for tumors containing abundant fat.

Diagnostic impact of echo planar diffusion-weighted magnetic resonance imaging (DWI) in musculoskeletal neoplastic masses using apparent diffusion coefficient (ADC) mapping as a quantitative assessment tool

PurposeTo evaluate the diagnostic impact of echo planar DW imaging in distinguishing benign from malignant musculoskeletal soft-tissue masses using ADC mapping as a quantitative assessment tool. Patients and methodsWe evaluated 73 tumors (21 bone tumors and 52 soft-tissue tumors). MR examinations were performed with a 1.5-T system. Diffusion-weighted single-shot EPI images were obtained in all patients. Apparent diffusion coefficients (ADCs) were calculated by using b factors of 0 and 1000s/mm2. ADC value measurements were compared with the histopathological findings. ResultsThe average ADC of benign tumors was 1.86±0.67×10−3mm2/s, and that of malignant soft-tissue tumors was 0.97±0.35×10−3mm2/s. ADC value of malignant tumors was significantly lower than that of the benign tumor group (p<0.0001). The highest ADC value was seen in the case of ganglion cyst (2.8±0.23×10−3mm2/s) and cystic neurofibroma (2.5±0.04×10−3mm2/s), and juxta cortical enchondroma (2.65±0.36×10−3mm2/s) while the lowest one was seen in aggressive fibromatosis (0.37±0.05×10−3mm2/s). For malignant soft-tissue masses, the highest ADC value was seen in mesenchymal chondrosarcoma (2.1±0.32) liposarcoma (intermediate grade) (1.4±0.21) while the lowest ADC value was seen in fibrosarcoma (high grade) (0.78±0.14). ConclusionMR diffusion provides additional information to the routine MRI sequences rendering it an effective non-invasive tool in differentiating between benign and malignant soft-tissue tumors.