Soft tissue masses with myxoid stroma: Can conventional magnetic resonance imaging differentiate benign from malignant tumors?

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors

BACKGROUND

To explore the potential of different quantitative dynamic contrast-enhanced (qDCE)-MRI tracer kinetic (TK) models and qDCE parameters in discriminating benign from malignant soft tissue tumors (STTs).

METHODS

This research included 92 patients (41females, 51 males; age range 16-86 years, mean age 51.24 years) with STTs. The qDCE parameters (Ktrans, Kep, Ve, Vp, F, PS, MTT and E) for regions of interest of STTs were estimated by using the following TK models: Tofts (TOFTS), Extended Tofts (EXTOFTS), adiabatic tissue homogeneity (ATH), conventional compartmental (CC), and distributed parameter (DP). We established a comprehensive model combining the morphologic features, time-signal intensity curve shape, and optimal qDCE parameters. The capacities to identify benign and malignant STTs was evaluated using the area under the curve (AUC), degree of accuracy, and the analysis of the decision curve.

RESULTS

TOFTS-Ktrans, EXTOFTS-Ktrans, EXTOFTS-Vp, CC-Vp and DP-Vp demonstrated good diagnostic performance among the qDCE parameters. Compared with the other TK models, the DP model has a higher AUC and a greater level of accuracy. The comprehensive model (AUC, 0.936, 0.884-0.988) demonstrated superiority in discriminating benign and malignant STTs, outperforming the qDCE models (AUC, 0.899-0.915) and the traditional imaging model (AUC, 0.802, 0.712-0.891) alone.

CONCLUSIONS

Various TK models successfully distinguish benign from malignant STTs. The comprehensive model is a noninvasive approach incorporating morphological imaging aspects and qDCE parameters, and shows significant potential for further development.

Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance.

METHODS

In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test.

RESULTS

Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment.

CONCLUSION

Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials.

CLINICAL RELEVANCE STATEMENT

Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.

Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives

This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.

Ultrasound-guided nerve block prior to biopsy of suspected neurogenic tumors: safety and feasibility in a pilot study

OBJECTIVE

The aim of this study is to investigate the safety and feasibility of ultrasound-guided nerve block prior to biopsy of potentially neurogenic tumors.

MATERIALS AND METHODS

A retrospective review of the medical record from June 2017 to June 2022 identified ultrasound-guided biopsies of potentially neurogenic tumors that were performed with a pre-procedural nerve block. Patient demographics, biopsy site, number of passes, needle gauge, use of sedation, pathology results, and procedural complications were recorded and summarized.

RESULTS

The structured search found 16 patients that underwent biopsies of 18 potentially neurogenic tumors with the use of a pre-procedural nerve block at a variety of upper and lower extremity locations. Average patient age was 52 (range 18-78) and 9 patients (56%) were female. Of the 16 patients, 10 were performed without intravenous sedation. Three patients were unable to tolerate biopsy until a nerve block was used. All biopsies yielded a diagnostic sample with 13 of the tumors neurogenic in origin. One patient reported mild postprocedural pain which resolved with conservative treatment; no other complications were reported.

CONCLUSION

Nerve block prior to ultrasound-guided biopsy of potentially neurogenic tumors is a safe and feasible technique. Further study is needed to determine the extent to which nerve block can decrease intra-procedural pain and reduce or eliminate the need for sedation during biopsy.

Myxomas and myxoid liposarcomas of the extremities: Our preliminary findings in conventional, perfusion, and diffusion magnetic resonance

Background

The differentiation between myxomas and myxoid liposarcomas (MLPS) often is a serious challenge for the radiologists. Magnetic resonance imaging (MRI) is the most useful imaging technique in characterization of the soft tissue tumors (STT).

Purpose

To evaluate in a sample of myxomas and MLPS of the extremities, what morphological findings in conventional MRI allow us to differentiate these two types of myxoid tumors, in addition to analyzing the validity of the apparent diffusion coefficient (ADC) values of diffusion-weighted MRI (DW-MRI).

Material and Methods

Magnetic resonance imaging studies in myxomas and MLPS of extremities searched in our PACS between 2015 and 2019. All studies had conventional MRI with T1, T2, and PD SPAIR sequences, while DW-MRI with ADC mapping and perfusion MRI with a T1 sequence repeated for 4 minutes after contrast injection were additional sequences only in some explorations. Two radiologists evaluated independently the MRI studies by examining the qualitative parameters. Apparent diffusion coefficient values were calculated using two methods-ADC global and ADC solid, and Receiver Operating Characteristic (ROC) curves were applied for analysis.

Results

The features were consistent with MLPS: size greater than 10 cm, heterogeneous signal on T1, and nodular enhancement, while the common findings for myxomas were a homogenously hypointense signal on T1 and diffuse peritumoral enhancement. The solid and global ADC values were higher in myxomas. We observed that the solid ADC value less than 2.06 x 10^-3mm² x s would support the diagnosis of MLPS against myxoma.

Conclusion

Overall, MRI with its different modalities improved the diagnostic accuracy when differentiating myxomas from MLPS of extremities.

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.

Multi-parametric MRI-based radiomics for the diagnosis of malignant soft-tissue tumor

PURPOSE

To develop and validate a multiparametric magnetic resonance imaging-based radiomics nomogram for differentiating malignant and benign soft-tissue tumors.

METHODS

A total of 91 patients with pathologically confirmed soft-tissue tumors were enrolled between January 2017 and October 2020. Forty-eight patients were consecutively enrolled between November 2020 and March 2022, as a time-independent cohort. All patients underwent contrast-enhanced T1-weighted and T2-weighted fat-suppression magnetic resonance scans at 3.0 T. Radiomics features were extracted and selected from the two modalities to develop the radiomics signature. Significant clinical/morphological characteristics were identified using a multivariate logistic regression analysis. The least absolute shrinkage and selection operator regression were applied to identify discriminative features. A clinical-radiomics nomogram was constructed based on clinical/morphological characteristics and radiomics features. Finally, the performance of the nomogram was validated using the receiver operating characteristic and decision curve analysis (DCA).

RESULTS

Six features were selected to establish the combined RS. Size, margin, and peritumoral edema were identified as the most important clinical and morphological factors, respectively. The radiomics signature outperformed the clinical model in terms of AUC and sensitivity. The nomogram integrating the combined RS, size, margin, and peritumoral edema achieved favorable predictive efficacy, generating AUCs of 0.954 (95% confidence interval [CI]: 0.907-1.000, Sen = 0.861, Spe = 0.917), 0.962 (95% CI: 0.901-1.000, Sen = 0.944, Spe = 0.923), and 0.935 (95% CI: 0.871-0.998, Sen = 0.815, Spe = 0.952) in the training (n = 60), validation (n = 31) and time-independent (n = 48) cohorts, respectively. The DCA curve indicated good clinical usefulness of the nomogram.

CONCLUSIONS

Our study demonstrated the clinical potential of multiparametric MRI-based radiomics in distinguishing malignant from benign soft-tissue tumors, which can be considered as a noninvasive tool for individual treatment management.

MRI imaging of soft tissues tumours and tumour like lesions-SLAM approach

Imaging is vital in characterising and delineating the extent of soft tissue tumours and there is abundant literature on this. A simplified approach is required to characterise the lesions on MR and we describe a simplified street-smart approach called SLAM (signal, location, age, multiplicity and matrix).

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

OBJECTIVES

Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

KEY POINTS

• 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.

Added-value of advanced magnetic resonance imaging to conventional morphologic analysis for the differentiation between benign and malignant non-fatty soft-tissue tumors

OBJECTIVES

To evaluate the added value of DWI, qualitative proton MR spectroscopy (H-MRS) and dynamic contrast-enhanced perfusion (DCE-P) to conventional MRI in differentiating benign and malignant non-fatty soft tissue tumors (NFSTT).

METHODS

From November 2009 to August 2017, 288 patients with NFSTT that underwent conventional and advanced MRI were prospectively evaluated. The study was approved by the local ethics committee. All patients signed an informed consent. A musculoskeletal (R1) and a general (R2) radiologist classified all tumors as benign, malignant, or indeterminate according to morphologic MRI features. Then, DWI, H-MRS, and DCE-P data of indeterminate tumors were analyzed by two additional radiologists (R3 and R4). Advanced techniques were considered individually and in combination for tumor benign-malignant differentiation using histology as the gold standard.

RESULTS

There were 104 (36.1%) malignant and 184 (63.9%) benign tumors. Conventional MRI analysis classified 99 tumors for R1 and 135 for R2 as benign or malignant, an accuracy for the identification of malignancy of 87.9% for R1 and 83.7% for R2, respectively. There were 189 indeterminate tumors for R1. For these tumors, the combination of DWI and H-MRS yielded the best accuracy for malignancy identification (77.4%). DWI alone provided the best sensitivity (91.8%) while the combination of DCE-P, DWI, and H-MRS yielded the best specificity (100%). The reproducibility of the advanced imaging parameters was considered good to excellent (Kappa and ICC > 0.86). An advanced MRI evidence-based evaluation algorithm was proposed allowing to characterize 28.1 to 30.1% of indeterminate non-myxoid tumors.

CONCLUSION

The prioritized use of advanced MRI techniques allowed to decrease by about 30% the number of non-myxoid NFSTT deemed indeterminate after conventional MRI analysis alone.

KEY POINTS

• When morphological characterization of non-fatty soft tissue tumors is possible, the diagnostic performance is high and there is no need for advanced imaging techniques. • Following morphologic analysis, advanced MRI techniques reduced by about 30% the number of non-myxoid indeterminate tumors. • DWI is the keystone of advanced imaging techniques yielding the best sensitivity (91.8%). Optimal specificity (> 90%) is obtained by a combination of advanced techniques.

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.

Radiomics nomogram for differentiating between benign and malignant soft-tissue masses of the extremities

BACKGROUND

Preoperative differentiation between malignant and benign tumors is important for treatment decisions.

PURPOSE/HYPOTHESIS

To investigate/validate a radiomics nomogram for preoperative differentiation between malignant and benign masses.

STUDY TYPE

Retrospective.

POPULATION

Imaging data of 91 patients.

FIELD STRENGTH/SEQUENCE

T₁ -weighted images (570 msec repetition time [TR]; 17.9 msec echo time [TE], 200-400 mm field of view [FOV], 208-512 × 208-512 matrix), fat-suppressed fast-spin-echo (FSE) T₂ -weighted images (T₂ WIs) (4331 msec TR; 87.9 msec TE, 200-400 mm FOV, 312 × 312 matrix), slice thickness 4 mm, and slice spacing 1 mm.

ASSESSMENT

Fat-suppressed FSE T₂ WIs were selected for extraction of features. Radiomics features were extracted from fat-suppressed T₂ WIs. A radiomics signature was generated from the training dataset using least absolute shrinkage and selection operator algorithms. Independent risk factors were identified by multivariate logistic regression analysis and a radiomics nomogram was constructed. Nomogram capability was evaluated in the training dataset and validated in the validation dataset. Performance of the nomogram, radiomics signature, and clinical model were compared.

STATISTICAL TESTS

1) Independent t-test or Mann-Whitney U-test: for continuous variables. Fisher's exact test or χ² test: comparing categorical variables between two groups. Univariate analysis: evaluating associations between clinical/morphological characteristics and malignancy. 2) Least absolute shrinkage and selection operator (LASSO)-logistic regression model: selection of malignancy features. 3) Significant clinical/morphological characteristics and radiomics signature were input variables for multiple logistic regression analysis. Area under the curve (AUC): evaluation of ability of the nomogram to identify malignancy. Hosmer-Lemeshow test and decision curve: evaluation and validation of nomogram results.

RESULTS

The radiomics nomogram was able to differentiate malignancy from benignity in the training and validation datasets with an AUC of 0.94. The nomogram outperformed both the radiomics signature and clinical model alone.

DATA CONCLUSION

This radiomics nomogram is a noninvasive, low-cost preoperative prediction method combining the radiomics signature and clinical model.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:155-163.

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.

18F-FDG PET/CT and MRI features of myxoid liposarcomas and intramuscular myxomas

OBJECTIVE

To examine the imaging characteristics of intramuscular myxomas (IM) and myxoid liposarcomas (MLS) on ¹⁸F-FDG PET/CT and MRI.

MATERIALS AND METHODS

With IRB approval, our institutional imaging database was searched for pathologically proven IM and MLS evaluated by ¹⁸F-FDG PET/CT and MRI. PET/CT and MRI imaging characteristics were recorded and correlated with pathologic diagnosis.

RESULTS

We found eight patients (2 M, 6 F) with IM (mean age 65.6 ± 10.4 years) and 16 patients (7 F, 9 M) with MLS (mean age 42.8 ± 16.3 years). MRI was available in 7/8 IM and 15/16 MLS patients. There was no significant difference between the two groups in SUVmax (IM 2.7 ± 0.8, MLS 3.0 ± 1.0; p = 0.35), SUVmean (1.7 ± 0.4, 1.5 ± 0.5; p = 0.40), total lesion glycolysis (101.8 ± 127.3, 2420.2 ± 4003.3 cm³*g/ml; p = 0.12), metabolic tumor volume (62.3 ± 71.1, 1742.9 ± 3308.0 cm³; p = 0.17) or CT attenuation (p = 0.70). MLS occurred in younger patients (p = 0.0015), were larger (16.4 ± 8.2 vs. 5.6 ± 2.5 cm; p = 0.0015), more often T1 hyperintense (p = 0.03), with nodular enhancement (p = 0.006), and macroscopic fat on CT (p = 0.0013) and MRI (p = < 0.001) compared to myxomas.

CONCLUSIONS

IM and MLS most commonly demonstrate low-grade FDG activity and overlapping metabolic measures on PET/CT. MRI is useful in differentiation, but MLS can present without macroscopic fat on MRI, underscoring the importance of radiologic-pathologic correlation for accurate diagnosis.