Can we use MRI to detect clinically silent recurrent soft-tissue sarcoma?

Soft tissue tumor imaging in adults: European Society of Musculoskeletal Radiology-Guidelines 2024: imaging immediately after neoadjuvant therapy in soft tissue sarcoma, soft tissue tumor surveillance, and the role of interventional radiology

OBJECTIVES

An update of the first European Society of Musculoskeletal Radiology (ESSR) consensus on soft tissue tumor imaging in 2015 became necessary due to technical advancements, further insights into specific entities, and the revised WHO classification (2020) and AJCC staging system (2017). The third part of the revised guidelines covers algorithms and techniques beyond initial imaging: (1) Imaging after neoadjuvant therapy in soft tissue sarcoma, (2) sarcoma surveillance, and (3) special aspects, including surveillance of non-malignant entities and the role of interventional radiology.

MATERIALS AND METHODS

A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements that had undergone interdisciplinary revision were scored online by level of agreement (0 to 10) during two iterative rounds that could result in either 'group consensus,' 'group agreement,' or 'lack of agreement.'

RESULTS

The three sections contain 47 statements with comments. Group consensus was reached in 91.5%, group agreement in 6.4%, lack of agreement in 2.1%. In sarcoma, imaging immediately after neoadjuvant therapy is pivotal for determining the therapy effects and for resection-planning; surveillance should include imaging at fixed grade- and type-dependent intervals. In general, MRI is the method of choice for loco-regional surveillance of soft tissue sarcomas, and chest CT to assess metastatic disease. Interventional radiology has a role, especially in oligometastatic disease, palliative tumor control and local recurrences.

CONCLUSION

Strategies for standardized soft tissue tumor imaging regarding therapy control, surveillance, and useful interventional procedures are provided.

KEY POINTS

Question An ESSR consensus update on soft tissue tumor imaging regarding surveillance became necessary due to technical advancements, further entity-specific insights, and revised WHO- and AJCC-classifications. Findings Imaging immediately after neoadjuvant therapy in soft tissue sarcoma is pivotal. Post-therapeutic surveillance should include imaging at regular intervals, stratified for tumor grade and type. Clinical relevance The updated ESSR soft tissue tumor imaging guidelines aim to provide best practice expert consensus for standardized imaging, to support radiologists in their decision-making, and to improve examination comparability, both in individual patients and in future studies on individualized strategies.

Radiomics model for predicting distant metastasis in soft tissue sarcoma of the extremities and trunk treated with surgery

PURPOSE

The aim of this study was to develop a radiomics model based on magnetic resonance imaging (MRI) for predicting metastasis in soft tissue sarcomas (STSs) treated with surgery.

METHODS/PATIENTS

MRI and clinical data of 73 patients with STSs of the extremities and trunk were obtained from TCIA database and Jiangsu Cancer Hospital as the training set, data of other 40 patients were retrospectively collected at our institution as the external validation set. Radiomics features were extracted from both intratumoral and peritumoral regions of fat-suppressed T2-weighted images (FS-T2WIs) of patients, and 3D ResNet10 was used to extract deep learning features. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) algorithms were used for the selection of features. Based on 4 different sets of features, 5 machine learning algorithms were used to construct intratumor, peritumor, combined intratumor and peritumor radiomics models and deep learning radiomics (DLR) model. The area under the ROC curve (AUC) and Decision curve analysis (DCA) were used to evaluate the ability of models to predict metastasis.

RESULTS AND CONCLUSIONS

Based on 20 selected features from the deep-learning and radiomics features set, the DLR model was able to predict metastasis in the validation dataset, with an AUC of 0.9770. The DCA and Hosmer-Lemeshow test revealed that the DLR model had good clinical benefit and consistency. By getting richer information from MRI, The DLR model is a noninvasive, low-cost method for predicting the risk of metastasis in STSs, and can help develop appropriate treatment programs.

Radiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk

RATIONALE AND OBJECTIVES

To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperatively based on fat saturation T2-weighted imaging (FS-T2WI) of patients.

MATERIALS AND METHODS

Data of 115 patients with STTs of extremities and trunk were collected from our hospital as the training set, and data of other 70 patients were collected from another center as the external validation set. Outlined Regions of interest included the intratumor and the peritumor region extending outward by 5 mm, then the corresponding radiomics features were extracted respectively. Deep learning was performed using pretrained 3D ResNet algorithms, and deep learning features were extracted from the entire FS-T2WI of patients. Recursive feature elimination and least absolute shrinkage and selection operator were used to select the radiomics and deep learning features with predictive value. Five machine learning algorithms were applied to build radiomics models, the area under the ROC curve (AUC) in the validation set were used to evaluate the diagnostic performance, and decision curve analysis (DCA) was used to evaluate the clinical benefit of models.

RESULTS

Based on 20 selected deep learning and radiomics features, the deep learning radiomics (DLR) model had the best predictive performance in the validation set, with an AUC of 0.9410. DCA and calibration curves showed that the DLR model had better clinical net benefit and goodness of fit.

CONCLUSION

By extracting more features from FS-T2WI, the DLR model is a noninvasive, low-cost, and highly accurate preoperative differential diagnosis of benign and malignant STTs.

Sonographic characteristics of local soft tissue recurrence in primary bone tumor and diagnostic efficacy versus MRI

BACKGROUND

The accurate diagnosis of local soft tissue recurrence (LR) in primary bone tumors is crucial for guiding clinical management and predicting patient outcomes. However, standardized postoperative surveillance protocols remain undefined. This study aims to compare the diagnostic efficacy of ultrasound (US) versus magnetic resonance imaging (MRI) in detecting LR following primary bone tumor surgery and to characterize the sonographic features of osteosarcoma recurrence.

METHODS

We conducted a retrospective review of medical records from patients who underwent postoperative surveillance for primary bone tumors at our institution between 01/06/2016 to 01/09/2023. Diagnostic performance was compared using McNemar's test for paired variables. Sonographic characteristics were analyzed using logistic regression analysis, with statistical significance set at p < 0.05.

RESULTS

Comparative analysis revealed no statistically significant differences (p > 0.05) in sensitivity, specificity, or accuracy between MRI and US, and the exact values for these parameters are provided in Table 1. Key sonographic features predictive of osteosarcoma recurrence included tumor size and anatomical location. The diagnostic model demonstrated excellent discriminative ability, with an area under the receiver operating characteristic (ROC) curve of 0.973. The diagnostic parameters were as follows: sensitivity (96.6%), specificity (90.9%), accuracy (94.6%), positive predictive value (95.0%), and negative predictive value (93.8%).

CONCLUSION

The findings from this study support the role of ultrasonography as a valuable tool in tumor surveillance paradigms, providing a scientific rationale for optimizing integrated management strategies in bone oncology.

Postoperative Imaging of Bone and Soft Tissue Tumors in the Extremity: A Comprehensive Review

Postoperative imaging of musculoskeletal tumors poses a significant diagnostic challenge for radiologists. The complexity arises from the need to differentiate between expected postoperative changes, potential complications, and local recurrence. The choice of imaging modality depends on the type of primary tumor. Standard radiological modalities such as radiography, computed tomography (CT), and magnetic resonance imaging (MRI) are widely utilized. Radiography and CT are especially valuable for assessing primary bone tumors, as they provide detailed views of bone structures and alignment, as well as revealing postoperative complications. MRIs are particularly effective for evaluating soft tissue tumors and identifying local recurrences due to its superior soft tissue contrast. The advanced imaging techniques, such as diffusion-weighted imaging and dynamic contrast-enhanced MRI, have significantly improved diagnostic accuracy in detecting tumor recurrence. An in-depth understanding of surgery-specific imaging findings, as well as the ability to detect recurrent disease, is crucial for early diagnosis of complications and improved patient outcomes. Familiarity with normal postoperative changes helps radiologists distinguish them from abnormal findings indicative of complications or tumor recurrence. This review article aims to outline the surgical options for musculoskeletal tumors, detail the various imaging techniques used in postoperative surveillance, and discuss the potential complications. By understanding the role of different imaging modalities and their applications associated with various surgical procedures, clinicians and radiologists can provide accurate and timely diagnoses.

Beyond Clinical Examination: Utilizing MRI Surveillance to Detect Recurrence of Soft Tissue Sarcomas and Differentiate from Posttherapeutic Changes

BACKGROUND

Early detection of soft tissue sarcoma (STS) recurrence is essential; however, the role and timeline of Magnetic resonance imaging (MRI) surveillance are still under debate. The aim of this study was to determine whether local recurrence (LR) could be identified via clinical examination alone and to assess the MRI morphology of primary STS and LR.

METHODS

This retrospective study included all patients with STS recurrence after surveillance for at least five years from the tumor database of the Medical University of Vienna from 2000 until December 2023. The characteristics of primary STS and LR and the time interval to recurrence and clinical detectability were assessed. The MRIs of LR and posttherapeutic changes (PTC) were compared with the initial MRIs.

RESULTS

A total of 57 patients (60% male; mean age 58.5 ± 18.0 years) with STS and histologically confirmed LR were included. The mean time interval to LR was 2.3 ± 1.8 years (range 108 to 3037 days). The clinically detectable recurrences were significantly larger than the inapparent ones (71.9 cm3 vs. 7.0 cm3; p < 0.01). The MRI morphology of all LRs (26/26) closely resembled the initial STS. For comparison, nine patients were included with clinically suspected LRs, which were histologically proven to be PTC. None of these resembled the primary STS.

CONCLUSION

Based on clinical symptoms alone, especially small and early recurrences can be missed, which supports the importance of MRI surveillance.

Detecting residual soft tissue sarcoma after unplanned excision; model-free analysis of dynamic contrast-enhanced MRI at short-term follow-up

OBJECTIVES

To evaluate diagnostic utility of additional DCE-MRI for detecting residual soft tissue sarcomas (STS) after unplanned excision (UPE).

METHODS

We retrospectively evaluated 32 patients with UPE of STS, followed by conventional MRI with DCE-MRI and wide excision (WE), between November 2019 and January 2022. Residual tumors on conventional MRI were categorized into three groups: Lesion-type-0, no abnormal enhancement, Lesion-type-1, an indeterminate lesion, and Lesion-type-2, a definite enhancing nodule. On DCE-MRI, ROIs were manually placed on enhancing areas of suspected residual tumor. The mean and 95th percentile values of AUC of time-intensity curve were calculated at 60, 90, and 120 s of Enhancement-cycle-1 and -2. Optimal DCE parameters were identified by ROC analysis. Diagnostic performance of conventional MRI and DCE-MRI was compared using McNemar's test.

RESULTS

On WE, residual tumor was present in 23 (71.9%) of 32 patients. On MRI, Lesion-type-1 was found in 16/32 (50%) patients and Lesion-type-2 in 16/32 (50%). The optimal DCE parameter was the 95th percentile value of AUC at 120s of Enhancement-cycle-2. The sensitivity, specificity, and AUC were as follows: 65.2% (95% CI, 45.8-85.7%), 88.9% (CI, 68.4-100%), and 0.77 (CI, 0.62-0.92) for conventional MRI, and 100%, 55.6% (CI, 23.1-88.0%), and 0.78 (CI, 0.61-0.95) for combined conventional and DCE-MRI.

CONCLUSIONS

Additional DCE-MRI aided in detecting residual STS after UPE, particularly in cases without definite soft tissue nodular enhancement.

ADVANCES IN KNOWLEDGE

Close follow up may be suggested for patients showing abnormality in DCE-MRI, with more suspicion of residual tumor.

Impact of postoperative baseline MRI on diagnostic confidence and performance in detecting local recurrence of soft-tissue sarcoma of the limb

OBJECTIVE

To evaluate the impact of a postoperative baseline (PB) MRI on diagnostic confidence and performance in detecting local recurrence (LR) of soft-tissue sarcoma (STS) of the limb.

MATERIALS AND METHODS

A total of 72 patients (8 with LR, 64 without LR) with primary STS of the limb were included. Routine follow-up MRI (1.5 T) at 6 and approximately 36 months (meanLR: 39.7 months; meanno LR: 34.9 months) after multimodal therapy or at time of LR were assessed by three independent readers using a 5-point Likert scale. Furthermore, the following imaging parameters were evaluated: presence of a mass, signal characteristics at T2- and T1-weighted imaging, contrast enhancement (CE), and in some of the cases signal intensity on the apparent diffusion coefficient (ADC). U-test, McNemar test, and ROC-analysis were applied. Interobserver reliability was calculated using Fleiss kappa statistics. A p value of 0.05 was considered statistically significant.

RESULTS

The presence of a PB MRI significantly improved diagnostic confidence in detecting LR of STS (p < 0.001) and slightly increased specificity (mean specificity without PE 74.1% and with presence of PB MRI 81.2%); however, not to a significant level. The presence of a mass showed highest diagnostic performance and highest interreader agreement (AUC [%]; κ: 73.1-83.6; 0.34) followed by T2-hyperintensity (50.8-66.7; 0.08), CE (52.4-62.5; 0.13), and T1-hypointensity (54.7-77.3; 0.23). ADC showed an AUC of 65.6-96.6% and a κ of 0.55.

CONCLUSION

The presence of a PB MRI increases diagnostic confidence in detecting LR of STS of the limb.

Diagnosis of Local Recurrence of Malignant Soft Tissue Tumors after Reconstructive Surgery on MRI

PURPOSE

Magnetic resonance imaging (MRI) is useful in the diagnosis of local recurrence, but few studies have explored recurrence in MRI in patients after reconstructive surgery. The purpose of this study was to analyze MRI findings of locoregional recurrence following reconstructive surgery after malignant soft tissue tumor resection.

METHOD

Fifty-three postoperative MRIs from 37 patients who underwent reconstructive surgery after malignant soft tissue tumor resection were retrospectively reviewed. A total of 76 enhancing lesions, including 40 locoregional recurrences and 36 postoperative changes, were analyzed regarding morphology (location on the transplanted tissue, border, and shape) and the signals on T1- and T2-weighted imaging (T1WI, T2WI), fat-suppressed (FS) T2WI, and contrast-enhanced FS T1WI. Diffusion-weighted imaging with an apparent diffusion coefficient was assessed. A chi-squared test and Fisher's exact test were used for statistical analysis.

RESULTS

The most common site of recurrent tumors and postoperative changes was the peripheral margin on transplanted tissue (63% and 61%, respectively p = 0.907). Recurrent tumors commonly appeared with well-defined borders (75%) as well as nodular appearance (98%), hyperintensity on T2WI (85%) and FS-T2WI (95%), isointensity on T1WI (65%), impeded water diffusion (55%), and intense (50%) or moderate (45%) enhancement. Postoperative changes showed ill-defined borders (75%), nodular appearance (56%), facilitated water diffusion (69%), and moderate (86%) enhancement, which were significantly different from those of recurrent tumors (p ≤ 0.020).

CONCLUSIONS

Common and partitioning MRI features of locoregional recurrence were well-defined borders, nodular shape, impeded water diffusion, and intense enhancement. Peripheral margins on transplanted tissue were common sites in both recurrent tumors and postoperative changes.

Sarcoma Imaging Surveillance

Soft tissue sarcomas (STS) are a heterogeneous group of solid tumors. There are many histologic subtypes. The prognosis after treatment may be estimated by the analysis of the type of tumor, grade, depth, size at diagnosis, and age of the patient. These type of sarcomas most commonly metastasize to the lungs and may have a relatively high rate of local recurrence, depending on the histologic type and surgical margins. Patients with recurrence have a poorer prognosis. The surveillance of patients with STS is therefore extremely important. This review analyzes the role of MR imaging and US in detecting local recurrence.

Impact of Dynamic Contrast Enhanced and Diffusion-Weighted MR Imaging on Detection of Early Local Recurrence of Soft Tissue Sarcoma

BACKGROUND

Diagnosis of residual or recurrent tumor in soft-tissue sarcomas (STS) is a differential diagnostic challenge since post-therapeutic changes impede diagnosis.

PURPOSE

To evaluate the diagnostic accuracy of quantitative dynamic contrast enhanced (DCE)-MRI and diffusion-weighted imaging (DWI) to detect local recurrence of STS of the limb.

STUDY TYPE

Prospective.

POPULATION

A totalof 64 consecutive patients with primary STS of the limbs were prospectively included 3-6 months after surgery between January 2016 and July 2021.

FIELD STRENGTH/SEQUENCE

A 1.5 T; axial DWI echo-planar imaging sequences and DCE-MRI using a 3D T1-weighted spoiled gradient-echo sequence.

ASSESSMENT

The quantitative DCE-MRI parameters relative plasma flow (rPF) and relative mean transit time (rMTT) were calculated and ADC mapping was used to quantify diffusion restriction. Regions of interest of tumor growth and postoperative changes were drawn in consensus by two experts for diffusion and perfusion analysis. An additional morphological assessment was done by three independent and blinded radiologists.

STATISTICAL TEST

Unpaired t-test, ROC-analysis, and a logistic regression model were applied. Interobserver reliability was calculated using Fleiss kappa statistics. A P value of 0.05 was considered statistically significant.

RESULTS

A total of 11 patients turned out to have local recurrence. rPF was significantly higher in cases of local recurrence when compared to cases without local recurrence (61.1-4.5) while rMTT was slightly and significantly lower in local recurrence. ROC-analysis showed an area under the curve (AUC) of 0.95 (SEM ± 0.05) for rPF while a three-factor multivariate logistic regression model showed a high diagnostic accuracy of rPF (R²  = 0.71). Compared with morphological assessment, rPF had a distinct higher specificity and true positive value in detection of LR.

DATA CONCLUSION

DCE-MRI is a promising additional method to differentiate local recurrence from benign postoperative changes in STS of the limb. Especially specificity in detection of LR is increased compared to morphological assessment.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Presurgical Perspective and Posttreatment Evaluation of Soft Tissue Tumors of the Ankle and Foot in Adults

This article discusses soft tissue tumors of the ankle and foot region in adults, including tumors of the joints, and also briefly addresses tumor-simulating lesions. We offer general recommendations and describe specific aspects of common entities in that region, such as typical imaging appearance, therapeutic strategies, and posttherapeutic considerations. Focal masses and diffuse swelling are common in the foot and ankle region; most of them are non-neoplastic. Some of the tumors, such as plantar fibromatosis, tenosynovial giant cell tumor, synovial chondromatosis, or schwannoma, have a very typical appearance on magnetic resonance imaging. Sarcomas are rare among true soft tissue tumors; however, they can be small and well demarcated, may grow slowly, and are often misinterpreted as benign. This is especially true for synovial sarcoma, one of the most common sarcomas in this region. Densely packed tissues in the foot and ankle may hamper determining the tissue of origin. Adherence to diagnostic guidelines and cooperation with tumor centers is crucial including for posttherapeutic surveillance. We also describe typical posttherapeutic changes and complications after surgery, radiation therapy, and chemotherapy, as well as parameters for the detection and exclusion of recurrence of soft tissue tumors of the ankle and foot.

Detection of Soft Tissue Sarcoma Recurrence: Feasibility of Ultrafast 3D Gradient-Echo Sequence in Addition to Conventional Contrast-Enhanced MRI to Provide Early-Phase Postcontrast Information

Objectives:

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been investigated to better detect recurrent tumors of malignant soft tissue sarcoma (STS), however, DCE-MRI is time-consuming and not available at all medical centers. This study aims to evaluate the feasibility of dual-phase postcontrast MRI sequences (early 3D spoiled gradient-echo [GRE] and delayed fast spin-echo [FSE] T1WI) for the differentiation of recurrent tumor from nonneoplastic lesions.

Materials and methods:

A total of 297 patients under postoperative surveillance for malignant STS were included in this retrospective study and divided into three subgroups, as follows: group A, recurrent tumors (n = 82); group B, pseudomasses (n = 55); and group C, postoperative inflammation (n = 160). All MRI examinations included dual-phase post-contrast sequences. The contrast-to-noise ratio (CNR) and the signal-intensity ratio (SIR) were used to evaluate the degree of contrast enhancement in target lesions. ROC curve analysis was performed to assess the diagnostic performance for recurrent tumor.

Results:

In the early phase, all mean CNR and SIR values were significantly higher in group A (all, p < 0.05). However, the difference of the CNR between early and delayed post-contrast MRI showed a significantly lesser increase in group A than in the other groups when muscle was used as the reference tissue (p = 0.026). A comparison of ROC curves showed that dual-phase MRI had significantly better diagnostic performance than conventional postcontrast MRI.

Conclusion:

The addition of an early postcontrast 3D GRE to conventional FSE-T1WI is useful to detect recurrent tumors by providing additional information on early enhancement.

Does the primary soft-tissue sarcoma configuration predict configuration of recurrent tumors on magnetic resonance imaging?

BACKGROUND

Soft-tissue sarcomas (STS) are rare malignancies of the soft tissue.

PURPOSE

To assess whether the magnetic resonance imaging (MRI) configuration of primary STS can predict the configuration of a recurring tumor and whether the MRI configuration of multiple recurrences differs in one and the same patient.

MATERIAL AND METHODS

Thirty-nine patients with histologically proven recurrent STS were included in this retrospective study and underwent pre- and post-treatment MRI. Three main configurations of primary and recurrent tumors were identified: polycyclic/multilobulated; ovoid/nodular; and streaky.

RESULTS

Sixty recurrent lesions were detected: 34 ovoid/nodular; 15 polycyclic/multilobulated; and 11 streaky. Five recurrences were multifocal and eight were bifocal. Of 39 patients, 28 (71.8%) presented one recurrence within the MRI follow-up period (P = 0.006); in 10 patients (25.6%), up to three different configurations of recurring STS were identified in one patient. Recurrences of polycyclic/multilobulated primaries were mostly ovoid/nodular (48%; P = 0.003) or polycyclic/multilobulated (37%; P = 0.014), and recurring ovoid/nodular STS significantly most often showed the same configuration as the primary tumor (85%; P < 0.001). Primary STS with a streaky configuration recurred in all three configurations in roughly equal proportions. Homogeneity/heterogeneity and tumor borders are significantly associated with the configuration of recurrences.

CONCLUSION

Primary STS configuration may help predict recurrent tumor configuration when the primary STS had a polycyclic/multilobulated or ovoid/nodular configuration. However, recurrent STS configuration can also differ from primary STS configuration, especially when the primary STS had a streaky configuration, rendering recurrent STS difficult to predict. Different configurations of recurrent STS in one and the same patient are common.

Diagnostic performance of MRI in detecting locally recurrent soft tissue sarcoma: systematic review and meta-analysis

OBJECTIVE

To systematically review the diagnostic criteria and performance of MRI in detecting locally recurrent soft tissue sarcoma.

METHODS

Medline and Embase were searched for original studies on the diagnostic performance of MRI detecting locally recurrent soft tissue sarcoma. Study quality was assessed using QUADAS-2. Sensitivity and specificity were pooled using a bivariate random-effects model.

RESULTS

Ten studies were included. There was a high risk of bias with respect to patient selection in 2 studies and a high risk of bias with respect to flow and timing in 8 studies. The presence of a mass yielded a pooled sensitivity of 80.9% and a pooled specificity of 77.0%. Hyperintensity at T2-weighted imaging yielded a pooled sensitivity of 82.4% and a pooled specificity of 11.0%. Hypo- or isointensity at T1-weighted imaging yielded a pooled sensitivity of 82.0% and a pooled specificity of 14.3%. Contrast enhancement images yielded a pooled sensitivity of 95.9% and a pooled specificity of 12.3%. Low signal mass on the apparent diffusion coefficient (ADC) map yielded a pooled sensitivity of 67.5% and a pooled specificity of 95.3%. Early and rapid arterial phase enhancement at dynamic contrast-enhanced (DCE) MRI yielded a pooled sensitivity of 91.3% and a pooled specificity of 84.7%.

CONCLUSION

The presence of a mass appears a useful criterion to diagnose locally recurrent soft tissue sarcoma. Signal characteristics at standard T2- and T1-weighted imaging and contrast enhancement seem less useful because they lack specificity. Functional MRI techniques, including DWI with ADC mapping and DCE, may help to make a correct diagnosis.

KEY POINTS

• The presence of a mass at MRI appears useful to diagnose locally recurrent soft tissue sarcoma, because both sensitivity and specificity are fairly high. • Signal characteristics at standard T2- and T1-weighted sequences and contrast enhancement suffer from poor specificity. • DWI with ADC mapping and DCE may help to make a correct diagnosis, but further research is needed to better understand the value of these functional MRI techniques.

Therapeutic and diagnostic targeting of fibrosis in metabolic, proliferative and viral disorders

Fibrosis is a common denominator in many pathologies and crucially affects disease progression, drug delivery efficiency and therapy outcome. We here summarize therapeutic and diagnostic strategies for fibrosis targeting in atherosclerosis and cardiac disease, cancer, diabetes, liver diseases and viral infections. We address various anti-fibrotic targets, ranging from cells and genes to metabolites and proteins, primarily focusing on fibrosis-promoting features that are conserved among the different diseases. We discuss how anti-fibrotic therapies have progressed over the years, and how nanomedicine formulations can potentiate anti-fibrotic treatment efficacy. From a diagnostic point of view, we discuss how medical imaging can be employed to facilitate the diagnosis, staging and treatment monitoring of fibrotic disorders. Altogether, this comprehensive overview serves as a basis for developing individualized and improved treatment strategies for patients suffering from fibrosis-associated pathologies.

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.

Diagnostic value of MRI for detecting recurrent soft-tissue sarcoma in a long-term analysis at a multidisciplinary sarcoma center

Background

Soft-tissue sarcomas (STS) are rare tumors of the soft tissue. Recent diagnostic studies on STS mainly dealt with only few cases of STS and did not investigate the post-therapeutic performance of MRI in a routine clinical setting. Therefore, we assessed the long-term diagnostic accuracy of MRI for detecting recurrent STS at a multidisciplinary sarcoma center.

Methods

In all, 1055 postoperative follow-up MRIs of 204 patients were included in the study. MRI follow-up scans were systematically reviewed for diagnostic values (true-positive/−negative and false-positive/−negative results) in detecting recurrences. Pathological reports and follow-up MRIs were set as baseline references.

Results

The median age of the patients was 55.3 ± 18.2 years. Of the patients, 34.8% presented with recurrences. Here, 65 follow-up scans were true positive, 23 false positive, 6 false negative, and 961 true negative. The overall sensitivity and specificity of MRI for detecting recurrences were 92 and 98%, respectively, with an accuracy of 97%. For intramuscular lesions and after surgery alone the sensitivity was higher (95 and 97%, respectively) than for subcutaneous lesions and surgery with additional radiation therapy (83 and 86%, respectively), at similarly high specificities (96–98%). The 6 false-negative results were found in streaky (n = 2) and small ovoid/nodular (n = 4) recurring lesions. The false-positive lesions imitated streaky (n = 14), ovoid/nodular (n = 8), and polycyclic/multilobulated recurring tumors (n = 1). All false-positive results were found in patients in whom the primary tumors were polycyclic/multilobulated in appearance.

Conclusion

MRI shows a high diagnostic accuracy for detecting recurrent STS, with a high sensitivity and specificity. The diagnostic accuracy decreases in subcutaneous lesions and after surgery with radiation therapy, compared to intramuscular lesions and surgery alone. Radiologists should pay particular attention to streaky and small ovoid/nodular recurring lesions and patients with polycyclic/multilobulated primary tumors.

Soft Tissue Sarcoma Follow-up Imaging: Strategies to Distinguish Post-treatment Changes from Recurrence

Soft tissue sarcomas encompass multiple entities with differing recurrence rates and follow-up intervals. The detection of recurrences and their differentiation from post-therapeutic changes is therefore complex, with a central role for the clinical radiologist. This article describes approved recommendations. Prerequisite is a precise knowledge of the current clinical management and surgical techniques. We review recurrence rates and treatment modalities. An adequate imaging technique is paramount, and comparison with previous imaging is highly recommended. We describe time-dependent therapy-related complications on magnetic resonance imaging compared with the spectrum of regular post-therapeutic changes. Early complications such as seromas, hematomas, and infections, late complications such as edema and fibrosis, and inflammatory pseudotumors are elucidated. The appearance of recurrences and radiation-associated sarcomas is contrasted with these changes. This systematic approach in follow-up imaging of soft tissue sarcoma patients will facilitate the differentiation of post-therapeutic changes from recurrences.

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.