Soft tissue sarcomas at a glance: clinical, histological, and MR imaging features of malignant extremity soft tissue tumors

: Myxofibrosarcoma is one of the most common soft tissue sarcomas in the elderly. It is characterized by an extremely high rate of local recurrence, higher than other soft tissue tumors, and a relatively low risk of distant metastases.Magnetic resonance imaging (MRI) is the imaging modality of choice for the assessment of myxofibrosarcoma and plays a key role in the preoperative setting of these patients.MRI features associated with high risk of local recurrence are: high myxoid matrix content (water-like appearance of the lesions), high grade of contrast enhancement, presence of an infiltrative pattern (“tail sign”). On the other hand, MRI features associated with worse sarcoma specific survival are: large size of the lesion, deep location, high grade of contrast enhancement. Recognizing the above-mentioned imaging features of myxofibrosarcoma may be helpful to stratify the risk for local recurrence and disease-specific survival. Moreover, the surgical planning should be adjusted according to the MRI features

Download Full-text

Is it necessary to routinely use gadolinium in the MR imaging evaluation of soft-tissue tumors of the extremities?

American Journal of Roentgenology ◽

10.2214/ajr.165.6.7484606 ◽

1995 ◽

Vol 165 (6) ◽

pp. 1545-1545 ◽

Cited By ~ 2

Author(s):

M J Kransdorf

Keyword(s):

Soft Tissue ◽

Mr Imaging ◽

Soft Tissue Tumors ◽

Imaging Evaluation

Download Full-text

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

Cancers ◽

10.3390/cancers13081929 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1929

Author(s):

Jan C. Peeken ◽

Jan Neumann ◽

Rebecca Asadpour ◽

Yannik Leonhardt ◽

Joao R. Moreira ◽

...

Keyword(s):

Soft Tissue ◽

Soft Tissue Sarcomas ◽

Treatment Decision ◽

Staging System ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Clinical Staging ◽

Imaging Features ◽

Prognostic Assessment ◽

Selection Operator

Background: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients’ risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). Methods: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. Results: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. Conclusions: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

Download Full-text

Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Cancers ◽

10.3390/cancers13122866 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2866

Author(s):

Fernando Navarro ◽

Hendrik Dapper ◽

Rebecca Asadpour ◽

Carolin Knebel ◽

Matthew B. Spraker ◽

...

Keyword(s):

Deep Learning ◽

Soft Tissue ◽

Mr Imaging ◽

Characteristic Curve ◽

External Validation ◽

Soft Tissue Sarcomas ◽

Treatment Decision ◽

Receiver Operator Characteristic Curve ◽

Low Grade ◽

Tumor Grading

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

Download Full-text