An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: Classification and regression tree (CART) analysis.

PURPOSE: To build and validate a decision tree model using classification and regression tree (CART) analysis to distinguish lipoma and lipoma variants from well-differentiated liposarcoma of the extremities and superficial trunk. METHODS: This retrospective study included patients who underwent surgical resection and preoperative contrast-enhanced MR imaging for lipoma, lipoma variants, and well-differentiated liposarcoma in two tertiary referral centers. Six MRI findings (tumor size, anatomical location, tumor depth, shape, enhancement pattern, and presence of intermingled muscle fibers) and two demographic factors (patient age and sex) were assessed to build a classification tree using CART analysis with minimal error cross-validation pruning based on a complexity parameter. RESULTS: The model building cohort consisted of 231 patients (186 lipoma and lipoma variants and 45 well-differentiated liposarcoma) from one center, while the validation cohort consisted of 157 patients (136 lipoma and lipoma variants and 21 well-differentiated liposarcoma) from another center. In the CART analysis, the contrast enhancement pattern (no enhancement or thin septal enhancement versus thick septal, nodular, confluent hazy, or solid enhancement) was the first partitioning predictor, followed by a maximal tumor size of 12.75 cm. The tree model allowed distinction of lipoma and lipoma variants from well-differentiated liposarcoma in both the model building cohort (C-statistics, 0.955; sensitivity 80 %, specificity 94.62 %, accuracy 91.77 %) and the external validation cohort (C-statistics, 0.917; sensitivity 66.67 %, specificity 95.59 %, accuracy 91.72 %). CONCLUSION: The distinction of lipoma and lipoma variants from well-differentiated liposarcoma can be achieved with the simple classification tree model.

Correlation between radiological assessment and histopathological diagnosis in retroperitoneal tumors: analysis of 291 consecutive patients at a tertiary reference sarcoma center.

OBJECTIVES: Aim of study was to assess the correlation between computed tomography scan (CT) findings and histopathology. MATERIAL AND METHODS: Data were collected on consecutive patients with suspected retroperitoneal sarcoma (RPS) referred to a tertiary sarcoma center. Patients underwent contrast enhanced multi-detector CT scans. Radiological features of lesions were classified according to the presence of a fatty (Group A) mass, or non-fatty (Group B) mass, both subdivided according to homogeneity and intralesional high-contrasted appearance. Radiological classification was compared with histopathological diagnosis. Sensitivity, specificity, positive/negative predictive value (PPV, NPV) were analyzed. RESULTS: Of 291 patients, 103/291 (35.4%) masses were classified in Group A and 188/291 (64.6%) in Group B. Diagnosis of mesenchymal tumor was obtained in 231/291 cases (79%) and non-mesenchymal tumor in 60/291 (21%). Sensitivity and specificity of Group A for liposarcoma were 76.7% and 92.0%; PPV and NPV were 86.4% and 85.6%. Sensitivity of Group B for a mesenchymal tumor was 55.4% and specificity was 0%; PPV and NPV were 68.1% and 0%. CONCLUSIONS: None of radiological criteria were sufficient to anticipate a specific diagnosis, with the only exception of well differentiated liposarcoma and angiomyolipoma. In a series of suspected RPS, 21% of the lesions were finally non-mesenchymal tumors.

Risk assessment in liposarcoma patients based on FDG PET imaging.

PURPOSE: Tumor grade and subtype are considered standard parameters for risk assessment in patients with liposarcoma. The aim of this study was to assess the clinical value of [(18)F]fluorodeoxyglucose (FDG) PET-derived maximum standardized uptake value (SUV(max)) for prediction of outcome in liposarcoma patients. METHODS: (18)F-FDG PET was performed in 54 patients with liposarcoma prior to therapy. SUV(max) was calculated for each tumor and results were correlated with tumor grade, subtype, and relapse-free survival. RESULTS: SUV(max) ranged from 0.4 to 15.9 (mean 3.6) and was significantly lower in grade I than in grade II and grade III tumors. SUV(max) was 2.3+/-1.7, 3.5+/-1.5, 4.8+/-2.5, and 5.6+/-5.8 in well-differentiated, myxoid/round cell, dedifferentiated, and pleomorphic subtypes, respectively. Borderline differences (p=0.059) were found between tumor SUV(max) in patients with and without relapse. Using a SUV of 3.6 as cut-off, the accuracy in predicting a relapse was 75%. Tumor grade yielded a lower accuracy for predicting relapse (50%), as did tumor subtype (35%). In Kaplan-Meier survival analysis, patients with a SUV(max) >3.6 had a significantly shorter disease-free survival of 21 months compared with 44 months in patients with a SUV(max)

Assessment of soft tissue lesions suspicious for liposarcoma by F18-deoxyglucose (FDG) positron emission tomography (PET).

BACKGROUND: F18-deoxyglucose (FDG) positron emission tomography (PET) is a promising imaging technique. The aim of this study was to investigate the use of FDG PET in patients with suspected liposarcomas (LS). PATIENTS AND METHODS: Forty-two masses were studied. The FDG uptake was estimated in tumor (T) and normal tissue (NT). The data were analyzed with respect to pathological findings. RESULTS: Pathology revealed 11 primary LS, 14 locally recurrent LS, 5 other sarcomas, 1 inflammation, 1 lymphoma and 10 benign lesions. FDG uptake (T-to-NT ratio) in 25 LS corresponded with the histological subtype. Pleomorphic, mixed and myxoid LS showed an increased T-to-NT ratio and were thus visualized. Four out of six well-differentiated LS presented a low FDG uptake. Like subtype, the tumor grade also corresponded to FDG uptake. The T-to-NT ratio of higher grade LS, contrary to low grade LS, was uniformly increased. Primary LS were distinguishable from benign tumors, while other sarcomas, inflammation and lymphoma were not. Recurrence was detected with a sensitivity of 86% and a specificity of 100%. False-negative diagnoses occurred only in well-differentiated recurrences. CONCLUSION: FDG uptake in LS correlates with the histological subtype and tumor grade. The diagnostic value of FDG PET in LS, therefore, is influenced by histomorphological parameters. Our data suggest that pleomorphic, mixed and higher-grade LS recurrences are preferentially amenable to FDG PET imaging.

High Z elements in human sarcomata: assessment by multienergy CT and neutron activation analysis.

"Tumor equivalent" phantoms containing inorganic salts (KH2PO4, CH3COOK, NaCl and Kl) were scanned on an EMI 5005 body scanner at 140 kVp, 28 mA; 120 kVp, 33 mA; and 81 kVp, 42 mA. Significant signal gain for the detection of higher atomic number elements by multiple energy scanning was noted. Certain sarcomas are known to accumulate high Z elements. Accordingly, excised specimens of various histologies of human sarcomata (chondrosarcoma, liposarcoma, and malignant fibrous histiocytoma) were scanned at 140 kVp and 81 kVp. Using selected areas of interest in the computed tomographic (CT) image to direct the in vitro biopsy of various regions of excised tumors, interesting correlations between the CT number variation and the respective, high Z elemental composition variation, as determined by thermal neutron activation analysis were observed. Further investigation with phantoms and excised sarcomata at 62 kVp and 42 mA suggested that dual energy CT scanning (at 140 kVp and 62 kVp) may be a method of monitoring "effective Z" and heavy element compositional changes. The authors are also attempting to develop these same low kilovoltage techniques as a method for the noninvasive clinical monitoring of an antisarcoma chemotherapeutic agent, cis-diammineaichloroplatinum (11).