Assessment of central cartilaginous tumor of the appendicular bone: inter-observer and intermodality agreement and comparison of diagnostic performance of CT and MRI.

BACKGROUND: Diagnostic performance, inter-observer agreement, and intermodality agreement between computed tomography (CT) and magnetic resonance imaging (MRI) in the depiction of the major distinguishing imaging features of central cartilaginous tumors have not been investigated. PURPOSE: To determine the inter-observer and intermodality agreement of CT and MRI in the evaluation of central cartilaginous tumors of the appendicular bones, and to compare their diagnostic performance. MATERIAL AND METHODS: Two independent radiologists retrospectively reviewed preoperative CT and MRI. Inter-observer and intermodality agreement between CT and MRI in the assessment of distinguishing imaging features, including lesion size, deep endosteal scalloping, cortical expansion, cortical disruption, pathologic fracture, soft tissue extension, and peritumoral edema, were evaluated. The agreement with histopathology and the accuracy of the radiologic diagnoses made with CT and MRI were also analyzed. RESULTS: A total of 72 patients were included. CT and MRI showed high inter-observer and intermodality agreements with regard to size, deep endosteal scalloping, cortical expansion, cortical disruption, and soft tissue extension (ICC = 0.96-0.99, k = 0.60-0.90). However, for the evaluation of pathologic fracture, MRI showed only moderate inter-observer agreement (k = 0.47). Peritumoral edema showed only fair intermodality agreement (k = 0.28-0.33) and moderate inter-observer agreement (k = 0.46) on CT. Both CT and MRI showed excellent diagnostic performance, with high agreement with the histopathology (k = 0.89 and 0.87, respectively) and high accuracy (91.7% for both CT and MRI). CONCLUSION: CT and MRI showed high inter-observer and intermodality agreement in the assessment of several distinguishing imaging features of central cartilaginous tumors of the appendicular bones and demonstrated comparable diagnostic performance.

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.

Role of whole-body MRI for treatment response assessment in multiple myeloma: comparison between clinical response and imaging response.

BACKGROUND: Whole-body MRI (WB-MRI) including diffusion-weighted image (DWI) have been widely used in patients with multiple myeloma. However, evidence for the value of WB-MRI in the evaluation of treatment response remains sparse. Therefore, we evaluated the role of WB-MRI in the response assessment. METHODS: In our WB-MRI registry, we searched multiple myeloma patients treated with chemotherapy who underwent both baseline and follow-up WB-MRI scans. Clinical responses were categorized as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD), using IMWG criteria. Using RECIST 1.1, MD Anderson (MDA) criteria, and MDA-DWI criteria, imaging responses on WB-MRI were rated as CR, PR, SD, or PD by two radiologists independently. Then, discrepancy cases were resolved by consensus. Weighted Kappa analysis was performed to evaluate agreement between the imaging and clinical responses. The diagnostic accuracy of image responses in the evaluation of clinical CR, objective response (CR and PR), and PD was calculated. RESULTS: Forty-two eligible patients were included. There was moderate agreement between imaging and clinical responses (κ = 0.54 for RECIST 1.1, κ = 0.58 for MDA criteria, κ = 0.69 for MDA-DWI criteria). WB-MRI showed excellent diagnostic accuracy in assessment of clinical PD (sensitivity 88.9%, specificity 94.7%, positive predictive value [PPV] 84.2%, negative predictive value [NPV] 96.4% in all three imaging criteria). By contrast, WB-MRI showed low accuracy in assessment of clinical CR (sensitivity 4.5%, specificity 98.1%, PPV 50.0%, NPV 71.2% in all three imaging criteria). As to the clinical objective response, the diagnostic accuracy was higher in MDA-DWI criteria than RECIST 1.1 and MDA criteria (sensitivity/specificity/PPV/NPV, 84.2%/94.4%/98.0%/65.4, 54.4%/100%/100%/40.9, and 61.4%/94.4%/97.2%/43.6%, respectively). CONCLUSIONS: In the imaging response assessment of multiple myeloma, WB-MRI showed excellent performance in the evaluation of PD, but not in the assessment of CR or objective response. When adding DWI to imaging response criteria, diagnostic accuracy for objective response was improved and agreement between imaging and clinical responses was increased.

Adaptive statistical iterative reconstruction and Veo: assessment of image quality and diagnostic performance in CT colonography at various radiation doses.

OBJECTIVES: To evaluate the diagnostic performance of computed tomography (CT) colonography (CTC) reconstructed with different levels of adaptive statistical iterative reconstruction (ASiR, GE Healthcare) and Veo (model-based iterative reconstruction, GE Healthcare) at various tube currents in detection of polyps in porcine colon phantoms. METHODS: Five porcine colon phantoms with 46 simulated polyps were scanned at different radiation doses (10, 30, and 50 mA s) and were reconstructed using filtered back projection (FBP), ASiR (20%, 40%, and 60%) and Veo. Eleven data sets for each phantom (10-mA s FBP, 10-mA s 20% ASiR, 10-mA s 40% ASiR, 10-mA s 60% ASiR, 10-mA s Veo, 30-mA s FBP, 30-mA s 20% ASiR, 30-mA s 40% ASiR, 30-mA s 60% ASiR, 30-mA s Veo, and 50-mA s FBP) yielded a total of 55 data sets. Polyp detection sensitivity and confidence level of 2 independent observers were evaluated with the McNemar test, the Fisher exact test, and receiver operating characteristic curve analysis. Comparative analyses of overall image quality score, measured image noise, and interpretation time were also performed. RESULTS: Per-polyp detection sensitivities and specificities were highest in 10-mA s Veo, 30-mA s FBP, 30-mA s 60% ASiR, and 50-mA s FBP (sensitivity, 100%; specificity, 100%). The area-under-the-curve values for the overall performance of each data set was also highest (1.000) at 50-mA s FBP, 30-mA s FBP, 30-mA s 60% ASiR, and 10-mA s Veo. Images reconstructed with ASiR showed statistically significant improvement in per-polyp detection sensitivity as the percent level of per-polyp sensitivity increased (10-mA s FBP vs 10-mA s 20% ASiR, P = 0.011; 10-mA s FBP vs 10-mA s 40% ASiR, P = 0.000; 10-mA s FBP vs 10-mA s 60% ASiR, P = 0.000; 10-mA s 20% ASiR vs 40% ASiR, P = 0.034). Overall image quality score was highest at 30-mA s Veo and 50-mA s FBP. The quantitative measurement of the image noise was lowest at 30-mA s Veo and second lowest at 10-mA s Veo. There was a trend of decrease in time required for image interpretation as the percent level of ASiR increased, and ASiR or Veo was used instead of FBP. However, differences from comparative analyses of overall image quality score, measured image noise, and interpretation time did not reach statistical significance. CONCLUSION: ASiR and Veo showed improved diagnostic performance with excellent sensitivity and specificity with less image noise and good image quality compared with FBP reconstruction of same radiation dose. Our study confirmed feasibility of low-dose CTC with iterative reconstruction as a promising screening tool with excellent diagnostic performance similar to that of the standard-dose CTC with FBP.