Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor.

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.

MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities.

PURPOSE: To determine diagnostic performance of MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor (ALT) of the extremities. MATERIAL AND METHODS: This retrospective study was performed at three tertiary sarcoma centers and included 150 patients with surgically treated and histology-proven lesions. The training-validation cohort consisted of 114 patients from centers 1 and 2 (n = 64 lipoma, n = 50 ALT). The external test cohort consisted of 36 patients from center 3 (n = 24 lipoma, n = 12 ALT). 3D segmentation was manually performed on T1- and T2-weighted MRI. After extraction and selection of radiomic features, three machine learning classifiers were trained and validated using nested fivefold cross-validation. The best-performing classifier according to previous analysis was evaluated and compared to an experienced musculoskeletal radiologist in the external test cohort. RESULTS: Eight features passed feature selection and were incorporated into the machine learning models. After training and validation (74% ROC-AUC), the best-performing classifier (Random Forest) showed 92% sensitivity and 33% specificity in the external test cohort with no statistical difference compared to the radiologist (p = 0.474). CONCLUSION: MRI radiomics-based machine learning may classify deep-seated lipoma and ALT of the extremities with high sensitivity and negative predictive value, thus potentially serving as a non-invasive screening tool to reduce unnecessary referral to tertiary tumor centers.

Uncommon site of metastatic neuroblastoma in a 15-year-old girl: case report and description of sonographic and radiographic features.

BACKGROUND: Although most breast masses in children are benign, breast cancer must be considered in the differential diagnosis. The majority are represented by sarcomas and secondary lesions. Literature reports only four cases of neuroblastoma breast metastasis, with no emphasis on radiologic features. Our work aims to furnish a description of radiologic and sonographic features of neuroblastoma metastasis in the breast. CASE DESCRIPTION: A 15-year-old girl had a round nodular mass in the outer upper quadrant of the left breast that had rapidly enlarged over the last month. An ultrasound showed two subcutaneous nodules (3.8 cm and 1.3 cm in maximum diameter), with an irregular shape, heterogeneous echogenicity (isohypoechoic), and hyperechoic foci with a posterior acoustic shadow inside. Overall, the features were highly suspicious of secondary malignant lesions. Computed tomographic scan was performed and found a large retroperitoneal mass and multiple mixed secondary lesions to the spine and hip. A 14G core needle biopsy of breast masses was performed and showed a secondary localization of neuroblastoma. CONCLUSIONS: In adolescents, metastases are the most frequent cause of malignant breast masses. Ultrasound examination should be preferred as the first imaging tool. For the differential diagnosis of breast metastasis with benign masses, a rapid enlargement, a heterogeneous echogenicity, and intralesional hyperechogenic foci could be considered features of malignancy.