Diagnostic accuracy of CT and MR features for detecting atypical lipomatous tumors and malignant liposarcomas: a systematic review and meta-analysis

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 Differential Diagnosis and Guidance for Puncture Biopsy of Musculoskeletal Dedifferentiated Liposarcoma and Well Differentiated Liposarcoma

Objective

The study aimed to investigate the significantly different imaging characteristics of musculoskeletal dedifferentiated liposarcoma (DDLP) and well differentiated liposarcoma (WDLP) on MRI, which in turn could guide puncture biopsy.

Materials and Methods

This study included 14 patients with DDLP and 16 patients with WDLP, all of whom were confirmed by histopathological examination. The MRI manifestations of these two pathologies were retrospectively reviewed and compared. Furthermore, a step-by-step procedure regarding preoperative puncture biopsy of fatty masses that are suspicious for WD/DD was designed.

Results

Fatty signals can be found in almost all WDs, with a greater proportion of non-fatty areas in DD compared to WD, and it is reasonable to consider WD more likely when the non-fatty areas of the tumor are <25% (p < 0.05), while it is reasonable to consider DD more likely when the non-fatty areas of the tumor are >50% (p < 0.05), and the MRI signals in DD are more complex, inhomogeneous (p < 0.01), usually showed significant enhancement (p < 0.01), and the margins of the tumor were usually indistinct (p < 0.01); and imaging features such as tumor size, vascularity, necrosis, and peritumoral edema did not serve as distinguishing features between the two (p > 0.05).

Conclusion

DD has a greater proportion of non-fatty components, with more complex and inhomogeneous MRI signals, and typically shows significant enhancement, with usually indistinct margins of the tumor, in which the inhomogeneous manifestations are associated with the histological components. The possibility of DD should be considered in fatty tumors with non-fatty areas > 25%, for which puncture biopsy is necessary, while simultaneous puncture of low, moderate, high-signal areas within the non-fatty area could improve the accuracy of preoperative puncture pathology.

Diagnostic Performance of Radiomics and Deep Learning to Identify Benign and Malignant Soft Tissue Tumors: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

To systematically evaluate the application value of radiomics and deep learning (DL) in the differential diagnosis of benign and malignant soft tissue tumors (STTs).

MATERIALS AND METHODS

A systematic review was conducted on studies published up to December 11, 2023, that utilized radiomics and DL methods for the diagnosis of STTs. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS) 2.0 system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. A bivariate random-effects model was used to calculate the summarized sensitivity and specificity. To identify factors contributing to heterogeneity, meta-regression and subgroup analyses were performed to assess the following covariates: diagnostic modality, region/volume of interest, imaging examination, study design, and pathology type. The asymmetry of Deeks' funnel plot was used to assess publication bias.

RESULTS

A total of 21 studies involving 3866 patients were included, with 13 studies using independent test/validation sets included in the quantitative statistical analysis. The average RQS was 21.31, with substantial or near-perfect inter-rater agreement. The combined sensitivity and specificity were 0.84 (95% CI: 0.76-0.89) and 0.88 (95% CI: 0.69-0.96), respectively. Meta-regression and subgroup analyses showed that study design and the region/volume of interest were significant factors affecting study heterogeneity (P < 0.05). No publication bias was observed.

CONCLUSION

Radiomics and DL can accurately distinguish between benign and malignant STTs. Future research should concentrate on enhancing the rigor of study designs, conducting multicenter prospective validations, amplifying the interpretability of DL models, and integrating multimodal data to elevate the diagnostic accuracy and clinical utility of soft tissue tumor assessments.