Qualitative evaluation of MRI features of lipoma and atypical lipomatous tumor: results from a multicenter study

Nardo, Lorenzo; Abdelhafez, Yasser; Acquafredda, Francesco; Schirò, Silvia; Wong, Andrew L.; Sarohia, Dani; Maroldi, Roberto; Darrow, Morgan A.; Guindani, Michele; Lee, Sonia; Zhang, Michelle; Moawad, Ahmed W; Elsayes, Khaled M.; Badawi, Ramsey D.; Link, Thomas M.

doi:10.1007/s00256-020-03372-5

Cited by 25 publications

(26 citation statements)

References 29 publications

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“…Deep and proximal lower limb lesions show inadequate fat suppression and architecture findings in ALT cases without the contrast enhancement. On the other hand, MRI with contrast enhancement changes the impression of diagnosis incorrectly changed from benign lipoma to ALT [30]. Therefore, these findings suggest to us the importance of the combination of investigation including physical examination and imaging for the diagnosis of ALT.…”

Section: Mrimentioning

confidence: 86%

Recent Advancement in Atypical Lipomatous Tumor Research

Mashima

Sawada

Nakamura

2021

IJMS

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After Evans and colleagues identified the lipomatous tumor with a well-differentiated liposarcoma in a subcutaneous location or within a muscle layer, namely, atypical lipomatous tumor (ALT), this malignancy has been investigated to clarify the characteristics of clinical behavior and genomic changes. As one of the important issues for clinicians, it is a hot topic of how to distinguish ALT from benign lipoma in the clinical aspect. Recent studies revealed novel findings to clarify the risk factor for the diagnosis of ALT and molecular targets for the treatment of ALT. Clinical characteristics of superficial-type ALT well reflect the subcutaneous location of the tumor and are slightly different compared to deep-type ALT, such as tumor size. In addition, there has been a recent discovery of novel findings in ALT-related genes, namely, HMG2A (high mobility group protein 2a), YEATS4 (YEATS domain containing 4), and CPM (Carboxypeptidase M). Recent updates on treatment for advanced ALT are well developed including immunotherapy and conducting clinical trials. Finally, this review introduces one of the hot topics of ALT research focused on epigenetic changes: their attention in recent updates on clinical characteristics and the novel discovery of related genes, treatment, and epigenetic modifications in atypical lipomatous tumors.

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Section: Mrimentioning

confidence: 86%

Recent Advancement in Atypical Lipomatous Tumor Research

Mashima

Sawada

Nakamura

2021

IJMS

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“…Several authors have suggested that enhancement is predictive of ALT (33,34) but a recent study (35) suggests similar accuracy of readers in lesion diagnosis with and without contrast studies. These authors went further to suggest that post contrast imaging did not significantly change confidence in diagnosis and in some cases led to changing of correct diagnoses prompting a conclusion that contrast enhancement in lipomatous lesions may be limited and occasionally misleading (35). Finally, a retrospective study design allows the evaluation of potential relationships between textual features and lesion characterisation, but can result in selection bias.…”

Section: Discussionmentioning

confidence: 99%

Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis

et al. 2020

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Aims This pilot study aims to determine if tumour heterogeneity assessed using magnetic resonance imaging (MRI) radiomics-based texture analysis (TA) can differentiate between lipoma and atypical lipomatous tumour (ALT)/well differentiated liposarcoma (WDL). Materials and Methods30 consecutive ALT/WDLs and 30 lipomas were included in the study, cases diagnosed both histologically and with murine double minute 2 (MDM2) gene amplification by fluoresence in situ hybridisation (FISH) in excision specimens. Multiple patient, MRI and MRTA factors were assessed. Heterogeneity was evaluated using a filtration-histogram technique based textural analysis on single axial proton density (PD) and coronal T1-W images of the most homogenously fatty component of the lesion.Results 33% of the diagnoses of ALT/WDL vs lipoma were confirmed using FISH MDM2 analysis.. ALT/WDLs were statistically different from lipomas in location (site in the body and depth from skin surface) and fat content, with p values of 0.021, 0.001, and 0.021 respectively. 9 of 36 (25%) texture parameters had significant differences between ALT/WDLs and lipomas on axial PD MRTA, with the most significant results at medium and coarse texture scales particularly mean intensity (p=0.003) at SSF=6, and kurtosis (p=0.012) at SSF=5. A cut-off value of <304 for coarse filtered texture on axial PD MRI identified ALT from lipoma with a sensitivity and specificity of 70% (AUC=0.73, p=0.003). ConclusionsTexture heterogeneity quantified at fine, medium, coarse texture scales are significant differentiators of lipoma and ALT/WDL with the difference particularly marked in medium and coarse texture scales for two MR TA parameters: mean and kurtosis.

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“…This study used non-fat saturated T1W or PD MR images from 185 patients; 83 males, 102 females; age 58.4 ± 11.9 years (range: 23-89 years), who underwent musculoskeletal MRI as part of the pre-operative evaluation of their LTs at 4 participating institutions. Details on cohort identification are similar to the ones that have been previously described [26]. The study was approved by the Institutional Review Board at the host university, and several data transfer agreements were in effect between host university and other universities.…”

Section: Mr Images and Training Data Preparationmentioning

confidence: 99%

AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data

et al. 2023

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Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 $$\pm$$ ± 0.16, 0.73 $$\pm$$ ± 0.168, and 0.99 $$\pm$$ ± 0.012, respectively, while for SL predictions were 0.80 $$\pm$$ ± 0.184, 0.78 $$\pm$$ ± 0.193, and 1.00 $$\pm$$ ± 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

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Qualitative evaluation of MRI features of lipoma and atypical lipomatous tumor: results from a multicenter study

Cited by 25 publications

References 29 publications

Recent Advancement in Atypical Lipomatous Tumor Research

Recent Advancement in Atypical Lipomatous Tumor Research

Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis

AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data

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