Differentiation of lipoma and atypical lipomatous tumor by a scoring system: implication of increased vascularity on pathogenesis of liposarcoma

Nagano, Satoshi; Yokouchi, Masahiro; Setoguchi, Takao; Ishidou, Yasuhiro; Sasaki, Hiromi; Shimada, Hirofumi; Komiya, Setsuro

doi:10.1186/s12891-015-0491-8

Cited by 66 publications

(80 citation statements)

References 17 publications

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“…Differentiating lipoma and ALT radiologically is not reliable but certain features seen on MRI can be helpful such as size and intratumoural septation [138, 139]. Histological and cytogenetic analysis of tumour allows confirmation of diagnosis, although small pre-operative biopsies may be misleading [140, 141].…”

Section: Borderline Tumoursmentioning

confidence: 99%

UK guidelines for the management of soft tissue sarcomas

Dangoor¹,

et al. 2016

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Soft tissue sarcomas (STS) are rare tumours arising in mesenchymal tissues, and can occur almost anywhere in the body. Their rarity, and the heterogeneity of subtype and location means that developing evidence-based guidelines is complicated by the limitations of the data available. However, this makes it more important that STS are managed by teams, expert in such cases, to ensure consistent and optimal treatment, as well as recruitment to clinical trials, and the ongoing accumulation of further data and knowledge. The development of appropriate guidance, by an experienced panel referring to the evidence available, is therefore a useful foundation on which to build progress in the field. These guidelines are an update of the previous version published in 2010 (Grimer et al. in Sarcoma 2010:506182, 2010). The original guidelines were drawn up following a consensus meeting of UK sarcoma specialists convened under the auspices of the British Sarcoma Group (BSG) and were intended to provide a framework for the multidisciplinary care of patients with soft tissue sarcomas. This current version has been updated and amended with reference to other European and US guidance. There are specific recommendations for the management of selected subtypes of disease including retroperitoneal and uterine sarcomas, as well as aggressive fibromatosis (desmoid tumours) and other borderline tumours commonly managed by sarcoma services. An important aim in sarcoma management is early diagnosis and prompt referral. In the UK, any patient with a suspected soft tissue sarcoma should be referred to one of the specialist regional soft tissues sarcoma services, to be managed by a specialist sarcoma multidisciplinary team. Once the diagnosis has been confirmed using appropriate imaging, plus a biopsy, the main modality of management is usually surgical excision performed by a specialist surgeon. In tumours at higher risk of recurrence or metastasis pre- or post-operative radiotherapy should be considered. Systemic anti-cancer therapy (SACT) may be utilized in some cases where the histological subtype is considered more sensitive to systemic treatment. Regular follow-up is recommended to assess local control, development of metastatic disease, and any late-effects of treatment. For local recurrence, and more rarely in selected cases of metastatic disease, surgical resection would be considered. Treatment for metastases may include radiotherapy, or systemic therapy guided by the sarcoma subtype. In some cases, symptom control and palliative care support alone will be appropriate.

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Section: Borderline Tumoursmentioning

confidence: 99%

UK guidelines for the management of soft tissue sarcomas

Dangoor¹,

et al. 2016

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“…In an effort to be reproducible, some teams proposed a score to differentiate lipoma from liposarcoma based on size, depth, septal architecture, and contrast enhancement with an average score of 1.7 for lipoma compared to 5.1 for WDL achieving 100% sensitivity and 77% specificity [2].…”

Section: Discussionmentioning

confidence: 99%

“…Lipoma and liposarcoma are soft-tissue tumors of mesenchymal origin, often containing visible fat on MRI examination [1]. Differentiating soft-tissue lipoma from liposarcoma on imaging is crucial for patient management, as their follow-up, treatment, and prognosis drastically differ (ranging from almost 100% 5-year survival for lipoma to 60-70% [2] for liposarcoma). While some radiological features might help identifying liposarcoma (such as size >10 cm, thick septations, globular and/or nodular nonadipose regions, or lesion containing less than 75% fat [3]), a significant number of benign lipoma also have imaging appearance mimicking liposarcoma.…”

Section: Introductionmentioning

confidence: 99%

Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists

Malinauskaitė

Hofmeister

Burgermeister

et al. 2020

Sarcoma

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Distinguishing lipoma from liposarcoma is challenging on conventional MRI examination. In case of uncertain diagnosis following MRI, further invasive procedure (percutaneous biopsy or surgery) is often required to allow for diagnosis based on histopathological examination. Radiomics and machine learning allow for several types of pathologies encountered on radiological images to be automatically and reliably distinguished. The aim of the study was to assess the contribution of radiomics and machine learning in the differentiation between soft-tissue lipoma and liposarcoma on preoperative MRI and to assess the diagnostic accuracy of a machine-learning model compared to musculoskeletal radiologists. 86 radiomics features were retrospectively extracted from volume-of-interest on T1-weighted spin-echo 1.5 and 3.0 Tesla MRI of 38 soft-tissue tumors (24 lipomas and 14 liposarcomas, based on histopathological diagnosis). These radiomics features were then used to train a machine-learning classifier to distinguish lipoma and liposarcoma. The generalization performance of the machine-learning model was assessed using Monte-Carlo cross-validation and receiver operating characteristic curve analysis (ROC-AUC). Finally, the performance of the machine-learning model was compared to the accuracy of three specialized musculoskeletal radiologists using the McNemar test. Machine-learning classifier accurately distinguished lipoma and liposarcoma, with a ROC-AUC of 0.926. Notably, it performed better than the three specialized musculoskeletal radiologists reviewing the same patients, who achieved ROC-AUC of 0.685, 0.805, and 0.785. Despite being developed on few cases, the trained machine-learning classifier accurately distinguishes lipoma and liposarcoma on preoperative MRI, with better performance than specialized musculoskeletal radiologists.

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“…The terms ALT and WD-LPS are synonymous as these tumours are morphologically and karyotypically identical 1. ALT describes lesions in superficial sites and extremities2 which are more amenable to complete surgical resection with a wide margin. WD-LPS describes lesions arising in the retroperitoneum, mediastinum and deep pelvis—locations that are less amenable to radical excision due to surrounding structures.…”

Section: Descriptionmentioning

confidence: 99%

“…WD-LPS describes lesions arising in the retroperitoneum, mediastinum and deep pelvis—locations that are less amenable to radical excision due to surrounding structures. Nagano et al 2 have devised a scoring system to differentiate lipomas and ALTs using MRI characteristics: diameter, depth, septa and fat saturation enhancement. The system appears promising, but requires further validation.…”

Section: Descriptionmentioning

confidence: 99%