Increasing differential diagnosis between lipoma and liposarcoma through radiomics: a narrative review

• Main Authors: Raffaele Natella (Author), Giulia Varriano (Author), Maria Chiara Brunese (Author), Marcello Zappia (Author), Michela Bruno (Author), Michele Gallo (Author), Flavio Fazioli (Author), Igino Simonetti (Author), Vincenza Granata (Author), Luca Brunese (Author), Antonella Santone (Author)
• Format: Book
• Published: Open Exploration Publishing Inc., 2023-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.37349/etat.2023.00147; 2692-3114
• Keywords: radiology; radiomics; soft tissue sarcomas; liposarcoma; magnetic resonance imaging; Internal medicine; RC31-1245
• Source: Exploration of Targeted Anti-tumor Therapy, Vol 4, Iss 3, Pp 498-510 (2023)

Soft tissue sarcomas (STSs) are rare, heterogeneous, and very often asymptomatic diseases. Their diagnosis is fundamental, as is the identification of the degree of malignancy, which may be high, medium, or low. The Italian Medical Oncology Association and European Society of Medical Oncology (ESMO) guidelines recommend magnetic resonance imaging (MRI) because the clinical examination is typically ineffective. The diagnosis of these rare diseases with artificial intelligence (AI) techniques presents reduced datasets and therefore less robust methods. However, the combination of AI techniques with radiomics may be a new angle in diagnosing rare diseases such as STSs. Results obtained are promising within the literature, not only for the performance but also for the explicability of the data. In fact, one can make tumor classification, site localization, and prediction of the risk of developing metastasis. Thanks to the synergy between computer scientists and radiologists, linking numerical features to radiological evidence with excellent performance could be a new step forward for the diagnosis of rare diseases.