부산대학교 도서관

Tenosynovial giant cell tumour (TGCT) is a rare soft-tissue tumour originating from synovial lining of joints, bursae and tendon sheaths. The tumour comprises two subtypes: the localised-type (L-TGCT) is characterised by a single, well-defined lesion, whereas the diffuse-type (D-TGCT) consists of multiple lesions without clear margins. D-TGCT was previously known as pigmented villonodular synovitis. Although benign, TGCT can behave locally aggressive, especially the diffuse-type. Magnetic resonance imaging (MRI) is the modality of choice to diagnose TGCT and discriminate between subtypes. MRI can also provide a preoperative map before synovectomy, the mainstay of treatment. Finally, since the arrival of colony-stimulating factor 1-receptor inhibitors, a novel systemic therapy for D-TGCT patients with relapsed or inoperable disease, MRI is key in assessing treatment response. As recurrence after treatment of D-TGCT occurs more often than in L-TGCT, follow-up imaging plays an important role in D-TGCT. Reading follow-up MRIs of these diffuse synovial tumours may be a daunting task. Therefore, this educational review focuses on MRI findings in D-TGCT of the knee, which represents the most involved joint site (approximately 70% of patients). We aim to provide a systematic approach to assess the knee synovial recesses, highlight D-TGCT imaging findings, and combine these into a structured report. In addition, differential diagnoses mimicking D-TGCT, potential pitfalls and evaluation of tumour response following systemic therapies are discussed. Finally, we propose automated volumetric quantification of D-TGCT as the next step in quantitative treatment response assessment as an alternative to current radiological assessment criteria.Key Points: TGCT is categorised according to its site of involvement and growth pattern. D-TGCT is characterised by irregular synovial proliferation infiltrating multiple synovial knee recesses. Systematic MRI approach is provided for preoperative and systemic treatment response assessment. An overview of synovial knee recesses guides the radiologist in assessing blind spots. Machine learning-based tumour segmentation may be applied to assess D-TGCT tumour volume in the near future, so that treatment response assessment can be quantified and improved by artificial intelligence. [ABSTRACT FROM AUTHOR]