[Diagnostic features of gastrointestinal stromal tumor: a pathological analysis of 347 biopsy cases]

Objective: To investigate the pathological diagnostic pitfalls in assessing biopsy specimens of gastrointestinal stromal tumors (GIST) and to explore strategies for improving the accuracy of biopsy-based diagnosis. Methods: A retrospective analysis was conducted on 347 GIST biopsy cases diagnosed at the Tianjin Medical University Cancer Institute and Hospital, Tianjin, China from January 2017 to March 2025. The clinicopathological features were analyzed, focusing on rare sites, atypical histomorphology, and atypical immunohistochemical expression of the GIST cases. Results: There were 198 males and 149 females. 18 patients aged ≤40 years. 281 cases were initial biopsies, while 66 were biopsies on recurrences. Some of these cases exhibited significant atypical features, which constituted diagnostic challenges.​ The anatomical distribution was broad, with a high proportion of non-gastrointestinal tract locations (194/347, 55.9%), including the abdominal cavity, liver, pelvis, and rare sites such as the vagina, mediastinum, ribs, and scapula. Histologically, the morphology was diverse. While spindle cell type dominated (334 cases), epithelioid (12 cases) and the rare, dedifferentiated types (1 case) were also observed. Although 334 (96.3%) of the cases simultaneously expressed the two GIST markers, CD117 and DOG1, 13 (3.7%) cases showed single negativity or double negativity of these markers. Molecular tests confirmed that these cases were frequently associated with mutations in specific exons of the KIT or PDGFRA gene. Conclusions: The primary diagnostic pitfalls for GIST biopsies include non-classical anatomical sites, atypical histologic changes, and aberrant immunophenotypes. Addressing these challenges requires​ thorough correlation with clinical and radiological findings, combined application of both immunohistochemical markers, and proactive genetic testing for cases with atypical immunohistochemical profile. These measures are crucial for improving diagnostic accuracy and guiding targeted therapies.