Development of a prognostic prediction model for tumor recurrence or progression in gastrointestinal stromal tumors: integrating inflammatory blood indices with 5 mm 2 versus 50 high-power field mitotic counts

Information from the abstract

Background: The behavior of gastrointestinal stromal tumors varies according to tumor size, location, mitotic rate, and rupture. Traditional mitotic counting in 50 high-power fields (HPF; 5.3 mm2) may overestimate progression risk, as 5 mm2 corresponds to 20-25 HPF under modern microscopy. However, the 50-HPF method remains widely used. This study evaluated the impact of two mitotic counting methods on risk classification and developed a prognostic model combining inflammatory biomarkers with clinicopathological factors. Methods: We retrospectively analyzed 150 patients who underwent resection at Songklanagarind Hospital between 2009 and 2021. Patients who had received neoadjuvant therapy or had a second primary cancer were excluded. Mitosis was evaluated using both methods. Clinicopathological and serum biomarker data were collected. The primary outcome was 5-year disease-free or progression-free survival. The model was developed using forward stepwise variable selection based on the time-dependent area under the curve and internally validated by bootstrapping. Results: The 50-HPF method overestimated the Armed Forces Institute of Pathology classification in 14.2% of cases. A model incorporating tumor size, location, rupture, mitotic count within 5 mm2, and systemic immune-inflammation index achieved the highest area under the curve of 0.939 in both training and validation sets, outperforming previous models. The 5-mm2 method demonstrated superior performance to the 50-HPF method across all models (0.939 vs. 0.919 for the proposed model). Conclusions: Mitotic counting in 50 HPF overestimated risk classification in certain cases. Incorporating systemic immune-inflammation index and using a 5 mm2 mitotic count enhanced prognostic model performance.

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Related topics: Gastrointestinal Tumor Research and Treatment · Inflammatory Biomarkers in Disease Prognosis · Cancer Immunotherapy and Biomarkers