ABSTRACT
Gastrointestinal stromal tumor (GIST) is the most common mesenchymal original tumor in gastrointestinal (GI) tract and is considered to have varying malignant potential. With the advancement of computer science, radiomics technology and deep learning had been applied in medical researches. It's vital to construct a more accurate and reliable multimodal predictive model for recurrence-free survival (RFS) aiding for clinical decision-making. A total of 254 patients underwent surgery and pathologically diagnosed with GIST in The First Hospital of China Medical University from 2019 to 2022 were included in the study. Preoperative contrast enhanced computerized tomography (CE-CT) and hematoxylin/eosin (H&E) stained whole slide images (WSI) were acquired for analysis. In the present study, we constructed a sum of 11 models while the multimodal model (average C-index of 0.917 on validation set in 10-fold cross validation) performed the best on external validation cohort with an average C-index of 0.864. The multimodal model also reached statistical significance when validated in the external validation cohort (n = 42) with a p-value of 0.0088 which pertained to the recurrence-free survival (RFS) comparison between the high and low groups using the optimal threshold on the predictive score. We also explored the biological significance of radiomics and pathomics features by visualization and quantitative analysis. In the present study, we constructed a multimodal model predicting RFS of GIST which was prior over unimodal models. We also proposed hypothesis on the correlation between morphology of tumor cell and prognosis.
ABSTRACT
Tyrosine kinase inhibitors (TKIs) have greatly improved the prognosis of unresectable and metastatic gastrointestinal stromal tumors (GISTs) in the last two decades. Imatinib and sunitinib are recommended as first-line and second-line therapies, respectively. However, there is a lack of precision therapy for refractory GISTs regarding therapy after imatinib and sunitinib. We comprehensively searched electronic databases, including PubMed, EMBASE, Web of Science, Cochrane Library, and ClinicalTrials, from inception to October 2022. Randomized controlled trials featuring comparisons with third-line or over third-line therapies against GISTs were eligible. The primary outcome was progression-free survival (PFS). All network calculations were performed using random effect models, and the ranking of regimens were numerically based on the surface under the cumulative ranking (SUCRA) statistics. A total of seven studies were eligible for inclusion in this network meta-analysis. After analysis, ripretinib was ranked at the top in progression-free survival (PFS), overall survival (OS), and disease control rate (DCR) (SUCRA statistics: 83.1%, 82.5%, and 86.5%, respectively), whereas nilotinib and pimitespib presented better tolerability (SUCRA statistics: 64.9% and 63.8%, respectively). We found that regorafenib seemed more reliable for clinical administration, and ripretinib showed good effectiveness for the over third-line therapy. Precise targeted therapy is a critical direction for the future treatment of GIST, and more high-quality studies of new agents are expected.
ABSTRACT
BACKGROUND: Sarcopenia predicts poor prognosis of a variety of gastrointestinal malignancies. However, there is a lack of study on the association between skeletal muscle index (SMI) and the prognosis of gastrointestinal stromal tumor (GIST). The aim of this study is to develop a novel nomogram based on sarcopenia for GIST patients to predict overall survival (OS). METHODS: SMI was measured by computed tomography scan of 107 patients who underwent resection for primary localized gastrointestinal stromal tumor (GIST). Sarcopenia was defined by cutoff values for SMI as 40.1 cm2/m2 and 39.8 cm2/m2 using optimum stratification for males and females respectively. Factors were included in the nomogram were specified by univariate and multiple Cox proportional hazard analysis. Concordance index (C-index) and calibration curves were conducted to measure the discrimination and accuracy of the nomogram. The utility of the nomogram was assessed by the decision curve analysis (DCA). RESULTS: Twenty-eight (26.2%) of 107 patients were sarcopenic. Sarcopenia was correlated significantly with body mass index, albumin, female sex, resection style, mitotic index, rupture status, survival. Sarcopenia was significantly related to decreased overall survival (p = 0.003).The nomogram including sarcopenia status, resection style and mitotic index had an excellent discrimination with C-index 0.794. The calibration curves represented a good accordance between the actual observation and nomogram prediction for overall survival. Decision curve analysis illustrated that the nomogram was helpful in clinic. CONCLUSIONS: We developed a nomogram based on sarcopenia to predict overall survival after resection of GISTs which is an effective and favorable prognostication tool.
