Gastrointestinal Stromal Tumors: Diagnosis, Follow-up and Role of Radiomics in a Single Center Experience

Multimode ultrasound imaging of an anal canal gastrointestinal stromal tumour (GIST)

Gastrointestinal stromal tumours (GISTs) can develop throughout the entire gastrointestinal tract, but these tumours are usually found in the stomach and small intestine. In this case, a rare GIST arising from the anal canal was investigated using high-frequency endoanal ultrasound and external three-dimensional ultrasound with tomographic ultrasound imaging. The endoanal approach revealed the inner structure of the tumour. External ultrasound was used to determine the relationship between the lesion and surrounding tissues. In the limited reports of anal canal GISTs, no other lesions have been correctly diagnosed preoperatively or displayed in detail on imaging. The multilayer structure of the anal sphincter and these lesions can be clearly displayed by a variety of ultrasound imaging methods, which are nonradiative, low-cost and easily accessible. Modern ultrasound has the potential for broad application in anal canal tumour diagnosis and surveillance.

Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery

Background

Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling.

Methods

A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created.

Results

Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence (P < .05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST.

Conclusions

The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.