A Case Report of Multiple Gastrointestinal Stromal Tumors: Imaging Findings, Surgical Approach, and Review of the Literature.

Introduction: Multiple gastrointestinal stromal tumors (GISTs) are rare tumors. Differential diagnosis between metastatic and multiple GISTs represents a challenge for a proper workup, prediction prognosis, and therapeutic strategy. Case presentation: We present the case of 67-year-old man with computed tomography (CT) evidence of multiple exophytic lesions in the abdomen, reaching diameters ranging from 1 to 9 cm, without any signs of organs infiltration, and resulting positive at 18F-FDG-PET/CT. Laparoscopic biopsy revealed multiple GISTs, and surgical resection by using an open approach was performed to achieve radicality. Moreover, an extensive review of the current literature was performed. Results: Small GISTs (<5 cm) can be treated by the laparoscopic approach, while in the case of large GISTs (>5 cm), tumor location and size should be taken into account to reach the stage of radical surgery avoiding tumor rupture. For metastatic GISTs, Imatinib represents the first choice of treatment, and surgery should be considered only in a few selected cases when all lesions are resectable. Conclusion: Sporadic multiple GISTs are a rare event, imaging findings are not specific for GISTs, and biopsy requires a secure diagnosis and proper management. In the case of large lesions, with a high risk of vessels injury, laparotomy excision should be considered to achieve radicality and to avoid tumor rupture.

Radiomic Cancer Hallmarks to Identify High-Risk Patients in Non-Metastatic Colon Cancer.

The study was aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups­High-risk and No-risk­following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC > 0.8). Stable features were compared between the two groups (T-test or Mann−Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≤ ICC < 0.92). Among these, 28 features were significantly different between the two groups (p < 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk disease.