Introduction: Glomus tumor (GT) is a rare mesenchymal neoplasm that can be found anywhere throughout the body, including the stomach. Our goal was to present a case and a systematic review of the literature, reporting clinical, radiological, surgical, and pathological features of the disease. Methods: We reviewed Pubmed and SCOPUS for all case reports and case series published after 2000. Papers written in languages different from English and letters to the editor were excluded. Screening and data extraction were performed following the PRISMA guidelines. Results: A total of 89 studies were included in the systematic review, consisting of 187 cases of gastric glomus tumor. Mean age was 52 (18–90); most patients were female (61%). The most common clinical presentation was epigastric pain (33.9% of cases). The gastric antrum was the most frequently involved site (75.3%). Mean tumor size was 2.82 cm (0.8–17). Preoperative diagnosis was achieved in 22 cases, mostly by endoscopic ultrasound (EUS)-guided biopsy. Wedge resection was performed in 62% of treated patients. Smooth muscle actin was expressed in all cases with available immunohistochemistry. Malignant GT was reported in 11 cases. Discussion: Epigastric pain and bleeding were the most common symptoms at presentation in patients with diagnosis of glomus tumor. EUS-guided fine needle aspiration can be useful for preoperative diagnosis. Endoscopic elastosonography is a promising tool for the differential diagnosis of gastric submucosal lesions, including glomus tumors. The treatment of choice is wedge resection with adequate free margins. A laparoscopic approach is warranted when technically feasible. Since malignant gastric GTs have been described, long-term follow-up is suggested after surgical excision.

Mesenchymal tumors originate from mesenchymal cells and can be either benign or malignant, such as bone, soft tissue, and visceral sarcomas. Surgery is a cornerstone treatment in the management of mesenchymal tumors, often requiring complex procedures performed in high-volume referral centers. However, the COVID-19 pandemic has highlighted this need for alternative non-surgical approaches due to limited access to surgical resources. This review explores the role of non-surgical treatments in different clinical scenarios: for improving surgical outcomes, as a bridge to surgery, as better alternatives to surgery, and for non-curative treatment when surgery is not feasible. We discuss the effectiveness of active surveillance, cryoablation, high-intensity focused ultrasound, and other ablative techniques in managing these tumors. Additionally, we examine the use of tyrosine kinase inhibitors in gastrointestinal stromal tumors and hypofractionated radiotherapy in soft tissue sarcomas. The Sarculator tool is highlighted for its role in stratifying high-risk sarcoma patients and personalizing treatment plans. While surgery remains the mainstay of treatment, integrating advanced non-surgical strategies can enhance therapeutic possibilities and patient care, especially in specific clinical settings with limitations. A multidisciplinary approach in referral centers is vital to determine the optimal treatment course for each patient.

Background: Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy. Objective: The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs. Methods: Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed. Results: Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model’s accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model. Conclusions: The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.

BACKGROUND Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy. OBJECTIVE The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs. METHODS Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed. RESULTS Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model’s accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model. CONCLUSIONS The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.

E23523 Background: How sarcoma metastasize is unknown. VETC has been described as an epithelial-to-mesenchymal-transition independent process of metastasis: endothelium covers neoplastic clusters allowing tumor dissemination. It has also been shown to be a predictive of tyrosine kinase inhibitors (TKI) response. The objectives of the present study are: 1) to assess the presence of VETC in sarcoma; 2) to model its prognostic role. Methods: The study was retrospective. We selected 54 cases of sarcomas (6 DDLPS, 10 GIST, 6 retroperitoneal LMS, 9 MLPS, 8 MPNST, 10 SFT, 5 UPS); of them 31 were metastatic (M1 group), 23 were not (M0 group, defined as least 5 years of negative follow-up). High risk GIST and SFT, and grade 3 FNCLCC were considered high grade. VETC was assessed with CD31 immunohistochemistry and defined as a continuous endothelial lining around tumor clusters. We used Bayesian probabilistic modeling to detect small effects and multilevel hierarchical modeling to reduce overfitting. Models were fit using Stan and R. CI were computed with the HPDI method for 89%. The whole posterior probability (PP) was used for calculations. Results: The two groups (M0 & M1) were substantially homogeneous: the CI of the contrast PP (CPP) included 0 in each histology for sex, age, size, and grade; VETC was more expressed in M1 SFT cases with almost all the CPP mass above the 0. Also, UPS and GIST showed VETC to be more present in metastatic diseases with 79% and 78% respectively of the CPP mass above 0 (see table). Metastasis free survival (MFS) analysis showed that VETC in SFT and UPS with a coefficient of 2.42 (CI 0.73 – 4.65) and 1.94 (CI 0.16 – 3.67); for the latter the median was reached with a PP density of median MFS of 65 months (mo) (standard deviation, SD: 74 mo) for VETC- Vs 11 mo (SD: 14 mo) for VETC+. Similarly, disease specific survival analysis showed – in SFT and UPS – that VETC was negatively associated with prognosis with coefficients of 1.24 and 2.34; however the CI covered slightly the 0 (-0.54 – 3.96 and -0.09 – 5.73, respectively). Conclusions: We found VETC in sarcoma and highlighted its prognostic role in SFT and UPS.