Value of CT Imaging in the Differentiation of Gastric Leiomyoma From Gastric Stromal Tumor

Cinematic Rendering of Gastrointestinal Stromal Tumours: A Review of Current Possibilities and Future Developments

Gastrointestinal stromal tumours (GISTs) are defined as CD117-positive primary, spindled or epithelioid, mesenchymal tumours of the gastrointestinal tract, omentum, or mesentery. While computed tomography (CT) is the recommended imaging modality for GISTs, overlap in imaging features between GISTs and other gastrointestinal tumours often make radiological diagnosis and subsequent selection of the optimal therapeutic approach challenging. Cinematic rendering is a novel CT post-processing technique that generates highly photorealistic anatomic images based on a unique lighting model. The global lighting model produces high degrees of surface detail and shadowing effects that generate depth in the final three-dimensional display. Early studies have shown that cinematic rendering produces high-quality images with enhanced detail by comparison with other three-dimensional visualization techniques. Cinematic rendering shows promise in improving the visualization of enhancement patterns and internal architecture of abdominal lesions, local tumour extension, and global disease burden, which may be helpful for lesion characterization and pretreatment planning. This article discusses and illustrates the application of cinematic rendering in the evaluation of GISTs and the unique benefit of using cinematic rendering in the workup of GIST with a specific emphasis on tumour characterization and preoperative planning.

CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives

Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.

CT features of calcified micro-gastric gastrointestinal stromal tumors: a case series

BACKGROUND

Due to the lack of corresponding clinical symptoms, small calcified gastric gastrointestinal stromal tumors (GISTs) are often overlooked in clinical practice. Therefore, there is an unmet need to define the imaging features of calcified micro-gastric GISTs to facilitate diagnosis. This study retrospectively analyzed the computed tomography (CT) features of pathologically confirmed calcified micro-gastric GISTs.

METHODS

The medical records (gastroscopy, pre-treatment gastric CT imaging [pre- and post-contrast scans], pathology) of patients with calcified gastric GISTs < 1 cm in diameter confirmed pathologically after endoscopic submucosal dissection, endoscopic submucosal excavation, or endoscopic full-thickness resection were retrospectively reviewed.

RESULTS

Seven patients had 8 calcified gastric GISTs < 1 cm in diameter. Six patients hadsingle lesions, and 1patients had multiple lesions. Six patients had lesions in the gastric fundus, 1 patient had a lesion in the body of the stomach. Lesions had a mean diameter of 5.2 mm (range, 1.3 mm ~ 7 mm). Unenhanced CT scans showed spots and high-density nodular calcifications in 3 submucosal lesions, 2 lesions in the muscularis propria, and 3 subserosal lesions that protruded outside the stomach. Among the 8 lesions, only two had solid soft tissue components surrounding the calcification, with one of these two showing post contrast enhancement of the solid soft tissue component.

CONCLUSIONS

Novel CT features of gastric GISTs included: commonly found in the gastric antrum, small size (< 1 cm in diameter), calcification, few solid soft tissue components, and no abnormal enhancement in most cases.

Machine learning-based predictive model for the differential diagnosis of ≤ 5 cm gastric stromal tumor and gastric schwannoma based on CT images

The clinical symptoms of ≤ 5 cm gastric stromal tumor (GST) and gastric schwannoma (GS) are similar, but the treatment regimens are different. This study explored the value of computed tomography (CT) combined with machine learning (ML) algorithms to find the best model to discriminate them. A total of 126 patients with GST ≤ 5 cm and 35 patients with GS ≤ 5 during 2013-2022 were included. CT imaging features included qualitative data (tumor location, growth pattern, lobulation, surface ulcer status, necrosis, calcification, and surrounding lymph nodes) and quantitative data [long diameter (LD); short diameter (SD); LD/SD ratio; degree of enhancement (DE); heterogeneous degree (HD)]. Patients were randomly divided into a training set (n = 112) and test set (n = 49) using 7:3 stratified sampling. The univariate and multivariate logistic regression analysis were used to identify independent risk factors. Five ML algorithms were used to build prediction models: Support Vector Machine, k-Nearest Neighbor, Random Forest, Extra Trees, and Extreme Gradient Boosting Machine. The analysis identified that HDv, lobulation, and tumor growth site were independent risk factors (P < 0.05). We should focus on these three imaging features of tumors, which are relatively easy to obtain. The area under the curve for the SVM, KNN, RF, ET, and XGBoost prediction models were, respectively, 0.790, 0.895, 0.978, 0.988, and 0.946 for the training set, and were, respectively, 0.848, 0.892, 0.887, 0.912, and 0.867 for the test set. The CT combined with ML algorithms generated predictive models to improve the differential diagnosis of ≤ 5 cm GST and GS which has important clinical practical value. The Extra Trees algorithm resulted in the optimal model.

Intelligent Reconstruction Algorithm-Based Computed Tomography Images for Automatic Detection of Gastric Tumor

The aim of this study was to explore the application of computed tomography (CT) images in the diagnosis of gastric tumor under the intelligent reconstruction algorithm (IRA). 120 patients with gastric cancer were selected and all the patients underwent CT scanning, and CT images were analyzed based on the Feldkamp-Davis-Kress algorithm (FDK algorithm) to evaluate the imaging features of gastric lesions. According to biopsy or surgical pathology, the detection rate of CT images was calculated. The results showed that there were three pathological types of benign tumors (polyps, leiomyomas, and mesenchymomas) and three pathological types of malignant tumors (mesenchymomas, adenomas, and lymphomas). In addition, the detection rates of CT scans were different, reaching 94.2% on different orientations of the stomach, 90.7% of benign tumors, and 90.9% of malignant tumors, so the detection rate of different orientations was relatively high. CT images based on the FDK IRA could realize a high detection rate in diagnosis, accurately locate the lesion, and display the characteristics of the lesion and the metastasis of surrounding tissues; there were significant differences between benign and malignant gastric tumors in CT images, and the detection effect was obvious, which is worthy of clinical application and promotion.

Mesenchymal tumors of the stomach: radiologic and pathologic correlation

Mesenchymal tumors of the stomach are uncommon, with gastrointestinal stromal tumor (GIST) being the most common among them. Majority of the tumors may arise from cells of Cajal, smooth muscle cells, neural cells, totipotent stem cells, adipocytes or fibroblasts. Imaging plays an important role not only in staging but also in characterizing these tumors. Many of these tumors have characteristic imaging features. GISTs usually present as large cavitating and necrotic tumors with exophytic component. Presence of fat tissue within the tumor suggests a lipoma or a teratoma, early phase hyperenhancement indicates glomus tumor and hemangioma, and delayed contrast enhancement is seen in schwannoma. Their differentiation from epithelial tumors like carcinoma and neuroendocrine tumors is often possible based on the location (mesenchymal tumors are intramural), spread, morphological appearance and enhancement patterns. However, overlapping features exist between these tumors with imaging often being only suggestive. A biopsy is necessary for a definitive diagnosis in many cases.

Prediction of Gastric Gastrointestinal Stromal Tumors before Operation: A Retrospective Analysis of Gastric Subepithelial Tumors

Gastrointestinal stromal tumors (GISTs), leiomyomas, and schwannomas are the most common gastric subepithelial tumors (GSETs) with similar endoscopic findings. Preoperative prediction of GSETs is difficult. This study analyzed and predicted GSET diagnosis through a retrospective review of 395 patients who underwent surgical resection of GISTs, leiomyomas, and schwannomas measuring 2–10 cm. GSETs were divided by size (group 2–5, >2 and ≤5 cm; group 5–10, >5 and ≤10 cm) for analysis. Demographics, clinical symptoms, and images were analyzed. A recursive partitioning analysis (RPA) was used to identify optimal classifications for specific GSET diagnoses. GIST patients were relatively older than other patients. Both groups had higher proportions of UGI bleeding, lower hemoglobin (Hb) levels, and a higher ratio of necrosis on their computed tomography (CT) scans. The RPA tree showed that (a) age ≤ 55, Hb ≥ 10.7, and CT necrosis; (b) age ≤ 55 and Hb < 10.7; (c) age >55 and Hb < 12.9; and (d) age >55 and CT hetero-/homogeneity can predict high GIST risk in group 2–5. Positive or negative CT necrosis, with age >55, can predict high GIST risk in group 5–10. GIST patients were older and presented with low Hb levels and tumor necrosis. In RPA, the accuracy reached 85% and 89% in groups 2–5 and 5–10, respectively.

Inflammation-related indicators to distinguish between gastric stromal tumors and leiomyomas: A retrospective study

BACKGROUND

Gastric leiomyomas and gastric stromal tumors are the most common types of gastric tumors encountered. In recent years, the incidence of the two types of tumors has been increasing, but the differential diagnosis is still a challenge in clinical work. However, as there are many reports on stromal tumors and inflammation-related indicators are gradually being paid attention to as important factors in predicting tumor prognosis, the two main purposes of this study were to explore the inflammation-related differences between the two types of tumors and to develop a nomogram as a predictive model.

AIM

To explore the differences in platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), lymphocyte mononuclear cell ratio (LMR), and SII between the two types of tumors, and simultaneously create the nomogram model.

METHODS

This study enrolled 88 patients in the gastric stromal tumor group and 56 patients in the gastric leiomyoma group, and the relevant data of the two groups were entered into the system for an integrated analysis. The primary objective of this study was to identify the differences in the inflammation index between the two types of tumors.

RESULTS

There were statistically significant differences between the two groups in sex, age, and tumor location. In comparison, gastric leiomyomas seem to be more common in women, young patients, and gastric cardia, which is in line with our previous research; the groups showed the following statistical differences: PLR (158.2% vs 134.3%, P = 0.028), NLR (2.35 vs 1.68, P = 0.000), LMR (5.75 vs 10.8, P = 0.004), and SII (546.2 vs 384.3, P = 0.003). The results of the multivariate logistic regression analysis showed that sex, age, tumor location, and LMR were independent risk factors for the identification of the two types of tumors. After considering the risk factors selected by the above analysis into the predictive model, a predictive model for distinguishing gastrointestinal stromal tumors from gastric leiomyomas was established as the nomogram.

CONCLUSION

Gastric leiomyomas and gastric stromal tumors are not only different in factors such as age of the patient, but also in inflammatory indicators such as LMR and PLR. We have established a predictive model related to the laboratory indicators and are looking forward to further research conducted in this clinical area.

Gastric calcifying fibrous tumors: Computed tomography findings and clinical manifestations

To retrospectively analyze the computed tomography (CT) findings and clinical manifestations of gastric calcifying fibrous tumor (CFTs).The features of 7 cases with pathologically proven gastric CFTs who had undergone CT were assessed, including tumor location, contour, growth, degree of enhancement, calcification and clinical data. In addition, the size and CT value of each lesion were measured. The mean values of these CT findings and clinical data were statistically analyzed only for continuous variables.Four patients were female and three were male (mean age: 33.3 years; range: 22 ∼ 47 years). Nonspecific clinical symptoms: abdominal pain and discomfort were observed in four cases and the CFTs were incidentally detected in the other three cases. Regarding tumor markers, lower ferritin levels were observed in three female patients. All of the gastric CFTs were solitary and mainly located inside the body; they were in round or oval shape and exhibited endophytic growth. Gastric CFTs are usually small sized and could contain confluent and coarse calcifications; cyst, necrosis, ulcer, bleeding and surrounding lymphadenopathy were not found in any of the cases. Unenhanced CT values of gastric CFTs were higher than those of same-transect soft tissue. Mild-to-moderate enhancement in the arterial phase and progressive enhancement in the portal venous phase were mainly noted.A gastric mass with a high unenhanced CT attenuation value, confluent and coarse calcifications and mild-to-moderate enhancement could prompt a diagnosis of gastric CFT. In addition, (1) being young- or middle-aged, (2) having relatively low ferritin levels, and (3) tumor located in the gastric body have critical reference value for diagnosis of gastric CFT.