Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.

BACKGROUND: Preoperative knowledge of mutational status of gastrointestinal stromal tumors (GISTs) is essential to guide the individualized precision therapy. AIM: To develop a combined model that integrates clinical and contrast-enhanced computed tomography (CE-CT) features to predict gastric GISTs with specific genetic mutations, namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions. METHODS: A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio. The models were constructed using selected clinical features, conventional CT features, and radiomics features extracted from abdominal CE-CT images. Three models were developed: ModelCT sign, modelCT sign + rad, and model CTsign + rad + clinic. The diagnostic performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis and the Delong test. RESULTS: The ROC analyses revealed that in the training cohort, the area under the curve (AUC) values for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic for predicting KIT exon 11 mutation were 0.743, 0.818, and 0.915, respectively. In the validation cohort, the AUC values for the same models were 0.670, 0.781, and 0.811, respectively. For predicting KIT exon 11 codons 557-558 deletions, the AUC values in the training cohort were 0.667, 0.842, and 0.720 for modelCT sign, modelCT sign + rad, and modelCT sign + rad + clinic, respectively. In the validation cohort, the AUC values for the same models were 0.610, 0.782, and 0.795, respectively. Based on the decision curve analysis, it was determined that the modelCT sign + rad + clinic had clinical significance and utility. CONCLUSION: Our findings demonstrate that the combined modelCT sign + rad + clinic effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions. This combined model has the potential to be valuable in assessing the genotype of GISTs.

Radiomics signature: A potential biomarker for ß-arrestin1 phosphorylation prediction in hepatocellular carcinoma.

BACKGROUND: The phosphorylation status of ß-arrestin1 influences its function as a signal strongly related to sorafenib resistance. This retrospective study aimed to develop and validate radiomics-based models for predicting ß-arrestin1 phosphorylation in hepatocellular carcinoma (HCC) using whole-lesion radiomics and visual imaging features on preoperative contrast-enhanced computed tomography (CT) images. AIM: To develop and validate radiomics-based models for predicting ß-arrestin1 phosphorylation in HCC using radiomics with contrast-enhanced CT. METHODS: Ninety-nine HCC patients (training cohort: n = 69; validation cohort: n = 30) receiving systemic sorafenib treatment after surgery were enrolled in this retrospective study. Three-dimensional whole-lesion regions of interest were manually delineated along the tumor margins on portal venous CT images. Radiomics features were generated and selected to build a radiomics score using logistic regression analysis. Imaging features were evaluated by two radiologists independently. All these features were combined to establish clinico-radiological (CR) and clinico-radiological-radiomics (CRR) models by using multivariable logistic regression analysis. The diagnostic performance and clinical usefulness of the models were measured by receiver operating characteristic and decision curves, and the area under the curve (AUC) was determined. Their association with prognosis was evaluated using the Kaplan-Meier method. RESULTS: Four radiomics features were selected to construct the radiomics score. In the multivariate analysis, alanine aminotransferase level, tumor size and tumor margin on portal venous phase images were found to be significant independent factors for predicting ß-arrestin1 phosphorylation-positive HCC and were included in the CR model. The CRR model integrating the radiomics score with clinico-radiological risk factors showed better discriminative performance (AUC = 0.898, 95%CI, 0.820 to 0.977) than the CR model (AUC = 0.794, 95%CI, 0.686 to 0.901; P = 0.011), with increased clinical usefulness confirmed in both the training and validation cohorts using decision curve analysis. The risk of ß-arrestin1 phosphorylation predicted by the CRR model was significantly associated with overall survival in the training and validation cohorts (log-rank test, P < 0.05). CONCLUSION: The radiomics signature is a reliable tool for evaluating ß-arrestin1 phosphorylation which has prognostic significance for HCC patients, providing the potential to better identify patients who would benefit from sorafenib treatment.

Preoperative prediction of gastrointestinal stromal tumors with high Ki-67 proliferation index based on CT features.

BACKGROUND: To determine whether preoperative computed tomography (CT) features can be used for the prediction of gastrointestinal stromal tumors (GISTs) with a high Ki-67 proliferation index (Ki-67 PI). METHODS: A total of 198 patients with surgically and pathologically proven GISTs were retrospectively included. All GISTs were divided into a low Ki-67 PI group (<10%) and a high Ki-67 PI group (≥10%). All imaging features were blindly interpreted by two radiologists. Receiver operating characteristic (ROC) curve analyses were conducted to evaluate the predictive performance of the imaging features. RESULTS: Imaging features were found to be significantly different between the low and the high Ki-67 PI groups (P<0.05). Wall thickness of necrosis showed the highest predictive ability, with an area under the curve (AUC) of 0.838 [95% confidence interval (CI): 0.627-0.957], followed by necrosis, necrosis degree, hyperenhancement of the overlying mucosa (HYOM), and long diameter (LD) (AUC >0.7, P<0.05). HYOM was the strongest predictive feature for the high Ki-67 PI GISTs group, with an odds ratio (OR) value of 30.037 (95% CI: 5.707-158.106). CONCLUSIONS: Imaging features, including the presence of necrosis, high necrosis degree, thick wall of necrosis, and HYOM were significant predictive indicators for the high Ki-67 PI GISTs group.

Insight into gastrointestinal heterotopic pancreas: imaging evaluation and differential diagnosis.

Heterotopic pancreas (HP) is an uncommon congenital abnormality in the developmental process of the pancreas, with gastrointestinal heterotopic pancreas (GHP) being the most common HP. The clinical manifestations of GHP may have variable patterns of presentation, dictated by both the anatomic location and the functional ability of the lesion. The most common imaging modality in detecting GHP is computed tomography (CT), while gastrointestinal barium fluoroscopy, endoscopic ultrasonography, and magnetic resonance imaging (MRI) are also applied. The density and enhancement patterns of GHP are consistent with histological classifications. GHP with a predominantly acinar tissue component manifests homogeneous and marked enhancement on CT images, whereas a predominantly ductal GHP presents heterogeneous and mild enhancement. On MRI, the appearance and signal intensity of GHP were paralleled to the normal pancreas on all sequences and were characterized by T1-weighted high signal and early marked enhancement. This article provides a comprehensive review of the histopathology, clinical manifestations, imaging features of various modalities, and differential diagnosis of GHP. It is hoped that this review will improve clinicians' knowledge of GHP and aid in accurate preoperative diagnosis, thereby reducing the misdiagnosis rate.

Massive Calcified Epithelioid Hemangioendothelioma With Multifocal Involvement: An Imaging Diagnosis Dilemma and a Rare Case Report.

Epithelioid hemangioendothelioma (EHE) is a rare malignant vascular tumor that develops from vascular endothelial or pre-endothelial cells. More than 60% patients have single-organ involvement, and involvement of multiple organs including the liver, lungs, and bones is extremely rare. The typical radiographic features of EHE include multiple small nodules in both lungs, which are usually located near small- and medium-sized blood vessels and the bronchi, and solitary, multiple, or diffuse lesions located at the hepatic periphery, spreading within the branches of the portal and hepatic veins. Radiologic calcification has been rarely reported in the literature. Here, we firstly described a case of a 53-year-old woman with EHE who presented with lungs, liver, bone, and right hilar lymph node involvement, manifesting as massive calcification on computed tomography. This case reminds physicians that EHE may present with unusual imaging manifestations, like massive calcification, and should be considered during the diagnostic process.

Use of computed tomography for distinguishing heterotopic pancreas from gastrointestinal stromal tumor and leiomyoma.

PURPOSE: To determine whether morphologic features and semiquantitative parameters of computed tomography (CT) could be used to distinguish heterotopic pancreas from gastrointestinal stromal tumor (GIST) and leiomyoma. METHODS: This retrospective study evaluated CT images of heterotopic pancreases (n = 28), GISTs (n = 57), and leiomyomas (n = 26) located in the upper gastrointestinal tract. Morphologic imaging features of lesions were analyzed, including location, contour, margin, attenuation, growth pattern, enhancement type, enhancement degree, enlarged vessels feeding or draining the mass, hyperenhancement of the overlying mucosa, low intralesional attenuation, calcification, and a duct-like structure. Semiquantitative parameters included long diameter (LD), short diameter (SD), LD/SD ratio, and lesion and aorta CT values during plain CT (Lp and Ap), arterial phase (La and Aa), and venous phase (Lv and Av). Diagnostic performance of these findings and parameters were evaluated by receiver operating characteristic (ROC) analysis. RESULTS: Morphologic CT findings (including lesion contour, margin, attenuation, growth pattern, enhancement type, and enhancement degree) and semiquantitative parameters except for LD/SD were demonstrated to be significant for differentiating heterotopic pancreas from GIST and leiomyoma (all P < 0.01). Of these, location, low intralesional attenuation, duct-like structure and LD, SD, Lv, and Sv values showed good diagnostic performance with the areas under curve (AUC) higher than 0.70. The presence of a duct-like structure demonstrated the best diagnostic ability with AUC of 0.929 [95% confidence interval (CI) 0.864-0.969], sensitivity of 5.7% (95% CI 67.3-96.0), and specificity of 100% (95% CI 95.7-100), respectively. When the three morphologic features (location, low intralesional attenuation, duct-like structure) were used in combination, the AUC was improved to 0.980 (95% CI 0.952-1). CONCLUSION: CT features, especially the morphologic features, could be used to differentiate heterotopic pancreas from GIST and leiomyoma in the upper gastrointestinal tract and, thus, provide a more accurate method for non-invasive preoperative diagnosis. Additionally, the presence of a duct-like structure demonstrated to be a reliable indicator for heterotopic pancreas among the morphologic and semiquantitative CT features.

Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging.

The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk staging, therapy, and follow-up. The recommended imaging method for staging and follow-up is computed tomography (CT) according to current guidelines. Artificial intelligence (AI) applies and elaborates theses, procedures, modes, and utilization systems for simulating, enlarging, and stretching the intellectual capacity of humans. Recently, researchers have done a few studies to explore AI applications in GIST imaging. This article reviews the present AI studies in GISTs imaging, including preoperative diagnosis, risk stratification and prediction of prognosis, gene mutation, and targeted therapy response.