Gastrointestinal stromal tumors: a comprehensive radiological review.

Gastrointestinal stromal tumors (GISTs) originating from the interstitial cells of Cajal in the muscularis propria are the most common mesenchymal tumor of the gastrointestinal tract. Multiple modalities, including computed tomography (CT), magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography, ultrasonography, digital subtraction angiography, and endoscopy, have been performed to evaluate GISTs. CT is most frequently used for diagnosis, staging, surveillance, and response monitoring during molecularly targeted therapy in clinical practice. The diagnosis of GISTs is sometimes challenging because of the diverse imaging findings, such as anatomical location (esophagus, stomach, duodenum, small bowel, colorectum, appendix, and peritoneum), growth pattern, and enhancement pattern as well as the presence of necrosis, calcification, ulceration, early venous return, and metastasis. Imaging findings of GISTs treated with antineoplastic agents are quite different from those of other neoplasms (e.g. adenocarcinomas) because only subtle changes in size are seen even in responsive lesions. Furthermore, the recurrence pattern of GISTs is different from that of other neoplasms. This review discusses the advantages and disadvantages of each imaging modality, describes imaging findings obtained before and after treatment, presents a few cases of complicated GISTs, and discusses recent investigations performed using CT and MRI to predict histological risk grade, gene mutations, and patient outcomes.

Clinical impact of low tube voltage computed tomography during hepatic arteriography with low iodine to detect hepatocellular carcinoma before transarterial chemoembolization.

PURPOSE: This study aimed to evaluate the clinical impact of low tube voltage computed tomography (CT) during hepatic arteriography (CTHA) using low iodine contrast to detect hepatocellular carcinoma (HCC). MATERIALS AND METHODS: CTHA images were obtained using a dual-spin technique (80 kVp and 135 kVp) with 30 ml of low-dose iodine contrast (75 mgI/ml). Three radiologists reviewed 135 kVp and 80 kVp CTHA images to diagnose HCC, recording their confidence scores and evaluations of sharpness, noise, artifact, and overall image quality. Lesion-to-liver contrast ratios and objective noise were measured by a non-reader radiologist. RESULTS: We included 23 patients (body mass index, 23.6 ± 2.6 kg/m2) with 89 HCCs. The mean radiation dose index volume was 21.3 mGy at 135 kVp and 9.4 mGy at 80 kVp (P < 0.001). The overall sensitivity and positive predictive value for diagnosing HCCs at 80 kVp vs. 135 kVp were 0.787 vs. 0.730 and 0.712 vs. 0.756, respectively. The lesion-to-liver contrast ratio at 80 kVp was significantly higher than at 135 kVp in the first (3.1 vs. 2.0; P = 0.008) and second phase (3.1 vs. 2.3; P = 0.016). Objective noise was significantly higher at 80 kVp than at 135 kVp in the first (15. 6 ± 4.9 vs. 11.0 ± 3.1; P < 0.001) and second (16.9 ± 5.2 vs. 15.0 ± 7.3; P = 0.046) phases. CONCLUSION: An 80 kVp CTHA, with lower-dose iodine, improved the sensitivity and reduced the radiation dose, despite a decreased positive predictive value in comparison with a 135-kVp CTHA with the same iodine dose.

Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with advanced lung cancer inflammation index, computed tomography and chest radiograph.

The purpose of the present study was to evaluate the feasibility of applying the advanced lung cancer inflammation index (ALI) in patients with coronavirus disease 2019 (COVID-19) and to establish a combined ALI and radiologic risk prediction model for disease exacerbation. The present study included patients diagnosed with COVID-19 infection in our single institution from March to October 2020. Patients without clinical information and/or chest computed tomography (CT) upon admission were excluded. A radiologist assessed the CT severity score and abnormality on chest radiograph. The combined ALI and radiologic risk prediction model was developed via random forest classification. Among 79 patients (age, 43±19 years; male/female, 45:34), 72 experienced improvement and seven patients experienced exacerbation after admission. Significant differences were observed between the improved and exacerbated groups in the ALI (median, 47.6 vs. 13.2; P=0.011), frequency of chest radiograph abnormality (24.7 vs. 83.3%; P<0.001), and chest CT score (CCTS; median, 1 vs. 9; P<0.001). For the accuracy of predicting exacerbation, the receiver-operating characteristic curve analysis demonstrated an area under the curve of 0.79 and 0.92 for the ALI and CCTS, respectively. The combined ALI and radiologic risk prediction model had a sensitivity of 1.00 and a specificity of 0.81. Overall, ALI alone and CCTS alone modestly predicted the exacerbation of COVID-19, and the combined ALI and radiologic risk prediction model exhibited decent sensitivity and specificity.