A scoring model for radiologic diagnosis of gastric leiomyomas (GLMs) with contrast-enhanced computed tomography (CE-CT): Differential diagnosis from gastrointestinal stromal tumors (GISTs)

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.

Preoperative differentiation of gastric schwannomas and gastrointestinal stromal tumors based on computed tomography: a retrospective multicenter observational study

Introduction

Gastric schwannoma is a rare benign tumor accounting for only 1-2% of alimentary tract mesenchymal tumors. Owing to their low incidence rate, most cases are misdiagnosed as gastrointestinal stromal tumors (GISTs), especially tumors with a diameter of less than 5 cm. Therefore, this study aimed to develop and validate a diagnostic nomogram based on computed tomography (CT) imaging features for the preoperative prediction of gastric schwannomas and GISTs (diameters = 2-5 cm).

Methods

Gastric schwannomas in 47 patients and GISTs in 230 patients were confirmed by surgical pathology. Thirty-four patients with gastric schwannomas and 167 with GISTs admitted between June 2009 and August 2022 at Hospital 1 were retrospectively analyzed as the test and training sets, respectively. Seventy-six patients (13 with gastric schwannomas and 63 with GISTs) were included in the external validation set (June 2017 to September 2022 at Hospital 2). The independent factors for differentiating gastric schwannomas from GISTs were obtained by multivariate logistic regression analysis, and a corresponding nomogram model was established. The accuracy of the nomogram was evaluated using receiver operating characteristic and calibration curves.

Results

Logistic regression analysis showed that the growth pattern (odds ratio [OR] 3.626; 95% confidence interval [CI] 1.105-11.900), absence of necrosis (OR 4.752; 95% CI 1.464-15.424), presence of tumor-associated lymph nodes (OR 23.978; 95% CI 6.499-88.466), the difference between CT values during the portal and arterial phases (OR 1.117; 95% CI 1.042-1.198), and the difference between CT values during the delayed and portal phases (OR 1.159; 95% CI 1.080-1.245) were independent factors in differentiating gastric schwannoma from GIST. The resulting individualized prediction nomogram showed good discrimination in the training (area under the curve [AUC], 0.937; 95% CI, 0.900-0.973) and validation (AUC, 0.921; 95% CI, 0.830-1.000) datasets. The calibration curve showed that the probability of gastric schwannomas predicted using the nomogram agreed well with the actual value.

Conclusion

The proposed nomogram model based on CT imaging features can be used to differentiate gastric schwannoma from GIST before surgery.

Clinical significance of dual-energy dual-layer CT parameters in differentiating small-sized gastrointestinal stromal tumors from leiomyomas

PURPOSE

Small gastrointestinal stromal tumors (GISTs) can generally have nonspecific CT findings similar to those with benign submucosal tumors of the stomach. The purpose of this study was to explore the potential dual-layer dual-energy CT (dlDECT) parameters to differentiate small-sized (≤ 4 cm) GISTs from leiomyomas of the stomach.

MATERIALS AND METHODS

This retrospective study included 26 SMTs ≤ 4 cm in diameter with pathological confirmation of either GIST (n = 17) or leiomyoma (n = 9) from May 2018 to January 2022. All patients received contrast-enhanced CT. The normalized iodine concentration (NIC) and spectral slope (λHU) were compared between GIST and leiomyoma. Receiver-operating characteristic (ROC) curves were plotted and the areas under the curve (AUCs) were calculated to estimate the diagnostic performance of these markers for differentiating GISTs from leiomyomas.

RESULTS

NIC was significantly higher in GIST than in leiomyoma in the portal (P = 0.0019) and delayed phases (P = 0.0011). λHU was significantly higher in GIST than in leiomyoma in the portal (P = 0.0006) and delayed phases (P = 0.0009). AUC of the ROC curves using NIC to differentiate between GIST and leiomyoma were 0.875 and 0.895 in the portal and delayed phase; using λHU, they were 0.918 and 0.902 in the portal and delayed phase.

CONCLUSION

dlDECT parameters including NIC and λHU show promise as indicators for differentiating small-sized GISTs from leiomyomas.

Development of a CT image analysis-based scoring system to differentiate gastric schwannomas from gastrointestinal stromal tumors

Purpose

To develop a point-based scoring system (PSS) based on contrast-enhanced computed tomography (CT) qualitative and quantitative features to differentiate gastric schwannomas (GSs) from gastrointestinal stromal tumors (GISTs).

Methods

This retrospective study included 51 consecutive GS patients and 147 GIST patients. Clinical and CT features of the tumors were collected and compared. Univariate and multivariate logistic regression analyses using the stepwise forward method were used to determine the risk factors for GSs and create a PSS. Area under the receiver operating characteristic curve (AUC) analysis was performed to evaluate the diagnostic efficiency of PSS.

Results

The CT attenuation value of tumors in venous phase images, tumor-to-spleen ratio in venous phase images, tumor location, growth pattern, and tumor surface ulceration were identified as predictors for GSs and were assigned scores based on the PSS. Within the PSS, GS prediction probability ranged from 0.60% to 100% and increased as the total risk scores increased. The AUC of PSS in differentiating GSs from GISTs was 0.915 (95% CI: 0.874-0.957) with a total cutoff score of 3.0, accuracy of 0.848, sensitivity of 0.843, and specificity of 0.850.

Conclusions

The PSS of both qualitative and quantitative CT features can provide an easy tool for radiologists to successfully differentiate GS from GIST prior to surgery.

Differentiation of Small Gastrointestinal Stromal Tumor and Gastric Leiomyoma with Contrast-Enhanced CT

Objective

The value of multiphase contrast-enhanced CT in differentiating gastrointestinal stromal tumors (GISTs) and gastric leiomyomas (GLMs) which were ≤3 cm was evaluated using machine learning.

Methods

A retrospective analysis was conducted on 45 cases of small gastric wall submucosal tumors (including 22 GISTs and 23 GLMs) with pathologically confirmed diameter ≤3 cm and completed multiphase CT-enhanced scan images. The CT features including tumor location, maximum diameter, shape, margins, growth pattern, plain/enhanced CT value, cystic degeneration, calcification, ulcer, progressive reinforcement, perilesional lymph nodes, and the CT value ratio of the tumor to the aorta at the same level in the enhanced phase III scan of the two groups were evaluated. Tumor location and maximum diameter were automatically evaluated by machine learning.

Results

The GISTs and GLMs with a diameter ≤3 cm showed clear margins, uniform density on plain scan CT, and progressive homogeneous enhancement. The age of the GISTs is greater than that of the GLMs group. The plain scan CT value of the GISTs group was lower than that in the GLMs group. In the GISTs group, the lesions were mostly located in the fundus (68.18%), showing a mixed growth pattern (54.55%), and in the GLMs group, most lesions were located in the cardia (47.82%), showing an intraluminal growth pattern (95.65%). The abovementioned differences were statistically significant.

Conclusions

Contrast-enhanced CT has limited value in differentiating small GISTs from GLMs, which are ≤3 cm. Older age (>49.0 years), a low plain CT value (<42.5 Hu), mixed growth inside and outside the cavity, and noncardiac location tended to be the criteria for the diagnosis of small GISTs of the gastric wall.

A diagnostic scoring model of ENKTCL in the nose-Waldeyer's ring based on logistic regression: Differential diagnosis from DLBCL

Objective

To establish a logistic regression model based on CT and MRI imaging features and Epstein-Barr (EB) virus nucleic acid to develop a diagnostic score model to differentiate extranodal NK/T nasal type (ENKTCL) from diffuse large B cell lymphoma (DLBCL).

Methods

This study population was obtained from two independent hospitals. A total of 89 patients with ENKTCL (n = 36) or DLBCL (n = 53) from January 2013 to May 2021 were analyzed retrospectively as the training cohort, and 61 patients (ENKTCL=27; DLBCL=34) from Jun 2021 to Dec 2022 were enrolled as the validation cohort. All patients underwent CT/MR enhanced examination and EB virus nucleic acid test within 2 weeks before surgery. Clinical features, imaging features and EB virus nucleic acid results were analyzed. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors of ENKTCL and establish a predictive model. Independent predictors were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the predictive model and score model.

Results

We searched for significant clinical characteristics, imaging characteristics and EB virus nucleic acid and constructed the scoring system via multivariate logistic regression and converted regression coefficients to weighted scores. The independent predictors for ENKTCL diagnosis in multivariate logistic regression analysis, including site of disease (nose), edge of lesion (blurred), T2WI (high signal), gyrus like changes, EB virus nucleic acid (positive), and the weighted score of regression coefficient was 2, 3, 4, 3, 4 points. The ROC curves, AUCs and calibration tests were carried out to evaluate the scoring models in both the training cohort and the validation cohort. The AUC of the scoring model in the training cohort were 0.925 (95% CI, 0.906-0.990) and the cutoff point was 5 points. In the validation cohort, the AUC was 0.959 (95% CI, 0.915-1.000) and the cutoff value was 6 points. Four score ranges were as follows: 0-6 points for very low probability of ENKTCL, 7-9 points for low probability; 10-11 points for middle probability; 12-16 points for very high probability.

Conclusion

The diagnostic score model of ENKTCL based on Logistic regression model which combined with imaging features and EB virus nucleic acid. The scoring system was convenient, practical and could significantly improve the diagnostic accuracy of ENKTCL and the differential diagnosis of ENKTCL from DLBCL.

Multi-slice spiral computed tomography in differential diagnosis of gastric stromal tumors and benign gastric polyps, and gastric stromal tumor risk stratification assessment

BACKGROUND

The biological characteristics of gastric stromal tumors are complex, and their incidence has increased in recent years. Gastric stromal tumors (GST) have potential malignant tendencies, and the probability of transformation into malignant tumors is as high as 20%-30%.

AIM

To investigate the value of multi-slice spiral computed tomography (MSCT) in the differential diagnosis of GST and benign gastric polyps, and GST risk stratification assessment.

METHODS

We included 64 patients with GST (GST group) and 60 with benign gastric polyps (control group), confirmed by pathological examination after surgery in PLA General Hospital, from January 2016 to June 2021. The differences in the MSCT imaging characteristic parameters and enhanced CT values between the two groups before surgery were compared. According to the National Institutes of Health’s standard, GST is divided into low- and high-risk groups for MSCT imaging characteristic parameters and enhanced CT values.

RESULTS

The incidences of extraluminal growth, blurred boundaries, and ulceration in the GST group were significantly higher than those in the control group (P < 0.05). The CT values and enhanced peak CT values in the arterial phase in the CST group were higher than those in the control group (P < 0.05). The MSCT differential diagnosis of GST and gastric polyp sensitivity, specificity, misdiagnosis rate, missed diagnosis rate, and areas under the curve (AUCs) were 73.44 %, 83.33%, 26.56%, 16.67%, 0.784, respectively. The receiver operating characteristic curves were plotted with the arterial CT value and enhanced peak CT value, with a statistical difference. The results showed that the sensitivity, specificity, misdiagnosis rate, missed diagnosis rate, and AUC value of arterial CT in the differential diagnosis of GST and gastric polyps were 80.18%, 62.20%, 19.82%, 37.80%, and 0.710, respectively. The sensitivity, specificity, misdiagnosis rate, missed diagnosis rate, and AUC value of the enhanced peak CT value in the differential diagnosis of GST and gastric polyps were 67.63%, 60.40%, 32.37%, 39.60%, and 0.710, respectively. The incidence of blurred lesion boundaries and ulceration in the high-risk group was significantly higher than that in the low-risk group (P < 0.05). The arterial phase and enhanced peak CT values in the high-risk group were significantly higher than those in the low-risk group (P < 0.05).

CONCLUSION

Presurgical MSCT examination has important value in the differential diagnosis of GST and gastric benign polyps and can effectively evaluate the risk grade of GST patients.

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.