Gastrointestinal stromal tumors (GIST): a proposal of a "CT-based predictive model of Miettinen index" in predicting the risk of malignancy

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.

Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study

Objective

gastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs.

Methods

Overall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1.

Results

The Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621-0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore.

Conclusion

The radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance.

Machine learning for predicting the risk stratification of 1-5 cm gastric gastrointestinal stromal tumors based on CT

BACKGROUD

To predict the malignancy of 1-5 cm gastric gastrointestinal stromal tumors (GISTs) by machine learning (ML) on CT images using three models - Logistic Regression (LR), Decision Tree (DT) and Gradient Boosting Decision Tree (GBDT).

METHODS

231 patients from Center 1 were randomly assigned into the training cohort (n = 161) and the internal validation cohort (n = 70) in a 7:3 ratio. The other 78 patients from Center 2 served as the external test cohort. Scikit-learn software was used to build three classifiers. The performance of the three models were evaluated by sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC). Diagnostic differences between ML models and radiologists were compared in the external test cohort. Important features of LR and GBDT were analyzed and compared.

RESULTS

GBDT outperformed LR and DT with the largest AUC values (0.981 and 0.815) in the training and internal validation cohorts and the greatest accuracy (0.923, 0.833 and 0.844) across all three cohorts. However, LR was found to have the largest AUC value (0.910) in the external test cohort. DT yielded the worst accuracy (0.790 and 0.727) and AUC values (0.803 and 0.700) in both the internal validation cohort and the external test cohort. GBDT and LR performed better than radiologists. Long diameter was demonstrated to be the same and most important CT feature for GBDT and LR.

CONCLUSIONS

ML classifiers, especially GBDT and LR with high accuracy and strong robustness, were considered to be promising in risk classification of 1-5 cm gastric GISTs based on CT. Long diameter was found the most important feature for risk stratification.

A New Online Dynamic Nomogram: Construction and Validation of a Predictive Model for Distant Metastasis Risk and Prognosis in Patients with Gastrointestinal Stromal Tumors

BACKGROUND

Gastrointestinal stromal tumor (GIST) is the most common sarcoma of the digestive tract, among which patients with distant metastases tend to have a poor prognosis. This study aimed to develop a model for predicting distant metastasis in GIST patients and to develop two models for monitoring overall survival (OS) and cancer-specific survival (CSS) in GIST patients with metastasis. This would allow us to develop an optimal, individualized treatment strategy.

METHODS

We reviewed demographic and clinicopathological characteristics data from 2010 to 2017 of patients diagnosed with GIST in the Surveillance, Epidemiology, and End Results (SEER) database. The data of the external validation group was reviewed from the Forth Hospital of Hebei Medical University. Univariate and multivariate logistic regression analyses were used to confirm the independent risk factors for distant metastasis in the GIST patients, and univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors for OS and CSS in the GIST patients with distant metastasis. Subsequently, three web-based novel nomograms were developed, which were evaluated by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

Of the 3639 patients who met the inclusion criteria, 418 (11.4%) had distant metastases. The risk factors for distant metastasis in GIST patients included sex, primary site, grade, N stage, tumor size, and mitotic count. For OS, the independent prognosis factors for GIST patients with metastasis included age, race, marital, primary site, chemotherapy, mitotic count, and metastasis at the lung, and for CSS, age, race, marital, primary site, and metastasis at the lung were the independent prognosis factors. Three web-based nomograms were constructed based on these independent factors, respectively. The ROC curves, calibration curves, and DCA were performed in the training, testing, and validation sets which confirmed the high accuracy and strong clinical practice power for the nomograms.

CONCLUSION

Population-based nomograms can help clinicians predict the occurrence and prognosis of distant metastases in patients with GIST, which may be helpful for clinicians to formulate clinical management and appropriate treatment strategies.

Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors

BACKGROUND

Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs).

AIM

To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs.

METHODS

The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification.

RESULTS

There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 ± 0.94 vs 6.63 ± 3.26 cm, P < 0.001) between the low-grade and high-grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours (P = 0.028; OR = 7.750; 95%CI: 1.253-47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively.

CONCLUSION

CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.

Imaging of Gastrointestinal Tract Ailments

Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management of GI diseases. This review mainly focuses on the imaging of several representative gastrointestinal ailments, such as inflammatory bowel disease, tumors, appendicitis, Meckel's diverticulum, and others. Various imaging modalities commonly used for the gastrointestinal tract, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), and photoacoustic tomography (PAT) and multimodal imaging with mode overlap are summarized. These achievements in single and multimodal imaging provide useful guidance for improved diagnosis, staging, and treatment of the corresponding gastrointestinal diseases. The review evaluates the strengths and weaknesses of different imaging techniques and summarizes the development of imaging techniques used for diagnosing gastrointestinal ailments.

Prognostic value of preoperative CT features for disease-free survival in patients with primary gastric gastrointestinal stromal tumors after resection

PURPOSE

Tumor size is an important prognostic factor without consideration of the necrotic and cystic components within tumor for patients with gastrointestinal stromal tumors (GISTs). We aimed to extract the enhancing viable component from the tumor using computed tomography (CT) post-processing software and evaluate the value of preoperative CT features for predicting the disease-free survival (DFS) after curative resection for patients with primary gastric GISTs.

METHODS

132 Patients with primary gastric GISTs who underwent preoperative contrast-enhanced CT and curative resection were retrospectively analyzed. We used a certain CT attenuation of 30 HU to extract the enhancing tissue component from the tumor. Enhancing tissue volume and other CT features were assessed on venous-phase images. We evaluated the value of preoperative CT features for predicting the DFS after surgery. Univariate and multivariate Cox regression analyses were performed to find the independent risk factor for predicting the DFS.

RESULTS

Of the 132 patients, 68 were males and 64 were females, with a mean age of 61 years. The median follow-up duration was 60 months, and 28 patients experienced disease recurrence and distant metastasis during the follow-up period. Serosal invasion (p < 0.001; HR = 5.277) and enhancing tissue volume (p = 0.005; HR = 1.447) were the independent risk factors for predicting the DFS after curative resection for patients with primary gastric GISTs.

CONCLUSION

Preoperative contrast-enhanced CT could be useful for predicting the DFS after the surgery of gastric GISTs, and serosal invasion and enhancing tissue volume were the independent risk factors.

Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study

Background

This study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas.

Methods

In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination.

Results

Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1.

Conclusion

Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.

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.

Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multi-center study

Purpose

This study was to investigate the diagnostic efficacy of radiomics models based on the enhanced CT images in differentiating the malignant risk of gastrointestinal stromal tumors (GIST) in comparison with the clinical indicators model and traditional CT diagnostic criteria.

Materials and methods

A total of 342 patients with GISTs confirmed histopathologically were enrolled from five medical centers. Data of patients wrom two centers comprised the training group (n=196), and data from the remaining three centers constituted the validation group (n=146). After CT image segmentation and feature extraction and selection, the arterial phase model and venous phase model were established. The maximum diameter of the tumor and internal necrosis were used to establish a clinical indicators model. The traditional CT diagnostic criteria were established for the classification of malignant potential of tumor. The performance of the four models was assessed using the receiver operating characteristics curve.

Reuslts

In the training group, the area under the curves(AUCs) of the arterial phase model, venous phase model, clinical indicators model, and traditional CT diagnostic criteria were 0.930 [95% confidence interval (CI): 0.895-0.965), 0.933 (95%CI 0.898-0.967), 0.917 (95%CI 0.872-0.961) and 0.782 (95%CI 0.717-0.848), respectively. In the validation group, the AUCs of the models were 0.960 (95%CI 0.930-0.990), 0.961 (95% CI 0.930-0.992), 0.922 (95%CI 0.884-0.960) and 0.768 (95%CI 0.692-0.844), respectively. No significant difference was detected in the AUC between the arterial phase model, venous phase model, and clinical indicators model by the DeLong test, whereas a significant difference was observed between the traditional CT diagnostic criteria and the other three models.

Conclusion

The radiomics model using the morphological features of GISTs play a significant role in tumor risk stratification and can provide a reference for clinical diagnosis and treatment plan.

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.

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.

Preoperative prediction of malignant potential of 2-5 cm gastric gastrointestinal stromal tumors by computerized tomography-based radiomics

BACKGROUND

The use of endoscopic surgery for treating gastrointestinal stromal tumors (GISTs) between 2 and 5 cm remains controversial considering the potential risk of metastasis and recurrence. Also, surgeons are facing great difficulties and challenges in assessing the malignant potential of 2-5 cm gastric GISTs.

AIM

To develop and evaluate computerized tomography (CT)-based radiomics for predicting the malignant potential of primary 2-5 cm gastric GISTs.

METHODS

A total of 103 patients with pathologically confirmed gastric GISTs between 2 and 5 cm were enrolled. The malignant potential was categorized into low grade and high grade according to postoperative pathology results. Preoperative CT images were reviewed by two radiologists. A radiological model was constructed by CT findings and clinical characteristics using logistic regression. Radiomic features were extracted from preoperative contrast-enhanced CT images in the arterial phase. The XGboost method was used to construct a radiomics model for the prediction of malignant potential. Nomogram was established by combing the radiomics score with CT findings. All of the models were developed in a training group (n = 69) and evaluated in a test group (n = 34).

RESULTS

The area under the curve (AUC) value of the radiological, radiomics, and nomogram models was 0.753 (95% confidence interval [CI]: 0.597-0.909), 0.919 (95%CI: 0.828-1.000), and 0.916 (95%CI: 0.801-1.000) in the training group vs 0.642 (95%CI: 0.379-0.870), 0.881 (95%CI: 0.772-0.990), and 0.894 (95%CI: 0.773-1.000) in the test group, respectively. The AUC of the nomogram model was significantly larger than that of the radiological model in both the training group (Z = 2.795, P = 0.0052) and test group (Z = 2.785, P = 0.0054). The decision curve of analysis showed that the nomogram model produced increased benefit across the entire risk threshold range.

CONCLUSION

Radiomics may be an effective tool to predict the malignant potential of 2-5 cm gastric GISTs and assist preoperative clinical decision making.

Comparison of Computed Tomography Features of Gastric and Small Bowel Gastrointestinal Stromal Tumors With Different Risk Grades

OBJECTIVE

This study aimed to compare the computed tomography (CT) features of gastric and small bowel gastrointestinal stromal tumors (GISTs) and further identify the predictors for risk stratification of them, respectively.

METHODS

According to the modified National Institutes of Health criteria, patients were classified into low-malignant potential group and high-malignant potential group. Two experienced radiologists reviewed the CT features including the difference of CT values between arterial phase and portal venous phase (PVPMAP) by consensus. The CT features of gastric and small bowel GISTs were compared, and the association of CT features with risk grades was analyzed, respectively. Determinant CT features were used to construct corresponding models.

RESULTS

Univariate analysis showed that small bowel GISTs tended to present with irregular contour, mixed growth pattern, ill-defined margin, severe necrosis, ulceration, tumor vessels, heterogeneous enhancement, larger size, and marked enhancement compared with gastric GISTs. According to multivariate analysis, tumor size (P < 0.001; odds ratio [OR], 3.279), necrosis (P = 0.008; OR, 2.104) and PVPMAP (P = 0.045; OR, 0.958) were the independent influencing factors for risk stratification of gastric GISTs. In terms of small bowel GISTs, the independent predictors were tumor size (P < 0.001; OR, 3.797) and ulceration (P = 0.031; OR, 4.027). Receiver operating characteristic curve indicated that the CT models for risk stratification of gastric and small bowel GISTs both achieved the best predictive performance.

CONCLUSIONS

Computed tomography features of gastric and small bowel GISTs are different. Furthermore, the qualitative and quantitative CT features of GISTs may be favorable for preoperative risk stratification.

Computed tomography in the size measurement of gastric gastrointestinal stromal tumors: Implication to risk stratification and "wait-and-see" tactics

INTRODUCTION

This study aimed to compare the radiologic size of gastric gastrointestinal stromal tumors (GISTs) on computed tomography (CT) with the pathologic size in a Chinese population, and elucidate the potential significance of the CT size in the preoperative risk stratification.

MATERIALS AND METHODS

The study enrolled 314 patients treated by endoscopic/surgical resection of gastric lesions that proved postoperatively to be GISTs. Bland-Altman analysis and intraclass correlation coefficient (ICC) were adopted to assess the size agreement between CT and pathology. Independent predictors of risk category underestimation and the optimal cut-off value of CT size were determined by logistic regression analysis and the receiver operating characteristic (ROC) curve.

RESULTS

CT underestimated gastric GISTs size by 0.30 cm [95% confidence interval (CI): (-0.42, - 0.19); p < 0.001]. In the subgroup analysis, the size underestimation was 0.10 cm in GISTs ≤ 5 cm [95% CI: (-0.19, -0.01); p = 0.024]; and 0.75 cm in GISTs >5 cm [95% CI: (-1.05, 0.45), p < 0.001]. Though ICC values showed well reliability for the corresponding pathologic size, with 0.95 in all size, 0.86 in size ≤ 5 cm, and 0.92 in size >5 cm respectively. Risk underestimation by CT imaging mainly occurred in gastric GISTs with smaller size (≤5 cm; p = 0.010) and lower mitotic index (≤5 per 50 high-power fields; p = 0.011). CT size of 3.65 cm was defined as an absolute cut-off to differentiate intermediate/high-risk patients from low-risk group, with 87.5% sensitivity at a specificity of 57.8%.

CONCLUSION

Preoperative CT underestimated the mean size by 0.30 cm in gastric GISTs. A CT size of 3.65 cm would facilitate the selection of potential intermediate/high-risk patients, instant intervention should be encouraged in the absence of contraindications.

Structured and shared CT radiological report of gastric cancer: a consensus proposal by the Italian Research Group for Gastric Cancer (GIRCG) and the Italian Society of Medical and Interventional Radiology (SIRM)

OBJECTIVES

Written radiological report remains the most important means of communication between radiologist and referring medical/surgical doctor, even though CT reports are frequently just descriptive, unclear, and unstructured. The Italian Society of Medical and Interventional Radiology (SIRM) and the Italian Research Group for Gastric Cancer (GIRCG) promoted a critical shared discussion between 10 skilled radiologists and 10 surgical oncologists, by means of multi-round consensus-building Delphi survey, to develop a structured reporting template for CT of GC patients.

METHODS

Twenty-four items were organized according to the broad categories of a structured report as suggested by the European Society of Radiology (clinical referral, technique, findings, conclusion, and advice) and grouped into three "CT report sections" depending on the diagnostic phase of the radiological assessment for the oncologic patient (staging, restaging, and follow-up).

RESULTS

In the final round, 23 out of 24 items obtained agreement ( ≥ 8) and consensus ( ≤ 2) and 19 out 24 items obtained a good stability (p > 0.05).

CONCLUSIONS

The structured report obtained, shared by surgical and medical oncologists and radiologists, allows an appropriate, clearer, and focused CT report essential to high-quality patient care in GC, avoiding the exclusion of key radiological information useful for multidisciplinary decision-making.

KEY POINTS

• Imaging represents the cornerstone for tailored treatment in GC patients. • CT-structured radiology report in GC patients is useful for multidisciplinary decision making.

Assessment of gastric wall structure using ultra-high-resolution computed tomography

PURPOSE

To evaluate the image quality of ultra-high-resolution CT (U-HRCT) in the comparison among four different reconstruction methods, focusing on the gastric wall structure, and to compare the conspicuity of a three-layered structure of the gastric wall between conventional HRCT (C-HRCT) and U-HRCT.

METHOD

Our retrospective study included 48 patients who underwent contrast-enhanced U-HRCT. Quantitative analyses were performed to compare image noise of U-HRCT between deep-learning reconstruction (DLR) and other three methods (filtered back projection: FBP, hybrid iterative reconstruction: Hybrid-IR, and Model-based iterative reconstruction: MBIR). The mean overall image quality scores were also compared between the DLR and other three methods. In addition, the mean conspicuity scores for the three-layered structure of the gastric wall at five regions were compared between C-HRCT and U-HRCT.

RESULTS

The mean noise of U-HRCT with DLR was significantly lower than that with the other three methods (P < 0.001). The mean overall image quality scores with DLR images were significantly higher than those with the other three methods (P < 0.001). Regarding the comparison between C-HRCT and U-HRCT, the mean conspicuity scores for the three-layered structure of the gastric wall on U-HRCT were significantly better than those on C-HRCT in the fornix (5 [5-5] vs. 3.5 [3-4], P < 0.001), body (4 [3.25-5] vs. 4 [3-4], P = 0.039), angle (5 [4-5] vs. 3 [2-4], P < 0.001), and antral posterior (4 [3.25-5] vs. 2 [2-4], P < 0.001), except for antral anterior (4 [3-5] vs. 3 [3-4], P = 0.230) CONCLUSION: U-HRCT using DLR improved the image noise and overall image quality of the gastric wall as well as the conspicuity of the three-layered structure, suggesting its utility for the evaluation of the anatomical details of the gastric wall structure.

Establishment of the Radiologic Tumor Invasion Index Based on Radiomics Splenic Features and Clinical Factors to Predict Serous Invasion of Gastric Cancer

Background

Currently, there are shortcomings in diagnosing gastric cancer with or without serous invasion, making it difficult for patients to receive appropriate treatment. Therefore, we aimed to develop a radiomic nomogram for preoperative identification of serosal invasion.

Methods

We selected 315 patients with gastric cancer, confirmed by pathology, and randomly divided them into two groups: the training group (189 patients) and the verification group (126 patients). We obtained patient splenic imaging data for the training group. A p-value of <0.05 was considered significant for features that were selected for lasso regression. Eight features were chosen to construct a serous invasion prediction model. Patients were divided into high- and low-risk groups according to the radiologic tumor invasion risk score. Subsequently, univariate and multivariate regression analyses were performed with other invasion-related factors to establish a visual combined prediction model.

Results

The diagnostic accuracy of the radiologic tumor invasion score was consistent in the training and verification groups (p<0.001 and p=0.009, respectively). Univariate and multivariate analyses of invasion risk factors revealed that the radiologic tumor invasion index (p=0.002), preoperative hemoglobin <100 (p=0.042), and the platelet and lymphocyte ratio <92.8 (p=0.031) were independent risk factors for serosal invasion in the training cohort. The prediction model based on the three indexes accurately predicted the serosal invasion risk with an area under the curve of 0.884 in the training cohort and 0.837 in the testing cohort.

Conclusions

Radiological tumor invasion index based on splenic imaging combined with other factors accurately predicts serosal invasion of gastric cancer, increases diagnostic precision for the most effective treatment, and is time-efficient.

Advancement in Diagnostic Imaging of Thymic Tumors

Simple Summary

Diagnostic imaging is pivotal for the diagnosis and staging of thymic tumors. It is important to distinguish thymoma and other tumor histotypes amenable to surgery from lymphoma. Furthermore, in cases of thymoma, it is necessary to differentiate between early and advanced disease before surgery since patients with locally advanced tumors require neoadjuvant chemotherapy for improving survival. This review aims to provide to radiologists a full spectrum of findings of thymic neoplasms using traditional and innovative imaging modalities.

Abstract

Thymic tumors are rare neoplasms even if they are the most common primary neoplasm of the anterior mediastinum. In the era of advanced imaging modalities, such as functional MRI, dual-energy CT, perfusion CT and radiomics, it is possible to improve characterization of thymic epithelial tumors and other mediastinal tumors, assessment of tumor invasion into adjacent structures and detection of secondary lymph nodes and metastases. This review aims to illustrate the actual state of the art in diagnostic imaging of thymic lesions, describing imaging findings of thymoma and differential diagnosis.

A CT-based nomogram for predicting the malignant potential of primary gastric gastrointestinal stromal tumors preoperatively

PURPOSE

To develop and validate a computerized tomography (CT)-based nomogram for predicting the malignant potential of primary gastric gastrointestinal stromal tumors (GISTs).

METHODS

The primary and validation cohorts consisted of 167 and 39 patients (single center, different time periods) with histologically confirmed primary gastric GISTs. Clinical data and preoperative CT images were reviewed. The association of CT characteristics with malignant potential was analyzed using univariate and stepwise logistic regression analyses. A nomogram based on significant CT findings was developed for predicting malignant potential. The predictive accuracy of the nomogram was determined by the concordance index (C-index) and calibration curves. External validation was performed with the validation cohort.

RESULTS

CT imaging features including tumor size, tumor location, tumor necrosis, growth pattern, ulceration, enlarged vessels feeding or draining the mass (EVFDM), tumor contour, mesenteric fat infiltration, and direct organ invasion showed significant differences between the low- and high-grade malignant potential groups in univariate analysis (P < 0.05). Only tumor size (> 5 cm vs ≤ 5 cm), location (cardiac/pericardial region vs other), EVFDM, and mesenteric fat infiltration (present vs absent) were significantly associated with high malignant potential in multivariate logistic regression analysis. Incorporating these four independent factors into the nomogram model achieved good C-indexes of 0.946 (95% confidence interval [CI] 0.899-0.975) and 0.952 (95% CI 0.913-0.977) in the primary and validation cohorts, respectively. The cutoff point was 0.33, with sensitivity, specificity, and diagnostic accuracy of 0.865, 0.915, and 0.780, respectively.

DISCUSSION

Primary gastric GISTs originating in the cardiac/pericardial region appear to be associated with higher malignant potential. The nomogram consisting of CT features, including size, location, EVFDM, and mesenteric fat infiltration, could be used to accurately predict the high malignant potential of primary gastric GISTs.

Utility of preoperative computed tomography features in predicting the Ki-67 labeling index of gastric gastrointestinal stromal tumors

PURPOSE

To evaluate the value of preoperative computed tomography (CT) features including morphologic and quantitative features for predicting the Ki-67 labeling index (Ki-67LI) of gastric gastrointestinal stromal tumors (GISTs).

METHODS

We retrospectively included 167 patients with gastric GISTs who underwent preoperative contrast-enhanced CT. We assessed the morphologic features of preoperative CT images and the quantitative features including the maximum diameter of tumor, total tumor volume, mean total tumor CT value, necrosis volume, necrosis volume ratio, enhanced tissue volume, and mean CT value of enhanced tissue. Potential predictive parameters to distinguish the high-level Ki-67LI group (>4%, n = 125) from the low-level Ki-67LI group (≤4%, n = 42) were compared and subsequently determined in multivariable logistic regression analysis.

RESULTS

Growth pattern (p = 0.036), shape (p = 0.000), maximum diameter (p = 0.018), total tumor volume (p = 0.021), mean total tumor CT value (p = 0.009), necrosis volume (p = 0.006), necrosis volume ratio (p = 0.000), enhanced tissue volume (p = 0.027), and mean CT value of enhanced tissue (p = 0.004) were significantly different between the two groups. Multivariate logistic regression analysis indicated that lobulated/irregular shape (odds ratio [OR] = 3.817; p = 0.000) and high necrosis volume ratio (OR = 1.935; p = 0.024) were independent factors of high-level Ki-67LI.

CONCLUSIONS

Higher necrosis volume ratio in combination with lobulated/irregular shape could potentially predict high expression of Ki-67LI for gastric GISTs.

Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer)

Background

To predict response to neoadjuvant chemotherapy (NAC) of gastric cancer (GC), prior to surgery, would be pivotal to customize patient treatment. The aim of this study is to investigate the reliability of computed tomography (CT) texture analysis (TA) in predicting the histo-pathological response to NAC in patients with resectable locally advanced gastric cancer (AGC).

Methods

Seventy (40 male, mean age 63.3 years) patients with resectable locally AGC, treated with NAC and radical surgery, were included in this retrospective study from 5 centers of the Italian Research Group for Gastric Cancer (GIRCG). Population was divided into two groups: 29 patients from one center (internal cohort for model development and internal validation) and 41 from other four centers (external cohort for independent external validation). Gross tumor volume (GTV) was segmented on each pre- and post-NAC multidetector CT (MDCT) image by using a dedicated software (RayStation), and 14 TA parameters were then extrapolated. Correlation between TA parameters and complete pathological response (tumor regression grade, TRG1), was initially investigated for the internal cohort. The univariate significant variables were tested on the external cohort and multivariate logistic analysis was performed.

Results

In multivariate logistic regression the only significant TA variable was delta gray-level co-occurrence matrix (GLCM) contrast (P=0.001, Nagelkerke R²: 0.546 for the internal cohort and P=0.014, Nagelkerke R²: 0.435 for the external cohort). Receiver operating characteristic (ROC) curves, generated from the logistic regression of all the patients, showed an area under the curve (AUC) of 0.763.

Conclusions

Post-NAC GLCM contrast and dissimilarity and delta GLCM contrast TA parameters seem to be reliable for identifying patients with locally AGC responder to NAC.

Assessment of morphological CT imaging features for the prediction of risk stratification, mutations, and prognosis of gastrointestinal stromal tumors

OBJECTIVES

To investigate the correlation between CT imaging features and risk stratification of gastrointestinal stromal tumors (GISTs), prediction of mutation status, and prognosis.

METHODS

This retrospective dual-institution study included patients with pathologically proven GISTs meeting the following criteria: (i) preoperative contrast-enhanced CT performed between 2008 and 2019; (ii) no treatments before imaging; (iii) available pathological analysis. Tumor risk stratification was determined according to the National Institutes of Health (NIH) 2008 criteria. Two readers evaluated the CT features, including enhancement patterns and tumor characteristics in a blinded fashion. The differences in distribution of CT features were assessed using univariate and multivariate analyses. Survival analyses were performed by using the Cox proportional hazard model, Kaplan-Meier method, and log-rank test.

RESULTS

The final population included 88 patients (59 men and 29 women, mean age 60.5 ± 11.1 years) with 45 high-risk and 43 low-to-intermediate-risk GISTs (median size 6.3 cm). At multivariate analysis, lesion size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) were independently associated with the high-risk GISTs. Hyperenhancement was significantly more frequent in PDGFRα-mutated/wild-type GISTs compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). Ill-defined margins were associated with shorter progression-free survival (HR 9.66) at multivariate analysis, while ill-defined margins and hemorrhage remained independently associated with shorter overall survival (HR 44.41 and HR 30.22). Inter-reader agreement ranged from fair to almost perfect (k: 0.32-0.93).

CONCLUSIONS

Morphologic contrast-enhanced CT features are significantly different depending on the risk status or mutations and may help to predict prognosis.

KEY POINTS

• Lesions size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) are independent predictors of high-risk GISTs. • PDGFRα-mutated/wild-type GISTs demonstrate more frequently hyperenhancement compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). • Ill-defined margins (hazard ratio 9.66) were associated with shorter progression-free survival at multivariate analysis, while ill-defined margins (hazard ratio 44.41) and intralesional hemorrhage (hazard ratio 30.22) were independently associated with shorter overall survival.

Evaluation of risk classifications for gastrointestinal stromal tumor using multi-parameter Magnetic Resonance analysis

BACKGROUND

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal malignancy of the gastrointestinal tract. At present, it is generally believed that the prognosis of GIST is closely related to its risk classification. It may add value to correctly diagnose and evaluate the risk of invasion using a noninvasive imaging examination prior to surgery. MRI has the advantages of multiple parameters and high soft tissue resolution, which may be the potential method to preoperatively evaluate the risk of GIST.

PURPOSE

To retrospectively evaluate the diagnostic accuracy of multi-parameter MR analysis for preoperative risk classification of GIST.

MATERIALS AND METHODS

In this 6-year retrospective study, full MRI examination was performed on all 60 GIST cases confirmed classified by pathology, including 35 cases of very low-to-low-risk GIST and 25 cases of intermediate-to-high-risk GIST. Dynamic contrast-enhanced T1- and T2-weighted images, and apparent diffusion coefficient (ADC) maps were reviewed independently by two radiologists blinded to pathologic results. Volume, ADC ratio, three wash-in indexes (WII) were calculated and compared using t-test or Kruskal-Wallis nonparametric test. Sensitivity and specificity analyses were performed to calculate diagnostic accuracy using ROC analyses. Differences were considered significant at p < 0.05.

RESULTS

All GISTs were resected. Patient age, sex, tumor location and tumor shape did not differ significantly across the two groups (p = 0.798, 0.767, 0.822 and 0.096, respectively). GIST in the intermediate-to-high-risk group presented significantly greater volume (p = 0.0045), lower ADC ratio (p = 0.0125) and faster enhancement (for WII2, p < 0.0001; for WII3, p = 0.0358) than that of GIST in the very low-to-low-risk group. This combination of the volume, ADC ratio and WII2 provided sensitivity of 88%, specificity of 94.29%, and accuracy of 91.7% for the risk classification of GIST.

CONCLUSION

Multi-parameter MR analysis provides a preoperative imaging standard for accurately distinguishing very low-to-low-risk GIST from intermediate-to-high-risk GIST.

Radiomics analysis of CT imaging for differentiating gastric neuroendocrine carcinomas from gastric adenocarcinomas

PURPOSE

To develop and evaluate a CT-based radiomics nomogram for differentiating gastric neuroendocrine carcinomas (NECs) from gastric adenocarcinomas (ADCs).

METHODS

CT images of 63 patients with gastric NECs were collected retrospectively, and 63 patients with gastric ADCs were selected as the control group. Univariate analysis was used to identify the significant factors of clinical characteristics and CT findings for differentiating gastric NECs from ADCs. Radiomics analysis was applied to CT images of unenhanced, arterial phase and venous phase, respectively. A radiomics nomogram incorporating the radiomics signature and the subjective CT findings was developed and its diagnostic ability was evaluated. The diagnostic performances of CT findings model, radiomics signature and radiomics nomogram were compared using DeLong test.

RESULTS

The tumor margin and lymph node (LN) metastasis were independent predictors for differentiating gastric NECs from ADCs. The radiomics signature based on venous phase presented superior AUC of 0.798 [95 % confidence interval (CI), 0.657-0.938] in validation cohort. The nomogram incorporated the radiomics signature, tumor margin and LN metastasis showed AUCs of 0.821 (95 %CI: 0.725-0.895) in the primary cohort and 0.809 (95 %CI: 0.649-0.918) in the validation cohort. Moreover, the radiomics nomogram showed good discrimination and calibration. The diagnostic performance of CT findings model was significantly lower than that of radiomics nomogram (p =  0.001) and radiomics signature (p = 0.025).

CONCLUSIONS

Radiomics analysis exhibited good performance in differentiating gastric NECs from ADCs, and the radiomics nomogram may have significant clinical implications on preoperative detection of gastric malignant tumors.

Could computed tomography be used as a surrogate of endoscopic ultrasonography in the screening and surveillance of small gastric Gastrointestinal stromal tumors?

PURPOSE

To investigate whether computed tomography (CT) could be used for screening and surveillance of small gastric gastrointestinal stromal tumors (gGISTs).

METHOD

A total of 162 pathologically confirmed small gGISTs (≤2 cm) between September 2007 and November 2019 were retrospectively enrolled. Thirty-six lesions received contrast-enhanced CT after they were identified by endoscopy and EUS, and forty-three lesions received CT alone before surgery. The detection rate of CT for ≤1 cm gGISTs (micro-gGISTs) and 1-2 cm gGISTs (mini-gGISTs) was investigated, and the detection rate of CT alone was compared with that of CT following endoscopy and EUS. The relationship between EUS- and CT-detected high-risk features were assessed.

RESULTS

CT demonstrated a favorable detection rate for mini-gGISTs previously identified by EUS and endoscopy, whereas CT alone showed an inferior detection rate (100 % vs. 75 %, p =  0.02). CT showed a poor detection rate for micro-gGISTs, both for lesions received CT after identified by EUS and endoscopy, and those received CT alone (33.3 % vs. 14.8 %, p =  0.372). CT-detected heterogeneous enhancement pattern and presence of calcification were strongly correlated with heterogeneous echotexture (Spearman's ρ=0.66, p < 0.001) and echogenic foci (Spearman's ρ=0.79, p < 0.001) on EUS, respectively. CT-detected necrosis was moderately correlated with cystic spaces on EUS (Spearman's ρ=0.42, p = 0.02). No correlation was found between EUS- and CT- assessed irregular border.

CONCLUSIONS

CT could potentially be considered as a surrogate of EUS for surveillance of mini-gGISTs instead of micro-gGISTs, whereas couldn't be used as a screening modality for either micro- or mini-gGISTs.

Dual energy CT in gland tumors: a comprehensive narrative review and differential diagnosis

Dual energy CT (DECT)with image acquisition at two different photon X-ray levels allows the characterization of a specific tissue or material/elements, the extrapolation of virtual unenhanced and monoenergetic images, and the quantification of iodine uptake; such special capabilities make the DECT the perfect technique to support oncological imaging for tumor detection and characterization and treatment monitoring, while concurrently reducing the dose of radiation and iodine and improving the metal artifact reduction. Even though its potential in the field of oncology has not been fully explored yet, DECT is already widely used today thanks to the availability of different CT technologies, such as dual-source, single-source rapid-switching, single-source sequential, single-source twin-beam and dual-layer technologies. Moreover DECT technology represents the future of the imaging innovation and it is subject to ongoing development that increase according its clinical potentiality, in particular in the field of oncology. This review points out recent state-of-the-art in DECT applications in gland tumors, with special focus on its potential uses in the field of oncological imaging of endocrine and exocrine glands.

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

OBJECTIVES

To investigate CT findings and develop a diagnostic score model to differentiate GLMs from GISTs.

METHODS

This retrospective study included 109 patients with pathologically confirmed GLMs (n = 46) and GISTs (n = 63) from January 2013 to August 2018 who received CE-CT before surgery. Demographic and radiological features was collected, including lesion location, contour, presence or absence of intralesional necrosis and ulceration, growth pattern, whether the tumor involved EGJ, the long diameter (LD) /the short diameter (SD) ratio, pattern and degree of lesion enhancement. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors and establish a predictive model. Independent predictors for GLMs were weighted with scores based on regression coefficients. A receiver operating characteristic (ROC) curve was created to determine the diagnostic ability of the model. Overall score distribution was divided into four groups to show differentiating probability of GLMs from GISTs.

RESULTS

Five CT features were the independent predictors for GLMs diagnosis in multivariate logistic regression analysis, including esophagogastric junction (EGJ) involvement (OR, 367.9; 95 % CI, 5.8-23302.8; P =  0.005), absence of necrosis (OR, 11.9; 95 % CI, 1.0-138. 1; P =  0.048) and ulceration (OR, 151.9; 95 % CI, 1.4-16899.6; P =  0.037), degree of enhancement (OR, 9.3; 95 % CI, 3.2-27.4; P <  0.001), and long diameter/ short diameter (LD/SD) ratio (OR,170.9; 95 % CI, 8.4-3493.4; P =  0.001). At a cutoff of 9 points, AUC for this score model was 0.95, with 95.65 % sensitivity, 79.37 % specificity, 77.19 % PPV, 96.15 % NPV and 86.24 % diagnostic accuracy. An increasing trend was showed in diagnostic probability of GLMs among four groups based on the score (P <  0.001).

CONCLUSIONS

The newly designed scoring system is reliable and easy-to-use for GLMs diagnosis by distinguishing from GISTs, including EGJ involvement, absence of ulceration and necrosis, mild enhancement and high LD/SD ratio. The overall score of model ranged from 1 to 17 points, which was divided into 4 groups: 1-7 points, 7-10 points, 10-13 points and 13-17 points, with a diagnostic probability of GLMs 0%, 45 %, 83 % and 100 %, respectively.