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World J Gastroenterol. Aug 28, 2020; 26(32): 4729-4738
Published online Aug 28, 2020. doi: 10.3748/wjg.v26.i32.4729
New advances in radiomics of gastrointestinal stromal tumors
Roberto Cannella, Ludovico La Grutta, Massimo Midiri, Tommaso Vincenzo Bartolotta, Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Palermo 90127, Italy
Tommaso Vincenzo Bartolotta, Department of Radiology, Fondazione Istituto Giuseppe Giglio, Ct.da Pietrapollastra, Cefalù (Palermo) 90015, Italy
ORCID number: Roberto Cannella (0000-0002-3808-0785); Ludovico La Grutta (0000-0002-3557-0084); Massimo Midiri (0000-0003-1824-7549); Tommaso Vincenzo Bartolotta (0000-0002-8808-379X).
Author contributions: Cannella R and Bartolotta TV wrote and revised the manuscript for important intellectual content; La Grutta L and Midiri M made critical revisions related to important intellectual content of the manuscript; all the Authors approved the final version of the article.
Conflict-of-interest statement: All authors have no any conflict of interests.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Tommaso Vincenzo Bartolotta, MD, PhD, Assistant Professor, Section of Radiology - BiND, University Hospital “Paolo Giaccone”, Via del Vespro 129, Palermo 90127, Italy.
Received: May 28, 2020
Peer-review started: May 28, 2020
First decision: June 4, 2020
Revised: June 16, 2020
Accepted: August 1, 2020
Article in press: August 1, 2020
Published online: August 28, 2020


Gastrointestinal stromal tumors (GISTs) are uncommon neoplasms of the gastrointestinal tract with peculiar clinical, genetic, and imaging characteristics. Preoperative knowledge of risk stratification and mutational status is crucial to guide the appropriate patients’ treatment. Predicting the clinical behavior and biological aggressiveness of GISTs based on conventional computed tomography (CT) and magnetic resonance imaging (MRI) evaluation is challenging, unless the lesions have already metastasized at the time of diagnosis. Radiomics is emerging as a promising tool for the quantification of lesion heterogeneity on radiological images, extracting additional data that cannot be assessed by visual analysis. Radiomics applications have been explored for the differential diagnosis of GISTs from other gastrointestinal neoplasms, risk stratification and prediction of prognosis after surgical resection, and evaluation of mutational status in GISTs. The published researches on GISTs radiomics have obtained excellent performance of derived radiomics models on CT and MRI. However, lack of standardization and differences in study methodology challenge the application of radiomics in clinical practice. The purpose of this review is to describe the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors, discuss the potential clinical applications that may impact patients’ management, report limitations of current radiomics studies, and future directions.

Key Words: Gastrointestinal stromal tumors, Radiomics, Texture analysis, Computed tomography, Magnetic resonance imaging, Clinical applications

Core tip: Radiomics researches have demonstrated promising results for the differential diagnosis of gastrointestinal stromal tumors (GISTs) with other gastrointestinal neoplasms in the stomach and duodenum. Excellent performances have been reported for the evaluation of risk status, the preoperative identification of high-risk tumors, and the prediction of prognosis after target therapies. Radiogenomics studies are still lacking, with only initial evidences describing the potential of radiomics for the diagnosis of GISTs without KIT mutations. In this work we review the new advances in radiomics applied to the computed tomography and magnetic resonance imaging of GISTs.


Gastrointestinal stromal tumors (GISTs) are uncommon mesenchymal neoplasms of the gastrointestinal tract, originating from the interstitial cells of Cajal[1]. GISTs may arise anywhere along all the gastrointestinal tract, being more commonly encountered in the stomach (50%-60% of cases) or small intestine (30%-40%), while they are rarely observed in the esophagus and colorectum[1,2]. All GISTs have malignant potential with varying degree of biological aggressiveness. Liver and peritoneum are the most common sites of metastatic disease or recurrence after curative resection, which occurs in about 40% of patients[3-5]. GISTs are also characterized by peculiar genetic alterations, with 85% of tumors presenting with activating mutations in the KIT proto-oncogene, while a minority of lesions show mutations of platelet-derived growth factor α (PDGFRα), or occasionally may lack of known mutations (wild type GISTs)[6]. The advent of imatinib mesylate, a selective tyrosine kinase inhibitor of the KIT and PDGFα receptors, has revolutionized the treatment of GISTs, significantly improving the patients’ survival even in advanced stages.

Contrast-enhanced computed tomography (CT) is the imaging modality of choice for preoperative diagnosis, staging, as well as postoperative follow-up and assessment of treatment response in patients with GISTs[7,8]. On contrast-enhanced CT, GISTs usually present with peculiar imaging features, most often with large (> 5 cm) abdominal mass, heterogeneous enhancement, and variable amount of necrosis[9-12]. Other imaging findings include presence of calcifications, ulceration or cystic degeneration[11,12]. Magnetic resonance imaging (MRI) may provide additional information for the evaluation of primary tumors in peculiar location (i.e., rectum) and may be preferred for the differential diagnosis of liver metastasis from other benign hepatic lesions[13,14]. In clinical practice, predicting the behavior of GISTs is challenging, unless the lesions have already metastasized at the time of diagnosis. Although some imaging predictors of malignant potential have been identified (size, location, margins, enhancement pattern) and variably correlated with prognosis and survival of GISTs, small tumors lacking of concerning imaging features may still metastasize, making difficult to predict aggressive tumors.

Radiomics is emerging as a promising tool that allows to quantify lesion heterogeneity, extracting additional quantitative data from radiological imaging that cannot be evaluated by human eyes[15,16]. In recent years, multiple researches have explored the performance of radiomics models in abdominal oncologic applications, with significant results for lesions characterization, evaluation of therapeutic response and prediction of patients’ survival after surgical or systemic treatments[17-22]. The application of radiomics in GISTs could be used to further improve the patients’ management and provide new advances in quantitative lesion evaluation due to the unique clinical, genetic, and imaging characteristics of these tumors.

With this review, we aim to describe the new advances of radiomics applied to CT and MR imaging for the evaluation of gastrointestinal stromal tumors, discuss the potential clinical applications that may impact patients’ management, report limitations of current radiomics studies, and future directions.


Radiomics is based on the mathematical quantification of images heterogeneity, through the analysis of distribution and relationships of pixel intensities within a region of interest (ROI)[15,16]. Radiomics analysis requires a multistep process, starting from imaging acquisition, and including lesion segmentation, features extraction, features selection and reduction, predictive model building, and finally validation and clinical interpretation of the results[19,20,23].

Radiomics can be potentially applied to any type of radiological images, including ultrasound, CT, MRI and positron emission tomography/CT, but most of studies are nowadays based on CT or MRI examinations[19]. Image acquisition is one of the most critical steps for radiomics, since scanning and technical parameters may influence the reproducibility of radiomics features. Particularly, reconstruction algorithm and slice thickness had demonstrated to largely impact on the reproducibility of radiomics features on CT[24-26]. The heterogeneous imaging acquisition may be problematic for evaluation of retrospective data acquired with different CT or MRI scanners, while prospective study should ensure that all patients will be imaged using standardized parameters[27]. It is also important to select the optimal phase/sequence for image analysis. Pre-contrast images are not affected by the contrast administration, but lesion segmentation is more difficult, especially for smaller tumors that are difficult to distinguish on non-contrast CT. Contrast-enhanced images may provide better assessment of lesion heterogeneity, but type and non-standardized timing of contrast agent administration may represent additional confounding factors, especially for images acquired on arterial phase.

Lesion segmentation is the most critical step of radiomics process. Segmentation may be performed manually by expert radiologists, using semi-automatic, or automatic software[27]. Although manual segmentation is time consuming and it is subject to intra- and inter-reader variability, it is still considered as the gold standard for most of radiomics studies[18,19,23]. The segmentation is usually realized by drawing a ROI within the tumor margins (Figure 1), avoiding the inclusion of any extra-tumoral tissues such as bowel mucosa, intestinal content, or peritumoral vessels. The ROI can be placed on a single slide (2D ROI) on the largest tumor cross section or include the whole lesion (3D ROI). Although the latter may capture more tissue heterogeneity, its clinical advantage remains debated.

Figure 1
Figure 1 Examples of lesion segmentation using a texture analysis software (LIFEx, www. on axial (A), coronal (B) and sagittal (C) contrast-enhanced computed tomography images on venous phase in an 82-year-old man with 4.5 cm gastric gastrointestinal stromal tumor.

Several in-house build or commercially available radiomics research software are nowadays used for extract a large number of radiomics features. These features may be divided into semantic (qualitative features usually reported by radiologists such as size, margins, enhancement pattern) or agnostic (which are mathematical and quantitative descriptors of heterogeneity) features. Agnostic features are further classified in first, second and third order features[19]. The first order features are obtained from the analysis of the gray level histogram within a defined ROI, without considering spatial relations among pixels. Most common histogram-based features include mean (average of the pixels within the ROI), standard deviation (dispersion from the mean), skewness (asymmetric of the histogram), kurtosis (peakedness/flatness of the histogram), and entropy (image irregularity or complexity)[20]. The second order texture features consider the spatial relationship among pixels, and most commonly include grey level co-occurrence matrix (GLCM), that quantifies the arrangements of pairs of pixels with the same values in specific directions, and grey-level run length matrix (GLRLM), that quantifies consecutive pixels with the same intensity along specific directions. Third or higher order features evaluate spatial relationship among three or more pixels through statistical methods after applying filters or mathematical transforms. These features include fractal analysis, wavelet transform, and Laplacian transforms of Gaussian-filtered image. Due to the large number of extracted parameters, features reduction should be performed in order to excluded features that are not reproducible or with high similarity (i.e., redundant features). This is a significant step to avoid overfitting problems, especially in small cohorts[18-20,24].

Only uncorrelated features with significant diagnostic performance are selected for final radiomics models. The choice of statistical methods and models may depend on multiple factors such as evaluation of primary outcome, number of features, and number of analyzed lesions[23]. These models can be also combined with other patient clinical characteristics in order to increase their predictive power[15]. Finally, radiomics models should be tested and validated using independent internal validation cohort or external population[18]. To assess the quality of radiomics studies, scores have been proposed, such as the Radiomics Quality Score developed by Lambin et al[28], which evaluates 16 key components of radiomics workflow[28,29].


Existing articles of radiomics in GISTs (Table 1) have been performed with heterogeneous methodology regarding the imaging studies, type of radiomics features and analysis[30-44]. Up to May 2020, all the radiomics research studies on GISTs were performed in retrospective population, and only four studies were multicentric[31,32,35,44]. The number of included GISTs widely ranged from 15 to 440 lesions. All except one of radiomics GIST studies used CT imaging for features extraction, while only one study[36] evaluated the MRI. On CT studies, radiomics analysis was most commonly carried out on venous phase (48%), followed by arterial phase (38%), and pre-contrast images (14%) (Figure 2). No study included the delayed phase in radiomics evaluation. First, second, and third order features were extracted in 80%, 67%, and 20% of studies, respectively. Volumetric analysis (3D ROI) was performed in 60% of cases, while 2D ROIs were placed in 47% cases. Only one study[41] compared the accuracy of 2D vs 3D ROIs in GISTs, reporting an excellent agreement between the two segmentation methods.

Table 1 Summary of radiomics studies including gastrointestinal stromal tumors.
Ref.Number of lesionsImagingRadiomics analysisKey radiomics results
Ba-Ssalamah et al[30]15 GISTs, 27 gastric adenocarcinomas, 5 lymphomasCTHistogram-based, GLCM, GLRLM, absolute gradient, autoregressive model, Wavelet transform.On AP texture features perfectly differentiated between GIST vs lymphoma. On PVP texture features differentiated GIST vs adenocarcinoma in 90% of cases and GIST vs lymphoma in 92% of cases.
Chen et al[31]222 GISTsCTGLCM, GLRLM, GLSZM, NGTDM. Support Vector Machine for model building.AUROC 0.84-0.86 of radiomics models for GIST risk stratification.
Chen et al[32]147 GISTsCTResidual Neural Network for model building.AUROC of 0.887-0.947 for ResNet nomogram and model for prediction of disease-free survival after surgical resection.
Choi et al[33]145 GISTsCTHistogram-based.AUROC of 0.782-0.779 of mpp and kurtosis for differentiation of high-risk GISTs.
Ekert et al[34]25 GISTsCTHistogram-based, GLCM, GLDM, GLRLM, GLSZM, NGLDM.Ten GLCM, GLRLM, NGLDM features significantly correlated with disease progression and progression free survival.
Feng et al[35]90 GISTsCTHistogram-based.AUROC of 0.823-0.830 of entropy for the differentiation of low from high-risk GISTs.
Fu et al[36]51 GISTsMRIFractal features, GLCM, GLRLM.Texture features on DWI and ADC map correlated with overall survival in metastatic GISTs.
Liu et al[37]78 GISTsCTHistogram-based.AUROC of 0.637-0.811 for the identification of very low and low-risk GISTs.
Lu et al[38]28 GISTs, 26 DACs, 20 PDACsCTHistogram-based.AUROC of 0.809-0.936 of 90th percentile for differentiation of GISTs from DACs and PDACs.
Ren et al[39]440 GISTsCTHistogram-based, GLCM.AUROC of 0.933-0.935 for the differentiation of low from high-risk GISTs.
Wang et al[40]333 GISTsCTHistogram-based, GLCM, GLRLM.AUROC of 0.882-0.920 for the differentiation of low from high-risk GISTs. AUROC of 0.769-0.820 for the differentiation of low from high mitotic count.
Xu et al[41]86 GISTsCTHistogram-based, GLCM, GLRLM.AUROC of 0.904-0.962 of standard deviation for diagnosis of GIST without KIT exon 11 mutations.
Yan et al[42]213 GISTsCTHistogram-based, GLCM, GLRLM, absolute gradient, autoregressive model, Wavelet transform.AUROC of 0.933 of texture analysis model for preoperative risk stratification.
Zhang et al[43]140 GISTsCTHistogram-based, shape-based, GLCM, GLRLM, GLSZM.AUROC of 0.809-0.935 for discrimination of advanced GISTs and four risk categories of GISTs
Zhang et al[44]339 GISTsCTGLCM, GLRLM, GLSZM, GLDM.AUROC of 0.754-0.787 of radiomics features for prediction of high Ki67 expression.
Figure 2
Figure 2 Chart shows the frequency of computed tomography imaging phases included in radiomics gastrointestinal stromal tumors studies. Corresponding computed tomography images shows an 8.6 cm gastric gastrointestinal stromal tumor in a 64-year-old woman.

Few studies have investigated the intra- and inter-reader variability of radiomics features in GISTs, with promising results for reproducibility of tumor segmentation and features extraction. A recent study[41] described an almost perfect intra- and inter-reader reproducibility of radiomics features (reported ICC > 0.98) using both single-ROI and whole lesions-ROI manual segmentations. Other studies assessed the inter-reader variability for manual segmentation, all reporting an excellent inter-observer agreement for whole tumor radiomics parameters extracted by two abdominal radiologists (ICC ranging from 0.85 to 0.99)[35,37-39].

Validation of radiomics models in independent cohorts was performed in 47% of studies. However only three of them[31,32,44] included external validation cohorts.

Differential diagnosis between GIST and other tumors

Stomach is the most common organ affected by GISTs. The differential diagnosis should be carried with other gastric benign mesenchymal neoplasms (i.e., schwannomas and leiomyomas) or malignant tumors (i.e., gastric adenocarcinomas and lymphomas), and it may be difficult due to the overlap in imaging appearance[45-47]. Using a texture analysis approach, Ba-Ssalamah et al[30] differentiated GISTs from gastric adenocarcinomas and lymphomas with a high successful rate on arterial and venous phase CT images.

Another challenging location for the differential diagnosis of GISTs from other gastrointestinal neoplasms is the duodenum[48]. GISTs occur rarely in the duodenum (less than 5% of cases) and the differentiation from more common duodenal adenocarcinomas (DACs), pancreatic ductal adenocarcinomas (PDACs), or pancreatic neuroendocrine tumors is significantly relevant for preoperative management and patient prognosis[48,49]. To improve the preoperative characterization of these lesions, a study by Lu et al[38] investigated the whole lesion histogram analysis on contrast-enhanced CT, reporting an excellent discrimination of GISTs from DACs and PDACs in the periampullary region.

Risk stratification and prediction of prognosis of GISTs

Accurate evaluation of malignant risk and outcome in GISTs is mainly based on tumor size, location (gastric vs non-gastric tumors), and mitotic count obtained with resection specimens. These factors are combined in the National Institutes of Health 2008 criteria[50], which classified GISTs four risk classes: very low, low, intermediate and high-risk tumors. However, in clinical practice risk stratification may be limited by the evaluation of mitotic count in patients treated with neoadjuvant therapies, or by the assessment of biopsy specimens that could not be representative of the whole tumor. Therefore, several studies have tried to predict risk stratification based on preoperative CT imaging[51-53]. CT features like size, growth pattern, or enlarged feeding vessels have been associated with high-risk tumors[51-53]. Nevertheless, risk stratification using qualitative imaging evaluation is affected by the readers’ experience, heterogeneous definition of imaging features, and subjective assessment with suboptimal reproducibility of qualitative features[54].

Radiomics models have demonstrated to improve the preoperative prediction of high-risk GISTs compared to the conventional visual evaluation[33,42]. The added value of radiomics and texture analysis on contrast-enhanced CT was firstly investigated by Yan et al[42] in a retrospective cohort of 213 small bowel GISTs. In that study, texture analysis model achieved a similar diagnostic accuracy compared to that of clinical and subjective imaging features for preoperative risk prediction of GISTs[42]. When combining the clinical and texture analysis features, the diagnostic performance (AUROC of 0.943) significantly improved compared to the model incorporating clinical and imaging features only[42]. In a more recent study, Choi et al[33] investigated the diagnostic performance of histogram-based texture parameters and qualitative analysis of CT imaging features for the differentiation of low-risk from high-risk GISTs. Their results confirmed that the radiomics features showed a higher diagnostic performance (AUROC of 0.782-0.779) compared to conventional qualitative evaluation (AUROC of 0.59-0.70) by two radiologists in the differential diagnosis of low-risk from high-risk GISTs[33].

The potential of radiomics for the risk stratification in GISTs have been further evaluated by other evidences with promising results and excellent diagnostic performance[35,37,39,40,43]. Liu et al[37] applied CT-based texture analysis for the identification of very low and low risk GISTs in a cohort of 78 patients, reporting a fair diagnostic performance (AUROC of 0.637-0.811) for the most discriminant features obtained on pre-contrast, arterial and venous phases CT images. Feng et al[35] extracted histogram-based parameters from arterial and venous phase CT images of 90 small bowel GISTs. Among them, entropy showed the highest diagnostic accuracy on arterial and venous phases (AUROC of 0.823 and 0.830, respectively) for the identification of high-risk GISTs. Zhang et al[43] analyzed 140 GISTs using arterial phase CT images, reporting an excellent diagnostic performance for preoperative prediction of advanced (i.e., high and intermediate risk) GISTs and four-class risk stratification (AUROC of 0.935 and 0.809, respectively).

In a large population of 440 pathologically proven GISTs, Ren et al[39] reported an excellent performance of radiomics models for the differentiation of low-risk from high-risk GISTs (AUROC of 0.935 and 0.933 in training and validation cohort, respectively). In that study, the prediction nomogram (incorporating lesion size, cystic degeneration, and texture-based mean) demonstrated a sensitivity of 90.6% and a specificity of 75.7% for the diagnosis of high-risk GISTs[39]. Similarly, Wang et al[40] analyzed the contrast-enhanced CT images of 333 GISTs and reported an excellent discrimination capacity of radiomics models between low-risk and high-risk GISTs in both training and validation cohorts (AUROC of 0.882 and 0.920, respectively). In addition, radiomics models enable to discriminate GISTs with low and high mitotic count with a good-to-excellent performance (AUROC: 0.769-0.820)[40].

In two subsequent studies[31,32], Chen et al[31,32] built support vector machine and residual neural network based models to predict malignant potential or 3-year and 5-year recurrence-free survival after complete surgical resection of localized GISTs, respectively. In those researches, the Authors enrolled an internal patients’ cohort for training the model, which was subsequently validated in internal and external cohorts, with a good-to almost perfect performance for GIST risk stratification and prediction of recurrence free survival at 3-year and 5-year, respectively[31,32].

Survival analysis for disease progression according to texture features was carried out also by Ekert et al[34] on contrast-enhanced CT, while only one study[36] has performed radiomics analysis on MRI. Fu et al[36] extracted texture features from T2-weighted, DWI and ADC map images to determine prognosis of metastatic GISTs, reporting that texture features on DWI and ADC map well-correlated with overall survival.

Finally, Ki67 index represents a marker of proliferation of tumor cells, which have also been associated with poor prognosis in GISTs[55]. In a study of 339 GISTs[44], radiomics signature from contrast-enhanced CT have demonstrated a significant correlation with Ki67 expression, providing an added value for prognosis assessment.

Assessment of mutational status

Genetic alterations and mutational status is crucial for GISTs optimal target therapy. About 80%-85% of GISTs have mutation in KIT genes, 10% of GISTs have mutations in PDGFRα, while the remaining 10%-15% GISTs are wild type due to the lack of mutations in either of these genes[6]. Particularly, PDGFRα and wild type GISTs have a lower response rate or resistance to the target therapies with tyrosine kinase inhibitors, depending on the specific mutational status[1,6].

Few data exist regarding the association between CT imaging features and mutations in GISTs[57,58]. The performance of radiomics features and radiologists visual analysis for the differentiation of GISTs with and without KIT exon 11 mutations have been explored by Xu et al[41] in a study cohort and validation group of 69 and 17 GISTs, respectively. In that investigation, the standard deviation was strongly correlated with absence of KIT exon 11 mutations, and achieved an AUROC of 0.904-0.962. Contrarily, there was no statistically significant differences in the visual ratings of lesions heterogeneity between GISTs with and without KIT exon 11 mutations. Further researches are needed to correlate the radiomics signature with the genomics patterns of mutational status (known as radiogenomics analysis[15]) in order to provide reliable information to guide the most appropriate treatment, especially in advanced GISTs that are not suitable for surgical resection.


Although radiomics has an enormous research potential for the improvement of quantitative tumors evaluation, there are some limitations that challenges its application in everyday clinical practice. Standardization of methodology is the primary problem for radiomics analysis. Differences in imaging acquisition, features extraction, and radiomics software challenge the comparisons between studies and the repeatability or application of radiomics models in different populations. All the current published studies on radiomics of GISTs are retrospective and mostly performed in single centers. The lack of standardization in CT and MRI acquisition is another major problem for radiomics assessment of GISTs. This latter is strictly related to the rarity of GISTs compared to other neoplasms, which require collection of imaging studies obtained during a long period of time. Moreover, the peculiar histopathological characteristics of GISTs, such as mitotic count and mutational status, require pathological diagnosis through resections specimens as reference standard for radiomics studies.

The evaluation of treatment response after tyrosine kinase inhibitors therapy needs also the be further investigated. Indeed, the response to target therapy may occur even without reduction of tumor size[58]. As consequence, Choi criteria[59], based on the measurements of CT attenuation values, have been adopted for the evaluation of treatment response in patients undergoing target therapies. The added values of radiomics in the imaging evaluation of treatment response is currently underexplored and may be investigated in futures studies.

Further prospective multicentric studies will be needed to validate the optimal diagnostic performance of radiomics models provided by retrospective analysis. Future works are also warranted for optimization and standardization of radiomics software, imaging acquisition, features extraction and models analysis.


Radiomics is emerging as a promising tool for quantitative evaluation of GISTs, with excellent diagnostic performance for the differential diagnosis with other gastrointestinal neoplasms, prediction of risk stratification, and evaluation of mutational status. Future implementation of radiomics models in clinical practice may provide additional information from radiological images that will be helpful to guide patients management and more tailored treatments.


Manuscript source: Invited Manuscript

Specialty type: Gastroenterology and hepatology

Country/Territory of origin: Italy

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1.  Parab TM, DeRogatis MJ, Boaz AM, Grasso SA, Issack PS, Duarte DA, Urayeneza O, Vahdat S, Qiao JH, Hinika GS. Gastrointestinal stromal tumors: a comprehensive review. J Gastrointest Oncol. 2019;10:144-154.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 80]  [Cited by in F6Publishing: 134]  [Article Influence: 22.3]  [Reference Citation Analysis (2)]
2.  Akahoshi K, Oya M, Koga T, Shiratsuchi Y. Current clinical management of gastrointestinal stromal tumor. World J Gastroenterol. 2018;24:2806-2817.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 180]  [Cited by in F6Publishing: 183]  [Article Influence: 30.5]  [Reference Citation Analysis (8)]
3.  Plumb AA, Kochhar R, Leahy M, Taylor MB. Patterns of recurrence of gastrointestinal stromal tumour (GIST) following complete resection: implications for follow-up. Clin Radiol. 2013;68:770-775.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 9]  [Article Influence: 0.8]  [Reference Citation Analysis (0)]
4.  D'Ambrosio L, Palesandro E, Boccone P, Tolomeo F, Miano S, Galizia D, Manca A, Chiara G, Bertotto I, Russo F, Campanella D, Venesio T, Sangiolo D, Pignochino Y, Siatis D, De Simone M, Ferrero A, Pisacane A, Dei Tos AP, Aliberti S, Aglietta M, Grignani G. Impact of a risk-based follow-up in patients affected by gastrointestinal stromal tumour. Eur J Cancer. 2017;78:122-132.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 18]  [Article Influence: 2.6]  [Reference Citation Analysis (0)]
5.  Burkill GJ, Badran M, Al-Muderis O, Meirion Thomas J, Judson IR, Fisher C, Moskovic EC. Malignant gastrointestinal stromal tumor: distribution, imaging features, and pattern of metastatic spread. Radiology. 2003;226:527-532.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 299]  [Cited by in F6Publishing: 268]  [Article Influence: 12.8]  [Reference Citation Analysis (1)]
6.  Oppelt PJ, Hirbe AC, Van Tine BA. Gastrointestinal stromal tumors (GISTs): point mutations matter in management, a review. J Gastrointest Oncol. 2017;8:466-473.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 43]  [Cited by in F6Publishing: 54]  [Article Influence: 7.7]  [Reference Citation Analysis (0)]
7.  Vernuccio F, Taibbi A, Picone D, LA Grutta L, Midiri M, Lagalla R, Lo Re G, Bartolotta TV. Imaging of Gastrointestinal Stromal Tumors: From Diagnosis to Evaluation of Therapeutic Response. Anticancer Res. 2016;36:2639-2648.  [PubMed]  [DOI]  [Cited in This Article: ]
8.  Inoue A, Ota S, Sato S, Nitta N, Shimizu T, Sonoda H, Tani M, Ban H, Inatomi O, Ando A, Kushima R, Murata K. Comparison of characteristic computed tomographic findings of gastrointestinal and non-gastrointestinal stromal tumors in the small intestine. Abdom Radiol (NY). 2019;44:1237-1245.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 7]  [Cited by in F6Publishing: 10]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
9.  Bartolotta TV, Taibbi A, Galia M, Cannella I, Lo Re G, Sparacia G, Midiri M, Lagalla R. Gastrointestinal stromal tumour: 40-row multislice computed tomography findings. Radiol Med. 2006;111:651-660.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 12]  [Article Influence: 0.7]  [Reference Citation Analysis (0)]
10.  Xing GS, Wang S, Sun YM, Yuan Z, Zhao XM, Zhou CW. Small Bowel Stromal Tumors: Different Clinicopathologic and Computed Tomography Features in Various Anatomic Sites. PLoS One. 2015;10:e0144277.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 13]  [Cited by in F6Publishing: 17]  [Article Influence: 1.9]  [Reference Citation Analysis (0)]
11.  Baheti AD, Shinagare AB, O'Neill AC, Krajewski KM, Hornick JL, George S, Ramaiya NH, Tirumani SH. MDCT and clinicopathological features of small bowel gastrointestinal stromal tumours in 102 patients: a single institute experience. Br J Radiol. 2015;88:20150085.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 25]  [Cited by in F6Publishing: 30]  [Article Influence: 3.3]  [Reference Citation Analysis (0)]
12.  Scola D, Bahoura L, Copelan A, Shirkhoda A, Sokhandon F. Getting the GIST: a pictorial review of the various patterns of presentation of gastrointestinal stromal tumors on imaging. Abdom Radiol (NY). 2017;42:1350-1364.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 29]  [Cited by in F6Publishing: 30]  [Article Influence: 4.3]  [Reference Citation Analysis (0)]
13.  Yu MH, Lee JM, Baek JH, Han JK, Choi BI. MRI features of gastrointestinal stromal tumors. AJR Am J Roentgenol. 2014;203:980-991.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 48]  [Cited by in F6Publishing: 51]  [Article Influence: 5.7]  [Reference Citation Analysis (0)]
14.  Kang TW, Kim SH, Jang KM, Choi D, Ha SY, Kim KM, Kang WK, Kim MJ. Gastrointestinal stromal tumours: correlation of modified NIH risk stratification with diffusion-weighted MR imaging as an imaging biomarker. Eur J Radiol. 2015;84:33-40.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 21]  [Article Influence: 2.1]  [Reference Citation Analysis (0)]
15.  Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278:563-577.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4541]  [Cited by in F6Publishing: 4516]  [Article Influence: 564.5]  [Reference Citation Analysis (2)]
16.  Vernuccio F, Cannella R, Comelli A, Salvaggio G, Lagalla R, Midiri M. [Radiomics and artificial intelligence: new frontiers in medicine.]. Recenti Prog Med. 2020;111:130-135.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 15]  [Reference Citation Analysis (0)]
17.  Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics. 2017;37:1483-1503.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 422]  [Cited by in F6Publishing: 520]  [Article Influence: 74.3]  [Reference Citation Analysis (0)]
18.  Wakabayashi T, Ouhmich F, Gonzalez-Cabrera C, Felli E, Saviano A, Agnus V, Savadjiev P, Baumert TF, Pessaux P, Marescaux J, Gallix B. Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int. 2019;13:546-559.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 69]  [Cited by in F6Publishing: 95]  [Article Influence: 19.0]  [Reference Citation Analysis (0)]
19.  Lewis S, Hectors S, Taouli B. Radiomics of hepatocellular carcinoma. Abdom Radiol (NY). 2020;.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 25]  [Cited by in F6Publishing: 39]  [Article Influence: 13.0]  [Reference Citation Analysis (0)]
20.  Miranda Magalhaes Santos JM, Clemente Oliveira B, Araujo-Filho JAB, Assuncao-Jr AN, de M Machado FA, Carlos Tavares Rocha C, Horvat JV, Menezes MR, Horvat N. State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations. Abdom Radiol (NY). 2020;45:342-353.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 19]  [Cited by in F6Publishing: 22]  [Article Influence: 5.5]  [Reference Citation Analysis (0)]
21.  Kocak B, Durmaz ES, Erdim C, Ates E, Kaya OK, Kilickesmez O. Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies. AJR Am J Roentgenol. 2020;214:129-136.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 24]  [Cited by in F6Publishing: 37]  [Article Influence: 7.4]  [Reference Citation Analysis (0)]
22.  Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol. 2020;21:387-401.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 80]  [Cited by in F6Publishing: 68]  [Article Influence: 17.0]  [Reference Citation Analysis (0)]
23.  Varghese BA, Cen SY, Hwang DH, Duddalwar VA. Texture Analysis of Imaging: What Radiologists Need to Know. AJR Am J Roentgenol. 2019;212:520-528.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 104]  [Cited by in F6Publishing: 131]  [Article Influence: 26.2]  [Reference Citation Analysis (0)]
24.  Berenguer R, Pastor-Juan MDR, Canales-Vázquez J, Castro-García M, Villas MV, Mansilla Legorburo F, Sabater S. Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. Radiology. 2018;288:407-415.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 295]  [Cited by in F6Publishing: 368]  [Article Influence: 61.3]  [Reference Citation Analysis (0)]
25.  Ahn SJ, Kim JH, Lee SM, Park SJ, Han JK. CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts. Eur Radiol. 2019;29:4008-4015.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 14]  [Cited by in F6Publishing: 20]  [Article Influence: 3.3]  [Reference Citation Analysis (0)]
26.  Meyer M, Ronald J, Vernuccio F, Nelson RC, Ramirez-Giraldo JC, Solomon J, Patel BN, Samei E, Marin D. Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings. Radiology. 2019;293:583-591.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 152]  [Cited by in F6Publishing: 151]  [Article Influence: 30.2]  [Reference Citation Analysis (0)]
27.  Koçak B, Durmaz EŞ, Ateş E, Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol. 2019;25:485-495.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 109]  [Cited by in F6Publishing: 173]  [Article Influence: 43.3]  [Reference Citation Analysis (0)]
28.  Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749-762.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1825]  [Cited by in F6Publishing: 2750]  [Article Influence: 392.9]  [Reference Citation Analysis (0)]
29.  Park JE, Kim D, Kim HS, Park SY, Kim JY, Cho SJ, Shin JH, Kim JH. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol. 2020;30:523-536.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 108]  [Cited by in F6Publishing: 155]  [Article Influence: 31.0]  [Reference Citation Analysis (0)]
30.  Ba-Ssalamah A, Muin D, Schernthaner R, Kulinna-Cosentini C, Bastati N, Stift J, Gore R, Mayerhoefer ME. Texture-based classification of different gastric tumors at contrast-enhanced CT. Eur J Radiol. 2013;82:e537-e543.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 71]  [Cited by in F6Publishing: 69]  [Article Influence: 6.3]  [Reference Citation Analysis (0)]
31.  Chen T, Ning Z, Xu L, Feng X, Han S, Roth HR, Xiong W, Zhao X, Hu Y, Liu H, Yu J, Zhang Y, Li Y, Xu Y, Mori K, Li G. Radiomics nomogram for predicting the malignant potential of gastrointestinal stromal tumours preoperatively. Eur Radiol. 2019;29:1074-1082.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 33]  [Cited by in F6Publishing: 35]  [Article Influence: 5.8]  [Reference Citation Analysis (0)]
32.  Chen T, Liu S, Li Y, Feng X, Xiong W, Zhao X, Yang Y, Zhang C, Hu Y, Chen H, Lin T, Zhao M, Liu H, Yu J, Xu Y, Zhang Y, Li G. Developed and validated a prognostic nomogram for recurrence-free survival after complete surgical resection of local primary gastrointestinal stromal tumors based on deep learning. EBioMedicine. 2019;39:272-279.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 28]  [Cited by in F6Publishing: 26]  [Article Influence: 5.2]  [Reference Citation Analysis (0)]
33.  Choi IY, Yeom SK, Cha J, Cha SH, Lee SH, Chung HH, Lee CM, Choi J. Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection. Abdom Radiol (NY). 2019;44:2346-2356.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 20]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
34.  Ekert K, Hinterleitner C, Horger M. Prognosis assessment in metastatic gastrointestinal stromal tumors treated with tyrosine kinase inhibitors based on CT-texture analysis. Eur J Radiol. 2019;116:98-105.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 11]  [Cited by in F6Publishing: 14]  [Article Influence: 2.8]  [Reference Citation Analysis (0)]
35.  Feng C, Lu F, Shen Y, Li A, Yu H, Tang H, Li Z, Hu D. Tumor heterogeneity in gastrointestinal stromal tumors of the small bowel: volumetric CT texture analysis as a potential biomarker for risk stratification. Cancer Imaging. 2018;18:46.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 27]  [Cited by in F6Publishing: 30]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
36.  Fu J, Fang MJ, Dong D, Li J, Sun YS, Tian J, Tang L. Heterogeneity of metastatic gastrointestinal stromal tumor on texture analysis: DWI texture as potential biomarker of overall survival. Eur J Radiol. 2020;125:108825.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 14]  [Article Influence: 3.5]  [Reference Citation Analysis (1)]
37.  Liu S, Pan X, Liu R, Zheng H, Chen L, Guan W, Wang H, Sun Y, Tang L, Guan Y, Ge Y, He J, Zhou Z. Texture analysis of CT images in predicting malignancy risk of gastrointestinal stromal tumours. Clin Radiol. 2018;73:266-274.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 18]  [Cited by in F6Publishing: 18]  [Article Influence: 2.6]  [Reference Citation Analysis (0)]
38.  Lu J, Hu D, Tang H, Hu X, Shen Y, Li Z, Peng Y, Kamel I. Assessment of tumor heterogeneity: Differentiation of periampullary neoplasms based on CT whole-lesion histogram analysis. Eur J Radiol. 2019;115:1-9.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 21]  [Cited by in F6Publishing: 20]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
39.  Ren C, Wang S, Zhang S. Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors. Cancer Imaging. 2020;20:5.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 16]  [Cited by in F6Publishing: 25]  [Article Influence: 6.3]  [Reference Citation Analysis (0)]
40.  Wang C, Li H, Jiaerken Y, Huang P, Sun L, Dong F, Huang Y, Dong D, Tian J, Zhang M. Building CT Radiomics-Based Models for Preoperatively Predicting Malignant Potential and Mitotic Count of Gastrointestinal Stromal Tumors. Transl Oncol. 2019;12:1229-1236.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 25]  [Cited by in F6Publishing: 23]  [Article Influence: 4.6]  [Reference Citation Analysis (0)]
41.  Xu F, Ma X, Wang Y, Tian Y, Tang W, Wang M, Wei R, Zhao X. CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation. Eur J Radiol. 2018;107:90-97.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 21]  [Cited by in F6Publishing: 30]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
42.  Yan J, Zhao X, Han S, Wang T, Miao F. Evaluation of Clinical Plus Imaging Features and Multidetector Computed Tomography Texture Analysis in Preoperative Risk Grade Prediction of Small Bowel Gastrointestinal Stromal Tumors. J Comput Assist Tomogr. 2018;42:714-720.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 10]  [Article Influence: 1.7]  [Reference Citation Analysis (0)]
43.  Zhang L, Kang L, Li G, Zhang X, Ren J, Shi Z, Li J, Yu S. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. Radiol Med. 2020;125:465-473.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 42]  [Cited by in F6Publishing: 35]  [Article Influence: 8.8]  [Reference Citation Analysis (0)]
44.  Zhang QW, Gao YJ, Zhang RY, Zhou XX, Chen SL, Zhang Y, Liu Q, Xu JR, Ge ZZ. Personalized CT-based radiomics nomogram preoperative predicting Ki-67 expression in gastrointestinal stromal tumors: a multicenter development and validation cohort. Clin Transl Med. 2020;9:12.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 24]  [Cited by in F6Publishing: 40]  [Article Influence: 10.0]  [Reference Citation Analysis (0)]
45.  Choi JW, Choi D, Kim KM, Sohn TS, Lee JH, Kim HJ, Lee SJ. Small submucosal tumors of the stomach: differentiation of gastric schwannoma from gastrointestinal stromal tumor with CT. Korean J Radiol. 2012;13:425-433.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 50]  [Cited by in F6Publishing: 53]  [Article Influence: 4.4]  [Reference Citation Analysis (0)]
46.  Choi YR, Kim SH, Kim SA, Shin CI, Kim HJ, Kim SH, Han JK, Choi BI. Differentiation of large (≥ 5 cm) gastrointestinal stromal tumors from benign subepithelial tumors in the stomach: radiologists' performance using CT. Eur J Radiol. 2014;83:250-260.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 40]  [Cited by in F6Publishing: 45]  [Article Influence: 4.1]  [Reference Citation Analysis (0)]
47.  Huh CW, Jung DH, Kim JS, Shin YR, Choi SH, Kim BW. CT Versus Endoscopic Ultrasound for Differentiating Small (2-5 cm) Gastrointestinal Stromal Tumors From Leiomyomas. AJR Am J Roentgenol. 2019;213:586-591.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 5]  [Cited by in F6Publishing: 7]  [Article Influence: 1.4]  [Reference Citation Analysis (0)]
48.  Cai PQ, Lv XF, Tian L, Luo ZP, Mitteer RA, Fan Y, Wu YP. CT Characterization of Duodenal Gastrointestinal Stromal Tumors. AJR Am J Roentgenol. 2015;204:988-993.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 27]  [Cited by in F6Publishing: 31]  [Article Influence: 3.4]  [Reference Citation Analysis (0)]
49.  Ren S, Chen X, Wang J, Zhao R, Song L, Li H, Wang Z. Differentiation of duodenal gastrointestinal stromal tumors from hypervascular pancreatic neuroendocrine tumors in the pancreatic head using contrast-enhanced computed tomography. Abdom Radiol (NY). 2019;44:867-876.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4]  [Cited by in F6Publishing: 5]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
50.  Joensuu H. Risk stratification of patients diagnosed with gastrointestinal stromal tumor. Hum Pathol. 2008;39:1411-1419.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 699]  [Cited by in F6Publishing: 785]  [Article Influence: 49.1]  [Reference Citation Analysis (0)]
51.  Zhou C, Duan X, Zhang X, Hu H, Wang D, Shen J. Predictive features of CT for risk stratifications in patients with primary gastrointestinal stromal tumour. Eur Radiol. 2016;26:3086-3093.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 41]  [Cited by in F6Publishing: 59]  [Article Influence: 6.6]  [Reference Citation Analysis (0)]
52.  O'Neill AC, Shinagare AB, Kurra V, Tirumani SH, Jagannathan JP, Baheti AD, Hornick JL, George S, Ramaiya NH. Assessment of metastatic risk of gastric GIST based on treatment-naïve CT features. Eur J Surg Oncol. 2016;42:1222-1228.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 19]  [Cited by in F6Publishing: 22]  [Article Influence: 2.8]  [Reference Citation Analysis (0)]
53.  Li H, Ren G, Cai R, Chen J, Wu X, Zhao J. A correlation research of Ki67 index, CT features, and risk stratification in gastrointestinal stromal tumor. Cancer Med. 2018;7:4467-4474.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 27]  [Cited by in F6Publishing: 42]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
54.  Maldonado FJ, Sheedy SP, Iyer VR, Hansel SL, Bruining DH, McCollough CH, Harmsen WS, Barlow JM, Fletcher JG. Reproducible imaging features of biologically aggressive gastrointestinal stromal tumors of the small bowel. Abdom Radiol (NY). 2018;43:1567-1574.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 16]  [Cited by in F6Publishing: 19]  [Article Influence: 3.2]  [Reference Citation Analysis (0)]
55.  Basilio-de-Oliveira RP, Pannain VL. Prognostic angiogenic markers (endoglin, VEGF, CD31) and tumor cell proliferation (Ki67) for gastrointestinal stromal tumors. World J Gastroenterol. 2015;21:6924-6930.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in CrossRef: 54]  [Cited by in F6Publishing: 55]  [Article Influence: 6.1]  [Reference Citation Analysis (0)]
56.  Tateishi U, Miyake M, Maeda T, Arai Y, Seki K, Hasegawa T. CT and MRI findings in KIT-weak or KIT-negative atypical gastrointestinal stromal tumors. Eur Radiol. 2006;16:1537-1543.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 21]  [Cited by in F6Publishing: 23]  [Article Influence: 1.3]  [Reference Citation Analysis (0)]
57.  Yin YQ, Liu CJ, Zhang B, Wen Y, Yin Y. Association between CT imaging features and KIT mutations in small intestinal gastrointestinal stromal tumors. Sci Rep. 2019;9:7257.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 14]  [Article Influence: 2.8]  [Reference Citation Analysis (0)]
58.  Apfaltrer P, Meyer M, Meier C, Henzler T, Barraza JM, Dinter DJ, Hohenberger P, Schoepf UJ, Schoenberg SO, Fink C. Contrast-enhanced dual-energy CT of gastrointestinal stromal tumors: is iodine-related attenuation a potential indicator of tumor response? Invest Radiol. 2012;47:65-70.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 97]  [Cited by in F6Publishing: 103]  [Article Influence: 8.6]  [Reference Citation Analysis (0)]
59.  Choi H, Charnsangavej C, Faria SC, Macapinlac HA, Burgess MA, Patel SR, Chen LL, Podoloff DA, Benjamin RS. Correlation of computed tomography and positron emission tomography in patients with metastatic gastrointestinal stromal tumor treated at a single institution with imatinib mesylate: proposal of new computed tomography response criteria. J Clin Oncol. 2007;25:1753-1759.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1094]  [Cited by in F6Publishing: 1067]  [Article Influence: 62.8]  [Reference Citation Analysis (0)]