Dataset Information

Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach.

ABSTRACT: Treatment planning of gastrointestinal stromal tumors (GISTs) includes distinguishing GISTs from other intra-abdominal tumors and GISTs' molecular analysis. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status, and mitotic index (MI). Patients diagnosed at the Erasmus MC between 2004 and 2017, with GIST or non-GIST intra-abdominal tumors and a contrast-enhanced venous-phase CT, were retrospectively included. Tumors were segmented, from which 564 image features were extracted. Prediction models were constructed using a combination of machine learning approaches. The evaluation was performed in a 100 × random-split cross-validation. Model performance was compared to that of three radiologists. One hundred twenty-five GISTs and 122 non-GISTs were included. The GIST vs. non-GIST radiomics model had a mean area under the curve (AUC) of 0.77. Three radiologists had an AUC of 0.69, 0.76, and 0.84, respectively. The radiomics model had an AUC of 0.52 for c-KIT, 0.56 for c-KIT exon 11, and 0.52 for the MI. The numbers of PDGFRA, BRAF, and other c-KIT mutations were too low for analysis. Our radiomics model was able to distinguish GISTs from non-GISTs with a performance similar to three radiologists, but less observer dependent. Therefore, it may aid in the early diagnosis of GIST, facilitating rapid referral to specialized treatment centers. As the model was not able to predict any genetic or molecular features, it cannot aid in treatment planning yet.

SUBMITTER: Starmans MPA

PROVIDER: S-EPMC8921463 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach.

Starmans Martijn P A MPA Timbergen Milea J M MJM Vos Melissa M Renckens Michel M Grünhagen Dirk J DJ van Leenders Geert J L H GJLH Dwarkasing Roy S RS Willemssen François E J A FEJA Niessen Wiro J WJ Verhoef Cornelis C Sleijfer Stefan S Visser Jacob J JJ Klein Stefan S

Journal of digital imaging 20220127 2

Treatment planning of gastrointestinal stromal tumors (GISTs) includes distinguishing GISTs from other intra-abdominal tumors and GISTs' molecular analysis. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status, and mitotic index (MI). Patients diagnosed at the Erasmus MC between 2004 and 2017, with GIST or non-GIST intra-abdominal tumors and a contrast-enhanced venous-phase CT, ...[more]

PMID: 35088185

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Project description:The objective of the study was to investigate whether special stains can differentiate gastrointestinal stromal tumors (GISTs) and gastrointestinal leiomyomas (GILs). In this retrospective study, 39 cases of GISTs (diameter, 0.2-8.8 cm) and 75 cases of GILs (diameter, 0.2-4.5 cm) were recruited, all biopsy specimens were obtained by endoscopic submucosal dissection (ESD) and endoscopic mucosal resection (EMR) excision, and the depth of excision included the whole mucosa, mucosal myometria, and most submucosa. GISTs and GILs were the most common types of mesenchymal tumors found anywhere along the gastrointestinal (GI) tract, from the esophagus to the rectum. GISTs were often associated with a higher risk of malignancy. In this study, the gender, age of onset, size and sites of the lesions, together with the number of mucosal or lamina propria lesions all have significant differences, nevertheless, there was no significant difference in cell morphology of GISTs and GILs tested by hematoxylin eosin (H&E) stain, and all showed low echo areas by EUS examination. In this retrospective study, the GISTs and GILs had been diagnosed by immunohistochemistry combined with clinical morphology. Subsequently, special stains including Masson's trichrome (MT) stain, Alcian blue periodic acid-Schiff (AB-PAS) stain (pH 2.5), Wright-Giemsa (W-G) stain and periodic acid-Schiff (PAS) combined with diastase periodic acid-Schiff (D-PAS) stains were also applied in the diagnosis, the retrospective study results showed that 92.3% GISTs were stained blue with MT stain, 97.3% GILs were stained red with MT stain (P < 0.01), almost all GISTs were PAS-negative (light purple), in contrast, all GILs were PAS-positive (rose red) (P < 0.01), all of these experiments set control using the blood vessels stained by MT and AB-PAS stains. Nevertheless, there was no significant difference between GISTs and GILs stained by W-G stain. These obvious and meaningful differential results were also confirmed in the detection of new GISTs and GILs cases using MT and AB-PAS stains. In conclusion, MT and AB-PAS stains could also identify GISTs and GILs cases, particularly, AB-PAS was more sensitive and more specific, providing a more cost-effective, simple, and high sensitivity and specificity inspection methods, which should be noticed and widely used in the future, especially in resource-limited grass-roots testing institution or in cases with inconclusive immunostains or insufficient material.

Project description:BackgroundMutation screening for gastrointestinal stromal tumor (GIST) is crucial and the c kit gene (KIT) exon 11 mutation is the most common type. This study aimed to explore the associations between GIST with KIT exon 11 mutation and contrast-enhanced computed tomography (CT) images.MethodsPathologically proven GISTs with definitive genotype testing results in our hospital were retrospectively included. Abdominal contrast-enhanced CT images were analyzed. Conventional CT image features and radiomic features were recorded and extracted to build the following models: model [CT], model [radiomic + clinic] and model [CT + radiomic + clinic]. The diagnostic performances of GISTs with KIT exon 11 mutation and KIT exon 11 deletion involving codons 557-558 were evaluated.ResultsIn total, 327 GISTs (255 with KIT exon 11 mutation, and 73 with KIT exon 11 mutation deletion involving codons 557-558) were included. Significant CT features were found for GISTs with KIT exon 11 mutation. The area under curves (AUCs) of the models for KIT exon 11 mutation were 0.7158, 0.7530, and 0.8375 in the training cohort, and 0.6777, 0.7349, and 0.8105 in validation cohort, respectively. The AUCs of the models for KIT exon 11 mutation deletion involving codons 557-558 were 0.7155, 8621, and 0.8691 in the training cohort, and 0.7099, 0.8355, and 0.8488 in the validation cohort, respectively. The model [CT + radiomic + clinic] demonstrated the highest AUCs for prediction of KIT exon 11 mutation and those with deletion involving codons 557-558 (P<0.05), respectively. The model [radiomic + clinic] showed higher diagnostic performance than model [CT] significantly.ConclusionsOur results demonstrated the associations between GIST with KIT exon 11 mutation and contrast-enhanced CT images. We found combing conventional image analysis and texture analysis is a useful tool to distinguish GIST with KIT exon 11 mutation. CT radiogenomics exhibited good application potential in predict the KIT exon 11 mutation of GIST.

Project description:BackgroundGastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Recent advent of tyrosine kinase inhibitors (TKIs) has significantly improved the prognosis of GIST patients. However, responses to TKI therapy can vary depending on the specific gene mutation. D842V, which is the most common mutation in platelet-derived growth factor receptor alpha exon 18, shows no response to imatinib and sunitinib. Radiomics features based on venous-phase contrast-enhanced computed tomography (CECT) have shown potential in non-invasive prediction of GIST genotypes. This study sought to determine whether radiomics features could help distinguish GISTs with D842V mutations.MethodsA total of 872 pathologically confirmed GIST patients with CECT data available from three independent centers were included and divided into the training cohort ( n=487 ) and the external validation cohort ( n=385 ). Clinical features including age, sex, tumor size and location were collected. Radiomics features on the largest axial image of venous-phase CECT were analyzed and a total of two radiomics features were selected after feature selection. Random forest models trained on non-radiomics features only (the non-radiomics model) and on both non-radiomics and radiomics features (the combined model) were compared.ResultsThe combined model showed better average precision (0.250 vs. 0.102, p = 0.039) and F1 score (0.253 vs. 0.155, p = 0.012) than the non-radiomics model. There was no significant difference in ROC-AUC (0.728 vs. 0.737, p = 0.836) and geometric mean (0.737 vs. 0.681, p = 0.352).ConclusionsThis study demonstrated the potential of radiomics features based on venous-phase CECT images to identify D842V mutation in GISTs. Our model may provide an alternative approach to guide TKI therapy for patients inaccessible to sequence variant testing, potentially improving treatment outcomes for GIST patients especially in resource-limited settings.

Project description:BackgroundWe conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).MethodsForty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal-Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.ResultsThe high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).ConclusionsRadiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.

Dataset Information

Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach.

Publications

Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach.

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