scholarly journals CT Radiomics for the Preoperative Prediction of Ki67 Index in Gastrointestinal Stromal Tumors: A Multi-Center Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Yilei Zhao ◽  
Meibao Feng ◽  
Minhong Wang ◽  
Liang Zhang ◽  
Meirong Li ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Gastrointestinal Stromal Tumors ◽  
External Validation ◽  
Region Of Interest ◽  
Feature Subset ◽  
Ki67 Index ◽  
Training Set ◽  
Stromal Tumors ◽  
Preoperative Prediction ◽  
Multi Center Study

PurposeThis study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs).Materials and MethodsA total of 344 patients with GISTs from three hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model’s performance and generalization ability.ResultsAfter dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761–0.908) in the training set and 0.784 (95% CI: 0.691–0.874) in the external validation cohort.ConclusionThe radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.

scholarly journals Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Bing Kang ◽  
Xianshun Yuan ◽  
Hexiang Wang ◽  
Songnan Qin ◽  
Xuelin Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Gastrointestinal Stromal Tumors ◽  
External Validation ◽  
Learning Model ◽  
Ct Images ◽  
Stromal Tumors ◽  
Contrast Enhanced Ct ◽  
Malignant Gists ◽  
Deep Learning Model

ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.

scholarly journals Phosphohistone H3 Immunohistochemical Labeling: a Potentially Useful Tool for Risk Stratification and Prognostic Analysis in Gastrointestinal Stromal Tumors.

2021 ◽  
Author(s):  
Xiaohong Li ◽  
Yutao Zhang ◽  
Feng Li ◽  
Yun Tang ◽  
Hongyuan Zhou
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Stromal Tumors ◽  
Ki67 Index ◽  
Stromal Tumors ◽  
Prognostic Analysis ◽  
Phosphohistone H3 ◽  
Primary Location ◽  
Risk Grade ◽  
Positive Index ◽  
Expression Status

Abstract BackgroundIt is well recognized that risk stratification of gastrointestinal stromal tumors (GISTs) is closely related to tumor size, mitotic index (MI), and primary location. Among these three parameters, tumor size and primary location are easily established, while MI is subjective and its repeatability is poor. It is thus necessary to identify a biomarker to represent the true MI. Expression status and biological or prognostic significance of mitotic marker phosphohistone H3 (PHH3) and cell proliferation marker Ki67 in GIST have not been clearly understood until now. MethodsAn immunohistochemistry experiment was performed to detect the expression status of PHH3 and Ki67 in 125 paraffin-embedded GIST samples. All of the patients were followed up until September 30, 2019. ResultsThe MI determined using stained hematoxylin and eosin (H&E) sections (MI-H&E) and immunohistochemically positive PHH3 index (PHH3-IHC) was compared among groups of different genders, ages, primary locations, and histological subtypes, showing that the difference was not statistically significant (P>0.05). MI-H&E and the immunohistochemically positive Ki67 index were positively correlated (r=0.273, P=0.001), but the correlation was lower than that with the PHH3-positive index (r=0.705, P=0.000). The PHH3-positive index was also positively correlated with the Ki67 index (r=0.224, P=0.006). MI-H&E were positively correlated with MI quantified using immunohistochemically stained PHH3 sections (MI-PHH3) (P<0.05). After using PHH3 to perform MI quantification, the risk stratification of five GIST cases was changed, where two cases were given a higher risk grade and three cases were given a lower risk grade. Follow-up data were obtained from 98 cases (98/125, 78.4%), including two cases of metastasis and one death. Both metastatic and death cases had high MI-H&E. One metastatic case and one death case had high PHH3-positive indexes, while the remaining metastatic case had a low PHH3-positive index. ConclusionImmunohistochemical PHH3 labeling is a potentially useful tool for risk stratification and prognostic analysis in GIST. Using immunohistochemical PHH3 labeling makes it more convenient for pathologists to determine the MI for GIST. MI quantification with immunohistochemical PHH3 sections can be used as an adjunct tool for risk stratification and prognostic analysis of GIST, but cannot completely replace MI quantification using stained H&E sections. The Ki67 index is positively correlated with MI-H&E, although the efficiency of tumor risk stratification is lower than that of PHH3.

scholarly journals Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

2020 ◽  
Author(s):  
Bo Liu ◽  
Hexiang Wang ◽  
Shunli Liu ◽  
Shifeng Yang ◽  
Yancheng Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastrointestinal Stromal Tumors ◽  
Ct Images ◽  
Arterial Phase ◽  
Training Set ◽  
Clinical Model ◽  
Stromal Tumors ◽  
Contrast Enhanced ◽  
Validation Set ◽  
Exon 11

Abstract Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors.Methods We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (D-phase), and combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval (CI), 0.640–0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397–0.790), 0.715 (95% CI, 0.506–0.873), and 0.679 (95% CI, 0.468–0.847), respectively, with the validation set.Conclusion The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

Association of ki67 Index with Recurrence in Gastrointestinal Stromal Tumors

2018 ◽  
Vol 49 (4) ◽  
pp. 543-547 ◽  
Author(s):  
Patricia Segales-Rojas ◽  
Leonardo S. Lino-Silva ◽  
Eduardo Aguilar-Cruz ◽  
Rosa A. Salcedo-Hernández
Keyword(s):  
Gastrointestinal Stromal Tumors ◽  
Ki67 Index ◽  
Stromal Tumors

scholarly journals Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors

2021 ◽  
Vol 11 ◽  
Author(s):  
Meihua Shao ◽  
Zhongfeng Niu ◽  
Linyang He ◽  
Zhaoxing Fang ◽  
Jie He ◽  
...  
Keyword(s):  
Clinical Features ◽  
Gastrointestinal Stromal Tumors ◽  
Roc Curves ◽  
Ct Images ◽  
Training Data ◽  
Training Set ◽  
Data Set ◽  
Stromal Tumors ◽  
Risk Rating ◽  
Validation Set

We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features that were extracted from a lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models, and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and model 3 yielded an accuracy of 88.3%, 85.8%, 86.4%, and 84.6%, a sensitivity of 85.4%, 84.2%, 76.8%, and 78.0%, and a specificity of 91.2%, 87.5%, 96.2%, and 91.2% in the training set and an accuracy of 88.4%, 84.1%, 82.6%, and 82.6%, a sensitivity of 88.6%, 77.1%, 74.3%, and 85.7%, and a specificity of 88.2%, 91.2%, 91.2%, and 79.4% in the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the training data set (Delong test, p &lt; 0.05). The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.

scholarly journals Computed-Tomography-Based Radiomics Model for Predicting the Malignant Potential of Gastrointestinal Stromal Tumors Preoperatively: A Multi-Classifier and Multicenter Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Minhong Wang ◽  
Zhan Feng ◽  
Lixiang Zhou ◽  
Liang Zhang ◽  
Xiaojun Hao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Risk Stratification ◽  
Gastrointestinal Stromal Tumors ◽  
Validation Cohort ◽  
External Validation ◽  
Random Forest Model ◽  
Training Cohort ◽  
Stromal Tumors ◽  
Forest Model

Background: Our goal was to establish and verify a radiomics risk grading model for gastrointestinal stromal tumors (GISTs) and to identify the optimal algorithm for risk stratification.Methods: We conducted a retrospective analysis of 324 patients with GISTs, the presence of which was confirmed by surgical pathology. Patients were treated at three different hospitals. A training cohort of 180 patients was collected from the largest center, while an external validation cohort of 144 patients was collected from the other two centers. To extract radiomics features, regions of interest (ROIs) were outlined layer by layer along the edge of the tumor contour on CT images of the arterial and portal venous phases. The dimensionality of radiomic features was reduced, and the top 10 features with importance value above 5 were selected before modeling. The training cohort used three classifiers [logistic regression, support vector machine (SVM), and random forest] to establish three GIST risk stratification prediction models. The receiver operating characteristic curve (ROC) was used to compare model performance, which was validated by external data.Results: In the training cohort, the average area under the curve (AUC) was 0.84 ± 0.07 of the logistic regression, 0.88 ± 0.06 of the random forest, and 0.81 ± 0.08 of the SVM. In the external validation cohort, the AUC was 0.85 of the logistic regression, 0.90 of the random forest, and 0.80 of the SVM. The random forest model performed the best in both the training and the external validation cohorts and could be generalized.Conclusion: Based on CT radiomics, there are multiple machine-learning models that can predict the risk of GISTs. Among them, the random forest algorithm had the highest prediction efficiency and could be readily generalizable. Through external validation data, we assume that the random forest model may be used as an effective tool to guide preoperative clinical decision-making.

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