MRI Texture-Based Models for Predicting Mitotic Index and Risk Classification of Gastrointestinal Stromal Tumors

How to update esophageal masses imaging using literature review (MRI and CT features)

MRI offers new opportunities for detailed visualization of the different layers of the esophageal wall, as well as early detection and accurate characterization of esophageal lesions. Staging of esophageal tumors including extramural extent of disease, and status of the adjacent organ can also be performed by MRI with higher accuracy compared to other imaging modalities including CT and esophageal endoscopy. Although MDCT appears to be the primary imaging modality that is indicated for preoperative staging of esophageal cancer to assess tumor resectability, MDCT is considered less accurate in T staging. This review aims to update radiologists about emerging imaging techniques and the imaging features of various esophageal masses, emphasizing the imaging features that differentiate between esophageal masses, demonstrating the critical role of MRI in esophageal masses. CRITICAL RELEVANCE STATEMENT: MRI features may help differentiate mucosal high-grade neoplasia from early invasive squamous cell cancer of the esophagus, also esophageal GISTs from leiomyomas, and esophageal malignant melanoma has typical MR features. KEY POINTS: MRI can accurately visualize different layers of the esophagus potentially has a role in T staging. MR may accurately delineate esophageal fistulae, especially small mediastinal fistulae. MRI features of various esophageal masses are helpful in the differentiation.

Predicting the progression-free survival of gastrointestinal stromal tumors after imatinib therapy through multi-sequence magnetic resonance imaging

PURPOSE

Identify radiomics features associated with progression-free survival (PFS) and develop a predictive model for accurate PFS prediction in liver metastatic gastrointestinal stromal tumor patients (GIST).

METHODS

This multi-center retrospective study involved a comprehensive review of clinical and imaging data pertaining to 211 patients with gastrointestinal stromal tumors (GIST) from Center A and B. A total of 147 patients with hepatic metastatic GIST were included, with 102 cases as the training set and 45 cases as the external validation set. Radiomics features were extracted from non-enhanced MR images, specifically T2WI, DWI, and ADC, and relevant features were selected through LASSO-Cox regression. A radiomics nomogram model was then constructed using multivariable Cox regression analysis to effectively predict PFS. The models performance were evaluated with the concordance index (C-index).

RESULTS

The median age of the patients was 53 years, with 82 males and 65 females. A total of 21 radiomics features were selected to generate the radiomics signature. Radiomics signature slightly outperformed the clinical model but without significant difference (P > 0.05). Integrated radiomics signature with clinical features to build a nomogram, which exhibited high predictive performance in both training (C-index 0.757, 95% CI 0.692-0.822) and validation cohorts (C-index 0.718, 95% CI 0.618-0.818). Nomogram significantly outperformed the clinical model (P = 0.002 for training cohort, P < 0.001 for validation cohort). Stable long-term predictions shown by time-dependent ROC analysis (AUC 0.765-0.919 in training, 0.766-0.893 in validation). Multivariable Cox regression confirmed radiomics signature as an independent prognostic factor for preoperative survival prediction in hepatic metastatic GIST patients (HR = 3.973).

CONCLUSION

Radiomics signature is valuable for predicting PFS in metastatic GIST patients. Integrating imaging features and clinical factors into a comprehensive nomogram improves accuracy and effectiveness of survival prognosis, guiding personalized treatment strategies.

Computed tomography texture-based models for predicting KIT exon 11 mutation of gastrointestinal stromal tumors

Background

KIT exon 11 mutation in gastrointestinal stromal tumors (GISTs) is associated with treatment strategies. However, few studies have shown the role of imaging-based texture analysis in KIT exon 11 mutation in GISTs. In this study, we aimed to show the association between computed tomography (CT)-based texture features and KIT exon 11 mutation.

Methods

Ninety-five GISTs confirmed by surgery and identified with mutational genotype of KIT were included in this study. By amplifying the samples using over-sampling technique, a total of 183 region of interest (ROI) segments were extracted from 63 patients as training cohort. The 63 new ROI segments were extracted from the 63 patients as internal validation cohort. Thirty-two patients who underwent KIT exon 11 mutation test during 2021-2023 was selected as external validation cohort. The textural parameters were evaluated both in training cohort and validation cohort. Least absolute shrinkage and selection operator (LASSO) algorithms and logistic regression analysis were used to select the discriminant features.

Results

Three of textural features were obtained using LASSO analysis. Logistic regression analysis showed that patients' age, tumor location and radiomics features were significantly associated with KIT exon 11 mutation (p < 0.05). A nomogram was developed based on the associated factors. The area under the curve (AUC) of clinical features, radiomics features and their combination in training cohort was 0.687 (95 % CI: 0.604-0.771), 0.829 (95 % CI: 0.768-0.890) and 0.874 (95 % CI: 0.822-0.926), respectively. The AUC of radiomics features in internal validation cohort and external cohort was 0.880 (95 % CI: 0.796-0.964) and 0.827 (95%CI: 0.667-0.987), respectively.

Conclusion

The CT texture-based model can be used to predict KIT exon 11 mutation in GISTs.

Radiomics in gastrointestinal stromal tumours: an up-to-date review

Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.

Predictive Value of a Radiomics Nomogram Model Based on Contrast-Enhanced Computed Tomography for KIT Exon 9 Gene Mutation in Gastrointestinal Stromal Tumors

OBJECTIVES

To establish and validate a radiomics nomogram model for preoperative prediction of KIT exon 9 mutation status in patients with gastrointestinal stromal tumors (GISTs).

MATERIALS AND METHODS

Eighty-seven patients with pathologically confirmed GISTs were retrospectively enrolled in this study. Imaging and clinicopathological data were collected and randomly assigned to the training set (n = 60) and test set (n = 27) at a ratio of 7:3. Based on contrast-enhanced CT (CE-CT) arterial and venous phase images, the region of interest (ROI) of the tumors were manually drawn layer by layer, and the radiomics features were extracted. The intra-class correlation coefficient (ICC) was used to test the consistency between observers. Least absolute shrinkage and selection operator regression (LASSO) were used to further screen the features. The nomogram of integrated radiomics score (Rad-Score) and clinical risk factors (extra-gastric location and distant metastasis) was drawn on the basis of multivariate logistic regression. The area under the receiver operating characteristic (AUC) curve and decision curve analysis were used to evaluate the predictive efficiency of the nomogram, and the clinical benefits that the decision curve evaluation model may bring to patients.

RESULTS

The selected radiomics features (arterial phase and venous phase features) were significantly correlated with the KIT exon 9 mutation status of GISTs. The AUC, sensitivity, specificity, and accuracy in the radiomics model were 0.863, 85.7%, 80.4%, and 85.0% for the training group (95% confidence interval [CI]: 0.750-0.938), and 0.883, 88.9%, 83.3%, and 81.5% for the test group (95% CI: 0.701-0.974), respectively. The AUC, sensitivity, specificity, and accuracy in the nomogram model were 0.902 (95% confidence interval [CI]: 0.798-0.964), 85.7%, 86.9%, and 91.7% for the training group, and 0.907 (95% CI: 0.732-0.984), 77.8%, 94.4%, and 88.9% for the test group, respectively. The decision curve showed the clinical application value of the radiomic nomogram.

CONCLUSION

The radiomics nomogram model based on CE-CT can effectively predict the KIT exon 9 mutation status of GISTs and may be used for selective gene analysis in the future, which is of great significance for the accurate treatment of GISTs.

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.

Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia

Objective

Prostate cancer and hyperplasia require different treatment strategies and have completely different outcomes; thus, preoperative identification of prostate cancer and hyperplasia is very important. The purpose of this study was to evaluate the application value of magnetic resonance imaging (MRI)-derived radiomic nomogram based on T2-weighted images (T2WI) in differentiating prostate cancer and hyperplasia.

Materials and Methods

One hundred forty-six patients (66 cases of prostate cancer and 80 cases of prostate hyperplasia) who were confirmed by surgical pathology between September 2019 and September 2019 were selected. We manually delineated T2WI of all patients using ITK-SNAP software and radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the effective features were selected using the LASSO algorithm, and the radiomic feature model was constructed. Next, combined with independent clinical risk factors, a multivariate Logistic regression model was used to establish a radiomic nomogram. The receiver operator characteristic (ROC) curve was used to evaluate the prediction performance of the radiomic nomogram. Finally, the clinical application value of the nomogram was evaluated by decision curve analysis.

Results

The PSA and the selected imaging features were significantly correlated with the differential diagnosis of prostate cancer and hyperplasia. The radiomic model had good discrimination efficiency for prostate cancer and hyperplasia. The training set (AUC = 0.85; 95% CI: 0.77–0.92) and testing set (AUC = 0.84; 95% CI: 0.72–0.96) were effective. The radiomic nomogram, combined with the radiomic characteristics of MRI and independent clinical risk factors, showed better differentiation efficiency in the training set (AUC = 0.91; 95% CI: 0.85–0.97) and testing set (AUC = 0.90; 95% CI: 0.81–0.99). The decision curve showed the clinical application value of the radiomic nomogram.

Conclusion

The radiomic nomogram of T2-MRI combined with clinical risk factors can easily identify prostate cancer and hyperplasia. It also provides suggestions for further clinical events.

Combined model based on enhanced CT texture features in liver metastasis prediction of high-risk gastrointestinal stromal tumors

PURPOSE

To investigate the use of the combined model based on clinical and enhanced CT texture features for predicting the liver metastasis of high-risk gastrointestinal stromal tumors (GISTs).

METHODS

This retrospective study was conducted including 204 patients with pathologically confirmed high-risk GISTs from the Zhejiang Cancer Hospital from January 2015 to June 2021, and 76 cases of them were diagnosed with simultaneous liver metastasis. We randomly divided the cohort into a training cohort (n = 142) and a validation cohort (n = 62) with a ratio of 7:3. All volumes of interest (VOIs) of the high-risk GISTs were manually segmented on the portal venous phase CT images using the ITK-SNAP software. The least absolute shrinkage and selection operator (Lasso) algorithm was performed to determine the most valuable features from a total of 110 texture features extracted by the A-K software to reflect the texture information of the given VOIs. Texture-based predictive model was built from the selected texture features. Independent clinical risk factors were identified through univariate logistic analysis. Then, the texture-based model incorporated the clinical predictors to develop a combined model by multivariate logistic regression. Receiver operating characteristic curve, calibration curve, and decision curve analysis were utilized to analyze the discrimination capacity and clinical application value of the predictive models.

RESULTS

The nine optimal texture features were remained after the reduction of dimension using Lasso method. Another four clinical parameters (BMI, location, gastrointestinal bleeding, and CA125 level) were included in the clinical-based predictive model. Finally, with the combination of remaining texture and clinical features, a multivariate logistic regression classifier was built to predict the liver metastasis potential of high-risk GISTs. The remarkable classification performance of the combined model for the prediction of liver metastasis in the subjects with high-risk GISTs was obtained with area under curve (AUC) = 0.919, sensitivity = 83.9%, specificity = 89.7%, and accuracy = 84.9% in our validation group.

CONCLUSION

The texture-based radiomic signature derived from the portal venous phase CT images could predict liver metastasis of high-risk GISTs in a non-invasive way. Integrating additional clinical variables into the model further leads to an improvement of liver metastasis risk prediction.

MRI-Based Radiomics Models for Predicting Risk Classification of Gastrointestinal Stromal Tumors

Background

We conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).

Methods

Forty-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.

Results

The 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).

Conclusions

Radiomics 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.

Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images

PURPOSE

Our objective was to establish a random forest model and to evaluate its predictive capability of the treatment effect of neoadjuvant chemotherapy-radiation therapy.

METHODS

This retrospective study included 82 patients with locally advanced cervical cancer who underwent scanning from March 2013 to May 2018. The random forest model was established and optimised based on the open source toolkit scikit-learn. Byoptimising of the number of decision trees in the random forest, the criteria for selecting the final partition index and the minimum number of samples partitioned by each node, the performance of random forest in the prediction of the treatment effect of neoadjuvant chemotherapy-radiation therapy on advanced cervical cancer (> IIb) was evaluated.

RESULTS

The number of decision trees in the random forests influenced the model performance. When the number of decision trees was set to 10, 25, 40, 55, 70, 85 and 100, the performance of random forest model exhibited an increasing trend first and then a decreasing one. The criteria for the selection of final partition index showed significant effects on the generation of decision trees. The Gini index demonstrated a better effect compared with information gain index. The area under the receiver operating curve for Gini index attained a value of 0.917.

CONCLUSION

The random forest model showed potential in predicting the treatment effect of neoadjuvant chemotherapy-radiation therapy based on high-resolution T2WIs for advanced cervical cancer (> IIb).