This study aimed to construct a Nomogram based on preoperative CT features to predict the mitotic index in gastrointestinal stromal tumors and to establish preoperative risk stratification. The constructed nomogram prediction model is targeted towards guiding preoperative risk stratification, facilitating the provision of rational drug administration regimens, and tailoring appropriate surgical plans for personalized treatment. The imaging and pathological data of 250 patients with gastrointestinal stromal tumors in Shanxi Provincial hospital from January 2019 to January 2024 were retrospectively analyzed. According to the pathological data, the patients were divided into high mitotic index and low mitotic index, and were divided into a training group (n = 176) and a validation group (n = 74) according to a stratified sampling ratio of 7:3. In the training group, statistically significant variables were screened out by univariate analysis for multivariate logistic regression analysis, and independent risk factors were screened out and a Nomogram prediction model was constructed. The receiver operating characteristic (ROC) was used to evaluate the model discrimination, and the predicted probability risk was stratified by the optimal cutoff value. The Hosmer-Lemeshow test (HL test) was performed, and the calibration curve was drawn by Bootstrap repeated sampling 1000 times to evaluate the model consistency. Finally, the clinical application value of the prediction model was evaluated by the decision curve analysis (DCA). There were no significant differences in the distribution of clinical characteristics and CT features between the training group and the validation group ( P>0.05). Univariate analysis showed that the differences in tumor size, tumor site, boundary, calcification, liquefaction/necrosis, morphological characteristics, growth pattern, and ulceration were statistically significant (P<0.05). Multivariate logistic regression analysis screened out tumor size (GIST ≤ 2 cm, P = 0.018; GIST 2-5 cm, p = 0.009; GIST 5-10 cm, P = 0.017), liquefaction/necrosis (P = 0.002), and morphological characteristics (P = 0.002) as independent risk factors for high mitotic index. The Nomogram was established based on these three factors. The area under the curve (AUC) of the training group and the validation group of the model were 0.851 (95%CI: 0.793-0.91) and 0.836 (95%CI: 0.735-0.937), the specificity was 0.696 and 0.735, and the sensitivity was 0.869 and 0.760, respectively. The HL test had good calibration (training group P = 0.461, validation group P = 0.822), indicating that the predicted risk was consistent with the actual risk. The DCA also showed good clinical practicality. The Nomogram prediction model that incorporates preoperative CT features of tumor size, liquefaction/necrosis, and morphological characteristics can effectively predict the number of mitotic figures in gastrointestinal stromal tumors, and can perform effective preoperative risk stratification to guide clinical decision-making and personalized treatment.

Risk assessment of gastric gastrointestinal stromal tumors (GISTs), particularly those with a diameter ≤ 5 cm, remains a clinical challenge. Previous research has primarily focused on tumor size, ulceration, necrosis, and enhancement patterns, with less emphasis on the role of tumor calcification, which remains controversial regarding its correlation with malignancy risk.

This study aims to explore the characteristics of calcification in gastric GISTs and its correlation with risk stratification as defined by the National Institutes of Health (NIH), to improve preoperative risk assessment for gastric GISTs ≤ 5 cm.

A retrospective analysis of 385 pathologically confirmed gastric GIST patients, including 178 with small gastric GISTs (< 2 cm), was conducted. Tumors were categorized into low-risk (very low / low) and high-risk (intermediate / high) groups based on NIH criteria. Variables such as age, gender, tumor long-axis diameter, calcification rates, calcification size, the number and distribution of calcification, calcification to tumor long-axis diameter ratio were analyzed. Logistic regression was used to identify independent predictors of malignancy for gastric GISTs, with predictive values assessed via receiver operating characteristic (ROC) curves.

Significant differences were found between high-risk and low-risk groups in treatment methods, tumor long-axis diameter, the ratio of calcification to tumor long-axis, and calcification distribution (P < 0.05). Calcification rates varied across risk categories, with 23.6% in very low-risk, 31.6% in low-risk, 9.8% in intermediate-risk, and 31.7% in high-risk categories (P < 0.05). In GISTs ≤ 5 cm, both tumor long-axis diameter (OR = 3.07, 95% CI: 2.29-4.10) and calcification (OR = 0.36, 95% CI: 0.13-0.97) were independent predictors of malignancy risk (both P < 0.05). ROC curve analysis yielded areas of 0.849 for tumor long-axis diameter, 0.578 for calcification, and 0.862 for their combination.

The study indicates lower calcification rates in intermediate-risk gastric GISTs and higher rates in other risk categories. Additionally, tumors of different sizes exhibit two distinct calcification patterns, suggesting possible differing mechanisms of calcification in tumors. Calcification in gastric GISTs ≤ 5 cm acts as a protective factor against higher malignancy risk, and when combined with tumor long-axis diameter, significantly enhances predictive accuracy over long-axis diameter alone.

Gastrointestinal stromal tumors (GIST) are prevalent neoplasm originating from the gastrointestinal mesenchyme. Approximately 50% of GIST patients experience tumor recurrence within 5 years. Thus, there is a pressing need to accurately evaluate risk stratification preoperatively.

To assess the application of a deep learning model (DLM) combined with computed tomography features for predicting risk stratification of GISTs.

Preoperative contrast-enhanced computed tomography (CECT) images of 551 GIST patients were retrospectively analyzed. All image features were independently analyzed by two radiologists. Quantitative parameters were statistically analyzed to identify significant predictors of high-risk malignancy. Patients were randomly assigned to the training (n = 386) and validation cohorts (n = 165). A DLM and a combined DLM were established for predicting the GIST risk stratification using convolutional neural network and subsequently evaluated in the validation cohort.

Among the analyzed CECT image features, tumor size, ulceration, and enlarged feeding vessels were identified as significant risk predictors (P < 0.05). In DLM, the overall area under the receiver operating characteristic curve (AUROC) was 0.88, with the accuracy (ACC) and AUROCs for each stratification being 87% and 0.96 for low-risk, 79% and 0.74 for intermediate-risk, and 84% and 0.90 for high-risk, respectively. The overall ACC and AUROC were 84% and 0.94 in the combined model. The ACC and AUROCs for each risk stratification were 92% and 0.97 for low-risk, 87% and 0.83 for intermediate-risk, and 90% and 0.96 for high-risk, respectively. Differences in AUROCs for each risk stratification between the two models were significant (P < 0.05).

A combined DLM with satisfactory performance for preoperatively predicting GIST stratifications was developed using routine computed tomography data, demonstrating superiority compared to DLM.

Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy.

The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs.

Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed.

Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model's accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model.

The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.

Gastrointestinal stromal tumors (GISTs) vary widely in prognosis, and traditional pathological assessments often lack precision in risk stratification. Advanced imaging techniques, especially magnetic resonance imaging (MRI), offer potential improvements. This study investigates how MRI imagomics can enhance risk assessment and support personalized treatment for GIST patients.

To assess the effectiveness of MRI imagomics in improving GIST risk stratification, addressing the limitations of traditional pathological assessments.

Analyzed clinical and MRI data from 132 GIST patients, categorizing them by tumor specifics and dividing into risk groups. Employed dimension reduction for optimal imagomics feature selection from diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), and contrast enhanced T1WI with fat saturation (CE-T1WI) fat suppress (fs) sequences.

Age, lesion diameter, and mitotic figures significantly correlated with GIST risk, with DWI sequence features like sphericity and regional entropy showing high predictive accuracy. The combined T1WI and CE-T1WI fs model had the best predictive efficacy. In the test group, the DWI sequence model demonstrated an area under the curve (AUC) value of 0.960 with a sensitivity of 80.0% and a specificity of 100.0%. On the other hand, the combined performance of the T1WI and CE-T1WI fs models in the test group was the most robust, exhibiting an AUC value of 0.834, a sensitivity of 70.4%, and a specificity of 85.2%.

MRI imagomics, particularly DWI and combined T1WI/CE-T1WI fs models, significantly enhance GIST risk stratification, supporting precise preoperative patient assessment and personalized treatment plans. The clinical implications are profound, enabling more accurate surgical strategy formulation and optimized treatment selection, thereby improving patient outcomes. Future research should focus on multicenter studies to validate these findings, integrate advanced imaging technologies like PET/MRI, and incorporate genetic factors to achieve a more comprehensive risk assessment.

Gastrointestinal stromal tumors (GISTs) are defined as mesenchymal tumors of the gastrointestinal tract that express positivity for CD117, which is a c-KIT proto-oncogene antigen. Expression of the c-KIT protein, a tyrosine kinase growth factor receptor, allows the distinction between GISTs and other mesenchymal tumors such as leiomyoma, leiomyosarcoma, schwannoma and neurofibroma. GISTs can develop anywhere in the gastrointestinal tract, as well as in the mesentery and omentum. Over the years, the management of GISTs has improved due to a better knowledge of their behaviors and risk or recurrence, the identification of specific mutations and the use of targeted therapies. This has resulted in a better prognosis for patients with GISTs. In parallel, imaging of GISTs has been revolutionized by tremendous progress in the field of detection, characterization, survival prediction and monitoring during therapy. Recently, a particular attention has been given to radiomics for the characterization of GISTs using analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence with the aim of better characterizing GISTs and providing a more precise assessment of tumor burden. This article sums up recent advances in computed tomography and magnetic resonance imaging of GISTs in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning.

The objective of this study is to determine the morphological computed tomography features of the tumor and texture analysis parameters, which may be a useful diagnostic tool for the preoperative prediction of high-risk gastrointestinal stromal tumors (HR GISTs).

This is a prospective cohort study that was carried out in the period from 2019 to 2022. The study included 79 patients who underwent CT examination, texture analysis, surgical resection of a lesion that was suspicious for GIST as well as pathohistological and immunohistochemical analysis.

Textural analysis pointed out min norm (p = 0.032) as a histogram parameter that significantly differed between HR and LR GISTs, while min norm (p = 0.007), skewness (p = 0.035) and kurtosis (p = 0.003) showed significant differences between high-grade and low-grade tumors. Univariate regression analysis identified tumor diameter, margin appearance, growth pattern, lesion shape, structure, mucosal continuity, enlarged peri- and intra-tumoral feeding or draining vessel (EFDV) and max norm as significant predictive factors for HR GISTs. Interrupted mucosa (p < 0.001) and presence of EFDV (p < 0.001) were obtained by multivariate regression analysis as independent predictive factors of high-risk GISTs with an AUC of 0.878 (CI: 0.797-0.959), sensitivity of 94%, specificity of 77% and accuracy of 88%.

This result shows that morphological CT features of GIST are of great importance in the prediction of non-invasive preoperative metastatic risk. The incorporation of texture analysis into basic imaging protocols may further improve the preoperative assessment of risk stratification.

The Armed Forces Institute of Pathology (AFIP) had higher accuracy and reliability in prognostic assessment and treatment strategies for patients with gastric stromal tumors (GSTs). The AFIP classification is frequently used in clinical applications. But the risk classification is only available for patients who are previously untreated and received complete resection. We aimed to investigate the feasibility of multi-slice MSCT features of GSTs in predicting AFIP risk classification preoperatively.

The clinical data and MSCT features of 424 patients with solitary GSTs were retrospectively reviewed. According to pathological AFIP risk criteria, 424 GSTs were divided into a low-risk group (n = 282), a moderate-risk group (n = 72), and a high-risk group (n = 70). The clinical data and MSCT features of GSTs were compared among the three groups. Those variables (p < 0.05) in the univariate analysis were included in the multivariate analysis. The nomogram was created using the rms package.

We found significant differences in the tumor location, morphology, necrosis, ulceration, growth pattern, feeding artery, vascular-like enhancement, fat-positive signs around GSTs, CT value in the venous phase, CT value increment in the venous phase, longest diameter, and maximum short diameter (all p < 0.05). Two nomogram models were successfully constructed to predict the risk of GSTs. Low- vs. high-risk group: the independent risk factors of high-risk GSTs included the location, ulceration, and longest diameter. The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.911 (95% CI: 0.872-0.951), and the sensitivity and specificity were 80.0% and 89.0%, respectively. Moderate- vs. high-risk group: the morphology, necrosis, and feeding artery were independent risk factors of a high risk of GSTs, with an AUC value of 0.826 (95% CI: 0.759-0.893), and the sensitivity and specificity were 85.7% and 70.8%, respectively.

The MSCT features of GSTs and the nomogram model have great practical value in predicting pathological AFIP risk classification between high-risk and non-high-risk groups before surgery.

Preoperative evaluation of the mitotic index (MI) of gastrointestinal stromal tumors (GISTs) represents the basis of individualized treatment of patients. However, the accuracy of conventional preoperative imaging methods is limited. The aim of this study was to develop a predictive model based on multiparametric MRI for preoperative MI prediction.

A total of 112 patients who were pathologically diagnosed with GIST were enrolled in this study. The dataset was subdivided into the development (n = 81) and test (n = 31) sets based on the time of diagnosis. With the use of T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map, a convolutional neural network (CNN)-based classifier was developed for MI prediction, which used a hybrid approach based on 2D tumor images and radiomics features from 3D tumor shape. The trained model was tested on an internal test set. Then, the hybrid model was comprehensively tested and compared with the conventional ResNet, shape radiomics classifier, and age plus diameter classifier.

The hybrid model showed good MI prediction ability at the image level; the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and accuracy in the test set were 0.947 (95% confidence interval [CI]: 0.927-0.968), 0.964 (95% CI: 0.930-0.978), and 90.8 (95% CI: 88.0-93.0), respectively. With the average probabilities from multiple samples per patient, good performance was also achieved at the patient level, with AUROC, AUPRC, and accuracy of 0.930 (95% CI: 0.828-1.000), 0.941 (95% CI: 0.792-1.000), and 93.6% (95% CI: 79.3-98.2) in the test set, respectively.

The deep learning-based hybrid model demonstrated the potential to be a good tool for the operative and non-invasive prediction of MI in GIST patients.

Background: The role of advanced functional imaging techniques in prediction of pathological risk categories of gastrointestinal stromal tumors (GIST) is still unknown. The purpose of this study was to evaluate classical CT features, CT-perfusion and magnetic-resonance-diffusion-weighted-imaging (MR-DWI)-related parameters in predicting the metastatic risk of gastric GIST. Patients and methods: Sixty-two patients with histologically proven GIST who underwent CT perfusion and MR-DWI using multiple b-values were prospectively included. Morphological CT characteristics and CT-perfusion parameters of tumor were comparatively analyzed in the high-risk (HR) and low-risk (LR) GIST groups. Apparent diffusion coefficient (ADC) and intravoxel-incoherent-motion (IVIM)-related parameters were also analyzed in 45 and 34 patients, respectively. Results: Binary logistic regression analysis revealed that greater tumor diameter (p < 0.001), cystic structure (p < 0.001), irregular margins (p = 0.007), irregular shape (p < 0.001), disrupted mucosa (p < 0.001) and visible EFDV (p < 0.001), as well as less ADC value (p = 0.001) and shorter time-to-peak (p = 0.006), were significant predictors of HR GIST. Multivariate analysis extracted irregular shape (p = 0.006) and enlarged feeding or draining vessels (EFDV) (p = 0.017) as independent predictors of HR GIST (area under curve (AUC) of predicting model 0.869). Conclusion: Although certain classical CT imaging features remain most valuable, some functional imaging parameters may add the diagnostic value in preoperative prediction of HR gastric GIST.

Malignant primary gastric gastrointestinal stromal tumors (gGISTs) without treatment with imatinib are prone to bleeding and peritoneum implantation during operation. Therefore, preoperative assessment of the malignant potential of gGIST is essential. The use of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) combined with computed tomography (PET/CT) as a non-invasive tool for diagnosis, staging and prognosis evaluation in oncology, may also be useful for gGISTs. In the present study, we analyzed the value of 18F-FDG PET-CT in assessing the malignant potential of gGISTs before treatment.

Patients who were diagnosed with gGIST by pathology and underwent 18F-FDG PET/CT at the same time were collected. The clinicopathological features of 26 patients with gGISTs were retrospectively analyzed at last. The gGIST risk classification was graded according to the US National Institutes of Health (NIH) GIST risk classification criteria [2008]. Lesions were classified as malignant group (moderate- or high-risk category) and benign group (low- or very low-risk category) according to pathology. The relationship between the maximal standard uptake value (SUVmax) and GIST risk category, tumor diameter, Ki-67 index, and mitotic count was analyzed. The cut-off level of SUVmax for the diagnosis of malignant gGIST with the highest sensitivity was calculated based on the receiver-operating characteristic (ROC) curve.

The SUVmax, tumor diameter, Ki-67 index, and mitotic count of the 26 gGIST patients were 5.90±4.49, 7.40±4.92 cm, 7.62%±11.76%, (5.96±3.19)/50 high-power field (HPF), respectively. SUVmax was significantly correlated with GIST risk category, Ki-67 index, and mitotic count (r=0.855, 0.860, and 0.690, all P<0.01) but not with tumor diameter (r=0.383, P=0.054). The SUVmax of gGIST was 7.00±4.57 in the malignant group (moderate or high NIH risk category in 20 patients), which was significantly different from that (2.25±0.77) in the benign group (low or extremely low NIH risk category in 6 patients) (t=4.566, P<0.01). ROC curve analysis showed that a SUVmax cut-off of 2.60 was most sensitive for predicting malignant gGIST. When the area under the curve was 0.967, the sensitivity was 100% and the specificity was 83.3%.

SUVmax may be used as a complementary indicator for predicting the malignant potential of gGISTs before treatment.

Our primary purpose was to search for computed tomography (CT) radiomic features of gastrointestinal stromal tumors (GISTs) that could potentially correlate with the risk class according to the Miettinen classification. Subsequently, assess the existence of features with possible predictive value in differentiating responder from non-responder patients to first-line therapy with Imatinib.

A retrospective study design was carried out using data from June 2009 to December 2020. We analyzed all the preoperative CTs of patients undergoing surgery for GISTs. We segmented non-contrast-enhanced CT (NCECT) and contrast-enhanced venous CT (CECT) images obtained either on three different CT scans (heterogeneous cohort) or on a single CT scan (homogeneous cohort). We then divided the patients into two groups according to Miettinen classification criteria and based on the predictive value of response to first-line therapy with Imatinib.

We examined 54 patients with pathological confirmation of GISTs. For the heterogeneous cohort, we found a statistically significant relationship between 57 radiomic features for NCECT and 56 radiomic features for CECT using the Miettinen risk classification. In the homogeneous cohort, we found the same relationship between 8 features for the NCECT and 5 features for CECT, all included in the heterogeneous cohort. The various radiomic features are distributed with different values in the two risk stratification groups according to the Miettinen classification. We also found some features for groups predictive of response to first-line therapy with Imatinib.

We found radiomic features that correlate with statistical significance for both the Miettinen risk classification and the molecular subtypes of response. All features found in the homogeneous study cohort were also found in the heterogeneous cohort. CT radiomic features may be useful in assessing the risk class and prognosis of GISTs.