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.

Deep learning and radiomics to predict the mitotic index of gastrointestinal stromal tumors based on multiparametric MRI.

Introduction: 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. Methods: 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. Results: 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. Discussion: 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.

The Value of First-Order Features Based on the Apparent Diffusion Coefficient Map in Evaluating the Therapeutic Effect of Low-Intensity Pulsed Ultrasound for Acute Traumatic Brain Injury With a Rat Model.

Purpose: In order to evaluate the neuroprotective effect of low-intensity pulsed ultrasound (LIPUS) for acute traumatic brain injury (TBI), we studied the potential of apparent diffusion coefficient (ADC) values and ADC-derived first-order features regarding this problem. Methods: Forty-five male Sprague Dawley rats (sham group: 15, TBI group: 15, LIPUS treated: 15) were enrolled and underwent magnetic resonance imaging. Scanning layers were acquired using a multi-shot readout segmentation of long variable echo trains (RESOLVE) to decrease distortion. The ultrasound transducer was applied to the designated region in the injured cortical areas using a conical collimator and was filled with an ultrasound coupling gel. Regions of interest were manually delineated in the center of the damaged cortex on the diffusion weighted images (b = 800 s/mm2) layer by layer for the TBI and LIPUS treated groups using the open-source software ITK-SNAP. Before analysis and modeling, the features were normalized using a z-score method, and a logistic regression model with a backward filtering method was employed to perform the modeling. The entire process was completed using the R language. Results: During the observation time, the ADC values ipsilateral to the trauma in the TBI and LIPUS groups increased rapidly up to 24 h. After statistical analysis, the 10th percentile, 90th percentile, mean, skewness, and uniformity demonstrated a significant difference among three groups. The receiver operating characteristic curve (ROC) analysis shows that the combined LR model exhibited the highest area under the curve value (AUC: 0.96). Conclusion: The combined LR model of first-order features based on the ADC map can acquire a higher diagnostic performance than each feature only in evaluating the neuroprotective effect of LIPUS for TBI. Models based on first-order features may have potential value in predicting the therapeutic effect of LIPUS in clinical practice in the future.

Multi-Parameter MR Radiomics Based Model to Predict 5-Year Progression-Free Survival in Endometrial Cancer.

Background: Relapse is the major cause of mortality in patients with resected endometrial cancer (EC). There is an urgent need for a feasible method to identify patients with high risk of relapse. Purpose: To develop a multi-parameter magnetic resonance imaging (MRI) radiomics-based nomogram model to predict 5-year progression-free survival (PFS) in EC. Methods: For this retrospective study, 202 patients with EC followed up for at least 5 years after hysterectomy. A radiomics signature was extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE). The radiomics score (RS) was calculated based on the least absolute shrinkage and selection operator (LASSO) regression. We have developed a radiomics based nomogram model (ModelN) incorporating the RS and clinical and conventional MR (cMR) risk factors. The performance was evaluated by the receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA). Results: The ModelN demonstrated a good calibration and satisfactory discrimination, with a mean area under the curve (AUC) of 0.840 and 0.958 in the training and test cohorts, respectively. In comparison with clinical prediction model (ModelC), the discrimination ability of ModelN showed an improvement with P < 0.001 for the training cohort and P=0.032 for the test cohort. Compared to the radiomics prediction model (ModelR), ModelN discrimination ability showed an improvement for the training cohort with P = 0.021, with no statistically significant difference in the test cohort (P = 0.106). Calibration curves suggested a good fit for probability (Hosmer-Lemeshow test, P = 0.610 and P = 0.956 for the training and test cohorts, respectively). Conclusion: This multi-parameter nomogram model incorporating clinical and cMR findings is a valid method to predict 5-year PFS in patients with EC.

Combination Analysis of a Radiomics-Based Predictive Model With Clinical Indicators for the Preoperative Assessment of Histological Grade in Endometrial Carcinoma.

BACKGROUND: Histological grade is one of the most important prognostic factors of endometrial carcinoma (EC) and when selecting preoperative treatment methods, conducting accurate preoperative grading is of great significance. PURPOSE: To develop a magnetic resonance imaging (MRI) radiomics-based nomogram for discriminating histological grades 1 and 2 (G1 and G2) from grade 3 (G3) EC. METHODS: This was a retrospective study included 358 patients with histologically graded EC, stratified as 250 patients in a training cohort and 108 patients in a test cohort. T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) were performed via 1.5-Tesla MRI. To establish ModelADC, the region of interest was manually outlined on the EC in an apparent diffusion coefficient (ADC) map. To establish the radiomic model (ModelR), EC was manually segmented by two independent radiologists and radiomic features were extracted. The Radscore was calculated based on the least absolute shrinkage and selection operator regression. We combined the Radscore with carbohydrate antigen 125 (CA125) and body mass index (BMI) to construct a mixed model (ModelM) and develop the predictive nomogram. Receiver operator characteristic (ROC) and calibration curves were assessed to verify the prediction ability and the degree of consistency, respectively. RESULTS: All three models showed some amount of predictive ability. Using ADC alone to predict the histological risk of EC was limited in both the cohort [area under the curve (AUC), 0.715; 95% confidence interval (CI), 0.6509-0.7792] and test cohorts (AUC, 0.621; 95% CI, 0.515-0.726). In comparison with ModelADC, the discrimination ability of ModelR showed improvement (Delong test, P < 0.0001 for both the training and test cohorts). ModelM, established based on the combination of radiomic and clinical indicators, showed the best level of predictive ability in both the training (AUC, 0.925; 95% CI, 0.898-0.951) and test cohorts (AUC, 0.915; 95% CI, 0.863-0.968). Calibration curves suggested a good fit for probability (Hosmer-Lemeshow test, P = 0.673 and P = 0.804 for the training and test cohorts, respectively). CONCLUSION: The described radiomics-based nomogram can be used to predict EC histological classification preoperatively.

Evaluation of risk classifications for gastrointestinal stromal tumor using multi-parameter Magnetic Resonance analysis.

BACKGROUND: Gastrointestinal stromal tumor (GIST) is the most common mesenchymal malignancy of the gastrointestinal tract. At present, it is generally believed that the prognosis of GIST is closely related to its risk classification. It may add value to correctly diagnose and evaluate the risk of invasion using a noninvasive imaging examination prior to surgery. MRI has the advantages of multiple parameters and high soft tissue resolution, which may be the potential method to preoperatively evaluate the risk of GIST. PURPOSE: To retrospectively evaluate the diagnostic accuracy of multi-parameter MR analysis for preoperative risk classification of GIST. MATERIALS AND METHODS: In this 6-year retrospective study, full MRI examination was performed on all 60 GIST cases confirmed classified by pathology, including 35 cases of very low-to-low-risk GIST and 25 cases of intermediate-to-high-risk GIST. Dynamic contrast-enhanced T1- and T2-weighted images, and apparent diffusion coefficient (ADC) maps were reviewed independently by two radiologists blinded to pathologic results. Volume, ADC ratio, three wash-in indexes (WII) were calculated and compared using t-test or Kruskal-Wallis nonparametric test. Sensitivity and specificity analyses were performed to calculate diagnostic accuracy using ROC analyses. Differences were considered significant at p < 0.05. RESULTS: All GISTs were resected. Patient age, sex, tumor location and tumor shape did not differ significantly across the two groups (p = 0.798, 0.767, 0.822 and 0.096, respectively). GIST in the intermediate-to-high-risk group presented significantly greater volume (p = 0.0045), lower ADC ratio (p = 0.0125) and faster enhancement (for WII2, p < 0.0001; for WII3, p = 0.0358) than that of GIST in the very low-to-low-risk group. This combination of the volume, ADC ratio and WII2 provided sensitivity of 88%, specificity of 94.29%, and accuracy of 91.7% for the risk classification of GIST. CONCLUSION: Multi-parameter MR analysis provides a preoperative imaging standard for accurately distinguishing very low-to-low-risk GIST from intermediate-to-high-risk GIST.

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

BACKGROUND: Treatment regimens and prognoses of gastrointestinal stromal tumors (GIST) are quite different for tumors in different risk categories. Accurate preoperative grading of tumors is important for avoiding under- or overtreatment. PURPOSE: To develop and validate an MRI texture-based model to predict the mitotic index and its risk classification. STUDY TYPE: Retrospective. POPULATION: Ninety-one patients with histologically-confirmed GIST; 64 patients in a training cohort, and 27 patients in a test cohort. FIELD STRENGTH/SEQUENCE: T2 -weighted imaging (T2 WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE) at 1.5T. ASSESSMENT: GIST images were manually segmented by two independent radiologists using ITK-SNAP software and MRI features were extracted using Pyradiomics. Two pathologists reviewed the tissue specimens of the tumors to identify the mitotic index and risk classification in consensus. STATISTICAL TESTS: The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. A logistic regression model was established based on the radiomic score (radscore), tumor location, and maximum diameter to predict tumor classification and develop a nomogram. Receiver operator characteristic (ROC) curves were used to evaluate the ability of the nomogram to distinguish between two tumors with different risk classifications, and a calibration curve was used to evaluate the consistency between the predicted risk and the actual risk. RESULTS: The texture signature achieved high efficacy in predicting the mitotic index area under the curve ([AUC], 0.906; 95% confidence interval [CI]: 0.813, 0.961). A nomogram for prediction of the risk classification of GIST, which incorporated this texture signature together with maximum tumor diameter and location, allowed good discrimination in the training cohort (AUC, 0.878; 95% CI: 0.769, 0.960) and the validation cohort (AUC, 0.903; 95% CI: 0.732, 0.922). DATA CONCLUSION: The texture-based model can be used to predict GIST mitotic index and risk classification preoperatively. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 3.