Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer.

BACKGROUND: To provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features. METHODS: Two cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software. We used a framework that incorporated 10 machine learning algorithms and generated 77 combinations to construct radiomics-based models for lymph node metastasis prediction. Weighted gene coexpression network analysis (WGCNA) was subsequently performed to determine the relationships between gene expression levels and radiomic features. Molecular pathways enrichment analysis was performed to uncover the underlying molecular features. RESULTS: Patients in the in-house cohort (mean age, 61.3 years ± 9.6 [SD]; 91 men [60%]) were separated into training (n = 105, 70%) and validation (n = 46, 30%) cohorts. A total of 1,239 features were extracted and subjected to machine learning algorithms. The 77 radiomic models showed moderate performance for predicting lymph node metastasis, and the combination of the StepGBM and Enet algorithms had the best performance in the training (AUC = 0.84, 95% CI = 0.77-0.91) and validation (AUC = 0.85, 95% CI = 0.73-0.98) cohorts. We determined that 15 features were core variables for lymph node metastasis. Proliferation-related processes may respond to the main molecular alterations underlying these features. CONCLUSIONS: Machine learning-based radiomics could predict the status of lymph node metastasis in pancreatic cancer, which is associated with proliferation-related alterations.

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

PURPOSE: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). METHODS: A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning-based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the compared models, SegNeXt-ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. CONCLUSION: This two-stage SegNeXt-ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

OBJECTIVE: This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). METHODS: This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). RESULTS: A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. CONCLUSION: Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Shear-wave elastography predicts T-restaging and pathologic complete response of rectal cancer post neoadjuvant chemoradiotherapy.

PURPOSE: To investigate the value of shear-wave elastography (SWE) in assessing the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer. METHODS: In this study, 455 participants with locally advanced rectal cancer who underwent nCRT at our hospital between September 2021 and December 2022 were prospectively enrolled. The participants were randomly divided into training and test cohorts in a 3:2 ratio. Clinical baseline data, endorectal ultrasound examination data, and SWE measurements were collected for all participants. Logistic regression models were used to predict whether rectal cancer after nCRT had a low T staging (ypT 0-2 stage, Model A) and pathological complete response (pCR) (Model B). Paired Chi-square tests were used to compare the diagnostic performances of the radiologists to those of Models A and B. RESULTS: In total, 256 participants were included. The area under the receiver operating characteristic curve of Models A and B in the test cohort were 0.94 (0.87, 1.00) and 0.88 (0.80, 0.97), respectively. The optimal diagnostic thresholds for Models A and B were 14.9 kPa for peritumoral mesangial Emean and 15.2 kPa for tumor Emean, respectively. The diagnostic performance of the radiologists was significantly lower than that of Models A and B, respectively (p < 0.05). CONCLUSION: SWE can be used as a feasible method to evaluate the treatment response of nCRT for locally advanced rectal cancer.

Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics.

PURPOSE: This study aimed to use conventional ultrasound features, ultrasound radiomics, and machine learning algorithms to establish a predictive model to assess the risk of post-surgical recurrence of gastrointestinal stromal tumors (GISTs). METHODS: This retrospective analysis included 230 patients with pathologically diagnosed GISTs. Radiomic features were extracted from manually annotated images. Radiomic features plus conventional ultrasound features were selected using the SelectKbest analysis of variance and stratified tenfold cross-validation recursive elimination methods. Finally, five different machine learning algorithms (logistic regression [LR], support vector machine [SVM], random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) were established to predict risk stratification of GISTs. The predictive performance of the established model was mainly evaluated based on the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy, whereas the predictive performance of the optimal machine learning algorithm and a radiologist's subjective assessment were compared using McNemar's test. RESULTS: Seven radiomics features and one conventional ultrasound feature were selected to construct the machine learning models for GIST risk classification. The mentioned five machine learning models were able to predict the malignant potential of GISTs. LR and SVM outperformed other classifiers on the test set, with LR achieving an accuracy of 0.852 (AUC, 0.881; sensitivity, 0.871; specificity, 0.826) and SVM achieving an accuracy of 0.852 (AUC, 0.879; sensitivity, 0.839; specificity, 0.870), and proved significantly better than the radiologist (accuracy, 0.691; sensitivity, 0.645; specificity, 0.813). CONCLUSION: Machine learning-based ultrasound radiomics features are able to noninvasively predict the biological risk of GISTs.

Use of a Convolutional Neural Network to Predict the Malignant Potential of Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Images: Visualization of the Focus of the Prediction Model.

OBJECTIVE: We established a deep convolutional neural network (CNN) model based on ultrasound images (US-CNN) for predicting the malignant potential of gastrointestinal stromal tumors (GISTs). METHODS: A total of 980 ultrasound images from 245 pathology-confirmed GIST patients after surgical operation were retrospectively collected and divided into a low (very-low-risk, low-risk) and a high (medium-risk, high-risk) malignant potential group. Eight pre-trained CNN models were used to extract the features. The CNN model with the highest accuracy in the test set was selected. The model's performance was evaluated by calculating accuracy, sensitivity, specificity, positive-predictive value (PPV), negative-predictive value (NPV) and the F1 score. Three radiologists with different experience levels also predicted the malignant potential of GISTs in the same test set. US-CNN and human assessments were compared. Subsequently, gradient-weighted class activation diagrams (Grad-CAMs) were used to visualize the model's final classification decisions. RESULTS: Among the eight transfer learning-based CNNs, ResNet18 performed best. The accuracy, sensitivity, specificity, PPV, NPV and F1 score were 0.88, 0.86, 0.89, 0.82, 0.92 and 0.90, respectively, which were significantly better than those achieved by radiologists (resident doctor: 0.66, 0.55, 0.79, 0.74, 0.62 and 0.69; attending doctor: 0.68, 0.59, 0.78, 0.70, 0.69 and 0.73; professor: 0.69, 0.63, 0.72, 0.51, 0.80 and 0.76). Model interpretation with Grad-CAMs revealed that the activated areas mainly focused on cystic necrosis and margins. CONCLUSION: The US-CNN model predicts GIST malignant potential well, which can assist in clinical treatment decision-making.

Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors.

This study aimed to develop and evaluate a nomogram based on an ultrasound radiomics model to predict the risk grade of gastrointestinal stromal tumors (GISTs). 216 GIST patients pathologically diagnosed between December 2016 and December 2021 were reviewed and divided into a training cohort (n = 163) and a validation cohort (n = 53) in a ratio of 3:1. The tumor region of interest was depicted on each patient's ultrasound image using ITK-SNAP, and the radiomics features were extracted. By filtering unstable features and using Spearman's correlation analysis, and the least absolute shrinkage and selection operator algorithm, a radiomics score was derived to predict the malignant potential of GISTs. a radiomics nomogram that combines the radiomics score and clinical ultrasound predictors was constructed and assessed in terms of calibration, discrimination, and clinical usefulness. The radiomics score from ultrasound images was significantly associated with the malignant potential of GISTs. The radiomics nomogram was superior to the clinical ultrasound nomogram and the radiomics score, and it achieved an AUC of 0.90 in the validation cohort. Based on the decision curve analysis, the radiomics nomogram was found to be more clinically significant and useful. A nomogram consisting of radiomics score and the maximum tumor diameter demonstrated the highest accuracy in the prediction of risk grade in GISTs. The outcomes of our study provide vital insights for important preoperative clinical decisions.

Two-dimensional shear wave elastography of the pancreas: measurement success rate, repeatability, and factors affecting measurement values.

PURPOSE: We aimed to evaluate the success rate, repeatability, and factors affecting the measurement values of two-dimensional ultrasonic shear wave elastography (2D-SWE) for measuring pancreatic stiffness. METHODS: This prospective study recruited 100 healthy participants. 2D-SWE was performed on the pancreatic head, body, and tail. We compared the success rates of pancreatic stiffness measurements of different body positions and ultrasonic scans, with and without probe pressurization, as well as the effects of sex, age, body mass index (BMI), and region of interest (ROI) depth on measurement values. Intra- and inter-operator repeatabilities were assessed in 20 participants. The influence of ROI depth was verified using a tissue-like phantom. RESULTS: The median 2D-SWE measurements of the pancreatic head, body, and tail were 1.44, 1.45, and 1.56 m/s, respectively. The success rates for the pancreatic head and body were significantly higher than that of the tail. The success rate for the semi-recumbent position was higher than that of the supine position (P < 0.001). The intra-operator values for same-day and inter-operator reliability were excellent. Univariate analyses showed that probe pressurization, age, BMI, and ROI depth were correlated with pancreatic shear wave velocity (SWV) (P < 0.05); only ROI depth had a significant effect on SWV values. The inclusion phantom showed that the SWV value increased as the ROI depth increased. CONCLUSIONS: 2D-SWE had a high success rate and good repeatability for measuring pancreatic head and body stiffness. The ROI depth was the main factor affecting pancreatic SWV, which increased with ROI depth.

BRAF-activated WT1 contributes to cancer growth and regulates autophagy and apoptosis in papillary thyroid carcinoma.

BACKGROUND: Papillary thyroid carcinoma (PTC) is one of most prevalent malignant endocrine neoplasms, and it is associated with a high frequency of BRAF gene mutations, which lead to lymphatic metastasis and distant metastasis that promote tumor progression. The molecular mechanism of PTC and the role of BRAF mutation in PTC progression and development need to be further elucidated. METHODS: In this study, a comprehensive bioinformatics analysis was performed to identify the differentially expressed genes and signaling pathways in thyroid cancer patients carrying mutant BRAF. Then, we confirmed the prognostic role of WT1 in thyroid cancer patients. Immunohistochemistry was performed to measure the expression profile of WT1 in PTC tissue. Lentivirus shWT1 was transfected into BRAFV600E (mutant) PTC cells to stably inhibit WT1 expression. CCK-8, EdU, immunofluorescence, colony formation, cell migration, cell wound healing, apoptosis and autophagy assays were performed to assess the biological functions of WT1 in BRAFV600E PTC cells. RNA sequencing, immunohistochemistry and immunoblotting were performed to explore the molecular mechanism of WT1 in BRAFV600E PTC cells. RESULTS: The results confirmed that "epithelial cell proliferation", "apoptosis" and "selective autophagy" were closely associated with this BRAF mutant in these thyroid cancer patients. Knocking down BRAF-activated WT1 effectively inhibited the proliferation and migration of BRAFV600E PTC cells. Silencing WT1 significantly inhibited autophagy and promoted the apoptosis of BRAFV600E PTC cells. Mechanistic investigations showed that silencing WT1 expression remarkably suppressed the AKT/mTOR and ERK/P65 signaling pathways in BRAFV600E PTC cells. CONCLUSION: All these results indicate that WT1 is a promising prognostic biomarker and facilitates PTC progression and development of cells carrying the BRAFV600E mutation.

Application of ultrasonography in predicting the biological risk of gastrointestinal stromal tumors.

OBJECTIVES: To explore and establish a reliable and noninvasive ultrasound model for predicting the biological risk of gastrointestinal stromal tumors (GISTs). MATERIALS AND METHODS: We retrospectively reviewed 266 patients with pathologically-confirmed GISTs and 191 patients were included. Data on patient sex, age, tumor location, biological risk classification, internal echo, echo homogeneity, boundary, shape, blood flow signals, presence of necrotic cystic degeneration, long diameter, and short/long (S/L) diameter ratio were collected. All patients were divided into low-, moderate-, and high-risk groups according to the modified NIH classification criteria. All indicators were analyzed by univariate analysis. The indicators with inter-group differences were used to establish regression and decision tree models to predict the biological risk of GISTs. RESULTS: There were statistically significant differences in long diameter, S/L ratio, internal echo level, echo homogeneity, boundary, shape, necrotic cystic degeneration, and blood flow signals among the low-, moderate-, and high-risk groups (all p < .05). The logistic regression model based on the echo homogeneity, shape, necrotic cystic degeneration and blood flow signals had an accuracy rate of 76.96% for predicting the biological risk, which was higher than the 72.77% of the decision tree model (based on the long diameter, the location of tumor origin, echo homogeneity, shape, and internal echo) (p = .008). In the low-risk and high-risk groups, the predicting accuracy rates of the regression model reached 87.34 and 81.82%, respectively. CONCLUSIONS: Transabdominal ultrasound is highly valuable in predicting the biological risk of GISTs. The logistic regression model has greater predictive value than the decision tree model.