Radiomics-Based Prediction of Microvascular Invasion Grade in Nodular Hepatocellular Carcinoma Using Contrast-Enhanced Magnetic Resonance Imaging.

Objective: The aim of this study is to develop and verify a magnetic resonance imaging (MRI)-based radiomics model for predicting the microvascular invasion grade (MVI) before surgery in individuals diagnosed with nodular hepatocellular carcinoma (HCC). Methods: A total of 198 patients were included in the study and were randomly stratified into two groups: a training group consisting of 139 patients and a test group comprising 59 patients. The tumor lesion was manually segmented on the largest cross-sectional slice using ITK SNAP, with agreement reached between two radiologists. The selection of radiomics features was carried out using the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm. Radiomics models were then developed through maximum correlation, minimum redundancy, and logistic regression analyses. The performance of the models in predicting MVI grade was assessed using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. Results: There were no notable statistical differences in sex, age, BMI (body mass index), tumor size, and location between the training and test groups. The AP and PP radiomic model constructed for predicting MVI grade demonstrated an AUC of 0.83 (0.75-0.88) and 0.73 (0.64-0.80) in the training group and an AUC of 0.74 (0.61-0.85) and 0.62 (0.48-0.74) in test group, respectively. The combined model consists of imaging data and clinical data (age and AFP), achieved an AUC of 0.85 (0.78-0.91) and 0.77 (0.64-0.87) in the training and test groups, respectively. Conclusion: A radiomics model utilizing-contrast-enhanced MRI demonstrates strong predictive capability for differentiating MVI grades in individuals with nodular HCC. This model could potentially function as a dependable and resilient tool to support hepatologists and radiologists in their preoperative decision-making processes.

Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study.

Background: Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. Methods: We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. Results: The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. Conclusions: The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension.

Predicting the rupture status of small middle cerebral artery aneurysms using random forest modeling.

Objective: Small intracranial aneurysms are increasingly being detected; however, a prediction model for their rupture is rare. Random forest modeling was used to predict the rupture status of small middle cerebral artery (MCA) aneurysms with morphological features. Methods: From January 2009 to June 2020, we retrospectively reviewed patients with small MCA aneurysms (<7 mm). The aneurysms were randomly split into training (70%) and internal validation (30%) cohorts. Additional independent datasets were used for the external validation of 78 small MCA aneurysms from another four hospitals. Aneurysm morphology was determined using computed tomography angiography (CTA). Prediction models were developed using the random forest and multivariate logistic regression. Results: A total of 426 consecutive patients with 454 small MCA aneurysms (<7 mm) were included. A multivariate logistic regression analysis showed that size ratio (SR), aspect ratio (AR), and daughter dome were associated with aneurysm rupture, whereas aneurysm angle and multiplicity were inversely associated with aneurysm rupture. The areas under the receiver operating characteristic (ROC) curves (AUCs) of random forest models using the five independent risk factors in the training, internal validation, and external validation cohorts were 0.922, 0.889, and 0.92, respectively. The random forest model outperformed the logistic regression model (p = 0.048). A nomogram was developed to assess the rupture of small MCA aneurysms. Conclusion: Random forest modeling is a good tool for evaluating the rupture status of small MCA aneurysms and may be considered for the management of small aneurysms.

Comparative analysis of two mathematical algorithms for the calculation of computed tomography perfusion parameters in the healthy and diseased pancreas.

BACKGROUND: The maximum slope (MS) and deconvolution (DC) algorithms are commonly used to post-process computed tomography perfusion (CTP) data. This study aims to analyze the differences between MS and DC algorithms for the calculation of pancreatic CTP parameters. METHODS: The pancreatic CTP data of 57 patients were analyzed using MS and DC algorithms. Two blinded radiologists calculated pancreatic blood volume (BV) and blood flow (BF). Interobserver correlation coefficients were used to evaluate the consistency between two radiologists. Paired t-tests, Pearson linear correlation analysis, and Bland-Altman analysis were performed to evaluate the correlation and consistency of the CTP parameters between the two algorithms. RESULTS: Among the 30 subjects with normal pancreas, the BV values in the three pancreatic regions were higher in the case of the MS algorithm than in the case of the DC algorithm (t = 39.35, p < 0.001), and the BF values in the three pancreatic regions were slightly higher for the MS algorithm than for the DC algorithm (t = 2.19, p = 0.031). Similarly, among the 27 patients with acute pancreatitis, the BV values obtained using the MS methods were higher than those obtained using the DC methods (t = 54.14, p < 0.001). Furthermore, the BF values were higher with the MS methods than the DC methods (t = 8.45, p < 0.001). Besides, Pearson linear correlation and Bland-Altman analysis showed that the BF and BV values showed a good correlation and a bad consistency between the two algorithms. CONCLUSIONS: The BF and BV values measured using MS and DC algorithms had a good correlation but were not consistent.

Classifying Ruptured Middle Cerebral Artery Aneurysms With a Machine Learning Based, Radiomics-Morphological Model: A Multicentral Study.

OBJECTIVE: Radiomics and morphological features were associated with aneurysms rupture. However, the multicentral study of their predictive power for specific-located aneurysms rupture is rare. We aimed to determine robust radiomics features related to middle cerebral artery (MCA) aneurysms rupture and evaluate the additional value of combining morphological and radiomics features in the classification of ruptured MCA aneurysms. METHODS: A total of 632 patients with 668 MCA aneurysms (423 ruptured aneurysms) from five hospitals were included. Radiomics and morphological features of aneurysms were extracted on computed tomography angiography images. The model was developed using a training dataset (407 patients) and validated with the internal (152 patients) and external validation (73 patients) datasets. The support vector machine method was applied for model construction. Optimal radiomics, morphological, and clinical features were used to develop the radiomics model (R-model), morphological model (M-model), radiomics-morphological model (RM-model), clinical-morphological model (CM-model), and clinical-radiomics-morphological model (CRM-model), respectively. A comprehensive nomogram integrating clinical, morphological, and radiomics predictors was generated. RESULTS: We found seven radiomics features and four morphological predictors of MCA aneurysms rupture. The R-model obtained an area under the receiver operating curve (AUC) of 0.822 (95% CI, 0.776, 0.867), 0.817 (95% CI, 0.744, 0.890), and 0.691 (95% CI, 0.567, 0.816) in the training, temporal validation, and external validation datasets, respectively. The RM-model showed an AUC of 0.848 (95% CI, 0.810, 0.885), 0.865 (95% CI, 0.807, 0.924), and 0.721 (95% CI, 0.601, 0.841) in the three datasets. The CRM-model obtained an AUC of 0.856 (95% CI, 0.820, 0.892), 0.882 (95% CI, 0.828, 0.936), and 0.738 (95% CI, 0.618, 0.857) in the three datasets. The CRM-model and RM-model outperformed the CM-model and M-model in the internal datasets (p < 0.05), respectively. But these differences were not statistically significant in the external dataset. Decision curve analysis indicated that the CRM-model obtained the highest net benefit for most of the threshold probabilities. CONCLUSION: Robust radiomics features were determined related to MCA aneurysm rupture. The RM-model exhibited good ability in classifying ruptured MCA aneurysms. Integrating radiomics features into conventional models might provide additional value in ruptured MCA aneurysms classification.

SPARC promotes pancreatic cancer cell proliferation and migration through autocrine secretion into the extracellular milieu.

SPARC is a secreted glycoprotein that plays a complex and multifaceted role in tumour formation and progression. However, whether SPARC is an oncogene or a tumour suppressor is still unclear. Moreover, SPARC demonstrates potential in clinical pancreatic adenocarcinoma (PAAD) treatment, although it has been identified as an oncogene in some studies and a tumor suppressor in others. In the present study, a pan-cancer analysis of SPARC was carried out using The Cancer genome Atlas data, which demonstrated that SPARC was an oncogene in most cancer types and a cancer suppressor in others. In addition, SPARC expression was significantly upregulated in PAAD and associated with poor prognosis. SPARC also promoted the proliferation and migration of PANC-1 and SW1990 cell lines in vitro. SPARC was detected in the culture supernatant of PAAD cells and pancreatic acinar AR42J cells. SPARC regulated PAAD cell proliferation only when secreted into the extracellular milieu, thus explaining why the prognosis of patients with PAAD is correlated with the SPARC expression of both tumour cells and stromal cells. Collectively, the present findings demonstrated that the function of SPARC was associated with tumour type and that SPARC may represent an important oncogene in PAAD that merits further study.

A nomogram to predict rupture risk of middle cerebral artery aneurysm.

BACKGROUND: Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique. METHODS: We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model. RESULTS: Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it. CONCLUSION: Our model can be used to predict the rupture risk of MCA aneurysm.

Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique.

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a devastating disease. Anterior communicating artery (ACoA) aneurysm is the most frequent location of intracranial aneurysms. The purpose of this study is to predict the clinical outcome at discharge after rupture of ACoA aneurysms using the random forest machine learning technique. Methods: A total of 607 patients with ruptured ACoA aneurysms were included in this study between December 2007 and January 2016. In addition to basic clinical variables, 12 aneurysm morphologic parameters were evaluated. A multivariate logistic regression analysis was performed to determine the independent predictors of poor outcome. Of the 607 patients, 485 patients were randomly selected for training and the remaining for internal testing. The random forest model was developed using the training data set. An additional 202 patients from February 2016 to December 2017 were collected for externally validating the model. The prediction performance of the random forest model was compared with two radiologists. Results: Patients' age (odds ratio [OR] = 1.04), ventilated breathing status (OR = 4.23), World Federation of Neurosurgical Societies (WFNS) grade (OR = 2.13), and Fisher grade (OR = 1.50) are significantly associated with poor outcome. None of the investigated morphological parameters of ACoA aneurysm is an independent predictor of poor outcome. The developed random forest model achieves sensitivities of 78.3% for internal test and 73.8% for external test. The areas under receiver operating characteristic (ROC) curve of the random forest model were 0.90 for the internal test and 0.84 for the external test. Both sensitivities and areas under ROC curves of our model are superior to those of two raters in both internal and external tests. Conclusions: The random forest model presents good performance in predicting the outcome after rupture of ACoA aneurysms, which may aid in clinical decision making.

Integrated texture parameter of 18F-FDG PET may be a stratification factor for the survival of nonoperative patients with locally advanced non-small-cell lung cancer.

OBJECTIVE: This study explored whether integrated texture parameter (ITP) of the fluorine-18-fluorodeoxyglucose PET (F-FDG PET) is a stratification factor for the survival of nonoperative patients with locally advanced non-small-cell lung cancer (LA-NSCLC). PATIENTS AND METHODS: Thirty-five patients with LA-NSCLC treated with chemoradiotherapy or radiotherapy were included in the retrospective study. Eight principal components (PCs) were extracted from 72 F-FDG PET texture features (TFs) using PC analysis. The survival rates between PC subgroups (group by median value) were compared using Kaplan-Meier method. Seventy-two factor loadings for PC7 were evaluated using t-test. Standardized values of the TFs with significant factor loading were multiplied by the corresponding PC7 component coefficient, and the products were added together to obtain ITP. The survival rates between ITP subgroups (group by median value) were compared using Kaplan-Meier method. Patient characteristics between ITP subgroups were compared using χ -test, Mann-Whitney U-test, or t-test. RESULTS: The median follow-up time was 20.7 months. The median overall survival (OS) and progression-free survival (PFS) were 32.5 and 14.4 months, respectively. The patients with high PC7 value had lesser OS (P=0.006) and PFS (P=0.010) than those with lower value. Factor loadings of standardized uptake value kurtosis, run percentage, and zone percentage were significant for PC7 (P<0.01). The patients with high ITP value had lesser OS (P=0.001) and PFS (P=0.002) than those with lower value. There were no significant differences in patient characteristics between ITP subgroups (P>0.2). CONCLUSION: This study demonstrated that ITP might be a stratification factor for the survival of nonoperative patients with LA-NSCLC.

Cardiovascular involvement in connective tissue disease: the role of interstitial lung disease.

OBJECTIVE: The aim of this study was to assess cardiovascular involvement in patients with connective tissue disease (CTD), and determine whether interstitial lung disease (ILD) in these patients is associated with elevated cardiovascular risk. METHODS: This study evaluated a retrospective cohort of 436 CTD patients admitted to a large teaching hospital in Zhejiang province, China, along with an additional 436 participants of an annual community health screening conducted in the physical examination center who served as age- and gender-matched controls. Demographic, clinical, serologic and imaging characteristics, as well as medications used by each participant were recorded. Cardiovascular involvement was defined by uniform criteria. Correlations between clinical/serologic factors and cardiovascular involvement were determined by univariate and multivariate analyses. RESULTS: CTD patients had a significantly higher cardiovascular involvement rate than controls (64.7% vs 23.4%), with higher rates of diabetes, hypertension, and hyperlipidemia, elevated systolic and diastolic pressures, C-reactive protein, total cholesterol, and low-density lipoprotein cholesterol, and lower albumin and high-density lipoprotein cholesterol (all p < 0.05). Furthermore, CTP patients with cardiovascular involvement were significantly older, had higher systolic and diastolic pressures, C-reactive protein, glucose, and uric acid, higher rates of diabetes, hypertension, and use of moderate- to high-dose glucocorticoids, and longer disease duration compared to patients without involvement (all p < 0.05). Moreover, CTD in patients with cardiovascular involvement was more likely to be complicated by ILD (p < 0.01), which manifested as a higher alveolar inflammation score (p < 0.05). In the multivariate analysis, cardiovascular involvement in CTD patients was associated with age, systolic pressure, body mass index, uric acid, disease duration > 2 years, use of moderate- to high-dose glucocorticoids, and ILD with a high alveolar inflammation score. CONCLUSION: Cardiovascular involvement is increased in CTD patients, and is associated with ILD with a higher alveolar inflammation score. Thus, early-stage echocardiography and CT scans should be used to detect potential cardiovascular complications in these patients.