Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.

BACKGROUND: Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (tumor-SLNs) dual-region computed tomography (CT) radiomics model for predicting SLNM in NSCLC. METHODS: This retrospective study included NSCLC patients who underwent lung resection and SLNs dissection between January 2017 and December 2020. The radiomic features of the tumor and SLNs were extracted from preoperative CT, respectively. Ninety machine learning (ML) models were developed based on tumor region, SLNs region, and tumor-SLNs dual-region. The model performance was assessed by the area under the curve (AUC) and validated internally by fivefold cross-validation. RESULTS: In total, 202 patients were included in this study. ML models based on dual-region radiomics showed good performance for SLNM prediction, with a median AUC of 0.794 (range, 0.686-0.880), which was superior to those of models based on tumor region (median AUC, 0.746; range, 0.630-0.811) and SLNs region (median AUC, 0.700; range, 0.610-0.842). The ML model, which is developed by using the naive Bayes algorithm and dual-region features, had the highest AUC of 0.880 (range of cross-validation, 0.825-0.937) among all ML models. The optimal logistic regression model was inferior to the optimal ML model for predicting SLNM, with an AUC of 0.727. CONCLUSIONS: The CT radiomics showed the potential for accurately predicting SLNM in NSCLC patients. The ML model with dual-region radiomic features has better performance than the logistic regression or single-region models.

MRI-Based Radiomics and Delta-Radiomics Models of the Patella Predict the Radiographic Progression of Osteoarthritis: Data From the FNIH OA Biomarkers Consortium.

RATIONALE AND OBJECTIVES: To analyse the MRI-based radiomics and delta-radiomics features to establish radiomics models for predicting the radiographic progression of osteoarthritis (OA). MATERIALS AND METHODS: The data used in this research come from the dataset of the FNIH Biomarker Consortium Project within the Osteoarthritis Initiative (OAI). 565 participants randomly divided into training and validation groups at a 7:3 ratio. The training cohort consisted of 395 participants and included 202 cases. The validation cohort consisted of 170 participants and included 87 cases. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Support vector machine (SVM) was used to establish radiomics models and clinical and biomarker models for predicting the radiographic progression of OA. The predictive ability of the model was evaluated by the area under the curve (AUC). RESULTS: The baseline, 24 M, Delta, and two combination radiomics models (Baseline and Delta, 24 M and Delta) all showed good predictive performance in the training and validation cohorts, with the combination model exhibiting the best performance. In the training cohort, the AUCs were 0.851 (95% CI: 0.812-0.890), 0.825 (95% CI: 0.784-0.865), 0.804 (95% CI: 0.761-0.847), 0.892 (95% CI: 0.860-0.924) and 0.884 (95% CI: 0.851-0.917), respectively. The AUCs in the validation cohort were 0.741 (95% CI: 0.667-0.814), 0.786 (95% CI: 0.716-0.856), 0.745 (95% CI: 0.671-0.819), 0.781 (95% CI: 0.711-0.851) and 0.802 (95% CI: 0.736-0.869), respectively. As compared, the clinical and biomarker models have AUC < 0.74. The DeLong test showed that the predictive performance of the radiomics models in the training and validation cohorts was significantly better than that of the clinical and biomarker models (P < 0.001). CONCLUSION: The MRI-based radiomics models of the patella all showed good predictive performance performed better than the clinical and biomarker models in predicting the radiographic progression of OA. Delta radiomics can improve the predictive performance of the single time model, the combined model of 24 M and Delta provided the best predictive performance.

Factors affecting osteogenesis and chondrogenic differentiation of mesenchymal stem cells in osteoarthritis.

Osteoarthritis (OA) is a common degenerative joint disease that often involves progressive cartilage degeneration and bone destruction of subchondral bone. At present, clinical treatment is mainly for pain relief, and there are no effective methods to delay the progression of the disease. When this disease progresses to the advanced stage, the only treatment option for most patients is total knee replacement surgery, which causes patients great pain and anxiety. As a type of stem cell, mesenchymal stem cells (MSCs) have multidirectional differentiation potential. The osteogenic differentiation and chondrogenic differentiation of MSCs can play vital roles in the treatment of OA, as they can relieve pain in patients and improve joint function. The differentiation direction of MSCs is accurately controlled by a variety of signaling pathways, so there are many factors that can affect the differentiation direction of MSCs by acting on these signaling pathways. When MSCs are applied to OA treatment, the microenvironment of the joints, injected drugs, scaffold materials, source of MSCs and other factors exert specific impacts on the differentiation direction of MSCs. This review aims to summarize the mechanisms by which these factors influence MSC differentiation to produce better curative effects when MSCs are applied clinically in the future.

Radiomics models based on multi-sequence MRI for preoperative evaluation of MUC4 status in pancreatic ductal adenocarcinoma: a preliminary study.

Background: Mucin 4 (MUC4) overexpression promotes tumorigenesis and increases the aggressiveness of pancreatic ductal adenocarcinoma (PDAC). To date, no study has reported the association between radiomics and MUC4 expression in PDAC. Thus, we aimed to explore the utility of radiomics based on multi-sequence magnetic resonance imaging (MRI) to predict the status of MUC4 expression in PDAC preoperatively. Methods: This retrospective study included 52 patients with PDAC who underwent MRI. The patients were divided into two groups based on MUC4 expression status. Two feature sets were extracted from the arterial and portal phases (PPs) of dynamic contrast-enhanced MRI (DCE-MRI). Univariate analysis, minimum redundancy maximum relevance (MRMR), and principal component analysis (PCA) were performed for the feature selection of each dataset, and features with a cumulative variance of 90% were selected to develop radiomics models. Clinical characteristics were gathered to develop a clinical model. The selected radiomics features and clinical characteristics were modeled by multivariable logistic regression. The combined model integrated radiomics features from different selected data sets and clinical characteristics. The classification metrics were applied to assess the discriminatory power of the models. Results: There were 22 PDACs with a high expression of MUC4 and 30 PDACs with a low expression of MUC4. The area under the receiver operating characteristic (ROC) curve (AUC) values of the arterial phase (AP) model, the PP model, and the combined model were 0.732 (0.591-0.872), 0.709 (0.569-0.849), and 0.861 (0.760-0.961), respectively. The AUC of the clinical model was 0.666 (0.600-0.682). The combined model that was constructed outperformed the AP, the PP, and the clinical models (P<0.05, although no statistical significance was observed in the combined model vs. AP model). Conclusions: Radiomics models based on multi-sequence MRI have the potential to predict MUC4 expression levels in PDAC.

Adiponectin, May Be a Potential Protective Factor for Obesity-Related Osteoarthritis.

Osteoarthritis (OA) is the most common joint disease in elderly individuals and seriously affects quality of life. OA has often been thought to be caused by body weight load, but studies have increasingly shown that OA is an inflammation-mediated metabolic disease. The current existing evidence suggests that OA is associated with obesity-related chronic inflammation as well as abnormal lipid metabolism in obesity, such as fatty acids (FA) and triglycerides. Adiponectin, a cytokine secreted by adipose tissue, can affect the progression of OA by regulating obesity-related inflammatory factors. However, the specific molecular mechanism has not been fully elucidated. According to previous research, adiponectin can promote the metabolism of FA and triglycerides, which indicates that it is a potential protective factor for OA through many mechanisms. This article aims to review the mechanisms of chronic inflammation, FA and triglycerides in OA, as well as the potential mechanisms of adiponectin in regulating chronic inflammation and promoting FA and triglyceride metabolism. Therefore, adiponectin may have a protective effect on obesity-related OA, which could provide new insight into adiponectin and the related mechanisms in OA.

Liver injury associated with acute pancreatitis: The current status of clinical evaluation and involved mechanisms.

Acute pancreatitis (AP) is a very common acute disease, and the mortality rate of severe AP (SAP) is between 15% and 35%. The main causes of death are multiple organ dysfunction syndrome and infections. The mortality rate of patients with SAP related to liver failure is as high as 83%, and approximately 5% of the SAP patients have fulminant liver failure. Liver function is closely related to the progression and prognosis of AP. In this review, we aim to elaborate on the clinical manifestations and mechanism of liver injury in patients with AP.

The characteristics of acute necrotizing pancreatitis in different age stages: An MRI study.

PURPOSE: To study the characteristics of acute necrotizing pancreatitis (ANP) in different age stages and their correlations with the clinical outcomes using magnetic resonance imaging (MRI). METHOD: MRI of 716 patients with acute pancreatitis was retrospectively reviewed to assess the incidence and characteristics of ANP. On MRI, ANP was classified into three subtypes: extrapancreatic necrosis (EPN) alone, pancreatic necrosis (PN) alone and combined necrosis. The extent of necrosis was also quantified on MRI. All patients were divided into three age groups, that is, young,middle-aged and elderly groups, and these characteristics of ANP were compared among the three age groups. The endpoints of patients' clinical outcome were compared among different age groups and different characteristics of ANP. RESULTS: Of the 716 patients, 129(18 %) were identified as ANP on MRI. The prevalence of ANP in the elderly group was the highest (28.9 %, p < 0.05). The patients in the middle-age and the elderly groups exhibited a higher risk of combined necrosis (56.9 %, 55.8 %; respectively), and elderly patients more frequently had extensive extrapancreatic involvement compared with young patients (65.9 % vs 21.4 %; p = 0.004); however, PN alone was more common in young patients. These characteristics of ANP were significantly bound up with clinical outcomes. CONCLUSIONS: Different subtypes of ANP have different outcomes. More importantly, age needs to be considered as a factor of special concern in development of ANP.

MR imaging for acute pancreatitis: the current status of clinical applications.

Acute pancreatitis is a common clinical acute abdomen. Imaging examinations play an important role in the management of acute pancreatitis. MR imaging is a noninvasive examination with high tissue contrast and a variety of acquisition sequences that can help determine the diagnosis, complications and severity of acute pancreatitis. The acute pancreatitis classification working group modified the Atlanta classification in 2012 to improve clinical evaluations and standardize the radiologic nomenclature for acute pancreatitis. In particular, the redefinition of necrotizing pancreatitis offers a new understanding of this disease. In clinical practice, there is still a lack of unifying standards between radiologists and physicians, such as for the imaging features of pseudocysts, walled-off necrosis, peripancreatic necrosis and especially for the MR imaging features of acute pancreatitis. In this article, we review the 2012 revised Atlanta classification of acute pancreatitis and recent advances in the clinical applications of MR imaging (MRI) in acute pancreatitis by showing how MRI can provide more optimized information for clinical diagnosis and treatment plan.

Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis.

OBJECTIVES: To predict the recurrence of acute pancreatitis (AP) by constructing a radiomics model of contrast-enhanced computed tomography (CECT) at AP first attack. METHODS: We retrospectively enrolled 389 first-attack AP patients (271 in the primary cohort and 118 in the validation cohort) from three tertiary referral centers; 126 and 55 patients endured recurrent attacks in each cohort. Four hundred twelve radiomics features were extracted from arterial and venous phase CECT images, and clinical characteristics were gathered to develop a clinical model. An optimal radiomics signature was chosen using a multivariable logistic regression or support vector machine. The radiomics model was developed and validated by incorporating the optimal radiomics signature and clinical characteristics. The performance of the radiomics model was assessed based on its calibration and classification metrics. RESULTS: The optimal radiomics signature was developed based on a multivariable logistic regression with 10 radiomics features. The classification accuracy of the radiomics model well predicted the recurrence of AP for both the primary and validation cohorts (87.1% and 89.0%, respectively). The area under the receiver operating characteristic curve (AUC) of the radiomics model was significantly better than that of the clinical model for both the primary (0.941 vs. 0.712, p = 0.000) and validation (0.929 vs. 0.671, p = 0.000) cohorts. Good calibration was observed for all the models (p > 0.05). CONCLUSIONS: The radiomics model based on CECT performed well in predicting AP recurrence. As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to potential precautions. KEY POINTS: • The incidence of recurrence after an initial episode of acute pancreatitis is high, and quantitative methods for predicting recurrence are lacking. • The radiomics model based on contrast-enhanced computed tomography performed well in predicting the recurrence of acute pancreatitis. • As a quantitative method, radiomics exhibits promising performance in terms of alerting recurrent patients to the potential need to take precautions.

Could intravoxel incoherent motion diffusion-weighted magnetic resonance imaging be feasible and beneficial to the evaluation of gastrointestinal tumors histopathology and the therapeutic response?

Gastrointestinal tumors (GTs) are among the most common tumors of the digestive system and are among the leading causes of cancer death worldwide. Functional magnetic resonance imaging (MRI) is crucial for assessment of histopathological changes and therapeutic responses of GTs before and after chemotherapy and radiotherapy. A new functional MRI technique, intravoxel incoherent motion (IVIM), could reveal more detailed useful information regarding many diseases. Currently, IVIM is widely used for various tumors because the derived parameters (diffusion coefficient, D; pseudo-perfusion diffusion coefficient, D*; and perfusion fraction, f) are thought to be important surrogate imaging biomarkers for gaining insights into tissue physiology. They can simultaneously reflect the microenvironment, microcirculation in the capillary network (perfusion) and diffusion in tumor tissues without contrast agent intravenous administration. The sensitivity and specificity of these parameters used in the evaluation of GTs vary, the results of IVIM in GTs are discrepant and the variability of IVIM measurements in response to chemotherapy and/or radiotherapy in these studies remains a source of controversy. Therefore, there are questions as to whether IVIM diffusion-weighted MRI is feasible and helpful in the evaluation of GTs, and whether it is worthy of expanded use.

The role and the potential regulatory pathways of high expression of forkhead box C1 in promoting tumor growth and metastasis of basal-like breast cancer.

PURPOSE: To investigate the role of high forkhead box C1 (FOXC1) expression in basal-like breast cancer (BLBC) in vitro and vivo and the underlying regulatory mechanism. METHODS: The lentivirus vector with green fluorescent protein (GFP) was used. MDA-MB-231 cells expressing consistently high levels of FOXC1 (FOXC1-MDA-MB-231) were established. The parental MDA-MB-231 cells served as controls. Western blot analysis was used to determine the FOXC1 expression. The invasion capability was tested using the Trans-well assay. The tumorigenicity and the pulmonary metastatic ability were determined in mice in vivo. Histopathology and microarray processing and analysis were performed, and the various pathways involved and the related genes were analyzed. RESULTS: The invasion ability of FOXC1-MDA-MB-231 cells was enhanced significantly (p<0.01). Pulmonary metastases were observed in vivo in 3 of 5 mice administered FOXC1-MDA-MB-231 cells through tail vein injection. However, no pulmonary metastatic lesions were observed with MDA-MB-231 cells. The average tumor volume was larger in the mice injected with FOXC1-MDA-MB-231 than in the control mice (p<0.05). The expression of Ki-67 in the FOXC1-MDA-MB-231 injected mice was higher than in the control mice. Ten of the most gene-enriched pathways and the critical genes (IL-6 and SNAI2) were found to be related to BLBC. CONCLUSION: Elevated expression of FOXC1 enhanced the invasion ability of BLCB cells in vitro and promoted tumor growth and metastatic ability in vivo. This function may be regulated by many gene-enriched pathways and some critical genes.

The effect of superparamagnetic iron oxide with iRGD peptide on the labeling of pancreatic cancer cells in vitro: a preliminary study.

The iRGD peptide loaded with iron oxide nanoparticles for tumor targeting and tissue penetration was developed for targeted tumor therapy and ultrasensitive MR imaging. Binding of iRGD, a tumor homing peptide, is mediated by integrins, which are widely expressed on the surface of cells. Several types of small molecular drugs and nanoparticles can be transfected into cells with the help of iRGD peptide. Thus, we postulate that SPIO nanoparticles, which have good biocompatibility, can also be transfected into cells using iRGD. Despite the many kinds of cell labeling studies that have been performed with SPIO nanoparticles and RGD peptide or its analogues, only a few have applied SPIO nanoparticles with iRGD peptide in pancreatic cancer cells. This paper reports our preliminary findings regarding the effect of iRGD peptide (CRGDK/RGPD/EC) combined with SPIO on the labeling of pancreatic cancer cells. The results suggest that SPIO with iRGD peptide can enhance the positive labeling rate of cells and the uptake of SPIO. Optimal functionalization was achieved with the appropriate concentration or concentration range of SPIO and iRGD peptide. This study describes a simple and economical protocol to label panc-1 cells using SPIO in combination with iRGD peptide and may provide a useful method to improve the sensitivity of pancreatic cancer imaging.

CT and MR imaging patterns for pancreatic carcinoma invading the extrapancreatic neural plexus (Part I): Anatomy, imaging of the extrapancreatic nerve.

Pancreatic carcinoma is an extremely high-grade malignant tumor with fast development and high mortality. The incidence of pancreatic carcinoma continues to increase. Peripancreatic invasion and metastasis are the main characteristics and important prognostic factors in pancreatic carcinoma, especially invasion into the nervous system; pancreatic nerve innervation includes the intrapancreatic and extrapancreatic nerves. A strong grasp of pancreatic nerve innervation may contribute to our understanding of pancreatic pain modalities and the metastatic routes for pancreatic carcinomas. Computed tomography (CT) and magnetic resonance imaging (MRI) are helpful techniques for depicting the anatomy of extrapancreatic nerve innervation. The purpose of the present work is to show and describe the anatomy of the extrapancreatic neural plexus and to elucidate its characteristics using CT and MRI, drawing on our own previous work and the research findings of others.

CT and MR imaging patterns for pancreatic carcinoma invading the extrapancreatic neural plexus (Part II): Imaging of pancreatic carcinoma nerve invasion.

Computed tomography (CT) and magnetic resonance imaging (MRI) are excellent modalities which have the ability to detect, depict and stage the nerve invasion associated with pancreatic carcinoma. The aim of this article is to review the CT and MR patterns of pancreatic carcinoma invading the extrapancreatic neural plexus and thus provide useful information which could help the choice of treatment methods. Pancreatic carcinoma is a common malignant neoplasm with a high mortality rate. There are many factors influencing the prognosis and treatment options for those patients suffering from pancreatic carcinoma, such as lymphatic metastasis, adjacent organs or tissue invasion, etc. Among these factors, extrapancreatic neural plexus invasion is recognized as an important factor when considering the management of the patients.