A dual-targeted Gd-based contrast agent for magnetic resonance imaging in tumor diagnosis.

Enhanced magnetic resonance imaging (MRI) has important clinical value in the diagnosis of tumors. Much effort has been made to improve the relaxivity and specificity of contrast agents (CAs) in tumor diagnosis over the past few decades. However, there is still a lack of CAs which not only enhance the signal intensity of tumors rather than surrounding tissues in MRI but also maintain a high signal intensity prolonged for a long time. Herein, we synthesized a dual-targeted CA, RGD-(DOTA-Gd)-TPP (RDP), in which RGD is used to target the αvß3 integrin receptor overexpressed in tumor cells and TPP is used to bind to a mitochondrion further. The structure of RDP was characterized and its properties, such as relaxivity and biosafety, were measured and in vitro and in vivo MRI assays were carried out. It has been proven that RDP has higher relaxivity of aqueous solution than Magnevist used in clinics. Moreover, RDP achieved higher signal intensity and a longer signal duration in tumor imaging. Therefore, RDP can be applied as the potential dual-targeted MRI CA for clinical tumor diagnosis.

Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules.

Objective: To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics. Methods: This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise. Results: The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%. Conclusion: The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study.

PURPOSE: To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol. METHODS: A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared. RESULTS: With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs. CONCLUSION: Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography.

PURPOSE: Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images. MATERIALS AND METHODS: Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently. RESULTS: The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set. CONCLUSION: The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

CT-Based Radiomic Nomogram for the Prediction of Chronic Obstructive Pulmonary Disease in Patients with Lung cancer.

RATIONALE AND OBJECTIVES: To develop and validate a model for predicting chronic obstructive pulmonary disease (COPD) in patients with lung cancer based on computed tomography (CT) radiomic signatures and clinical and imaging features. MATERIALS AND METHODS: We retrospectively enrolled 443 patients with lung cancer who underwent pulmonary function test as the primary cohort. They were randomly assigned to the training (n = 311) or validation (n = 132) set in a 7:3 ratio. Additionally, an independent external cohort of 54 patients was evaluated. The radiomic lung nodule signature was constructed using the least absolute shrinkage and selection operator algorithm, while key variables were selected using logistic regression to develop the clinical and combined models presented as a nomogram. RESULTS: COPD was significantly related to the radiomics signature in both cohorts. Moreover, the signature served as an independent predictor of COPD in the multivariate regression analysis. For the training, internal, and external cohorts, the area under the receiver operating characteristic curve (ROC, AUC) values of our radiomics signature for COPD prediction were 0.85, 0.85, and 0.76, respectively. Additionally, the AUC values of the radiomic nomogram for COPD prediction were 0.927, 0.879, and 0.762 for the three cohorts, respectively, which outperformed the other two models. CONCLUSION: The present study presents a nomogram that incorporates radiomics signatures and clinical and radiological features, which could be used to predict the risk of COPD in patients with lung cancer with one-stop chest CT scanning.

Selenite-induced ROS/AMPK/FoxO3a/GABARAPL-1 signaling pathway modulates autophagy that antagonize apoptosis in colorectal cancer cells.

Previous studies have shown that selenium possessed chemotherapeutic effect against multiple malignant cancers, inducing diverse stress responses including apoptosis and autophagy. Selenite was previously shown to induce apoptosis and autophagy in colorectal cancer cells. However, the relationship between selenite-induced apoptosis and autophagy was not fully understood. Our results revealed a pro-survival role of selenite-induced autophagy against apoptosis in colorectal cancer cells. Real-time PCR array of autophagy-related genes showed that GABARAPL-1 was significantly upregulated in colorectal cancer cells, which was confirmed by western blot and immunofluorescence results. Knockdown of GABARAPL-1 significantly inhibited selenite-induced autophagy and enhanced apoptosis. Furthermore, we found that selenite-induced upregulation of GABARAPL-1 was caused by upregulated p-AMPK and FoxO3a level. Their interaction was correlated with involved in regulation of GABARAPL-1. We observed that activation and inhibition of AMPK influenced both autophagy and apoptosis level via FoxO3a/ GABARAPL-1 signaling, implying the pro-survival role of autophagy against apoptosis. Importantly, we corroborated these findings in a colorectal cancer xenograft animal model with immunohistochemistry and western blot results. Collectively, these results show that sodium selenite could induce ROS/AMPK/FoxO3a/GABARAPL-1-mediated autophagy and downregulate apoptosis in both colorectal cancer cells and colon xenograft model. These findings help to explore sodium selenite as a potential anti-cancer drug in clinical practices.

Application of total parathyroidectomy with auto-transplantation for uremia secondary hyperparathyroidism treatment.

BACKGROUND: Secondary hyperparathyroidism (sHPT) is a common acquired disorder in patients with chronic renal failure. Despite the development of new therapeutic agents, a majority of patients will require parathyroidectomy. The aim of this study was to evaluate total parathyroidectomy with auto-transplantation of trace amounts of parathyroid tissue as a surgical option in uremia sHPT treatment. METHODS: Clinical data of 50 sHPT patients who underwent total parathyroidectomy with auto-transplantation between January 2011 and December 2013 were reviewed retrospectively. Symptoms such as bone pain and fractures, concentrations of intact parathyroid hormone (iPTH), levels of ionized calcium and serum phosphorus, and activity of alkaline phosphatase were recorded before and after parathyroidectomy. RESULTS: After operation, signs of pruritus, bone pain and muscle weakness was disappeared, iPTH level and serum phosphate concentration were declined markedly. No serious postoperative complications were observed. Follow-up observation was around 28 months. One female patient (2%) died 3 months after surgery due to heart failure, and another patient (2%) had persistent disease. All other patients recovered during the follow-up period. CONCLUSIONS: Total parathyroidectomy with auto-transplantation of trace amounts of parathyroid tissue was considered to be a feasible, safe and effective surgical option for the treatment of sHPT.

The effect of continuous blood purification on the prognosis of cardiorenal syndrome patients.

To discuss the effect of continuous blood purification (CBP) on the prognosis of cardiorenal syndrome (CRS) patients. Twenty-seven patients were selected for this study, who had previously been treated at the Blood Purification Center of the General Hospital of Jinan Military Region from May 2007 to October 2010. All patients suffered from chronic heart failure and acute kidney injury. Using a non-invasive hemodynamics monitoring system, we observed the dynamic changes in their cardiac output (CO), systemic vascular resistance (SVR), thoracic fluid capacity (TFC), central venous pressure (CVP), urinary volume, and Acute Physiology and Chronic Health Evaluation II system (APACHEII) score. Individual measurements were obtained at 24 h, 48 h, 72 h, 96 h, and 120 h after CBP treatment. At the same time, levels of serum creatinine (Scr), cysteine proteinase inhibitor Cystatin C (CysC), high sensitivity C-reactive protein (hs-CRP), and serum sodium were observed. The fatality rate at 28 days was also recorded. After 24 h of CBP treatment, acidosis, CO, and disturbances in water and electrolyte levels were all improved. As well, concentrations of Scr and CysC were significantly decreased, and the levels of SVR, TFC, and CVP were also decreased. After 72 h of CBP treatment, hs-CRP levels and APACHE II scores were significantly lower (P < 0.01). From 72 to 120 h of CBP treatment, oliguria was resolved in 24 patients, with improved sensitivity to diuretics. The fatality rate at 28 days was 11.12 %. CBP treatment could improve myocardial function, shorten the oliguria period, decrease the fatality rate, and improve the prognosis of CRS patients.

Total parathyroidectomy with trace amounts of parathyroid tissue autotransplantation as the treatment of choice for secondary hyperparathyroidism: a single-center experience.

BACKGROUND: The aim of the study was to evaluate total parathyroidectomy with trace amounts of parathyroid tissue (30 mg) as a surgical option in secondary hyperparathyroidism (sHPT) treatment. METHODS: From January 2008 to March 2012, 47 patients underwent parathyroidectomy. Comparisons of demographic data, symptoms, and preoperative or postoperative biochemistry were made between total parathyroidectomy with trace amounts of parathyroid tissue autotransplantation group and total parathyroidectomy group. RESULTS: Out of 47 cases, 45 had successful operation. 187 parathyroid glands identified at the initial operation were reported in 47 patients. 43 patients had been diagnosed with parathyroid hyperplasia, and 4 patients had a benign adenoma. After operation, pruritus, bone pain and muscle weakness disappeared, also serum PTH and serum phosphate were declined markedly as well. After discharge, two patients (in total parathyroidectomy group) were readmitted because of postoperative hypoparathyroidism. Graft-dependent recurrence was not observed in an average follow-up of 42 months. CONCLUSIONS: Total parathyroidectomy with sternocleidomastoid muscle trace amounts of parathyroid tissue autotransplantation is considered to be a feasible, safe and effective surgical option for the patients with sHPT.