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Abstract Radiotherapy-induced immune activation holds great promise for optimizing cancer treatment efficacy. Here, we describe a clinically used radiosensitizer hafnium oxide (HfO2) that was core coated with a MnO2 shell followed by a glucose oxidase (GOx) doping nanoplatform (HfO2@MnO2@GOx, HMG) to trigger ferroptosis adjuvant effects by glutathione depletion and reactive oxygen species production. This ferroptosis cascade potentiation further sensitized radiotherapy by enhancing DNA damage in 4T1 breast cancer tumor cells. The combination of HMG nanoparticles and radiotherapy effectively activated the damaged DNA and Mn2+-mediated cGAS-STING immune pathway in vitro and in vivo. This process had significant inhibitory effects on cancer progression and initiating an anticancer systemic immune response to prevent distant tumor recurrence and achieve long-lasting tumor suppression of both primary and distant tumors. Furthermore, the as-prepared HMG nanoparticles “turned on” spectral computed tomography (CT)/magnetic resonance dual-modality imaging signals, and demonstrated favorable contrast enhancement capabilities activated by under the GSH tumor microenvironment. This result highlighted the potential of nanoparticles as a theranostic nanoplatform for achieving molecular imaging guided tumor radiotherapy sensitization induced by synergistic immunotherapy.

Abstract As a famous drug delivery system (DDS), mesoporous organosilica nanoparticles (MON) are degraded slowly in vivo and the degraded components are not useful for cell nutrition or cancer theranostics, and superparamagnetic iron oxide nanoparticles (SPION) are not mesoporous with low drug loading content (DLC). To overcome the problems of MON and SPION, we developed mesoporous SPIONs (MSPIONs) with an average diameter of 70 nm and pore size of 3.9 nm. Sorafenib (SFN) and/or brequinar (BQR) were loaded into the mesopores of MSPION, generating SFN@MSPION, BQR@MSPION and SFN/BQR@MSPION with high DLC of 11.5% (SFN), 10.1% (BQR) and 10.0% (SNF + BQR), demonstrating that our MSPION is a generic DDS. SFN/BQR@MSPION can be used for high performance ferroptosis therapy of tumors because: (1) the released Fe2+/3+ in tumor microenvironment (TME) can produce •OH via Fenton reaction; (2) the released SFN in TME can inhibit the cystine/glutamate reverse transporter, decrease the intracellular glutathione (GSH) and GSH peroxidase 4 levels, and thus enhance reactive oxygen species and lipid peroxide levels; (3) the released BQR in TME can further enhance the intracellular oxidative stress via dihydroorotate dehydrogenase inhibition. The ferroptosis therapeutic mechanism, efficacy and biosafety of MSPION-based DDS were verified on tumor cells and tumor-bearing mice.

BackgroundAccurate tumor target contouring and T staging are vital for precision radiation therapy in nasopharyngeal carcinoma (NPC). Identifying T-stage and contouring the Gross tumor volume (GTV) manually is a laborious and highly time-consuming process. Previous deep learning-based studies have mainly been focused on tumor segmentation, and few studies have specifically addressed the tumor staging of NPC.ObjectivesTo bridge this gap, we aim to devise a model that can simultaneously identify T-stage and perform accurate segmentation of GTV in NPC.Materials and methodsWe have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: delineating the tumor contour and identifying T-stage. Our retrospective study involved contrast-enhanced T1-weighted images (CE-T1WI) of 320 NPC patients (T-stage: T1-T4) collected between 2017 and 2020 at our institution, which were randomly allocated into three cohorts for three-fold cross-validations, and conducted the external validation using an independent test set. We evaluated the predictive performance using the area under the receiver operating characteristic curve (ROC-AUC) and accuracy (ACC), with a 95% confidence interval (CI), and the contouring performance using the Dice similarity coefficient (DSC) and average surface distance (ASD).ResultsOur multi-task model exhibited sound performance in GTV contouring (median DSC: 0.74; ASD: 0.97 mm) and T staging (AUC: 0.85, 95% CI: 0.82–0.87) across 320 patients. In early T category tumors, the model achieved a median DSC of 0.74 and ASD of 0.98 mm, while in advanced T category tumors, it reached a median DSC of 0.74 and ASD of 0.96 mm. The accuracy of automated T staging was 76% (126 of 166) for early stages (T1-T2) and 64% (99 of 154) for advanced stages (T3-T4). Moreover, experimental results show that our multi-task model outperformed the other single-task models.ConclusionsThis study emphasized the potential of multi-task model for simultaneously delineating the tumor contour and identifying T-stage. The multi-task model harnesses the synergy between these interrelated learning tasks, leading to improvements in the performance of both tasks. The performance demonstrates the potential of our work for delineating the tumor contour and identifying T-stage and suggests that it can be a practical tool for supporting clinical precision radiation therapy.

Abstract Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

In order to investigate the diffusion mechanism of slurry in post-pile grouting, this study develops a formula for calculating the diameter and the climb height of the cement core of jet grouting (CCJG). This research conducts field orthogonal tests using a self-developed grouting nozzle to analyze the effects of disturbance pressure (DP), disturbance time (DT), grouting pressure (GP), and the water–cement ratio (W/C) on the dimensions and strength of the CCJG. The findings revealed that the theoretical formula for calculating the diameter of the CCJG deviates by approximately 8% from the field test results, confirming the formula’s validity. In addition, the DP and DT significantly influence the volume of the CCJG, displaying a linear increase with their augmentation. Conversely, the W/C and DP predominantly affect the CCJG’s strength. Notably, an increase in the W/C results in diminished strength, whereas an increase in the DP enhances it. In addition, soil reinforcement is achieved through replacement, mixing, and compaction as the grout flows outward from the center of the grouting hole. These insights offer a theoretical foundation and technical support for effective grouting construction practices.

Background Despite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of this study was to develop and validate a CT imaging biomarker to predict the immunotherapy response in patients with GC and investigate the associated immune infiltration patterns.Methods This retrospective study included 294 GC patients who received anti-PD-1/PD-L1 immunotherapy from three independent medical centers between January 2017 and April 2022. A radiomics score (RS) was developed from the intratumoral and peritumoral features on pretreatment CT images to predict immunotherapy-related progression-free survival (irPFS). The performance of the RS was evaluated by the area under the time-dependent receiver operating characteristic curve (AUC). Multivariable Cox regression analysis was performed to construct predictive nomogram of irPFS. The C-index was used to determine the performance of the nomogram. Bulk RNA sequencing of tumors from 42 patients in The Cancer Genome Atlas was used to investigate the RS-associated immune infiltration patterns.Results Overall, 89 of 294 patients (median age, 57 years (IQR 48–66 years); 171 males) had an objective response to immunotherapy. The RS included 13 CT features that yielded AUCs of 12-month irPFS of 0.787, 0.810 and 0.785 in the training, internal validation, and external validation 1 cohorts, respectively, and an AUC of 24-month irPFS of 0.805 in the external validation 2 cohort. Patients with low RS had longer irPFS in each cohort (p

The preparation of Pickering emulsions from nanoparticles commonly requires modification of the particles’ surface chemistry. Here, the authors demonstrate a chemical modifier-free approach for the preparation of long-term stable Pickering emulsions for SERS sensing and catalytic applications.

For most healthcare organizations, a significant challenge today is predicting diseases with incomplete data information, often resulting in isolation. Federated learning (FL) solves the issue of data silos by enabling remote local machines to train a globally optimal model collaboratively without the need for sharing data. In this research, we present FedDK, a serverless framework designed to obtain personalized models for each federation through data from local federations using convolutional neural networks and training through FL. Our approach involves using convolutional neural networks (CNNs) to accumulate common knowledge and transfer it using knowledge distillation, which helps prevent common knowledge forgetting. Additionally, the missing common knowledge is filled circularly between each federation, culminating in a personalized model for each group. This novel design leverages federated, deep, and integrated learning methods to produce more accurate machine-learning models. Our federated model exhibits superior performance to local and baseline FL methods, achieving significant advantages.

Exposure to aristolochic acid (AA) is of increased concern due to carcinogenic and nephrotoxic effects, and incidence of aristolochic acid nephropathy (AAN) is increasing. This study characterizes renal alterations during the acute phase of AAN using parametric magnetic resonance imaging (MRI). An AAN and a control group of male Wistar rats received administration of aristolochic acid I (AAI) and polyethylene glycol (PEG), respectively, for six days. Both groups underwent MRI before and 2, 4 and 6 days after AAI or PEG administration. T2 relaxation times and apparent diffusion coefficients (ADCs) were determined for four renal layers. Serum creatinine levels (sCr) and blood urea nitrogen (BUN) were measured. Tubular injury scores (TIS) were evaluated based on histologic findings. Increased T2 values were detected since day 2 in the AAN group, but decreased ADCs and increased sCr levels and BUN were not detected until day 4. Significant linear correlations were observed between T2 of the cortex and the outer stripe of outer medulla and TIS. Our results demonstrate that parametric MRI facilitates early detection of renal injury induced by AAI in a rat model. T2 mapping may be a valuable tool for assessing kidney injury during the acute phase of AAN.

Objective The present systematic review and meta-analysis of randomized clinical trials (RCTs) aimed to investigate the effects of pulmonary rehabilitation in individuals with chronic obstructive pulmonary disease (COPD). Methods The RCTs of pulmonary rehabilitation programs published between 1999 and 2021 were retrieved from electronic databases (PubMed, Cochrane Library, and Embase). Two reviewers independently assessed the topical relevance and trial quality and extracted data for meta-analysis using the Stata software version 14.0. Results A total of 39 trials involving 2,397 participants with COPD were evaluated. We found that patients who received pulmonary rehabilitation program had significant improvement in the 6-min walk test (6MWT), St. George Respiratory Questionnaire score, and the modified British Medical Research Council score as compared to those who received usual care. Yoga and Tai Chi showed significant improvement in the forced expiratory volume (FEV1)% in 1 s predicted value. However, no significant difference was detected in the modified Borg score, forced vital capacity (FVC), and FEV1/FVC predicted value between the pulmonary rehabilitation and usual care groups. Conclusion Yoga and Tai Chi showed a significant improvement in the FEV1% predicted value. Also, pulmonary rehabilitation program improved the exercise capacity, the quality of life, and dyspnoea in patients with COPD.Key messages A total of 39 trials involving 2,397 participants with COPD were evaluated. We found that patients who received pulmonary rehabilitation program had significant improvement in the 6MWT, St. George Respiratory Questionnaire score, and the modified British Medical Research Council score as compared to those who received usual care. Yoga and Tai Chi showed significant improvement in the FEV1% predicted value. No significant difference was detected in the modified Borg score, FVC, and FEV1/FVC predicted value between the pulmonary rehabilitation and usual care groups.

Tumour microenvironment has been linked with immunotherapy response in gastric cancer. Here, the authors use CT-based radiomics to predict neutrophils-to-lymphocyte ratio and response to immunotherapy.

Abstract Magnetic resonance imaging (MRI) has been widely using in clinical diagnosis, and contrast agents (CAs) can improve the sensitivity MRI. To overcome the problems of commercial Gd chelates-based T 1 CAs, commercial magnetic iron oxide nanoparticles (MIONs)-based T 2 CAs, and reported exceedingly small MIONs (ES-MIONs)-based T 1 CAs, in this study, a facile co-precipitation method was developed to synthesize biodegradable and biocompatible ES-MIONs with excellent water-dispersibility using poly (aspartic acid) (PASP) as a stabilizer for T 1-weighted MRI of tumors. After optimization of the synthesis conditions, the final obtained ES-MION9 with 3.7 nm of diameter has a high r 1 value (7.0 ± 0.4 mM−1 s−1) and a low r 2/r 1 ratio (4.9 ± 0.6) at 3.0 T. The ES-MION9 has excellent water dispersibility because of the excessive –COOH from the stabilizer PASP. The pharmacokinetics and biodistribution of ES-MION9 in vivo demonstrate the better tumor targetability and MRI time window of ES-MION9 than commercial Gd chelates. T 1-weighted MR images of aqueous solutions, cells and tumor-bearing mice at 3.0 T or 7.0 T demonstrate that our ES-MION9 has a stronger capability of enhancing the MRI contrast comparing with the commercial Gd chelates. The MTT assay, live/dead staining of cells, and H&E-staining indicate the non-toxicity and biosafety of our ES-MION9. Consequently, the biodegradable and biocompatible ES-MION9 with excellent water-dispersibility is an ideal T 1-weighted CAs with promising translational possibility to compete with the commercial Gd chelates.

Abstract Cartilage regeneration after injury is still a great challenge in clinics, which suffers from its avascularity and poor proliferative ability. Herein we designed a novel biocompatible cellulose nanocrystal/GelMA (gelatin‐methacrylate anhydride)/HAMA (hyaluronic acid‐methacrylate anhydride)‐blended hydrogel scaffold system, loaded with synthetic melanin nanoparticles (SMNP) and a bioactive drug kartogenin (KGN) for theranostic purpose. We found that the SMNP‐KGN/Gel showed favorable mechanical property, thermal stability, and distinct magnetic resonance imaging (MRI) contrast enhancement. Meanwhile, the sustained release of KGN could recruit bone‐derived mesenchymal stem cells to proliferate and differentiate into chondrocytes, which promoted cartilage regeneration in vitro and in vivo. The hydrogel degradation and cartilage restoration were simultaneously monitored by multiparametric MRI for 12 weeks, and further confirmed by histological analysis. Together, these results validated the multifunctional hydrogel as a promising tissue engineering platform for noninvasive imaging‐guided precision therapy in cartilage regenerative medicine.

Abstract To surmount the major concerns of commercial small molecule Gd chelates and reported Gd‐based contrast agents (GBCAs) for magnetic resonance imaging (MRI), a new concept of organogadolinium macrochelates (OGMCs) constructed from the coordination between Gd3+ and macromolecules is proposed. A library of macromolecules were screened for Gd3+ coordination, and two candidates [i.e., poly(acrylic acid) (PAA), and poly(aspartic acid) (PASP)] succeeded in OGMC formation. Under optimized synthesis conditions, both Gd‐PAA12 and Gd‐PASP11 OGMCs are outstanding T1‐weighted CAs owing to their super high r1 values (> 50 mm−1 s−1, 3.0 T) and ultralow r2/r1 ratios (< 1.6, 3.0 T). The ferromagnetism of OGMCs is completely different from the paramagnetism of commercial and reported GBCAs. The ferromagnetism is very weak (Ms

ObjectiveThis study aimed to evaluate cerebral amide proton transfer signal intensity (SI) among controls, hypoxic–ischemic encephalopathy (HIE) neonates with normal conventional magnetic resonance imaging (HIE/MRI−) findings, and HIE neonates with abnormal conventional MRI (HIE/MRI+) findings.MethodsForty neonates diagnosed with neonatal HIE and sixteen normal neonates were evaluated. All neonates underwent conventional MRI scans and APT imaging. Cerebral APT SIs were compared to identify cerebral regions with significant APT changes among sixteen controls, thirteen HIE/MRI− patients, and twenty–seven HIE/MRI+ patients.ResultsSignificantly increased APT SIs were observed in the HIE/MRI− group compared with controls, in the left insula, right occipital lobe, left cingulate gyrus (posterior part), and corpus callosum. Significantly increased APT SIs were found in the HIE/MRI+ group compared with controls, in the right anterior temporal lobe (medial part), anterior parts of the right parahippocampal and ambient gyri, left superior temporal gyrus (middle part), left insula, left cingulate gyrus (posterior part), and right lentiform nucleus. No significant APT SI differences were observed in the cerebellum and brainstem among the three groups.ConclusionAmide proton transfer imaging plays an important role in detecting hypoxic–ischemic encephalopathy regardless of conventional MRI findings. Changes in APT signal intensity may provide important insights into the characterization of the cerebral internal environment. This study suggests that APT imaging could be used as a complement to conventional MRI in the detection of hypoxic–ischemic encephalopathy in clinical practice.

Coastline changes have significant impacts on coastal hydrodynamics. Xiangshan Bay is a semi-closed and long-narrow bay located in Zhejiang Province, China. Its coastline has changed dramatically in recent decades; however, the variations in the small-scale hydrodynamics in the changed coastline areas have not been carefully studied. This study uses the Finite-Volume Community Ocean Model (FVCOM) to design a set of control experiments and five sets of compared experiments targeting the areas with significant coastline changes in Xiangshan Bay over the past 21 years. It was found that the coastline changes at the mouth of the bay, such as areas near Meishan Island and Dasong, have a significant impact on both residual currents and tidal currents, changing the amplitudes and phase distributions of the tides. Coastline changes in the inner bay have lesser impacts on hydrodynamics, mainly affecting the small-scale areas in the vicinity.

Chemometric analysis is often used as an effective indicator of the supply capacity of nutrients in soil–plant systems and their biogeochemical cycles. Understanding ecological stoichiometric characteristics of C, N and P in soils under various land uses is crucial to guide ecological restoration and agricultural cultivation in karst rocky desertification region. However, data on ecological stoichiometry at different land uses in karst areas is limited. This study aimed to evaluate the effects of different land uses on soil ecological stoichiometric ratios and further identify the factors that influence soil ecological stoichiometric ratios. The topsoil from forest, shrub and cultivated land (paddy field and dry land) both in a karst area and non-karst area (as a reference) of Mashan County was sampled to investigate the spatial variance of the ecological stoichiometric characteristics of C, N and P under different land uses. The results show that: (1) Land-use types significantly determined the spatial heterogeneity of soil ecological stoichiometry in karst areas. (2) Soil organic carbon (SOC) was not significantly different between shrubs in the karst area and forests in the non-karst area (p = 0.595), but there were virtual differences in total nitrogen (TN), total phosphorus (TP), C:N, C:P and N:P between shrubs in the karst area and forests in the non-karst area (p < 0.01). (3) The contents of SOC, TN, and C:P, N:P in the study area were all generally higher in forests than those in cultivated land, and the content of TP was lower in forests than cultivated land, while C:N in cultivated land was higher than in shrubs in karst areas, and C:N was higher in forests than in cultivated land in non-karst areas. (4) Available nitrogen (AvN) was the main factor influencing stoichiometry in shrubs in karst areas, while pH, AvN, available phosphorus and elevation were the main factors in forests in non-karst areas, indicating that these factors significantly affect the soil ecological stoichiometric ratio during land-use changes. This study helps to understand the variations in soil ecological stoichiometric ratios under land-use changes. It provides guidance for the sustainable management of revegetation in karst regions in southwest China.

The Emotional Inhibition Scale (EIS) is a brief measure based on a four-factor model with documented validity in a mood disorder sample that may be useful for assessing emotional inhibition in patient populations, such as individuals with cancer. The present study adapted the EIS to Chinese conditions and examined the psychometric characteristics of the EIS in patients with cancer. The recruited participants comprised a sample of 100 patients (sample 1) and a sample of 202 patients (sample 2) with cancer. The two samples (sample 1 and sample 2) with cancer completed surveys including the EIS. The Toronto Alexithymia Scale-20 was completed by the two sample groups to assess criterion validity. Statistical analyses included internal consistency (sample 1), exploratory factor analyses (EFAs; sample 1), and confirmatory factor analyses (CFAs; sample 2). The results showed that EFA and CFA confirmed the four-factor solution proposed by the original authors (verbal inhibition, self-control, disguise of feelings, and timidity). The internal consistency and test-retest reliability of the EIS were satisfactory. In conclusion, the EIS demonstrated acceptable reliability and validity for assessing emotional inhibition in Chinese-speaking patients with cancer and may be a useful measure for assessing the level of emotional inhibition and the effect of emotional disclosure interventions.

Summary: Background: The tumour stroma microenvironment plays an important part in disease progression and its composition can influence treatment response and outcomes. Histological evaluation of tumour stroma is limited by access to tissue, spatial heterogeneity, and temporal evolution. We aimed to develop a radiological signature for non-invasive assessment of tumour stroma and treatment outcomes. Methods: In this multicentre, retrospective study, we analysed CT images and outcome data of 2209 patients with resected gastric cancer from five independent cohorts recruited from two centres (Nanfang Hospital of Southern Medical University [Guangzhou, China] and Sun Yat-sen University Cancer Center [Guangzhou, China]). Patients with histologically confirmed gastric cancer, at least 15 lymph nodes harvested, preoperative abdominal CT available, and complete clinicopathological and follow-up data were eligible for inclusion. Tumour tissue was collected for patients in the training cohort (321 patients), internal validation cohort one (246 patients), and external validation cohort one (128 patients). Four stroma classes were defined according to the protein expression of α-smooth muscle actin and periostin assessed by immunohistochemistry. The primary objective was to predict the histologically based stroma classes by using preoperative CT images. We trained a deep convolutional neural network model using the training cohort and tested the model in the internal and external validation cohort one. We evaluated the model's association with prognosis in the training cohort, two internal, and two external validation cohorts and compared outcomes of patients who received or did not receive adjuvant chemotherapy. Findings: The deep-learning model achieved a high diagnostic accuracy for assessing tumour stroma in both internal validation cohort one (area under the receiver operating characteristic curve [AUC] 0·96–0·98]) and external validation cohort one (AUC 0·89–0·94). The stromal imaging signature was significantly associated with disease-free survival and overall survival in all cohorts (p

PurposeThis study aimed to identify preoperative gadoxetic acid-enhanced MRI features and establish a nomogram for predicting early recurrence (≤ 2 years) of hepatocellular carcinoma (HCC) after ablation therapy.MethodsA total of 160 patients who underwent gadoxetic acid-enhanced MRI and ablation HCC therapy from January 2015 to June 2018, were included retrospectively and divided into a training cohort (n = 112) and a validation cohort (n = 48). Independent clinical risk factors and gadoxetic acid-enhanced MRI features associated with early recurrence were identified by univariate and multivariate logistic regression analysis and used for construction of a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsAlpha-fetoprotein (AFP) level, tumor number, arterial peritumoral enhancement, satellite nodule and peritumoral hypointensity at hepatobiliary phases in the training cohort were identified as independent risk factors for early recurrence after ablation. A new nomogram that was constructed with these five features showed an area under the curve (AUC) of 0.843 (95%CI 0.771-0.916) and 0.835 (95%CI 0.713-0.956) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had good consistency and clinical utility.ConclusionsA new nomogram that was constructed using four preoperative gadoxetic acid-enhanced MRI features and serum AFP level can predict the risk of early HCC recurrence after ablation therapy with AUC up to 0.843. The strong performance of this nomogram may help hepatologists to categorize patients’ recurrent risk to guide selecting treatment options and improve postoperative management.

This study aimed to develop and validate a prognostic nomogram for recurrence-free survival (RFS) after surgery in the absence of adjuvant therapy to guide the selection for adjuvant imatinib therapy based on Residual Neural Network (ResNet).The ResNet model was developed based on contrast-enhanced computed tomography (CE-CT) in a training cohort consisted of 80 patients pathologically diagnosed gastrointestinal sromal tumors (GISTs) and validated in internal and external validation cohort respectively. Independent clinicopathologic factors were integrated with the ResNet model to construct the individualized nomogram. The performance of the nomogram was evaluated in regard to discrimination, calibration, and clinical usefulness.The ResNet model was significantly associated with RFS. Integrable predictors in the individualized ResNet nomogram included the tumor site, size, and mitotic count. Compared with modified NIH, AFIP, and clinicopathologic nomogram, both ResNet nomogram and ResNet model showed a better discrimination capability with AUCs of 0·947(95%CI, 0·910–0·984) for 3-year-RFS, 0·918(0·852–0·984) for 5-year-RFS, and AUCs of 0·912 (0·851–0·973) for 3-year-RFS, 0·887(0·816–0·960) for 5-year-RFS, respectively. Calibration curve shows the good calibration of the nomogram in terms of the agreement between the estimated and the observed 3- and 5- year outcomes. Decision curve analysis showed that the ResNet nomogram had a higher overall net benefit.In conclusion, we presented a deep learning-based prognostic nomogram to predict RFS after resection of localized primary GISTs with excellent performance and could be a potential tool to select patients for adjuvant imatinib therapy. Keywords: Gastrointestinal Stromal Tumors, Deep Learning, Residual Neural Network, Recurrence-free Survival, Imatinib

Objective: The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status.Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram.Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703–0.799), 0.802 (95%CI, 0.691–0.912), and 0.745 (95%CI, 0.683–0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748–0.836), 0.870 (95%CI, 0.795–0.946), and 0.815 (95%CI, 0.763–0.867), respectively.Conclusions: CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.

Numerous tissue-engineered constructs have been investigated as bone scaffolds in regenerative medicine. However, it remains challenging to non-invasively monitor the biodegradation and remodeling of bone grafts after implantation. Herein, silk fibroin/hydroxyapatite scaffolds incorporated with ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles were successfully synthesized, characterized, and implanted subcutaneously into the back of nude mice. The USPIO labeled scaffolds showed good three-dimensional porous structures and mechanical property, thermal stability for bone repair. After loaded with bone marrow-derived mesenchymal stem cells (BMSCs), the multifunctional scaffolds promoted cell adhesion and growth, and facilitated osteogenesis by showing increased levels of alkaline phosphatase activity and up-regulation of osteoblastic genes. Furthermore, in vivo quantitative magnetic resonance imaging (MRI) results provided valuable information on scaffolds degradation and bone formation simultaneously, which was further confirmed by computed tomography and histological examination. These findings demonstrated that the incorporation of USPIO into BMSCs-loaded multifunctional scaffold system could be feasible to noninvasively monitor bone regeneration by quantitative MRI. This tissue engineering strategy provides a promising tool for translational application of bone defect repair in clinical scenarios.

To develop and validate a radiomics signature for the prediction of gastric cancer (GC) survival and chemotherapeutic benefits. In this multicenter retrospective analysis, we analyzed the radiomics features of portal venous-phase computed tomography in 1591 consecutive patients. A radiomics signature was generated by using the Lasso-Cox regression model in 228 patients and validated in internal and external validation cohorts. Radiomics nomograms integrating the radiomics signature were constructed, demonstrating the incremental value of the radiomics signature to the traditional staging system for individualized survival estimation. The performance of the nomograms was assessed with respect to calibration, discrimination, and clinical usefulness. The radiomics signature consisted of 19 selected features and was significantly associated with DFS (disease-free survival) and OS (overall survival). Multivariate analysis demonstrated that the radiomics signature was an independent prognostic factor. Incorporating the radiomics signature into the radiomics-based nomograms resulted in better performance for the estimation of DFS and OS than the clinicopathological nomograms and TNM staging system, with improved accuracy of the classification of survival outcomes. Further analysis showed that stage II and III patients with higher radiomics scores exhibited a favorable response to chemotherapy. In conclusion, the newly developed radiomics signature is a powerful predictor of DFS and OS, and it may predict which patients with stage II and III GC benefit from chemotherapy. Keywords: Gastric cancer, Radiomics signature, Prognosis, Chemotherapy

PURPOSE:We aimed to develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).METHODS:A total of 304 eligible patients with HCC were randomly divided into training (n=184) and independent validation (n=120) cohorts. Portal venous and arterial phase computed tomography data of the HCCs were collected to extract radiomic features. Using the least absolute shrinkage and selection operator algorithm, the training set was processed to reduce data dimensions, feature selection, and construction of a radiomics signature. Then, a prediction model including the radiomics signature, radiologic features, and alpha-fetoprotein (AFP) level, as presented in a radiomics nomogram, was developed using multivariable logistic regression analysis. The radiomics nomogram was analyzed based on its discrimination ability, calibration, and clinical usefulness. Internal cohort data were validated using the radiomics nomogram.RESULTS:The radiomics signature was significantly associated with MVI status (P < 0.001, both cohorts). Predictors, including the radiomics signature, nonsmooth tumor margin, hypoattenuating halos, internal arteries, and alpha-fetoprotein level were reserved in the individualized prediction nomogram. The model exhibited good calibration and discrimination in the training and validation cohorts (C-index [95% confidence interval]: 0.846 [0.787–0.905] and 0.844 [0.774–0.915], respectively). Its clinical usefulness was confirmed using a decision curve analysis.CONCLUSION:The radiomics nomogram, as a noninvasive preoperative prediction method, shows a favorable predictive accuracy for MVI status in patients with HBV-related HCC.

Purpose: Systemic lupus erythematosus (SLE) is a chronic inflammatory female-predominant autoimmune disease that can affect the central nervous system and exhibit neuropsychiatric symptoms. In SLE patients without neuropsychiatric symptoms (non-NPSLE), recent diffusion tensor imaging studies showed white matter abnormalities in their brains. The present study investigated the entire brain white matter structural connectivity in non-NPSLE patients by using probabilistic tractography and connectivity-based analyses. Methods: Whole-brain structural networks of 29 non-NPSLE patients and 29 healthy controls (HCs) were examined. The structural networks were constructed with interregional probabilistic connectivity. Graph theory analysis was performed to investigate the topological properties, and network-based statistic was employed to assess the alterations of the interregional connections among non-NPSLE patients and controls. Results: Compared with HCs, non-NPSLE patients demonstrated significantly decreased global and local network efficiencies and showed increased characteristic path length. This finding suggests that the global integration and local specialization were impaired. Moreover, the regional properties (nodal efficiency and degree) in the frontal, occipital, and cingulum regions of the non-NPSLE patients were significantly changed and negatively correlated with the disease activity index. The distribution pattern of the hubs measured by nodal degree was altered in the patient group. Finally, the non-NPSLE group exhibited decreased structural connectivity in the left median cingulate-centered component and increased connectivity in the left precuneus-centered component and right middle temporal lobe-centered component. Conclusion: This study reveals an altered topological organization of white matter networks in non-NPSLE patients. Furthermore, this research provides new insights into the structural disruptions underlying the functional and neurocognitive deficits in non-NPSLE patients.

The data presented in this article is related to the research article entitled “A One-Pot Method for Building Colloidal Nanoparticles into Bulk Dry Powders with Nanoscale Magnetic, Plasmonic and Catalytic Functionalities” (Ye et al., 2019). The data shows the hydrophobicity of the nanoparticle (NP) building blocks used for constructing NMPs obtained through contact angle measurements, along with the effect of NP hydrophobicity on the stability of the parent Pickering emulsions. SEM data of the morphology of NMPs is presented. Finally, a mathematical model is presented to predict the average diameter of NMPs produced via different experimental parameters.

Here, we present surface-enhanced Raman data for the calculation of signal uniformity and enhancement factor in SENSERS (surface-exposed nanoparticle sheet enhanced Raman spectroscopy). SEM was used to characterize the microstructure of the solid sample. The interaction between the solid sample and surface-exposed nanoparticle sheet was characterized using SERS and SEM. Based on these data a “skin” versus “sheet” type calculation method was used to calculate the magnitude of Raman signal enhancement within SENSERS. The data presented in this article is related to the research article entitled “Pressing Solids Directly Into Sheets of Plasmonic Nanojunctions Enables Solvent-Free Surface-Enhanced Raman Spectroscopy” (Xu et al., 2018).

Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status.Methods: We retrospectively analyzed radiomics features of CT images in 1,689 consecutive patients from three cancer centers. The prediction model was developed in the training cohort and validated in internal and external validation cohorts. Lasso regression model was utilized to select features and build radiomics signature. Multivariable logistic regression analysis was utilized to develop the model. We integrated the radiomics signature, clinical T and N stage, and other independent clinicopathologic variables, and this was presented as a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness.Results: The radiomics signature was significantly associated with pathological LN stage in training and validation cohorts. Multivariable logistic analysis found the radiomics signature was an independent predictor of LN metastasis. The nomogram showed good discrimination and calibration.Conclusions: The newly developed radiomic signature was a powerful predictor of LN metastasis and the radiomics nomogram could facilitate the preoperative individualized prediction of LN status.

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies the magnetic susceptibility distribution within biological tissues. QSM calculates the underlying magnetic susceptibility by deconvolving the tissue magnetic field map with a unit dipole kernel. However, this deconvolution problem is ill-posed. The morphology enabled dipole inversion (MEDI) introduces total variation (TV) to regularize the susceptibility reconstruction. However, MEDI results still contain artifacts near tissue boundaries because MEDI only imposes TV constraint on voxels inside smooth regions. We introduce a Morphology-Adaptive TV (MATV) for improving TV-regularized QSM. The MATV method first classifies imaging target into smooth and nonsmooth regions by thresholding magnitude gradients. In the dipole inversion for QSM, the TV regularization weights are a monotonically decreasing function of magnitude gradients. Thus, voxels inside smooth regions are assigned with larger weights than those in nonsmooth regions. Using phantom and in vivo datasets, we compared the performance of MATV with that of MEDI. MATV results had better visual quality than MEDI results, especially near tissue boundaries. Preliminary brain imaging results illustrated that MATV has potential to improve the reconstruction of regions near tissue boundaries.

PURPOSE:To study whole-brain microstructural alterations in patients with end-stage renal disease (ESRD) and examine the relationship between brain microstructure and physiological indictors in the disease. MATERIALS AND METHODS:Diffusion tensor imaging data were collected from 35 patients with ESRD (28 men, 18-61 years) and 40 age- and gender-matched healthy controls (HCs, 32 men, 22-58 years). A voxel-wise analysis was then used to identify microstructural alterations over the whole brain in the ESRD patients compared with the HCs. Multiple biochemical measures of renal metabolin, vascular risk factors, general cognitive ability and dialysis duration were correlated with microstructural integrity for the patients. RESULTS:Compared to the HCs, the ESRD patients exhibited disrupted microstructural integrity in not only white matter (WM) but also gray matter (GM) regions, as characterized by decreased fractional anisotropy (FA) and increased mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). Further correlation analyses revealed that the in MD, AD and RD values showed significantly positive correlations with the blood urea nitrogen in the left superior temporal gyrus and significantly negative correlations with the calcium levels in the left superior frontal gyrus (orbital part) in the patients. CONCLUSION:Our findings suggest that ESRD is associated with widespread diffusion abnormalities in both WM and GM regions in the brain, and microstructural integrity of several GM regions are related to biochemical alterations in the disease.

PURPOSE:To investigate the aberrant functional connectivity of the default mode network (DMN) in patients with end-stage renal disease (ESRD) and their clinical relevance. MATERIALS AND METHODS:Resting-state functional MRI data were collected from 31 patients with ESRD (24 men, 24-61 years) and 31 age- and gender-matched healthy controls (HCs, 21 men, 26-61years). A whole-brain seed-based functional connectivity analysis of these collected R-fMRI data was performed by locating the seeds in the posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC) to investigate the functional connectivity of the posterior and anterior DMN over the whole brain, respectively. RESULTS:Compared to the HCs, the patients exhibited significantly decreased functional connectivity with the PCC in the left middle temporal gyrus, the right anterior cingulate gyrus, and the bilateral medial superior frontal gyrus. For the vmPFC seed, only the right thalamus showed significantly decreased functional connectivity in the patients with ESRD compared to HCs. Interestingly, functional connectivity between the PCC and right medial superior frontal gyrus exhibited a significantly positive correlation with the hemoglobin level in the patients. CONCLUSION:Our findings suggest a spatially specific disruption of functional connectivity in the DMN in patients with ESRD, thereby providing novel insights into our understanding of the neurophysiology mechanism that underlies the disease.

PurposeThis study aimed to investigate the topological organization of intrinsic functional brain networks in patients with end-stage renal disease (ESRD).Materials and methodsResting-state functional MRI data were collected from 22 patients with ESRD (16 men, 18-61 years) and 29 age- and gender-matched healthy controls (HCs, 19 men, 32-61 years). Whole-brain functional networks were obtained by calculating the interregional correlation of low-frequency fluctuations in spontaneous brain activity among 1,024 parcels that cover the entire cerebrum. Weighted graph-based models were then employed to topologically characterize these networks at different global, modular and nodal levels.ResultsCompared to HCs, the patients exhibited significant disruption in parallel information processing over the whole networks (P < 0.05). The disruption was present in all the functional modules (default mode, executive control, sensorimotor and visual networks) although decreased functional connectivity was observed only within the default mode network. Regional analysis showed that the disease disproportionately weakened nodal efficiency of the default mode components and tended to preferentially affect central or hub-like regions. Intriguingly, the network abnormalities correlated with biochemical hemoglobin and serum calcium levels in the patients. Finally, the functional changes were substantively unchanged after correcting for gray matter atrophy in the patients.ConclusionOur findings provide evidence for the disconnection nature of ESRD's brain and therefore have important implications for understanding the neuropathologic substrate of the disease from disrupted network organization perspective.

BACKGROUND:Little is known about connectivity within the default mode network (DMN) in heroin-dependent individuals (HDIs). In the current study, diffusion-tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) were combined to investigate both structural and functional connectivity within the DMN in HDIs. METHODS:Fourteen HDIs and 14 controls participated in the study. Structural (path length, tracts count, (fractional anisotropy) FA and (mean diffusivity) MD derived from DTI tractography)and functional (temporal correlation coefficient derived from rs-fMRI) DMN connectivity changes were examined in HDIs. Pearson correlation analysis was performed to compare the structural/functional indices and duration of heroin use/Iowa gambling task(IGT) performance in HDIs. RESULTS:HDIs had lower FA and higher MD in the tract connecting the posterior cingulate cortex/precuneus (PCC/PCUN) to right parahippocampal gyrus (PHG), compared to the controls. HDIs also had decreased FA and track count in the tract connecting the PCC/PCUN and medial prefrontal cortex (MPFC), as well as decreased functional connectivity between the PCC/PCUN and bilateral PHG and MPFC, compared to controls. FA values for the tract connecting PCC/PCUN to the right PHG and connecting PCC/PCUN to the MPFC were negatively correlated to the duration of heroin use. The temporal correlation coefficients between the PCC/PCUN and the MPFC, and the FA values for the tract connecting the PCC/PCUN to the MPFC were positively correlated to IGT performance in HDIs. CONCLUSIONS:Structural and functional connectivity within the DMN are both disturbed in HDIs. This disturbance progresses as duration of heroin use increases and is related to deficits in decision making in HDIs.

Denoising is critical for improving visual quality and reliability of associative quantitative analysis when magnetic resonance (MR) images are acquired with low signal-to-noise ratios. The classical non-local means (NLM) filter, which averages pixels weighted by the similarity of their neighborhoods, is adapted and demonstrated to effectively reduce Rician noise without affecting edge details in MR magnitude images. However, the Rician NLM (RNLM) filter usually blurs small high-contrast particle details which might be clinically relevant information. In this paper, we investigated the reason of this particle blurring problem and proposed a novel particle-preserving RNLM filter with combined patch and pixel (RNLM-CPP) similarity. The results of experiments on both synthetic and real MR data demonstrate that the proposed RNLM-CPP filter can preserve small high-contrast particle details better than the original RNLM filter while denoising MR images.