Non-contrast-enhanced magnetic resonance urography for measuring split kidney function in pediatric patients with hydronephrosis: comparison with renal scintigraphy.

BACKGROUND: Split kidney function (SKF) is critical for treatment decision in pediatric patients with hydronephrosis and is commonly measured using renal scintigraphy (RS). Non-contrast-enhanced magnetic resonance urography (NCE-MRU) is increasingly used in clinical practice. This study aimed to investigate the feasibility of using NCE-MRU as an alternative to estimate SKF in pediatric patients with hydronephrosis, compared to RS. METHODS: Seventy-five pediatric patients with hydronephrosis were included in this retrospective study. All patients underwent NCE-MRU and RS within 2 weeks. Kidney parenchyma volume (KPV) and texture analysis parameters were obtained from T2-weighted (T2WI) in NCE-MRU. The calculated split KPV (SKPV) percent and texture analysis parameters percent of left kidney were compared with the RS-determined SKF. RESULTS: SKPV showed a significant positive correlation with SKF (r = 0.88, p < 0.001), while inhomogeneity was negatively correlated with SKF (r = - 0.68, p < 0.001). The uncorrected and corrected prediction models of SKF were established using simple and multiple linear regression. Bland-Altman plots demonstrated good agreement of both predictive models. The residual sum of squares of the corrected prediction model was lower than that of the uncorrected model (0.283 vs. 0.314) but not statistically significant (p = 0.662). Subgroup analysis based on different MR machines showed correlation coefficients of 0.85, 0.95, and 0.94 between SKF and SKPV for three different scanners, respectively (p < 0.05 for all). CONCLUSIONS: NCE-MRU can be used as an alternative method for estimating SKF in pediatric patients with hydronephrosis when comparing with RS. Specifically, SKPV proves to be a simple and universally applicable indicator for predicting SKF.

Experimental Demonstration of Fully Passive Quantum Key Distribution.

The passive approach to quantum key distribution (QKD) consists of removing all active modulation from the users' devices, a highly desirable countermeasure to get rid of modulator side channels. Nevertheless, active modulation has not been completely removed in QKD systems so far, due to both theoretical and practical limitations. In this Letter, we present a fully passive time-bin encoding QKD system and report on the successful implementation of a modulator-free QKD link. According to the latest theoretical analysis, our prototype is capable of delivering competitive secret key rates in the finite key regime.

Is the radiomics-clinical combined model helpful in distinguishing between pancreatic cancer and mass-forming pancreatitis?

PURPOSE: To develop CT-based radiomics models for distinguishing between resectable PDAC and mass-forming pancreatitis (MFP) and to provide a non-invasive tool for cases of equivocal imaging findings with EUS-FNA needed. METHODS: A total of 201 patients with resectable PDAC and 54 patients with MFP were included. Development cohort: patients without preoperative EUS-FNA (175 PDAC cases, 38 MFP cases); validation cohort: patients with EUS-FNA (26 PDAC cases, 16 MFP cases). Two radiomic signatures (LASSOscore, PCAscore) were developed based on the LASSO model and principal component analysis. LASSOCli and PCACli prediction models were established by combining clinical features with CT radiomic features. ROC analysis and decision curve analysis (DCA) were performed to evaluate the utility of the model versus EUS-FNA in the validation cohort. RESULTS: In the validation cohort, the radiomic signatures (LASSOscore, PCAscore) were both effective in distinguishing between resectable PDAC and MFP (AUCLASSO = 0.743, 95% CI: 0.590-0.896; AUCPCA = 0.788, 95% CI: 0.639-0.938) and improved the diagnostic accuracy of the baseline onlyCli model (AUConlyCli = 0.760, 95% CI: 0.614-0.960) after combination with variables including age, CA19-9, and the double-duct sign (AUCPCACli = 0.880, 95% CI: 0.776-0.983; AUCLASSOCli = 0.825, 95% CI: 0.694-0.955). The PCACli model showed comparable performance to FNA (AUCFNA = 0.810, 95% CI: 0.685-0.935). In DCA, the net benefit of the PCACli model was superior to that of EUS-FNA, avoiding biopsies in 70 per 1000 patients at a risk threshold of 35%. CONCLUSIONS: The PCACli model showed comparable performance with EUS-FNA in discriminating resectable PDAC from MFP.

Avoid non-diagnostic EUS-FNA: a DNN model as a possible gatekeeper to distinguish pancreatic lesions prone to inconclusive biopsy.

OBJECTIVE: This work aimed to explore the utility of CT radiomics with machine learning for distinguishing the pancreatic lesions prone to non-diagnostic ultrasound-guided fine-needle aspiration (EUS-FNA). METHODS: 498 patients with pancreatic EUS-FNA were retrospectively reviewed [Development cohort: 147 pancreatic ductal adenocarcinoma (PDAC); Validation cohort: 37 PDAC]. Pancreatic lesions not PDAC were also tested exploratively. Radiomics extracted from contrast-enhanced CT was integrated with deep neural networks (DNN) after dimension reduction. The receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were performed for model evaluation. And, the explainability of the DNN model was analyzed by integrated gradients. RESULTS: The DNN model was effective in distinguishing PDAC lesions prone to non-diagnostic EUS-FNA (Development cohort: AUC = 0.821, 95% CI: 0.742-0.900; Validation cohort: AUC = 0.745, 95% CI: 0.534-0.956). In all cohorts, the DNN model showed better utility than the logistic model based on traditional lesion characteristics with NRI >0 (p < 0.05). And, the DNN model had net benefits of 21.6% at the risk threshold of 0.60 in the validation cohort. As for the model explainability, gray-level co-occurrence matrix (GLCM) features contributed the most averagely and the first-order features were the most important in the sum attribution. CONCLUSION: The CT radiomics-based DNN model can be a useful auxiliary tool for distinguishing the pancreatic lesions prone to nondiagnostic EUS-FNA and provide alerts for endoscopists preoperatively to reduce unnecessary EUS-FNA. ADVANCES IN KNOWLEDGE: This is the first investigation into the utility of CT radiomics-based machine learning in avoiding non-diagnostic EUS-FNA for patients with pancreatic masses and providing potential pre-operative assistance for endoscopists.

Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney Disease.

Early diagnosis and prediction of chronic kidney disease (CKD) progress within a given duration are critical to ensure personalized treatment, which could improve patients' quality of life and prolong survival time. In this study, we explore the intelligibility of machine-learning and deep-learning models on end-stage renal disease (ESRD) prediction, based on readily-accessible clinical and laboratory features of patients suffering from CKD. Eight machine learning models were used to predict whether a patient suffering from CKD would progress to ESRD within three years based on demographics, clinical,and comorbidity information. LASSO, random forest, and XGBoost were used to identify the most significant markers. In addition, we introduced four advanced attribution methods to the deep learning model to enhance model intelligibility. The deep learning model achieved an AUC-ROC of 0.8991, which was significantly higher than that of baseline models. The interpretation generated by deep learning with attribution methods, random forest, and XGBoost was consistent with clinical knowledge, whereas LASSO-based interpretation was inconsistent. Hematuria, proteinuria, potassium, urine albumin to creatinine ratio were positively associated with the progression of CKD, while eGFR and urine creatinine were negatively associated. In conclusion, deep learning with attribution algorithms could identify intelligible features of CKD progression. Our model identified a number of critical, but under-reported features, which may be novel markers for CKD progression. This study provides physicians with solid data-driven evidence for using machine learning for CKD clinical management and treatment.

Incremental prognostic value of ADC histogram analysis in patients with high-risk prostate cancer receiving adjuvant hormonal therapy after radical prostatectomy.

Purpose: To investigate the incremental prognostic value of preoperative apparent diffusion coefficient (ADC) histogram analysis in patients with high-risk prostate cancer (PCa) who received adjuvant hormonal therapy (AHT) after radical prostatectomy (RP). Methods: Sixty-two PCa patients in line with the criteria were enrolled in this study. The 10th, 50th, and 90th percentiles of ADC (ADC10, ADC50, ADC90), the mean value of ADC (ADCmean), kurtosis, and skewness were obtained from the whole-lesion ADC histogram. The Kaplan-Meier method and Cox regression analysis were used to analyze the relationship between biochemical recurrence-free survival (BCR-fs) and ADC parameters and other clinicopathological factors. Prognostic models were constructed with and without ADC parameters. Results: The median follow-up time was 53.4 months (range, 41.1-79.3 months). BCR was found in 19 (30.6%) patients. Kaplan-Meier curves showed that lower ADCmean, ADC10, ADC50, and ADC90 and higher kurtosis could predict poorer BCR-fs (all p<0.05). After adjusting for clinical parameters, ADC50 and kurtosis remained independent prognostic factors for BCR-fs (HR: 0.172, 95% CI: 0.055-0.541, p=0.003; HR: 7.058, 95% CI: 2.288-21.773, p=0.001, respectively). By adding ADC parameters to the clinical model, the C index and diagnostic accuracy for the 24- and 36-month BCR-fs were improved. Conclusion: ADC histogram analysis has incremental prognostic value in patients with high-risk PCa who received AHT after RP. Combining ADC50, kurtosis and clinical parameters can improve the accuracy of BCR-fs prediction.

Noninvasive Assessment of the Renal Function, Oxford Classification and Prognostic Risk Stratification of IgAN by Using Intravoxel Incoherent Motion Diffusion-Weighted Imaging and Blood Oxygenation Level-Dependent MRI.

BACKGROUND: Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis worldwide. Oxford classification including mesangial hypercellularity (M), endothelial hypercellularity (E), segmental sclerosis (S), interstitial fibrosis/tubular atrophy (T), and crescent (C) were recommended to predict the prognosis of IgAN. PURPOSE: To explore whether multiparametric magnetic resonance imaging (MRI) can be applied to assess the renal function, Oxford classification, and risk of progression to end-stage kidney disease within 5 years of IgAN. STUDY TYPE: Prospective. POPULATION: A total of 46 patients with pathologically confirmed IgAN and 20 healthy volunteers. FIELD STRENGTH/SEQUENCE: A 3-T, blood oxygenation level-dependent (BOLD)-MRI, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). ASSESSMENT: Two radiologists measured the cortex and medulla T2*, apparent diffusion coefficient (ADC), true diffusion (Dt), pseudo-diffusion (Dp), perfusion fraction (fp). All participants were divided into three groups: group 1, healthy volunteers; group 2, patients with estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2 ; group 3, patients with eGFR <60 mL/min/1.73 m2 . Or two groups: group A, 5-year risk scores ≤10% and group B, 5-year risk scores >10%. STATISTICAL TESTS: Intraclass correlation coefficient, one-way analysis of variance, least-significant difference, Student's t-test, Pearson product-moment correlation, Spearman's rank correlation, and receiver operating characteristics (ROC) with the area under the curve (AUC). A P value <0.05 was considered statistically significant. RESULTS: Except for cortical Dp, all other MRI parameters showed significant differences between group 1 and group 2. None of the MRI parameters showed a significant correlation with M, E, or C scores. Cortical T2*, Dt, fp, and medullary Dt and fp showed low-to-moderate significant correlations with S scores. Except for cortical and medullary Dp, all other MRI parameters were significantly correlated with T scores. Cortical Dt showed the largest AUC for differentiating group A from group B (AUC = 0.927) and T0 from T1/T2 (AUC = 0.963). DATA CONCLUSION: Imaging by IVIM-DWI and BOLD-MRI could facilitate noninvasive assessment of the renal function, Oxford classification, and prognostic risk of IgAN patients. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

Non-invasive evaluation of the pathological and functional characteristics of chronic kidney disease by diffusion kurtosis imaging and intravoxel incoherent motion imaging: comparison with conventional DWI.

OBJECTIVE: To explore the diagnostic performance of diffusion kurtosis imaging (DKI) and incoherent intravoxel movement (IVIM) in evaluating the clinical and pathological characteristics in chronic kidney disease (CKD) compared to conventional diffusion-weighted imaging (DWI). METHODS: Forty-nine CKD patients and 24 healthy volunteers were included in this retrospective study from September 2020 to September 2021. All participants underwent MRI examinations before percutaneous renal biopsy. Coronal T2WI, axial T1WI and T2WI, and DWI (including IVIM and DKI) sequences obtained in one scan. We measured the apparent diffusion coefficient (ADC), true diffusion coefficient (Dt), pseudo-diffusion coefficient (Dp), perfusion fraction (fp), mean kurtosis (MK), and mean diffusivity (MD) values. One-way analysis of variance, correlation analysis, and receiver operating characteristic curve analysis were used in our study. RESULTS: Cortex and medulla ADC, MK, Dt, fp were significantly different between the healthy volunteers and CKD stages 1-2 (all p < 0.05). All diffusion parameters showed significant differences between CKD stages 1-2 and CKD stages 3-5 (all p < 0.05). Except for the uncorrelation between MDMedulla and vascular lesion score, all other diffusion parameters were low-to-moderately related to clinical and pathological indicators. fpMedulla was the best parameter to differentiate healthy volunteers from CKD stages 1-2. MKCortex was the best parameter to differentiate CKD stages 1-2 from that CKD stages 3-5. CONCLUSION: Renal cortex and medulla fp, Dt, and MK can provide more valuable information than ADC values for the evaluation of clinical and pathological characteristics of CKD patients, and thus can provide auxiliary diagnosis for fibrosis assessment and clinical management of CKD patients. ADVANCES IN KNOWLEDGE: IVIM and DKI can provide more diagnostic valuable information for CKD patients than conventional DWI.

Noninvasive assessment of renal function and fibrosis in CKD patients using histogram analysis based on diffusion kurtosis imaging.

PURPOSE: To investigate the potential of histogram analysis based on diffusion kurtosis imaging (DKI) in evaluating renal function and fibrosis associated with chronic kidney disease (CKD). MATERIALS AND METHODS: Thirty-six CKD patients were enrolled, and DKI was performed in all patients before the renal biopsy. The histogram parameters of diffusivity (D) and kurtosis (K) were obtained using FireVoxel. The histogram parameters between the stable [estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73 m2] and impaired (eGFR < 60 ml/min/1.73 m2) eGFR group were compared. Besides, patients were classified into mild, moderate, and severe fibrosis group using a semi-quantitative standard. The correlations of histogram parameters with eGFR and fibrosis scores were investigated and the diagnostic performances of histogram parameters in assessing renal dysfunction and fibrosis were analyzed. The added value of combination of most significant parameter with 24 h urinary protein (24 h-UPRO) in evaluating fibrosis was also explored. RESULTS: Seven D histogram parameters in cortex (mean, median, 10th, 25th, 75th, 90th percentiles and entropy), two D histogram parameters in medulla (75th, 90th percentiles), seven K histogram parameters in cortex (mean, min, median, 10th, 25th, 75th, 90th percentiles) and three K histogram parameters in medulla (mean, median, 25th percentile) were significantly different between the two groups. The Dmean of cortex was the most relevant parameter to eGFR (r = 0.648, P < 0.001) and had the largest area under the curve (AUC) for differentiating the stable from impaired eGFR group [AUC = 0.889; 95% confidence interval (CI) 0.728-0.970]. The K90th of cortex presented the strongest correlation with fibrosis scores (r = 0.575, P < 0.001) and achieved the largest AUC for distinguishing the mild from moderate to severe fibrosis group (AUC = 0.849, 95% CI 0.706-0.993). Combining the K90th in cortex with 24 h-UPRO gained statistically higher AUC value (AUC = 0.880, 95% CI 0.763-0.996). CONCLUSION: Histogram analysis based on DKI is practicable for the noninvasive assessment of renal function and fibrosis in CKD patients.

WHO/ISUP grade and pathological T stage of clear cell renal cell carcinoma: value of ZOOMit diffusion kurtosis imaging and chemical exchange saturation transfer imaging.

OBJECTIVES: To evaluate the value of ZOOMit diffusion kurtosis imaging (DKI) and chemical exchange saturation transfer (CEST) imaging in predicting WHO/ISUP grade and pathological T stage in clear cell renal cell carcinoma (ccRCC). METHODS: Forty-six patients with ccRCC were included in this retrospective study. All participants underwent MRI including ZOOMit DKI and CEST. The non-Gaussian mean kurtosis (MK), mean diffusivity (MD), magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)), and Ssat (3.5 ppm)/S0 were analyzed based on different WHO/ISUP grades and pT stages. Binary logistic regression was used to identify the best combination of the parameters. Pearson's correlation coefficients were calculated between CEST and diffusion-related parameters. RESULTS: The ADC, MD, and Ssat (3.5 ppm)/S0 values were significantly lower for higher WHO/ISUP grade tumors, whereas the MK and MTRasym (3.5 ppm) were higher in higher WHO/ISUP grade and higher pT stage tumors. MTRasym (3.5 ppm) combined with MD (AUC, 0.930; 95% CI, 0.858-1.000) showed the best diagnostic efficacy in evaluating the WHO/ISUP grade. MTRasym (3.5 ppm) and MK were mildly positively correlated (r = 0.324, p = 0.028). Ssat (3.5 ppm)/S0 was moderately positively correlated with ADC (r = 0.580, p < 0.001), mildly positively correlated with MD (r = 0.412, p = 0.005), and moderately negatively correlated with MK (r = -0.575, p < .001). CONCLUSION: The microstructural and biochemical assessment of ZOOMit DKI and CEST allowed for the characterization of different WHO/ISUP grades and pT stages in ccRCC. MTRasym (3.5 ppm) combined with MD showed the best diagnostic performance for WHO/ISUP grading. KEY POINTS: • Both diffusion kurtosis imaging (DKI) and chemical exchange saturation transfer (CEST) can be used to predict the WHO/ISUP grade and pathological T stage. • MTRasym (3.5 ppm) combined with MD showed the highest AUC (0.930; 95% CI, 0.858-1.000) in WHO/ISUP grading. • MTRasym at 3.5 ppm showed a positive correlation with mean kurtosis.

High visceral adipose tissue area is independently associated with early allograft dysfunction in liver transplantation recipients: a propensity score analysis.

OBJECTIVES: To evaluate the association between adipose tissue distribution and early allograft dysfunction (EAD) in liver transplantation (LT) recipients. METHODS: A total of 175 patients who received LT from April 2015 to September 2020 were enrolled in this retrospective study. The areas of abdominal adipose tissue and skeletal muscle of all patients were measured based on the preoperative CT images. The appropriate statistical methods including the propensity score-matched (PSM) analysis were performed to identify the association between adipose tissue distribution and EAD. RESULTS: Of 175 LT recipients, 55 patients (31.4%) finally developed EAD. The multivariate logistic analysis revealed that preoperative serum albumin (odds ratio (OR) 0.34, 95% confidence interval (CI) 0.17-0.70), platelet-lymphocyte ratio (OR 2.35, 95% CI 1.18-4.79), and visceral adipose tissue (VAT) area (OR 3.17, 95% CI 1.56-6.43) were independent associated with EAD. After PSM analysis, VAT area was still significantly associated with EAD (OR 3.95, 95% CI 1.16-13.51). In survival analysis, no significant difference was identified in one-year graft failure (log-rank: p = 0.487), and conversely result was identified in overall survival (OS) (log-rank: p = 0.012; hazard ratio (HR) 4.10, 95% CI 1.27-13.16). CONCLUSIONS: LT recipients with high VAT area have higher risk for the occurrence of EAD, and high VAT area might have certain clinical value for predicting the poor OS of patients. For LT candidates with large amount of VAT, the clinicians can take clinical interventions by suggesting physical and nutritional treatments to improve outcomes after LT.

Afterpulse effect in measurement-device-independent quantum key distribution.

There is no doubt that measurement-device-independent quantum key distribution (MDI-QKD) is a crucial protocol that is immune to all possible detector side channel attacks. In the preparation phase, a simulation model is usually employed to get a set of optimized parameters, which is utilized for getting a higher secure key rate in reality. With the implementation of high-speed QKD, the afterpulse effect which is an intrinsic characteristic of the single-photon avalanche photodiode is no longer ignorable, this will lead to a great deviation compared with the existing analytical model. Here we develop an afterpulse-compatible MDI-QKD model to get the optimized parameters. Our results indicate that by using our afterpulse-compatible model, we can get a much higher key rate than the prior afterpulse-omitted model. It is significant to take the afterpulse effect into consideration because of the improvement of the system working frequency.

Integration in the C-band between quantum key distribution and the classical channel of 25 dBm launch power over multicore fiber media.

The quantum-classical coexistence can be implemented based on wavelength division multiplexing (WDM), but due to Raman noise, the wavelength spacing between quantum and classical signals and launch power from classical channels are restricted. Space division multiplexing (SDM) can now be availably achieved by multicore fiber (MCF) to reduce Raman noise, thereby loosening the restriction for coexistence in the same band and obtaining a high communication capacity. In this paper, we realize the quantum-classical coexistence over a 7-core MCF. Based on the SDM, the highest launch power of 25 dBm is achieved which has been extended nearly 19 times in previous work. Moreover, both the quantum and classical channels are allocated in the C-band and the minimum wavelength spacing between them is only 1.6 nm. The coexistence system eliminates the need for adding a narrowband filter.

Ureteral calculi lithotripsy for single ureteral calculi: can DNN-assisted model help preoperatively predict risk factors for sepsis?

OBJECTIVES: To explore the utility of radiomics and deep learning model in assessing the risk factors for sepsis after flexible ureteroscopy lithotripsy (FURL) or percutaneous nephrolithotomy (PCNL) in patients with ureteral calculi. METHODS: This retrospective analysis included 847 patients with treatment-naive proximal ureteral calculi who received FURL or PCNL. All participants were preoperatively conducted non-contrast computed tomography scans, and relevant clinical information was meanwhile collected. After propensity score matching, the radiomics model was established to predict the onset of sepsis. A deep learning model was also adapted to further improve the prediction accuracy. Performance of these trained models was verified in another independent external validation set including 40 cases of ureteral calculi patients. RESULTS: The overall incidence of sepsis after FURL or PCNL was 5.9%. The least absolute shrinkage and selection operator (LASSO) regression analysis revealed 26 predictive variables, with an overall AUC of 0.881 (95% CI, 0.813-0.931) and an AUC of 0.783 (95% CI, 0.766-0.801) in external validation cohort. Judicious adaption of a deep neural network (DNN) model to our dataset improved the AUC to 0.920 (95% CI, 0.906-0.933) in the internal validation. To eliminate the overfitting, external validation was carried out for DNN model (AUC = 0.874 (95% CI, 0.858-0.891)). CONCLUSIONS: The DNN was more effective than the LASSO model in revealing risk factors for sepsis after FURL or PCNL in single ureteral calculi patients, and females are more susceptible to sepsis than males. Deep learning models have the potential to act as gatekeepers to facilitate patient stratification. KEY POINTS: • Both the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models were shown to be effective in sepsis prediction. • The DNN model achieved superior prediction capability, with an AUC of 0.920 (95% CI, 0.906-0.933). • DNN-assisted model has potential to serve as a gatekeeper to facilitate patient stratification.

All-in-one approaches for triple-negative breast cancer therapy: metal-phenolic nanoplatform for MR imaging-guided combinational therapy.

BACKGROUND: Conventional chemotherapy has poor efficacy in triple-negative breast cancer (TNBC) which is highly heterogeneous and aggressive. Imaging-guided therapy is usually combined with diverse treatment modalities, could realize the integration of diagnosis and treatments. Therefore, the primary challenge for combinational therapy is designing proper delivery systems to accomplish multiple synergistic effects. RESULTS: Herein, a facile nanoplatform was manufactured to fulfill the all-in-one approaches for TNBC combinational therapy. Fe3+-based metal-phenolic networks (MPNs) with bovine serum albumin (BSA) modification served as drug delivery carriers to encapsulate bleomycin (BLM), forming BFE@BSA NPs. The self-assembly mechanism, pH-responsive drug release behavior, and other physicochemical properties of this system were characterized. The potential of BFE@BSA NPs as photothermal transduction agents and magnetic resonance imaging (MRI) contrast agents was explored. The synergistic anti-tumor effects consisting of BLM-induced chemotherapy, Fenton reactions-mediated chemodynamic therapy, and photothermal therapy-induced apoptosis were studied both in vitro and in vivo. Once internalized into tumor cells, released BLM could cause DNA damage, while Fenton reactions were initiated to produce highly toxic •OH. Upon laser irradiation, BFE@BSA NPs could convert light into heat to achieve synergistic effects. After intravenous administration, BFE@BSA NPs exhibited great therapeutic effects in 4T1 tumor xenograft model. Moreover, as T1-weighted MRI contrast agents, BFE@BSA NPs could provide diagnosis and treatment monitoring for individualized precise therapy. CONCLUSIONS: A nano-system that integrated imaging and combinational therapy (chemotherapy, chemodynamic therapy and photothermal therapy) were developed to kill the tumor and monitor therapeutic efficacy. This strategy provided an all-in-one theranostic nanoplatform for MRI-guided combinational therapy against TNBC.

Renal functional and interstitial fibrotic assessment with non-Gaussian diffusion kurtosis imaging.

OBJECTIVES: To evaluate the application value of diffusion kurtosis imaging (DKI) for monitoring renal function and interstitial fibrosis. METHODS: Forty-two patients suspected of having primary nephropathy, hypertension or diabetes with impaired renal function were examined with DKI. DKI metrics of renal cortex and medulla on both sides of each patient were measured, including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), mean diffusivity (MD) and fractional anisotropy (FA). The differences in DKI metrics between stable and impaired estimated glomerular filtration rate (eGFR) patients as well as between mild and severe interstitial fibrosis patients were compared. Correlations of DKI metrics with clinical indicators and pathology were analyzed. Diagnostic performance of DKI to assess the degree of renal dysfunction was analyzed. RESULTS: Cortical MK, parenchymal Ka, MD and medullary FA were different in stable vs impaired eGFR patients and mild vs severe interstitial fibrosis patients (all p < .05). Negative correlation was found between Ka and eGFR (cortex: r = - 0.579; medulla: r = - 0.603), between MD and interstitial fibrosis (cortex: r = - 0.899; medulla: r = - 0.770), and positive correlation was found between MD and eGFR (cortex: r = 0.411; medulla: r = 0.344), between Ka and interstitial fibrosis (cortex: r = 0.871; medulla: r = 0.844) (all p < .05). DKI combined with mean arterial blood pressure (MAP) and urea showed good diagnostic power for assessing the degree of renal dysfunction (sensitivity: 90.5%; specificity: 89.5%). CONCLUSIONS: Noninvasive DKI has certain application value for monitoring renal function and interstitial fibrosis.

DWI-based radiomic signature: potential role for individualized adjuvant chemotherapy in intrahepatic cholangiocarcinoma after partial hepatectomy.

OBJECTIVES: To develop a diffusion-weighted imaging (DWI) based radiomic signature for predicting early recurrence (ER) (i.e., recurrence within 1 year after surgery), and to explore the potential value for individualized adjuvant chemotherapy. METHODS: A total of 124 patients with intrahepatic cholangiocarcinoma (ICC) were randomly divided into the training (n = 87) and the validation set (n = 37). Radiomic signature was built using radiomic features extracted from DWI with random forest. An integrated radiomic nomogram was constructed with multivariate logistic regression analysis to demonstrate the incremental value of the radiomic signature beyond clinicopathological-radiographic factors. A clinicopathological-radiographic (CPR) model was constructed as a reference. RESULTS: The radiomic signature showed a comparable discrimination performance for predicting ER to CPR model in the validation set (AUC, 0.753 vs. 0.621, p = 0.274). Integrating the radiomic signature with clinicopathological-radiographic factors further improved prediction performance compared with CPR model, with an AUC of 0.821 (95%CI 0.684-0.959) in the validation set (p = 0.01). The radiomic signature succeeded to stratify patients into distinct survival outcomes according to their risk index of ER, and remained an independent prognostic factor in multivariable analysis (disease-free survival (DFS), p < 0.0001; overall survival (OS), p = 0.029). Furthermore, adjuvant chemotherapy improved prognosis in high-risk patients defined by the radiomic signature (DFS, p = 0.029; OS, p = 0.088) and defined by the nomogram (DFS, p = 0.031; OS, p = 0.023), whereas poor chemotherapy efficacy was detected in low-risk patients. CONCLUSIONS: The preoperative DWI-based radiomic signature could improve prognostic prediction and help to identify ICC patients who may benefit from postoperative adjuvant chemotherapy.

Noninvasive assessment of clinical and pathological characteristics of patients with IgA nephropathy by diffusion kurtosis imaging.

OBJECTIVES: To explore the diagnostic performance of diffusion kurtosis imaging (DKI) in evaluating the clinical and pathological characteristics of patients with immunoglobulin A nephropathy (IgAN) compared with conventional DWI. MATERIALS AND METHODS: A total of 28 IgAN patients and 14 healthy volunteers prospectively underwent MRI examinations including coronal T2WI, axial T1WI, T2WI, and DWI sequences from September 2020 to August 2021. We measured mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) by using MR Body Diffusion Toolbox v1.4.0 (Siemens Healthcare). Patients were divided into three groups according to their estimated glomerular filtration rate (eGFR) (Group1, healthy volunteers without kidney disease or other diseases that affect renal function; Group2, IgAN patients with eGFR > 60 mL/min/1.73 m2; Group3, IgAN patients with eGFR < 60 mL/min/1.73 m2). One-way analysis of variance, Pearson or Spearman correlation, and receiver operating characteristic curves were applied in our statistical analysis. RESULTS: MKCortex and ADCCortex showed significant differences between the Group1 and Group2. MKCortex, MDCortex, ADCCortex, MKMedulla, and ADCMedulla showed significant differences between Group2 and Group3. MKCortex had the highest correlation with CKD stages (r = 0.749, p < 0.001), and tubulointerstitial lesion score (r = 0.656, p < 0.001). MDCortex had the highest correlation with glomerular lesion score (r = - 0.475, p = 0.011). MKCortex had the highest AUC (AUC = 0.923) for differentiating Group1 from Group2, and MDCortex had the highest AUC (AUC = 0.924) for differentiating Group2 from Group3, followed by MKMedulla (AUC = 0.923). CONCLUSIONS: DKI is a feasible and reliable technique that can assess the clinical and pathological characteristics of IgAN patients and can provide more valuable information than conventional DWI, especially MKCortex.

Multiparametric MRI-Based Radiomic Signature for Preoperative Evaluation of Overall Survival in Intrahepatic Cholangiocarcinoma After Partial Hepatectomy.

BACKGROUND: The clinical outcomes of patients with intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy remain suboptimal. Identifying patients with poor outcomes before surgery is urgently required. PURPOSE: To develop a multiparametric magnetic resonance imaging (MRI)-based radiomic signature to evaluate overall survival (OS) preoperatively and to investigate its incremental value for disease stratification. STUDY TYPE: Retrospective. SUBJECTS: One hundred and sixty-three patients with pathologically defined ICC, divided into training (N = 115) and validation sets (N = 48). SEQUENCE: Three-dimensional T1-weighted gradient-echo sequence with and without contrast agent, T2-weighted fast spin-echo sequence, and diffusion-weighted imaging with single-shot echo-planar sequence at 1.5 T or 3.0 T. ASSESSMENT: OS was defined as the time from the date of surgery to death or last contact. The radiomic signature was built based on the least absolute shrinkage and selection operator regression model. A clinicopathologic-radiographic (CPR) model and a combined model integrating radiomic signature with CPR factors were developed with multivariable Cox regression models. STATISTICAL TESTS: Harrell's concordance index (C-index) was used to compare the discrimination of different models. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to quantify the improvement of prognostic accuracy after adding radiomic signature. RESULTS: The high-risk patients of death defined by the radiomic signature showed significantly lower OS compared with low-risk patients in validation set (3-year OS 17.1% vs. 56.4%, P < 0.001). Integrating radiomic signature into tumor, node, and metastasis (TNM) staging system significantly improved the prognostic accuracy compared with TNM stage alone (validation set C-index 0.745 vs. 0.649, P = 0.039, NRI improvement 39.9%-43.8%, IDI improvement 16.1%-19.4%). The radiomic signature showed no significant difference of C-index with postoperative CPR model (validation set, 0.698 vs. 0.674, P = 0.752). Incorporating the radiomic signature into CPR model significantly improved prognostic accuracy (NRI improvement 32.5%-34.3%, IDI improvement 8.1%-12.9%). DATA CONCLUSION: Multiparametric MRI-based radiomic signature is a potential biomarker for preoperative prognostic evaluation of ICC patients. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

Body composition in relation to postoperative anastomotic leakage and overall survival in patients with esophageal cancer.

BACKGROUND: Body composition was reported to be related to the prognosis of patients with cancer. This study aimed to investigate the influence of preoperative body composition on anastomotic leakage and overall survival in patients with esophageal cancer. METHODS: In this retrospective study, 93 patients with esophageal cancers were evaluated. Skeletal muscle area, intermuscular adipose tissue, visceral adipose tissue (VAT), and subcutaneous adipose tissue were measured on computed tomography images at the level of the third lumbar vertebra. Subsequently, each body composition index was also calculated by dividing the body composition by the square of the height. The cut-off values of body compositions were defined using X-tile software (version 3.6.1; Yale University, New Haven, CTA). Univariate and multivariate analyses were performed to evaluate the risk factors of anastomotic leakage. Kaplan-Meier method and Cox regression analysis were used to evaluate the risk factors of overall survival. RESULTS: VAT and visceral fat index (VFI) were higher in patients with anastomotic fistula than in those without anastomotic fistula, but none of them were independent risk factors. Patients with higher body mass index (BMI), higher VFI, and higher subcutaneous fat index (SFI) had better overall survival. By multivariate analysis, SFI >27.6 cm2/m2 was still significantly associated with overall survival. CONCLUSION: Patients with higher VAT and VFI were prone to have an anastomotic leakage. Lower BMI, VFI, and SFI were associated with a reduction in overall survival.