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Abstract Background T1 mapping can quantify the longitudinal relaxation time of tissues. This study aimed to investigate the repeatability and reproducibility of T1 mapping radiomics features of lung cancer and the feasibility of T1 mapping-based radiomics model to predict its pathological types. Methods The chest T1 mapping images and clinical characteristics of 112 lung cancer patients (54 with adenocarcinoma and 58 with other types of lung cancer) were collected retrospectively. 54 patients underwent twice short-term T1 mapping scans. Regions of interest were manually delineated on T1 mapping pseudo-color images to measure the mean native T1 values of lung cancer, and radiomics features were extracted using the semi-automatic segmentation method by two independent observers. The patients were randomly divided into training group (77 cases) and validation group (35 cases) with the ratio of 7:3. Interclass correlation coefficients (ICCs), Student’s t-test or Mann-Whitney U tests and least absolute shrinkage and selection operator (LASSO) were used for feature selection. The optimum features were selected to establish a logistic regression (LR) radiomics model. Independent sample t-test, Mann Whitney U-test or chi square test were used to compare the differences of clinical characteristics and T1 values. Performance was compared by the area under the receiver operating characteristic (ROC) curve (AUC). Results In the training group, smoking history, lesion type and native T1 values were different between adenocarcinoma and non-adenocarcinoma patients (P = 0.004–0.038). There were 1035 (54.30%) radiomics features meet the intra-and inter-observer, and test-retest reproducibility with ICC > 0.80. After feature dimensionality reduction and model construction, the AUC of T1 mapping-based radiomics model for predicting the pathological types of lung cancer was 0.833 and 0.843, respectively, in the training and validation cohorts. The AUCs of T1 value and clinical model (including smoking history and lesion type) were 0.657 and 0.692 in the training group, and 0.722 and 0.686 in the validation group. Combined with T1 mapping radiomics, clinical model and T1 value to establish a combined model, the prediction efficiency was further improved to 0.895 and 0.915 in the training and validation groups. Conclusions About 50% of the T1 mapping-based radiomics features displayed relatively poor repeatability and reproducibility. While T1 mapping-based radiomics model is valuable in identification of histological types of lung cancer despite the measurement variability. Combination of T1 mapping radiomics model, clinical characteristics and native T1 value can improve the predictive value of pathological types of lung cancer.

Abstract Intrahepatic circulation is essential for the repair of acute liver injury (ALI); however, very limited information is available concerning changes in the circulatory pathways during ALI. Therefore, multi-scale X-ray phase-contrast CT combined with three-dimensional (3D) visualization is used to quantitatively analyze the intrahepatic circulation pathway (including the hepatic vein, portal vein and hepatic sinusoid) in the mouse model via the intraperitoneal injection of carbon tetrachloride (CCl4) from acute injury to recovery. The results demonstrate that the liver still preserves some vessel-like channels accessed to the central vein when the injury causes the severe collapse of the hepatic sinusoids that cannot be observed in two-dimensional pathologic slices. Moreover, angiogenesis is observed in the terminal branches of the hepatic vein and portal vein. Additionally, we extend the two-dimensional primary lobule to a 3D model and find that the sinusoids in zone III have the most severe injury. The sinusoids in different zones also show changes in parameters such as density and mean diameter during the ALI. In conclusion, phase-contrast CT can reveal the intact vascular system within the liver lobes, thus providing critical information for studying the mechanisms involved in the evolution of circulatory structures from damage to repair.

Abstract Background Although gray matter (GM) volume alterations have been extensively documented in previous voxel-based morphometry studies on vestibular migraine (VM), little is known about the impact of this disease on the topological organization of GM morphological networks. This study investigated the altered network patterns of the GM connectome in patients with VM. Methods In this study, 55 patients with VM and 57 healthy controls (HCs) underwent structural T1-weighted MRI. GM morphological networks were constructed by estimating interregional similarity in the distributions of regional GM volume based on the Kullback–Leibler divergence measure. Graph-theoretical metrics and interregional morphological connectivity were computed and compared between the two groups. Partial correlation analyses were performed between significant GM connectome features and clinical parameters. Logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers were used to examine the performance of significant GM connectome features in distinguishing patients with VM from HCs. Results Compared with HCs, patients with VM exhibited increased clustering coefficient and local efficiency, as well as reduced nodal degree and nodal efficiency in the left superior temporal gyrus (STG). Furthermore, we identified one connected component with decreased morphological connectivity strength, and the involved regions were mainly located in the STG, temporal pole, prefrontal cortex, supplementary motor area, cingulum, fusiform gyrus, and cerebellum. In the VM group, several connections in the identified connected component were correlated with clinical measures (i.e., symptoms and emotional scales); however, these correlations did not survive multiple comparison corrections. A combination of significant graph- and connectivity-based features allowed single-subject classification of VM versus HC with significant accuracy of 77.68%, 77.68%, and 72.32% for the LR, SVM, and RF models, respectively. Conclusion Patients with VM had aberrant GM connectomes in terms of topological properties and network connections, reflecting potential dizziness, pain, and emotional dysfunctions. The identified features could serve as individualized neuroimaging markers of VM.

Abstract Background The initial randomized, double‐blinded, actively controlled, phase III ANEAS study (NCT03849768) demonstrated that aumolertinib showed superior efficacy relative to gefitinib as first‐line therapy in epidermal growth factor receptor (EGFR)‐mutated advanced non‐small cell lung cancer (NSCLC). Metastatic disease in the central nervous system (CNS) remains a challenge in the management of NSCLC. This study aimed to compare the efficacy of aumolertinib versus gefitinib among patients with baseline CNS metastases in the ANEAS study. Methods Eligible patients were enrolled and randomly assigned in a 1:1 ratio to orally receive either aumolertinib or gefitinib in a double‐blinded fashion. Patients with asymptomatic, stable CNS metastases were included. Follow‐up imaging of the same modality as the initial CNS imaging was performed every 6 weeks for 15 months, then every 12 weeks. CNS response was assessed by a neuroradiological blinded, independent central review (neuroradiological‐BICR). The primary endpoint for this subgroup analysis was CNS progression‐free survival (PFS). Results Of the 429 patients enrolled and randomized in the ANEAS study, 106 patients were found to have CNS metastases (CNS Full Analysis Set, cFAS) at baseline by neuroradiological‐BICR, and 60 of them had CNS target lesions (CNS Evaluable for Response, cEFR). Treatment with aumolertinib significantly prolonged median CNS PFS compared with gefitinib in both cFAS (29.0 vs. 8.3 months; hazard ratio [HR] = 0.31; 95% confidence interval [CI], 0.17‐0.56; P < 0.001) and cEFR (29.0 vs. 8.3 months; HR = 0.26; 95% CI, 0.11‐0.57; P < 0.001). The confirmed CNS overall response rate in cEFR was 85.7% and 75.0% in patients treated with aumolertinib and gefitinib, respectively. Competing risk analysis showed that the estimated probability of CNS progression without prior non‐CNS progression or death was consistently lower with aumolertinib than with gefitinib in patients with and without CNS metastases at baseline. No new safety findings were observed. Conclusions These results indicate a potential advantage of aumolertinib over gefitinib in terms of CNS PFS and the risk of CNS progression in patients with EGFR‐mutated advanced NSCLC with baseline CNS metastases. Trial registration ClinicalTrials.gov number, NCT03849768

Abstract Purpose To evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC). Materials and methods A retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test. Results In predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807–0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1). Conclusions The combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making.

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor–lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Abstract Active surveillance (AS) is the primary strategy for managing patients with low or favorable-intermediate risk prostate cancer (PCa). Identifying patients who may benefit from AS relies on unpleasant prostate biopsies, which entail the risk of bleeding and infection. In the current study, we aimed to develop a radiomics model based on prostate magnetic resonance images to identify AS candidates non-invasively. A total of 956 PCa patients with complete biopsy reports from six hospitals were included in the current multicenter retrospective study. The National Comprehensive Cancer Network (NCCN) guidelines were used as reference standards to determine the AS candidacy. To discriminate between AS and non-AS candidates, five radiomics models (i.e., eXtreme Gradient Boosting (XGBoost) AS classifier (XGB-AS), logistic regression (LR) AS classifier, random forest (RF) AS classifier, adaptive boosting (AdaBoost) AS classifier, and decision tree (DT) AS classifier) were developed and externally validated using a three-fold cross-center validation based on five classifiers: XGBoost, LR, RF, AdaBoost, and DT. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) were calculated to evaluate the performance of these models. XGB-AS exhibited an average of AUC of 0.803, ACC of 0.693, SEN of 0.668, and SPE of 0.841, showing a better comprehensive performance than those of the other included radiomic models. Additionally, the XGB-AS model also presented a promising performance for identifying AS candidates from the intermediate-risk cases and the ambiguous cases with diagnostic discordance between the NCCN guidelines and the Prostate Imaging-Reporting and Data System assessment. These results suggest that the XGB-AS model has the potential to help identify patients who are suitable for AS and allow non-invasive monitoring of patients on AS, thereby reducing the number of annual biopsies and the associated risks of bleeding and infection.

Abstract Background SUZ12 is one of the core members of the polycomb repressive complex 2 (PRC2), but its expression and role in lung adenocarcinoma (LUAD) are unclear. We aimed to explore the expression, prognosis, biological functions and roles of SUZ12 in LUAD. Methods The expression of SUZ12 was detected by immunohistochemical staining, qRT‐PCR, and western blotting in LUAD tissues and cells. The biological functions and molecular mechanisms of SUZ12 were characterized by a range of in vitro and in vivo experiments. Results SUZ12 was overexpressed in LUAD tissues, and high SUZ12 expression was correlated with worse clinicopathological features and a poorer prognosis. Knockdown of SUZ12 significantly inhibited cell growth, colony formation, invasion, and migration, and induced apoptosis and G1/S phase arrest, while overexpression of SUZ12 had the opposite effects. Knockdown of SUZ12 decreased the tumorigenic capacity of A549 cells in vivo. The expression of key signaling molecules related to the cell cycle, apoptosis, migration, and immunity were altered by the knockdown or overexpression of SUZ12. SUZ12 can directly bind to the Bax promoter region, EZH2 and H3K27me3 levels dependents on SUZ12. The expression levels of SUZ12 and Bax were negatively correlated in LUAD tissues. Conclusions SUZ12 is a new oncogene related to the poor prognosis of LUAD. SUZ12 regulates LUAD progression by regulating the expression of related signaling molecules, and as a part of the PRC2 complex, it may bind to the Bax promoter to silence Bax expression.

Abstract Objective To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD). Materials and methods The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic. Results Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p

Abstract Bile infarct is a pivotal characteristic of obstructive biliary disease, but its evolution during the disease progression remains unclear. Our objective, therefore, is to explore morphological alterations of the bile infarct in the disease course by means of multiscale X-ray phase-contrast CT. Bile duct ligation is performed in mice to mimic the obstructive biliary disease. Intact liver lobes of the mice are scanned by phase-contrast CT at various resolution scales. Phase-contrast CT clearly presents three-dimensional (3D) images of the bile infarcts down to the submicron level with good correlation with histological images. The CT data illustrates that the infarct first appears on day 1 post-BDL, while a microchannel between the infarct and hepatic sinusoids is identified, the number of which increases with the disease progression. A 3D model of hepatic acinus is proposed, in which the infarct starts around the portal veins (zone I) and gradually progresses towards the central veins (zone III) during the disease process. Multiscale phase-contrast CT offers the comprehensive analysis of the evolutionary features of the bile infarct in obstructive biliary disease. During the course of the disease, the bile infarcts develop infarct-sinusoidal microchannels and gradually occupy the whole liver, promoting the disease progression.

Abstract Much evidence has accumulated to show that inflammation and nutritional status are associated with the prognosis of patients with various cancers. The present study was designed to explore the prognostic role of the LANR in NPC patients receiving definitive radiotherapy and to construct a nomogram for predicting patient survival. This study retrospectively reviewed 805 NPC patients (604 in the training cohort and 201 in the validation cohort) who received definitive radiotherapy between January 2013 and December 2019. The clinical data and pretreatment laboratory test data, including lymphocyte count, neutrophil count, and serum ALB concentration, were collected for all patients. The LANR was calculated as the albumin × lymphocyte/neutrophil ratio. Patients in the training cohort and validation cohort were categorized into high-LANR and low-LANR groups according to the corresponding cutoff values. The independent prognostic factors for overall survival (OS), progression-free survival (PFS), relapse-free survival (RFS), and metastasis-free survival (MFS) were evaluated by univariate and multivariate Cox regression analyses, and a nomogram was subsequently constructed. The performance of the nomogram was evaluated by the concordance index (C-index) and calibration curve. A low LANR (

Abstract Background Cardiac involvement in patients with immunoglubin light-chain amyloidosis (AL) is a major determinant of treatment choice and prognosis, and early identification of high-risk patients can initiate intensive treatment strategies to achieve better survival. This study aimed to investigate the prognostic value of native T1 and ECV in patients with AL-cardiac amyloidosis (CA). Methods A total of 38 patients (mean age 59 ± 11 years) with AL diagnosed histopathologically from July 2017 to October 2021 were collected consecutively. All patients were performed 3.0-T cardiac magnetic resonance (CMR) including cine, T1 mapping, and late gadolinium enhancement (LGE). Pre- and post-contrast T1 mapping images were transferred to a dedicated research software package (CVI42 v5.11.3) to create parametric T1 and ECV values. In addition, clinical and laboratory data of all patients were collected, and patients or their family members were regularly followed up by telephone every 3 months. The starting point of follow-up was the time of definitive pathological diagnosis, and the main endpoint was all-cause death. Kaplan-Meier analysis and Cox proportional risk model were used to evaluate the association between native T1 and ECV and death in patients with CA. Results After a median follow-up of 27 (16, 37) months, 12 patients with CA died. Kaplan-Meier analysis showed that elevated native T1 and ECV were closely associated with poor prognosis in patients with CA. The survival rate of patients with ECV > 44% and native T1 > 1389ms were significantly lower than that of patients with ECV ≤ 44% and native T1 ≤ 1389ms (Log-rank P

Abstract Purpose To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. Material and methods A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. Results The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p

Abstract Background The detection of local recurrence for prostate cancer (PCa) patients following radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was to construct and verify machine learning models with three different algorithms based on post-operative mpMRI for predicting local recurrence of PCa after RP and explore their potential clinical value compared with the Prostate Imaging for Recurrence Reporting (PI-RR) score of expert-level radiologists. Methods A total of 176 patients were retrospectively enrolled and randomly divided into training (n = 123) and testing (n = 53) sets. The PI-RR assessments were performed by two expert-level radiologists with access to the operative histopathological and pre-surgical clinical results. The radiomics models to predict local recurrence were built by utilizing three different algorithms (i.e., support vector machine [SVM], linear discriminant analysis [LDA], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]). The combined model integrating radiomics features and PI-RR score was developed using the most effective classifier. The classification performances of the proposed models were assessed by receiver operating characteristic (ROC) curve analysis. Results There were no significant differences between the training and testing sets concerning age, prostate-specific antigen (PSA), Gleason score, T-stage, seminal vesicle invasion (SVI), perineural invasion (PNI), and positive surgical margins (PSM). The radiomics model based on LR-LASSO exhibited superior performance than other radiomics models, with an AUC of 0.858 in the testing set; the PI-RR yielded an AUC of 0.833, and there was no significant difference between the best radiomics model and the PI-RR score. The combined model achieved the best predictive performance with an AUC of 0.924, and a significant difference was observed between the combined model and PI-RR score. Conclusions Our radiomics model is an effective tool to predict PCa local recurrence after RP. By integrating radiomics features with the PI-RR score, our combined model exhibited significantly better predictive performance of local recurrence than expert-level radiologists’ PI-RR assessment.

Abstract Background Prior MRI studies on vestibular migraine (VM) have revealed abnormalities in static regional intrinsic brain activity (iBA) and dynamic functional connectivity between brain regions or networks. However, the temporal variation and concordance of regional iBA measures remain to be explored. Methods 57 VM patients during the interictal period were compared to 88 healthy controls (HC) in this resting-state functional magnetic resonance imaging (fMRI) study. The dynamics and concordance of regional iBA indices, including amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), were examined by utilizing sliding time-window analysis. Partial correlation analyses were performed between clinical parameters and resting-state fMRI indices in brain areas showing significant group differences. Results The VM group showed increased ALFF and ReHo dynamics, as well as increased temporal concordance between ALFF and ReHo in the bilateral paracentral lobule and supplementary motor area relative to the HC group. We also found decreased ReHo dynamics in the right temporal pole, and decreased ALFF dynamics in the right cerebellum posterior lobe, bilateral angular gyrus and middle occipital gyrus (MOG) in the VM group compared with the HC group. Moreover, a positive correlation was observed between ALFF dynamics in the left MOG and vertigo disease duration across all VM patients. Conclusion Temporal dynamics and concordance of regional iBA indices were altered in the motor cortex, cerebellum, occipital and temporoparietal cortex, which may contribute to disrupted multisensory processing and vestibular control in patients with VM. ALFF dynamics in the left MOG may be useful biomarker for evaluating vertigo burden in this disorder.

Polyethylene mulching film, which is widely utilized in arid and semi-arid agriculture, leaves residual pollution. A novel approach to addressing this issue is microbial degradation. To screen the strains that degrade polyethylene efficiently and clarify the effect of degrading strains on the turnover of soil organic carbon, a polyethylene-degrading fungus PF2, identified as Trichoderma asperellum, was isolated from long-time polyethylene-covered soil. Strain PF2 induced surface damage and ether bonds, ketone groups and other active functional groups in polyethylene, with 4.15% weight loss after 30 days, where laccase plays a key role in the degradation of polyethylene. When applied to soil, the Trichoderma-to-soil weight ratios were the following: B1: 1:100; B2: 1:200; B3: 1:300 and B4: 1:400. Trichoderma asperellum significantly increased the cumulative CO2 mineralization and soil organic carbon mineralization in the B1 and B2 treatments compared with the control (B0). The treatments B1, B3 and B4 increased the stable organic carbon content in soil. An increase in the soil organic carbon content was observed with the application of Trichoderma asperellum, ranging from 27.87% to 58.38%. A positive correlation between CO2 emissions and soil organic carbon was observed, with the soil carbon pool management index (CPMI) being most correlated with active organic carbon. Trichoderma treatments improved the CPMI, with B3 showing the most favorable carbon retention value. Thus, Trichoderma asperellum not only degrades polyethylene but also contributes to carbon sequestration and soil fertility when applied appropriately.

Abstract There is mounting evidence that malnutrition and systemic inflammation status are involved in the prognosis of various cancers. In this study, we aimed to evaluate the prognostic value of the pretreatment fibrinogen-albumin ratio index (FARI) in nasopharyngeal carcinoma (NPC) patients receiving definite radiotherapy. NPC patients who received definite radiotherapy between January 2013 and December 2019 were included. A receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value. The clinicopathological characteristics of the patients were compared via the Chi-square test. Survival curves were analyzed by the Kaplan‒Meier method. The prognostic factors were evaluated by univariate and multivariate analyses via Cox hazards regression analysis. A total of 225 patients were enrolled, and the median follow-up time was 48.5 months. High FARI was correlated with worse ECOG score (p = 0.003), higher EBV-DNA titer (p = 0.047), and more advanced clinical stage (p

Abstract Transboundary river cooperation provides an effective pathway to maintain regional security and sustainable development; however, its implementation is a pressing and prominent concern due to lack of appropriate compensation measures and effective incentive strategies. Here we develop a dual water-electricity cooperation (DWEC) framework that combines water and electricity trading to meet the often-conflicting demands of participating countries. The results from the Lancang-Mekong River Basin reveal that substantial benefits in both economic and social aspects can be achieved through coupling regional water and electricity trades. Economic benefits can be obtained by expanding cooperation space and thereby greatly improving the willingness of countries to participate in basin-wide cooperation. Electricity trading plays a key role in loss compensation for water exporters, ensuring no loss for any party and maximizing basin-wide benefits. Furthermore, the DWEC improves regional water use equality, especially in water shortage periods when there is severe competition among water users. The proposed cooperation framework provides a viable way to implement cooperation in transboundary river basins.

Abstract Purpose To predict ductal carcinoma in situ with microinvasion (DCISMI) based on clinicopathologic, conventional breast magnetic resonance imaging (MRI), and dynamic contrast enhanced MRI (DCE-MRI) radiomics signatures in women with biopsy-confirmed ductal carcinoma in situ (DCIS). Methods Eighty-six women with eighty-seven biopsy-proven DCIS who underwent preoperative MRI and underwent surgery were retrospectively identified. Clinicopathologic, conventional MRI, DCE-MRI radiomics, combine (based on conventional MRI and DCE-MRI radiomics), traditional (based on clinicopathologic and conventional MRI) and mixed (based on clinicopathologic, conventional MRI and DCE-MRI radiomics) models were constructed by logistic regression (LR) with a 3-fold cross-validation, all evaluated using receiver operating characteristic (ROC) curve analysis. A clinical radiomics nomogram was then built by incorporating the Radiomics score, significant clinicopathologic and conventional MRI features of mixed model. Results The area under the curves (AUCs) of clinicopathologic, conventional MRI, DCE-MRI radiomics, traditional, combine, and mixed model were 0.76 (95% confidence interval [CI] 0.59–0.94), 0.77 (95%CI 0.59–0.95), 0.74 (95%CI 0.55–0.93), 0.87 (95%CI 0.73–1), 0.8 (95%CI 0.63–0.96), and 0.93 (95%CI 0.84–1) in the validation cohort, respectively. The clinical radiomics nomogram based on mixed model showed higher AUCs than both clinicopathologic and DCE-MRI radiomics models in training/test (all P

Abstract Background Differentiating between low-grade glioma and brain glial cell hyperplasia is crucial for the customized clinical treatment of patients. Objective Based on multiparametric MRI imaging and clinical risk factors, a radiomics-clinical model and nomogram were constructed for the distinction of brain glial cell hyperplasia from low-grade glioma. Methods Patients with brain glial cell hyperplasia and low-grade glioma who underwent surgery at the First Affiliated Hospital of Soochow University from March 2016 to March 2022 were retrospectively included. In this study, A total of 41 patients of brain glial cell hyperplasia and 87 patients of low-grade glioma were divided into training group and validation group randomly at a ratio of 7:3. Radiomics features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1-enhanced). Then, LASSO, SVM, and RF models were created in order to choose a model with a greater level of efficiency for calculating each patient’s Rad-score (radiomics score). The independent risk factors were identified via univariate and multivariate logistic regression analysis to filter the Rad-score and clinical risk variables in turn. A radiomics-clinical model was next built of which effectiveness was assessed. Results Brain glial cell hyperplasia and low-grade gliomas from the 128 cases were randomly divided into 10 groups, of which 7 served as training group and 3 as validation group. The mass effect and Rad-score were two independent risk variables used in the construction of the radiomics-clinical model, and their respective AUCs for the training group and validation group were 0.847 and 0.858. The diagnostic accuracy, sensitivity, and specificity of the validation group were 0.821, 0.750, and 0.852 respectively. Conclusion Combining with radiomics constructed by multiparametric MRI images and clinical features, the radiomics-clinical model and nomogram that were developed to distinguish between brain glial cell hyperplasia and low-grade glioma had a good performance.

Abstract Background Type 2 diabetes mellitus (T2DM) patients have a higher incidence of coronary artery disease than the general population. The aim of this study was to develop a radiomics nomogram of pericoronary adipose tissue (PCAT) based on non-contrast CT to predict haemodynamically significant coronary stenosis in T2DM patients. Methods The study enrolled 215 T2DM patients who underwent non-contrast CT and coronary computed tomography angiography (CCTA). CCTA derived fractional flow reserve (FFRCT) ≤ 0.80 was defined as hemodynamically significant stenosis.1691 radiomics features were extracted from PCAT on non-contrast CT. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to select useful radiomics features to construct Radscore. Logistic regression was applied to select significant factors among Radscore, fat attenuation index (FAI) and coronary artery calcium score (CACS) to construct radiomics nomogram. Results Radscore [odds ratio (OR) = 2.84; P

Background: Blood-based immune-inflammation indexes have been widely used to predict survival in a variety of cancers. In this research, we seeked to evaluate a novel immune-inflammation marker, named the pan-immune-inflammation value (PIV), in patients with nasopharyngeal carcinoma (NPC) undergoing definitive radiotherapy. Methods: A group of 377 patients with NPC was retrospectived analyzed. Clinical data and laboratory data were collected. Receiver operating characteristic (ROC) curve analysis was performed in order to determine the optimal PIV cut-off value. Survival curves were estimated by Kaplan-Meier method, and prognostic variables were identified using a Cox regression model. Additionally, we developed a nomogram and assessed its acuracy using the concordance index (C-index) and a calibration curve. Results: The optimal PIV cut-off value was 146.24 according to ROC analysis. High PIV was related to poorer Eastern Cooperative Oncology Group Performance Status (ECOG PS) score (p = 0.017), more advanced T (p＜0.001) and clinical stages (p = 0.024). In univariate analysis, older Age, poorer ECOG PS, higher Epstein–Barr virus DNA (EBV-DNA), advanced T, N and clinical stage, and higher PIV levels were related to patients' poorer overall survival (OS). Poorer ECOG PS, higher EBV-DNA, later T stage, later clinical stage, and higher PIV were associated with patients' poorer progression free survival (PFS). Male sex and later T stage were associated with patients' poorer locoregional recurrence free survival (LRRFS). Poorer ECOG PS, higher EBV-DNA, later T stage, later clinical stage, and higher PIV were associated with patients’ poorer distant metastasis free survival (DMFS). Multivariate analysis demonstrated that PIV was an independent prognostic index for OS (HR 2.231, 95 % CI 1.241–4.011, P = 0.007), PFS (HR 1.664, 95 % CI 1.003–2.760, P = 0.049), and DMFS(HR 2.081, 95 % CI 1.071–4.044, P = 0.031). Nomogram C-indexes for the nomogram of OS were 0.684, and PFS were 0.62, respectively. Survival predictions and actual survival were consistent according to the calibration curve. Conclusions: Pre-treatment PIV is a promising biomarker for predicting survival in patients with NPC.

Breast cancer is one of the most prevalent cancers worldwide, and early diagnosis and screening are vital to its successful treatment. Although medical imaging methods can assist in the early detection of breast cancer, imaging methods that are currently used for clinical diagnosis have drawbacks, such as low sensitivity and accuracy. Contrast agents are often used in diagnostic imaging to address these drawbacks. Nanocontrast agents have attracted considerable attention in recent years due to their unique physicochemical characteristics. Among these agents, inorganic nanoprobes have been substantially developed through improvements in synthesis techniques and pairings with other organic molecules. This paper mainly summarizes the specific applications of inorganic nanoprobes in the magnetic resonance imaging, fluorescence imaging, radionuclide imaging, and bimodal/multimodal imaging of breast cancer.

This study discovered non-monotonic variations in river flows for seven rivers originating from the Tibetan Plateau at warming levels of 1.5 °C, 2.0 °C, and 3.0 °C, which then resulted in different consequences for riparian countries

Abstract Background Epidermal growth factor receptor (EGFR)‐mutated non‐small cell lung cancer (NSCLC) develops resistance to tyrosine kinase inhibitors (TKIs). Here, we evaluated the efficacy of radiotherapy and continuation of TKIs in patients with advanced NSCLC with oligoprogression after EGFR‐TKIs. Methods From January 2011 to January 2019, 33 patients with EGFR‐mutated NSCLC on TKIs were treated by radiotherapy and continuation of TKIs for oligoprogressive disease. The primary endpoints were median progression‐free survival 1 (mPFS1), mPFS2, and median overall survival (mOS). PFS1 was measured from the start of EGFR‐TKIs therapy to the oligoprogression of the disease. PFS2 was measured from the date of oligoprogression to the further progression of the disease, while OS was calculated from oligoprogression to death from any cause or was censored at the last follow‐up date. Result The mPFS1, mPFS2, and mOS were 11.0 (95% CI, 4.4–17.6), 6.5 (95% CI, 1.4–11.6) and 21.8 (95% CI, 14.8–28.8) months, respectively. Univariate analysis showed that EGFR mutation type (p = 0.024), radiotherapy method (p = 0.001), and performance status (p = 0.017) were significantly correlated with PFS2. Univariate analysis showed that sex (p = 0.038), smoking history (p = 0.031), EGFR mutation type (p = 0.012), and radiotherapy method (p = 0.009) were significantly correlated with OS. Multivariate analysis suggested that radiotherapy method (p = 0.001) and performance status (p = 0.048) were prognostic factors for PFS2, and radiotherapy method (p = 0.040) was a prognostic factor for OS. Conclusion Radiotherapy with continued TKIs is effective for EGFR‐mutated NSCLC with oligoprogression, and it should be conducted as soon as possible. T790M+ patients have higher sensitivity to radiotherapy, and patients with good performance status and stereotactic body radiation therapy have better PFS2 and OS.

Abstract Background It is difficult to predict normal-sized lymph node metastasis (LNM) in cervical cancer clinically. We aimed to investigate the feasibility of using deep learning (DL) nomogram based on readout segmentation of long variable echo-trains diffusion weighted imaging (RESOLVE-DWI) and related patient information to preoperatively predict normal-sized LNM in patients with cervical cancer. Methods A dataset of MR images [RESOLVE-DWI and apparent diffusion coefficient (ADC)] and patient information (age, tumor size, International Federation of Gynecology and Obstetrics stage, ADC value and squamous cell carcinoma antigen level) of 169 patients with cervical cancer between November 2013 and January 2022 were retrospectively collected. The LNM status was determined by final histopathology. The collected studies were randomly divided into a development cohort (n = 126) and a test cohort (n = 43). A single-channel convolutional neural network (CNN) and a multi-channel CNN based on ResNeSt architectures were proposed for predicting normal-sized LNM from single or multi modalities of MR images, respectively. A DL nomogram was constructed by incorporating the clinical information and the multi-channel CNN. These models’ performance was analyzed by the receiver operating characteristic analysis in the test cohort. Results Compared to the single-channel CNN model using RESOLVE-DWI and ADC respectively, the multi-channel CNN model that integrating both two MR modalities showed improved performance in development cohort [AUC 0.848; 95% confidence interval (CI) 0.774–0.906] and test cohort (AUC 0.767; 95% CI 0.613–0.882). The DL nomogram showed the best performance in development cohort (AUC 0.890; 95% CI 0.821–0.938) and test cohort (AUC 0.844; 95% CI 0.701–0.936). Conclusion The DL nomogram incorporating RESOLVE-DWI and clinical information has the potential to preoperatively predict normal-sized LNM of cervical cancer.

Abstract Background The histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades. Methods A total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP–IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP–IMD and clinical features. The performance of these models was evaluated and compared. Results Model-CP–IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP–IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP–IMD and model-Combine outperformed model-CP according to decision curve analysis. Conclusion DESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

A new study reconstructs eight-century streamflow over the South and East Tibetan Plateau, showing that observations underestimate the full range of long-term streamflow variability and revealing contrasting regional variability in streamflow between south and north study regions.

Abstract This retrospective study compared the efficacy and safety of nedaplatin-based chemoradiotherapy and cisplatin-based chemoradiotherapy in stage II-IVa nasopharyngeal carcinoma (NPC) patients. Patients treated with cisplatin-based or nedaplatin-based chemoradiotherapy between January 2012 and December 2015 were evaluated. Survival was estimated by the Kaplan‒Meier method and compared by the log-rank test. Multivariate analysis was performed using the Cox proportional hazards model. A cohort of 538 NPC patients was enrolled. There were no significant differences in the 5-year overall survival (OS), progression-free survival (PFS), locoregional relapse-free survival (LRRFS), or distant metastasis-free survival (DMFS) between the cisplatin and nedaplatin groups. During the whole treatment course, patients in the cisplatin group had higher incidences of grade 3‒4 vomiting and anorexia, while patients in the nedaplatin group had higher incidences of grade 3‒4 leucopenia and mucositis. In terms of late toxicities, patients in the cisplatin group had a higher incidence of xerostomia. In multivariate analysis, T stage, N stage, and clinical stage were prognostic factors for OS, PFS, and DMFS. In subgroup analyses, nedaplatin-based chemotherapy achieved comparable treatment outcomes in specific populations stratified by age, sex, ECOG PS score and clinical stage. Cisplatin and nedaplatin are effective choices for stage II-IVa NPC patients, with a different spectrum of side effects.

Abstract Immunotherapy has become the standard of care for non-small cell lung cancer (NSCLC), either in combination or monotherapy. However, there are still some patients who cannot benefit from it. Immunization strategies for NSCLC are based on the expression of PD-L1 on tumor cells and TMB, and although these indicators have a certain predictive effect, their predictive performance is not good. Therefore, clinicians must make adjustments to recognize markers. This is a review article that summarized immunotherapeutic biomarkers according to the “seed-soil-environment”, generalizes primary resistance to immunotherapy, and summarizes the integration of markers.

Lung cancer is the most lethal malignancy around the world and non-small cell lung cancer (NSCLC) accounts for 80% of all cases. Most of the NSCLC patients has “driver gene mutations” and targeted therapy achieved a relatively good efficacy, but some patients progressed or relapsed after treatment. Previous studies demonstrated that immune checkpoint inhibitor could improve the prognosis of advanced-stage NSCLC and prolong the survival time. However, the efficacy of immune therapy varies in NSCLC patients with different immune and molecular features. The efficacy of immune therapy was controversial in NSCLC patients with driver gene mutation. The present review will summarize the immune characteristics of NSCLC patients with driver mutation and the directions of immunotherapy for patients with driver mutation.

Abstract Background Lipusu is the first commercialized liposomal formulation of paclitaxel and has demonstrated promising efficacy against locally advanced lung squamous cell carcinoma (LSCC) in a small‐scale study. Here, we conducted a multicenter, randomized, phase 3 study to compare the efficacy and safety of cisplatin plus Lipusu (LP) versus cisplatin plus gemcitabine (GP) as first‐line treatment in locally advanced or metastatic LSCC. Methods Patients enrolled were aged between 18 to 75 years, had locally advanced (clinical stage IIIB, ineligible for concurrent chemoradiation or surgery) or metastatic (Stage IV) LSCC, had no previous systemic chemotherapy and at least one measurable lesion as per the Response Evaluation Criteria in Solid Tumors (version 1.1) before administration of the trial drug. The primary endpoint was progression‐free survival (PFS). The secondary endpoints included objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety profiles. To explore the possible predictive value of plasma cytokines for LP treatment, plasma samples were collected from the LP group at baseline and first efficacy evaluation time and were then subjected to analysis by 45‐Plex ProcartaPlex Panel 1 to detect the presence of 45 cytokines using the Luminex xMAP technology. The correlation between treatment outcomes and dynamic changes in the levels of cytokines were evaluated in preliminary analyses. Results The median duration of follow‐up was 15.4 months. 237 patients in the LP group and 253 patients in the GP group were included in the per protocol set (PPS). In the PPS, the median PFS was 5.2 months versus 5.5 months in the LP and GP group (hazard ratio [HR]: 1.03, P = 0.742) respectively. The median OS was 14.6 months versus 12.5 months in the LP and GP group (HR: 0.83, P = 0.215). The ORR (41.8% versus 45.9%, P = 0.412) and DCR (90.3% versus 88.1%, P = 0.443) were also similar between the LP and GP group. A significantly lower proportion of patients in the LP group experienced adverse events (AEs) leading to treatment interruptions (10.9% versus 26.4%, P < 0.001) or treatment termination (14.3% versus 23.1%, P = 0.011). The analysis of cytokine levels in the LP group showed that low baseline levels of 27 cytokines were associated with an increased ORR, and 15 cytokines were associated with improved PFS, with 14 cytokines, including TNF‐α, IFN‐γ, IL‐6, and IL‐8, demonstrating an overlapping trend. Conclusion The LP regimen demonstrated similar PFS, OS, ORR and DCR as the GP regimen for patients with locally advanced or metastatic LSCC but had more favorable toxicity profiles. The study also identified a spectrum of different cytokines that could be potentially associated with the clinical benefit in patients who received the LP regimen.

BackgroundNeoadjuvant chemoimmunotherapy (NCIO) is more effective than neoadjuvant immunotherapy alone for pathological response in non-small cell lung cancer (NSCLC) patients, but the processes for determining patient suitability for its implementation are not clear. We aimed to identify the most relevant factors and build a convenient model to select NSCLC patients who would benefit most from NCIO.Methods We retrospectively collected the clinical data of patients with locally advanced NSCLC who received NCIO followed by surgery at our institution between January 2019 and July 2022.ResultsA total of 101 eligible stage IIB-IIIC NSCLC patients were included. After NCIO, all patients successfully underwent surgical resection. A total of 46.53% (47/101) of patients achieved pathological complete response (pCR), and 70.30% (71/101) achieved major pathologic response (MPR). Tumor regression rate (adjusted odds ratio OR = 12.33), PD-L1 expression (adjusted odds ratio (OR) = 9.66), pembrolizumab/nab-paclitaxel–based regimens (adjusted OR = 4.92), and comorbidities (adjusted OR = 0.16) were independently associated with pCR rate (all P < 0.05). Tumor regression rate (adjusted OR = 8.45), PD-L1 expression (adjusted OR = 5.35), and presence of squamous cell carcinoma (adjusted OR = 7.02) were independently associated with MPR rate (all P < 0.05). We established and validated an easy-to-use clinical model to predict pCR (with an area under the curve [AUC] of 0.848) and MPR (with an AUC of 0.847). Of note, the present study showed that CD4+ T-cell count/rate and total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) levels in the peripheral blood of pre-NCIO patients were also significantly correlated with pathological response in univariate analyses.ConclusionsThe tumor regression rate, PD-L1 expression, pembrolizumab/nab-paclitaxel–based regimens, presence of squamous cell carcinoma, and comorbidities were the main influential factors for incidence of pCR/MPR in patients with stage IIB-IIIC NSCLC in the present study. Through predictive models, we can predict who will benefit most from NCIO prior to the emergence of clinical outcomes in locally advanced NSCLC.

Abstract Background Fluorescence imaging as the beacon for optical navigation has wildly developed in preclinical studies due to its prominent advantages, including noninvasiveness and superior temporal resolution. However, the traditional optical methods based on ultraviolet (UV, 200–400 nm) and visible light (Vis, 400–650 nm) limited by their low penetration, signal-to-noise ratio, and high background auto-fluorescence interference. Therefore, the development of near-infrared-II (NIR-II 1000–1700 nm) nanoprobe attracted significant attentions toward in vivo imaging. Regrettably, most of the NIR-II fluorescence probes, especially for inorganic NPs, were hardly excreted from the reticuloendothelial system (RES), yielding the anonymous long-term circulatory safety issue. Results Here, we develop a facile strategy for the fabrication of Nd3+-doped rare-earth core–shell nanoparticles (Nd-RENPs), NaGdF4:5%Nd@NaLuF4, with strong emission in the NIR-II window. What’s more, the Nd-RENPs could be quickly eliminated from the hepatobiliary pathway, reducing the potential risk with the long-term retention in the RES. Further, the Nd-RENPs are successfully utilized for NIR-II in vivo imaging and magnetic resonance imaging (MRI) contrast agents, enabling the precise detection of breast cancer. Conclusions The rationally designed Nd-RENPs nanoprobes manifest rapid-clearance property revealing the potential application toward the noninvasive preoperative imaging of tumor lesions and real-time intra-operative supervision. Graphical abstract

Abstract Objective This study investigated the feasibility of predicting the expression levels of Ki-67 in breast cancer using ultrasonographic findings and clinical features. Methods Fifty-eight breast cancer patients, with 82 lesions confirmed by surgical pathology, were selected retrospectively for this study. Conventional ultrasound examination and elastography examination were performed before surgery. Clinical features (age, estrogen receptor (ER), progesterone receptor, and human epidermal growth factor receptor-2 expression levels), ultrasonographic findings, and elastography scores, including the maximum size, location, number, margin, borderline, blood flow, and elastography score of the mass, were collected. The expression of Ki-67 was recorded using immunohistochemical staining, and the patients were divided into a high (≥ 20%) expression group and a low ( 0.05). Multiple factor regression analysis showed that a large mass and a mass with a rich blood flow had an independent predictive value for Ki-67. When the diameter was > 21.5 mm, the diagnostic sensitivity and specificity were 91.9% and 71.3%, respectively. The expression level of Ki-67 was negatively correlated with that of ER. Conclusion The tumor size and blood flow of breast cancer is correlated with the expression level of Ki-67 and, thus, it could be used to predict the expression level of Ki-67 in ultrasound diagnosis. The margin condition and the expression level of ER of an ultrasonic mass could also indirectly reflect the Ki-67 expression level of the mass.

Summary: Background: In the global REACH-2 study, ramucirumab significantly improved overall survival (OS) compared with placebo in patients with advanced hepatocellular carcinoma (HCC) and elevated alpha-fetoprotein (AFP). REACH-2 China study aimed to evaluate the efficacy and safety of ramucirumab in Chinese patients with advanced HCC (NCT02435433). Methods: REACH-2 China was a randomised, double-blind, placebo-controlled, phase 3 study done at 31 centres in China between Sep 16, 2015, and March 15, 2021. Patients with advanced HCC and AFP ≥400 ng/mL after first-line sorafenib were randomly assigned (2:1) to receive ramucirumab 8 mg/kg intravenously or placebo Q2W, until disease progression or unacceptable toxicity. The primary endpoint was OS. Efficacy was assessed per intention-to-treat, and safety in patients who received any treatment. Findings: Of 104 Chinese patients enrolled (44 in the global study and 60 in the China extension study), 70 received ramucirumab and 34 received placebo. Median OS was 9·1 months in the ramucirumab group and 6·2 months in the placebo group (HR = 0·854 [95% CI: 0·536, 1·359]). The most common grade 3 or worse treatment-emergent adverse event were hypertension (5 [7·1%] of 70 patients in the ramucirumab group vs 1 [2.9%] of 34 in the placebo group), pneumonia (5 [7·1%] vs 1 [2·9%]), and hyponatraemia (4 [5·7%] vs 0 [0%]). Interpretation: Ramucirumab demonstrated clinically meaningful improvement in OS compared to placebo for Chinese patients with advanced HCC and elevated AFP, although lacking statistical superiority. Ramucirumab was well tolerated, with a manageable safety profile. The results are consistent with those of the global REACH-2 study, supporting a favourable risk-benefit profile for ramucirumab in this population. Funding: Eli Lilly and Company, USA.

ObjectivesIn the present study, we aimed to determine the prognostic value of the 18F-FDG PET/CT-based radiomics model when predicting progression-free survival (PFS) and overall survival (OS) in patients with non-small cell lung cancer (NSCLC).MethodsA total of 368 NSCLC patients who underwent 18F-FDG PET/CT before treatment were randomly assigned to the training (n = 257) and validation (n = 111) cohorts. Radiomics signatures from PET and CT images were obtained using LIFEx software, and then clinical and complex models were constructed and validated by selecting optimal parameters based on PFS and OS to construct radiomics signatures.ResultsIn the training cohort, the C-index of the clinical model for predicting PFS and OS in NSCLC patients was 0.748 and 0.834, respectively, and the AUC values ​​were 0.758 and 0.846, respectively. The C-index of the complex model for predicting PFS and OS was 0.775 and 0.881, respectively, and the AUC values ​​were 0.780 and 0.891, respectively. The C-index of the clinical model for predicting PFS and OS in the validation group was 0.729 and 0.832, respectively, and the AUC values ​​were 0.776 and 0.850, respectively. The C-index of the complex model for predicting PFS and OS was 0.755 and 0.867, respectively, and the AUC values ​​were 0.791 and 0.874, respectively. Moreover, decision curve analysis showed that the complex model had a higher net benefit than the clinical model.Conclusions18F-FDG PET/CT radiomics before treatment could predict PFS and OS in NSCLC patients, and the predictive power was higher when combined with clinical factors.

ObjectivesThis study aimed to determine whether texture analysis (TA) and machine learning-based classifications can be applied in differential diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) using non-contrast cine cardiac magnetic resonance (CMR) images.MethodsIn this institutional review board-approved study, we consecutively enrolled 167 patients with CA (n = 85), HCM (n = 82), and 84 patients with normal CMR served as controls. All cases were randomized into training [119 patients (70%)] and validation [48 patients (30%)] groups. A total of 275 texture features were extracted from cine images. Based on regression analysis with the least absolute shrinkage and selection operator (LASSO), nine machine learning models were established and their diagnostic performance determined.ResultsNineteen radiomics texture features derived from cine images were used to differentiate CA and HCM. In the validation cohort, the support vector machine (SVM), which had an accuracy of 0.85, showed the best performance (MCC = 0.637). Gray level non-uniformity (GLevNonU) was the single most effective feature. The combined model of radiomics texture features and conventional MR metrics had superior discriminatory performance (AUC = 0.89) over conventional MR metrics model (AUC = 0.79). Moreover, results showed that GLevNonU levels in HCM patients were significantly higher compared with levels in CA patients and control groups (P < 0.001). A cut-off of GLevNonU ≥ 25 was shown to differentiate between CA and HCM patients, with an area under the curve (AUC) of 0.86 (CI:0.804–0.920). Multiple comparisons tests showed that GLevNonU was significantly greater in LGE+, relative to LGE-patient groups (CA+ vs. CA- and HCM+ vs. HCM-, P = 0.01, 0.001, respectively).ConclusionMachine learning-based classifiers can accurately differentiate between CA and HCM on non-contrast cine images. The radiomics-MR combined model can be used to improve the discriminatory performance. TA may be used to assess myocardial microstructure changes that occur during different stages of cardiomyopathies.

BackgroundPreclincal studies showed the promising efficacy of tumor cell-derived microparticles packaging methotrexate (TMPs-MTX) to treat advanced non-squamous non-small cell lung cancer (NSCLC) with malignant pleural effusion (MPE).MethodsThis randomized, double-blind, placebo-controlled study was conducted at six hospitals in China from 20 July 2015 to 25 April 2019. Patients newly diagnosed with non-squamous NSCLC with MPE were randomly assigned to receive TMPs-MTX (group A) or saline (group B). Patients in both groups received pemetrexed (500 mg/m2 d1) and cisplatin (75 mg/m2 in total for d1-d2). Intrapleural infusion (50 mL saline containing 5 units of TMPs-MTX per perfusion, once every 48 hours, six total perfusions) was initiated on day 5 after pemetrexed-cisplatin chemotherapy. The primary outcome was the objective response rate (ORR) of MPE. Secondary outcomes included the ORR of target lesions, progression-free survival (PFS), overall survival (OS), toxicity, and pleural fluid properties.ResultsA total of 86 patients were enrolled in this study and randomly assigned to either group A or group B. Of these, 79 patients were evaluable for response. The ORR of MPE in group A was significantly higher than that in group B (82.50% vs. 58.97%, P = 0.0237). The ORR of target lesions was 25.64% in group A and 20.51% in group B (P = 0.5909), respectively. With a median follow-up time of 18.8 months, median PFS were 6.4 (95% CI, 4.5-12.3) months in group A and 7.3 (95% CI, 6.1-10.4) months in group B (P = 0.6893), and median OS were 19.9 (95% CI, 17.1-28.5) months and 17.5 (95% CI, 11.6-25.0) months (P = 0.4500), respectively. The incidence rates of adverse events were similar in the two groups. The most common treatment-related adverse events were chemotherapy-induced toxicities, including fever, gastrointestinal reactions, hepatic dysfunction, and leukopenia.ConclusionIntrapleural infusion of TMPs-MTX combined with pemetrexed-cisplatin chemotherapy is safe and effective against MPE in patients with advanced non-squamous NSCLC.Clinical trial registrationhttp://www.chictr.org.cn (ChiCTR-ICR-15006304).

Recently, it have been reported that Hepatitis A Virus-Cellular Receptor 2(HAVCR2,encoding T-cell immunoglobulin and Mucin-Containing Protein 3[TIM3]) mutations are associated with severe hemophagocytic syndrome(HLH) in subcutaneous panniculitis-like T-cell lymphoma(SPTCL),and there are also frequent mutations in sporadic SPTCL, suggesting the individuals harboring HAVCR2(TIM-3) germline mutations are highly susceptible to familial or sporadic SPTCL. Here, we identify a novel germline compound heterozygous mutation of TIM-3 gene,c.245A>G (p.Tyr82Cys) and c.265C>T(p.Arg89Cys) variations in a single familial case with EBV-positive peripheral T-cell lymphoma(NOS),accompanied HLH;we also detected Tyr82Cys germline mutation in TIM-3 gene in one sporadic patient with cutaneous T cell lymphoma. We screened the distributive frequencies for TIM-3 mutations in healthy controls(n=87), B-(n=79) or T-cell lymphoma(n=25) not SPTCL, and the results showed that the mutation was found in two out of 25 patients with T-cell lymphoma but was not detected in 79 patients with B-cell lymphoma nor in a group of 87 controls. The mRNA expression of TIM-3 on primary cells and transfected HEK293 cells reduced significantly, indicating Tyr82Cys and Arg89Cys mutations is a loss-of function mutations on TIM-3,resulting in a weakened TIM-3 signaling. Our results suggest Tyr82Cys TIM-3 germline mutations are not only limited in SPTCL, and also occurred in other types of T-cell lymphoma, especially complicated HLH. TIM-3 mutations may be an predisposing factor for T-cell lymphoma and molecular marker for auxiliary diagnosis in T cell lymphoma,especially complicated with HLH.

Objective: To explore whether preoperative contrast-enhanced computed tomogrpahy (CT) can predict lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC), and provide a reliable reference for the formulation of clinical individualized treatment plans. Methods: This retrospective study enrolled 228 patients with surgically resected and pathologically confirmed ESCC, including 36 patients with LVI and 192 patients without LVI. All patients underwent contrast-enhanced CT (CECT) scan within 2 weeks before the operation. Tumor size (including tumor length and maximum tumor thickness), tumor-to-normal wall enhancement ratio (TNR), and gross tumor volume (GTV) were obtained. All clinical features and CECT-derived parameters associated with LVI were analyzed by univariate and multivariate analysis. The independent predictors for LVI were identified, and their combination was built by multivariate logistic regression analysis, using the significant variables from the univariate analysis as inputs. Results: Univariate analysis of clinical features and CECT-derived parameters revealed that age, TNR, and clinical N stage (cN stage) were significantly associated with LVI. The multivariable analysis results demonstrated that age (odds ratio [OR]: 5.32, 95% confidence interval [CI]: 2.224-12.743, P

ObjectiveExtracranial vertebral artery dissection (EVAD) is one of the main causes of stroke in young and middle-aged patients. However, the diagnosis is challenging. This study aimed to identify the characteristics of EVAD on color duplex ultrasonography (CDU) and high-resolution magnetic resonance imaging (hrMRI), hoping to improve the accuracy and determine the relative contribution of vessel findings and clinical factors to acute ischemic events.MethodsPatients with unilateral EVAD were recruited and divided into ischemia and non-ischemia groups. Clinical features of patients and the lesion location; a variety of signs which indicate dissection, including the presence of an intimal flap, double lumen, intramural hematoma, dissecting aneurysm, intraluminal thrombus, and irregular lumen; and other quantitative parameters of each dissected segment on CDU and hrMRI were reviewed, respectively. Multiple logistic regression was performed to explore the association between clinical, imaging characteristics, and ischemic events in patients with unilateral EVAD.ResultsNinety-six patients with unilateral EVAD who met the inclusion criteria were enrolled during a six-year period. Overall, 41 cases (42.7%) were confirmed as ischemic stroke (n = 40) or transient ischemic attack (n = 1) during the 48 h after the onset of symptoms. Men, infections during the last week, and smoking were more common in the ischemia group. Intraluminal thrombus and occlusion on CDU were more prevalent in patients with cerebral ischemia than in those without (36.6 vs. 5.5%; p < 0.001, and 39.0 vs. 9.1%; p = 0.001, respectively). On hrMRI, intraluminal thrombus and occlusion were also more frequent in the ischemia group than in the non-ischemia group (34.1 vs. 5.5%; p < 0.001, and 34.1 vs. 9.1%; p = 0.003, respectively). In addition, lesion length on hrMRI was significantly longer for patients with ischemia (81.5 ± 41.7 vs. 64.7 ± 30.8 mm; p = 0.025). In multivariable logistic regression analysis, male gender, infections during the last week, and the presence of intraluminal thrombus on CDU and hrMRI were independently associated with acute ischemic events.ConclusionMale sex, infections during the last week, and the presence of intraluminal thrombus due to dissection are associated with an increased risk of ischemic events in patients with unilateral EVAD.

Soil salinity is an important abiotic stress factor that seriously affects the crop growth and yield. Use of plant-derived microorganisms is a promising strategy to alleviate salt stress. In a previous study, the endophytic strain Bacillus altitudinis WR10 isolated from wheat roots showed high salt resistance. In this study, we investigated the efficacy of WR10 in improving the salt tolerance of wheat and its potential mechanisms using a hydroponic test. Under salt stress, WR10 inoculation significantly increased the lengths and dry weights of the roots and shoots, indicating that WR10 improves wheat salt tolerance at the seedling stage. WR10 inoculation significantly reduced Na+ accumulation and enhanced K+, P, and Ca2+ uptake in salt-stressed plants, which can be attributed to the upregulated gene expression of H+-ATPase as well as the P-solubilizing and biofilm-producing characteristics of WR10. At the transcriptional level, L-ascorbate peroxidase (APX), glutathione (GSH) synthetase related to GSH biosynthesis, and phenylpropanoid biosynthesis genes (CYP73A, 4CL, and CAD) were significantly upregulated, whereas those of GSH metabolism genes (glutathione S-transferase and gamma-glutamyltranspeptidase) were significantly downregulated in WR10-applied wheat roots under salt stress. These changes increased the APX activity and GSH levels and resulted in a decrease in hydrogen peroxide levels. Additionally, a decrease in proline content was observed in WR10-inoculated plants under salt stress because of WR10-induced upregulation of proline dehydrogenase gene expression. These results provide supporting evidence that WR10 improves wheat salt tolerance via more than one mechanism and open a window of opportunity for WR10 application in salinized soil.

Abstract Background Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. Methods This retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. Results The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30–60%, the combined model added more net benefit compared with the AP radiomics model. Conclusions A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

Abstract Background Convenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia. Methods A total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed. Results In both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775–0.918] and the test set (AUC, 0.867; 95% CI, 0.732–949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts. Conclusions The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.

Background: The present exploration is aimed to determine whether diffusion kurtosis imaging (DKI)-derived mean diffusivity (MD) and mean kurtosis (MK) are possible parameters for the invasive breast cancer grading and whether MD/MK is related to breast cancer clinical-pathologic factors including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2) and Ki-67. Methods: Tumors from 108 invasive breast carcinoma patients (45.6 ± 11.2 years old; range, 20–84 years), diagnosed by pathological examination between January 2016 and August 2017, were included. DKI data (with b values of 0, 1000, 2000 sec/mm2) and MD/MK were measured. The expression of ER, PR, HER-2, and Ki-67 was evaluated by immunohistochemistry. Results: It was found that MD was significantly lower in grade III breast cancer than in grade II breast cancer (0.82 ± 0.21 vs 1.17 ± 0.24, p < 0.0001), while MK was significantly higher in grade III breast cancer than that in grade II breast cancer (1.00 ± 0.31 vs 0.85 ± 0.21, respectively; p < 0.05). In addition, MD was negatively associated with Ki-67 level (r = –0.39, p < 0.05), while MK was positively associated with Ki-67 level (r = 0.56, p < 0.05). Furthermore, MD was significantly increased in ER and PR positive expression group compared with ER and PR negative expression group, while there is no significant difference of MK in ER and PR positive expression group compared with ER and PR negative expression group. Additionally, there is no significant difference of MD and MK in HER-2 positive expression group compared with HER-2 negative expression group. Conclusions: These results demonstrate that DKI has value in the evaluation on the classification of invasive breast carcinoma. MD values were associated with breast cancer clinical-pathologic factors. DKI can provide useful information in the assessment of tumor proliferation activity.

Objectives: The purpose of this research was to investigate whether MRI and Simultaneous Hybrid PET/MRI images were consistent in the histological classification of patients with focal cortical dysplasia. Additionally, this research aimed to evaluate the postoperative outcomes with the MRI and Simultaneous Hybrid PET/MRI images of focal cortical dysplasia. Methods: A total of 69 cases in this research were evaluated preoperatively for drug-resistant seizures, and then surgical resection procedures of the epileptogenic foci were performed. The postoperative result was histopathologically confirmed as focal cortical dysplasia, and patients then underwent PET and MRI imaging within one month of the seizure. In this study, head MRI was performed using a 3.0 T magnetic resonance scanner (Philips) to obtain 3D T1WI images. The Siemens Biograph 16 scanner was used for a routine scanning of the head to obtain PET images. BrainLAB’s iPlan software was used to fuse 3D T1 images with PET images to obtain PET/MRI images. Results: Focal cortical dysplasia was divided into three types according to ILAE: three patients were classified as type I, twenty-five patients as type II, and forty-one patients as type III. Patients age of onset under 18 and age of operation over 18 had a longer duration (p = 0.036, p = 0.021). MRI had a high lesion detection sensitivity of type III focal cortical dysplasia (p = 0.003). Simultaneous Hybrid PET/MRI showed high sensitivity in detecting type II and III focal cortical dysplasia lesions (p = 0.037). The lesions in Simultaneous Hybrid PET/MRI-positive focal cortical dysplasia patients were mostly located in the temporal and multilobar (p = 0.005, 0.040). Conclusion: Simultaneous Hybrid PET/MRI has a high accuracy in detecting the classification of focal cortical dysplasia. The results of this study indicate that patients with focal cortical dysplasia with positive Simultaneous Hybrid PET/MRI have better postoperative prognoses.

Vulnerable carotid plaques are closely related to the occurrence of ischemic stroke. Therefore, accurate and rapid identification of the nature of carotid plaques is essential. This study aimed to determine whether texture analysis based on a vascular ultrasound can be applied to identify vulnerable plaques. Data from a total of 150 patients diagnosed with atherosclerotic plaque (AP) by carotid ultrasound (CDU) and high-resolution magnetic resonance imaging (HRMRI) were collected. HRMRI is the in vivo reference to assess the nature of AP. MaZda software was used to delineate the region of interest and extract 303 texture features from ultrasonic images of plaques. Following regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm, the overall cohort was randomized 7:3 into the training (n = 105) and testing (n = 45) sets. In the training set, the conventional ultrasound model, the texture feature model, and the conventional ultrasound-texture feature combined model were constructed. The testing set was used to validate the model’s effectiveness by calculating the area under the curve (AUC), accuracy, sensitivity, and specificity. Based on the combined model, a nomogram risk prediction model was established, and the consistency index (C-index) and the calibration curve were obtained. In the training and testing sets, the AUC of the prediction performance of the conventional ultrasonic-texture feature combined model was higher than that of the conventional ultrasonic model and the texture feature model. In the training set, the AUC of the combined model was 0.88, while in the testing set, AUC was 0.87. In addition, the C-index results were also favorable (0.89 in the training set and 0.84 in the testing set). Furthermore, the calibration curve was close to the ideal curve, indicating the accuracy of the nomogram. This study proves the performance of vascular ultrasound-based texture analysis in identifying the vulnerable carotid plaques. Texture feature extraction combined with CDU sonogram features can accurately predict the vulnerability of AP.

ObjectivesTo develop and validate a nomogram model based on radiomics features for preoperative prediction of visceral pleural invasion (VPI) in patients with lung adenocarcinoma.MethodsA total of 659 patients with surgically pathologically confirmed lung adenocarcinoma underwent CT examination. All cases were divided into a training cohort (n = 466) and a validation cohort (n = 193). CT features were analyzed by two chest radiologists. CT radiomics features were extracted from CT images. LASSO regression analysis was applied to determine the most useful radiomics features and construct radiomics score (radscore). A nomogram model was developed by combining the optimal clinical and CT features and the radscore. The model performance was evaluated using ROC analysis, calibration curve and decision curve analysis (DCA).ResultsA total of 1316 radiomics features were extracted. A radiomics signature model with a selection of the six optimal features was developed to identify patients with or without VPI. There was a significant difference in the radscore between the two groups of patients. Five clinical features were retained and contributed as clinical feature models. The nomogram combining clinical features and radiomics features showed improved accuracy, specificity, positive predictive value, and AUC for predicting VPI, compared to the radiomics model alone (specificity: training cohort: 0.89, validation cohort: 0.88, accuracy: training cohort: 0.84, validation cohort: 0.83, AUC: training cohort: 0.89, validation cohort: 0.89). The calibration curve and decision curve analyses suggested that the nomogram with clinical features is beyond the traditional clinical and radiomics features.ConclusionA nomogram model combining radiomics and clinical features is effective in non-invasively prediction of VPI in patients with lung adenocarcinoma.

The acquired resistance of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) is inevitable and heterogeneous. The strategies to overcome acquired resistance are significant. For patients with secondary T790M-positive after early generation EGFR-TKIs, osimertinib is the standard second-line therapy. In patients resistant to prior early generation EGFR-TKIs, the acquired T790M mutation overlaps with other driver gene resistance, such as HER2-and MET amplification, accounting for 4–8%. The efficacy of osimertinib is unclear in patients with concurrent multiple driver gene resistance. We here report a patient who acquired EGFR T790M, STRN-ALK fusion, and EGFR amplification after gefitinib progression and subsequent MET amplification acquired from osimertinib. The other patient acquired EGFR T790M and MET amplification post-dacomitinib and acquired CCDC6-RET fusion after osimertinib treatment. Besides, subsequent new bypass activations were the possible resistance mechanisms to second-line osimertinib. Both patients had progression-free survival (PFS) less than 4 months and limited benefits from osimertinib second-line therapy. The T790M accompanying driver gene resistance will be a new subtype after EGFR-TKIs progression, needing effective treatment options.