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The tumor immune microenvironment (TIME) phenotypes have been reported to mainly impact the efficacy of immunotherapy. Given the increasing use of immunotherapy in cancers, knowing an individual's TIME phenotypes could be helpful in screening patients who are more likely to respond to immunotherapy. Our study intended to establish, validate, and apply a machine learning model to predict TIME profiles in non-small cell lung cancer (NSCLC) by using F-FDG PET/CT radiomics and clinical characteristics. The RNA-seq data of 1145 NSCLC patients from The Cancer Genome Atlas (TCGA) cohort were analyzed. Then, 221 NSCLC patients from Daping Hospital (DPH) cohort received F-FDG PET/CT scans before treatment and CD8 expression of the tumor samples were tested. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images and develop a radiomics signature. The models were established by radiomics, clinical features, and radiomics-clinical combination, respectively, the performance of which was calculated by receiver operating curves (ROCs) and compared by DeLong test. Moreover, based on radiomics score (Rad-score) and clinical features, a nomogram was established. Finally, we applied the combined model to evaluate TIME phenotypes of NSCLC patients in The Cancer Imaging Archive (TCIA) cohort (n = 39). TCGA data showed CD8 expression could represent the TIME profiles in NSCLC. In DPH cohort, PET/CT radiomics model outperformed CT model (AUC: 0.907 vs. 0.861, = 0.0314) to predict CD8 expression. Further, PET/CT radiomics-clinical combined model (AUC = 0.932) outperformed PET/CT radiomics model (AUC = 0.907, = 0.0326) or clinical model (AUC = 0.868, = 0.0036) to predict CD8 expression. In the TCIA cohort, the predicted CD8-high group had significantly higher immune scores and more activated immune pathways than the predicted CD8-low group ( = 0.0421). Our study indicates that F-FDG PET/CT radiomics-clinical combined model could be a clinically practical method to non-invasively detect the tumor immune status in NSCLCs.

ObjectiveThe current study aimed to evaluate the clinical practice for hemodynamic tissue signature (HTS) method in IDH genotype prediction in three groups derived from high-grade gliomas.MethodsPreoperative MRI examinations of 44 patients with known grade and IDH genotype were assigned into three study groups: glioblastoma multiforme, grade III, and high-grade gliomas. Perfusion parameters were analyzed and were used to automatically draw the four reproducible habitats (high-angiogenic enhancing tumor habitats, low-angiogenic enhancing tumor habitats, infiltrated peripheral edema habitats, vasogenic peripheral edema habitats) related to vascular heterogeneity. These four habitats were then compared between inter-patient with IDH mutation and their wild-type counterparts at these three groups, respectively. The discriminating potential for HTS in assessing IDH mutation status prediction was assessed by ROC curves.ResultsCompared with IDH wild type, IDH mutation had significantly decreased relative cerebral blood volume (rCBV) at the high-angiogenic enhancing tumor habitats and low-angiogenic enhancing tumor habitats. ROC analysis revealed that the rCBVs in habitats had great ability to discriminate IDH mutation from their wild type in all groups. In addition, the Kaplan-Meier survival analysis yielded significant differences for the survival times observed from the populations dichotomized by low (< 4.31) and high (> 4.31) rCBV in the low-angiogenic enhancing tumor habitat.ConclusionsThe HTS method has been proven to have high prediction capabilities for IDH mutation status in high-grade glioma patients, providing a set of quantifiable habitats associated with tumor vascular heterogeneity.Key Points• The HTS method has a high accuracy for molecular stratification prediction for all subsets of HGG.• The HTS method can give IDH mutation–related hemodynamic information of tumor-infiltrated and vasogenic edema.• IDH-relevant rCBV difference in habitats will be a great prognosis factor in HGG.

Background High-flow priapism is uncommon, and its association with bilateral cavernous pseudoaneurysms has seldom been reported. Case description This paper presents an exceedingly rare case of high-flow priapism resulting from bilateral cavernous pseudoaneurysms that developed following a straddle injury. The diagnosis was established through blood gas analysis of cavernosal aspirate, ultrasound, and magnetic resonance imaging (MRI). Digital subtraction angiography (DSA) revealed that both cavernous pseudoaneurysms were irrigated by the right internal pudendal artery. Following three months of conservative treatment, the priapism resolved; however, voluntary erectile function was not restored. Conclusions To the best of our knowledge, there have been no reported cases of bilateral cavernous pseudoaneurysms irrigated by the same side of internal pudendal artery. Through this case presentation, we aim to draw clinicians’ attention to this unique presentation of bilateral cavernous pseudoaneurysms perfused by a single internal pudendal artery and its potential implications for treatment approach and prognosis.

Systemic volume changes during acute lung injury (ALI) are closely related to lung injury severity, disease progression, and treatment methods. Twenty-one goats were divided into control, mild injury, and severe injury groups via oleic acid injection. Carotid ultrasound measured carotid diameter and corrected flow time (FTc), while cardiac ultrasound assessed aortic and pulmonary artery velocity–time integral (VTI). Post-euthanasia at 6 h, lung wet-to-dry (W/D) ratio and pathological scores were analyzed. Statistical trends, correlations between ultrasound parameters and lung injury markers, and diagnostic performance via ROC analysis were evaluated. The severe injury group had significantly higher lung W/D ratios and pathological scores than the mild injury group. Carotid ultrasound showed a progressive decrease in carotid diameter and FTc post-injury, with FTc significantly lower in the severe injury group at 6-h. FTc was negatively correlated with lung W/D ratio and pathological scores. Cardiac ultrasound indicated a decreasing trend in aortic and pulmonary artery VTI post-injury, with pulmonary artery VTI significantly lower in the severe injury group at all times and negatively correlated with lung W/D ratio and pathological scores. ROC analysis showed that pulmonary artery VTI had the highest area under the curve (AUC), with values greater than 0.8 at all time points. The combined use of pulmonary artery VTI and carotid FTc had AUC values greater than 0.85 at all time points, peaking at 6-h (AUC = 0.951). In conclusion, pulmonary artery VTI is an excellent indicator for evaluating ALI severity post-injury, and the combination of pulmonary artery VTI and carotid FTc shows strong diagnostic performance for assessing ALI severity.

ObjectivesTo analyze the relationship between the dietary preparation status prior to contrast-enhanced CT (CECT) and adverse drug reactions (ADR) and emetic complications.MethodsNon-emergency adult patients who underwent routine CECT in our hospital from January 2019 to December 2020 were retrospectively analyzed. Stratified dietary preparation regimens were implemented for different clinical scenarios. The relationship between actual dietary preparation status and ADR and emetic complications was analyzed. ResultsA total of 127,200 cases were enrolled, including 49,676 cases in the fasting group (57 years ± 13, 56.79% men) and 77,524 cases in the non-fasting group (60 years ± 13, 54.55% men). No statistical difference was found in the overall incidence of ADR (0.211% vs. 0.254%, p = 0.126) or emetic complications (0.030% vs. 0.046%, p = 0.158) between the two groups, and no aspiration pneumonia or death occurred. For patients with an ICM-ADR history, the ADR incidence in non-fasting group was significantly lower than fasting group (2.424% vs. 12.371%, p = 0.002). For patients with hypertension, injection dose ≥ 100 mL, injection rate ≥ 5 mL/s, and Iopromide 370 usage, non-fasting was associated with higher ADR incidence (p < 0.05). 36.67% of the patients experienced unnecessary excessive fasting in practice. Excessive fasting (≥ 10 h) and more water ingestion (≥ 500 mL) within 1 h prior to CECT were associated with higher ADR incidence (p < 0.05).ConclusionUnrestricted food ingestion would not increase the overall risk of ADR and emetic complications. For some special patient subgroups, non-fasting, excessive fasting, and more water ingestion were associated with higher ADR incidence.

The secondary injury is more serious after traumatic brain injury (TBI) compared with primary injury. Release of excessive reactive oxygen species (ROS) and Ca 2 + influx at the damaged site trigger the secondary injury. Herein, a neutrophil-like cell membrane-functionalized nanoparticle was developed to prevent ROS-associated secondary injury. NCM@MP was composed of three parts: (1) Differentiated neutrophil-like cell membrane (NCM) was synthesized, with inflammation-responsive ability to achieve effective targeting and to increase the retention time of Mn 3 O 4 and nimodipine (MP) in deep injury brain tissue via C-X-C chemokine receptor type 4, integrin beta 1 and macrophage antigen-1. (2) Nimodipine was used to inhibit Ca 2 + influx, eliminating the ROS at source. (3) Mn 3 O 4 further eradicated the existing ROS. In addition, NCM@MP also exhibited desirable properties for T 1 enhanced imaging and low toxicity which may serve as promising multifunctional nanoplatforms for precise therapies. In our study, NCM@MP obviously alleviated oxidative stress response, reduced neuroinflammation, protected blood–brain barrier integrity, relieved brain edema, promoted the regeneration of neurons, and improved the cognition of TBI mice. This study provides a promising TBI management to relieve the secondary spread of damage. Graphical Abstract

ObjectivesTo develop and validate a machine learning model using 18F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma.MethodsEight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from the patients without.ResultsPET/CT radiomics-clinical combined model could well distinguish patients with MP/S components from those without MP/S components (AUC = 0.87), which performed better than PET (AUC = 0.829, p < 0.05) or CT (AUC = 0.827, p < 0.05) radiomics models in the training cohort. In test cohorts, radiomics-clinical combined model outperformed the PET radiomics model in test cohort 1 (AUC = 0.859 vs 0.799, p < 0.05) and the CT radiomics model in test cohort 2 (AUC = 0.880 vs 0.829, p < 0.05). Calibration curve indicated good coherence between all model prediction and the actual observation in training and test cohorts. DCA revealed PET/CT radiomics-clinical model exerted the highest clinical benefit.Conclusion18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of MP/S components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. The PET/CT radiomics-clinical combined model performed best.Critical relevance statement18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of micropapillary and solid components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies.Key points• 18F-FDG PET/CT radiomics signature is valuable to identify the presence of MP/S components in lung adenocarcinoma non-invasively.• Gender and N stage are independent predictors of differentiation in patients with or without MP/S components.• The nomogram integrating 18F-FDG PET/CT radiomics and clinical characteristics improves predictive performance.

IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and evaluate machine learning models using ultrasound-derived radiomics and clinical features to predict TIL levels in BC.MethodsThis retrospective study included 256 BC patients between January 2019 and August 2023, who were randomly divided into training (n = 179) and test (n = 77) cohorts. Radiomics features were extracted from the intratumor and peritumor regions in ultrasound images. Feature selection was performed using the \"Boruta\" package in R to iteratively remove non-significant features. Extra Trees Classifier was used to construct radiomics and clinical models. A combined radiomics-clinical (R-C) model was also developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical utility. A nomogram was created based on the best-performing model.ResultsA total of 1712 radiomics features were extracted from the intratumor and peritumor regions. The Boruta method selected five key features (four from the peritumor and one from the intratumor) for model construction. Clinical features, including immunohistochemistry, tumor size, shape, and echo characteristics, showed significant differences between high (≥10%) and low (<10%) TIL groups. Both the R-C and radiomics models outperformed the clinical model in the test cohort (area under the curve values of 0.869/0.838 vs 0.627,  < .05). Calibration curves and Brier scores demonstrated superior accuracy and calibration for the R-C and radiomics models. DCA revealed the highest net benefit of the R-C model at intermediate threshold probabilities.ConclusionUltrasound-derived radiomics effectively predicts TIL levels in BC, providing valuable insights for personalized treatment and surveillance strategies.

Background The purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast. Methods The preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI 2min and DCE-T1WI 7min ) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency. Results For ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADC Minimum ) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI 7min also showed significant differences between these two groups. Except for ADC Minimum , angular second moment of FS-T2WI (FS-T2WI ASM ), and the maximum gray values of DCE-T1WI 7min (DCE-T1WI 7min-Maximum ) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADC Contrast ) and entropy of FS-T2WI (FS-T2WI Entropy ) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793–0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%. Conclusion Texture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.

Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to developing dementia, especially for those with mild cognitive impairment (MCI), but its underlying cause is still unclear. This study aims to investigate the early detection of white matter structural network changes in T2DM patients with MCI and assess the relationship between cognitive impairment and structural network alterations in T2DM patients. In this study, we performed a battery of neuropsychological tests and diffusion tensor MRI in 30 T2MD-MCI patients, 30 T2DM patients with normal cognition (T2DM-NC) and 30 age-, sex-, and education-matched healthy control (HC) individuals. Cognitive performance exhibited obvious differences among the three groups. The structural network was significantly disrupted in both global and regional levels in T2DM patients. The T2DM-MCI group showed more severe impairment of global network efficiency, and lower nodal efficiency and fewer connections within multiple regions like the limbic system, basal ganglia, and several cortical structures. Moreover, a subnetwork impaired in T2DM-MCI patients was characterized by cortical-limbic fibers, and commissural fibers and pathways within the frontal, temporal, and occipital lobes. These altered global and nodal parameters were significantly correlated with cognitive function in T2DM-MCI patients. In particular, executive dysfunction and working memory impairment in T2DM-MCI patients correlated with nodal efficiency in the right opercular part and triangular part of the inferior frontal gyrus, which indicated that white matter disruption in these regions may act as potential biomarkers for T2DM-associated MCI detection. Our investigation provides a novel insight into the neuropathological effects of white matter network disruption on cognition impairments induced by T2DM.