Dual-Modality Imaging-Guided Manganese-Based Nanotransformer for Enhanced Gas-Photothermal Therapy Combined Immunotherapeutic Strategy Against Triple-Negative Breast Cancer.

Activating the stimulator of the interferon gene (STING) is a promising immunotherapeutic strategy for converting "cold" tumor microenvironment into "hot" one to achieve better immunotherapy for malignant tumors. Herein, a manganese-based nanotransformer is presented, consisting of manganese carbonyl and cyanine dye, for MRI/NIR-II dual-modality imaging-guided multifunctional carbon monoxide (CO) gas treatment and photothermal therapy, along with triggering cGAS-STING immune pathway against triple-negative breast cancer. This nanosystem is able to transfer its amorphous morphology into a crystallographic-like formation in response to the tumor microenvironment, achieved by breaking metal-carbon bonds and forming coordination bonds, which enhances the sensitivity of magnetic resonance imaging. Moreover, the generated CO and photothermal effect under irradiation of this nanotransformer induce immunogenic death of tumor cells and release damage-associated molecular patterns. Simultaneously, the Mn acts as an immunoactivator, potentially stimulating the cGAS-STING pathway to augment adaptive immunity, resulting in promoting the secretion of type I interferon, the proliferation of cytotoxic T lymphocytes and M2-macrophages repolarization. This nanosystem-based gas-photothermal treatment and immunoactivating therapy synergistic effect exhibit excellent antitumor efficacy both in vitro and in vivo, reducing the risk of triple-negative breast cancer recurrence and metastasis; thus, this strategy presents great potential as multifunctional immunotherapeutic agents for cancer treatment.

MicroRNA-377-3p exacerbates chronic obstructive pulmonary disease through suppressing ZFP36L1 expression and inducing lung fibroblast senescence.

Chronic obstructive pulmonary disease (COPD) is a leading aging related cause of global mortality. Small airway narrowing is recognized as an early and significant factor for COPD development. Senescent fibroblasts were observed to accumulate in lung of COPD patients and promote COPD progression through aberrant extracellular matrix (ECM) deposition and senescence-associated secretory phenotype (SASP). On the basis of our previous study, we further investigated the the causes for the increased levels of miR-377-3p in the blood of COPD patients, as well as its regulatory function in the pathological progression of COPD. We found that the majority of up-regulated miR-377-3p was localized in lung fibroblasts. Inhibition of miR-377-3p improved chronic smoking-induced COPD in mice. Mechanistically, miR-377-3p promoted senescence of lung fibroblasts, while knockdown of miR-377-3p attenuated bleomycin-induced senescence in lung fibroblasts. We also identified ZFP36L1 as a direct target for miR-377-3p that likely mediated its pro senescence activity in lung fibroblasts. Our data reveal that miR-377-3p is crucial for COPD pathogenesis, and may serve as a potential target for COPD therapy.

Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer.

OBJECTIVE: This study aimed to establish a MRI-based deep learning radiomics (DLR) signature to predict the human epidermal growth factor receptor 2 (HER2)-low-positive status and further verified the difference in prognosis by the DLR model. METHODS: A total of 481 patients with breast cancer who underwent preoperative MRI were retrospectively recruited from two institutions. Traditional radiomics features and deep semantic segmentation feature-based radiomics (DSFR) features were extracted from segmented tumors to construct models separately. Then, the DLR model was constructed to assess the HER2 status by averaging the output probabilities of the two models. Finally, a Kaplan‒Meier survival analysis was conducted to explore the disease-free survival (DFS) in patients with HER2-low-positive status. The multivariate Cox proportional hazard model was constructed to further determine the factors associated with DFS. RESULTS: First, the DLR model distinguished between HER2-negative and HER2-overexpressing patients with AUCs of 0.868 and 0.763 in the training and validation cohorts, respectively. Furthermore, the DLR model distinguished between HER2-low-positive and HER2-zero patients with AUCs of 0.855 and 0.750, respectively. Cox regression analysis showed that the prediction score obtained using the DLR model (HR, 0.175; p = 0.024) and lesion size (HR, 1.043; p = 0.009) were significant, independent predictors of DFS. CONCLUSIONS: We successfully constructed a DLR model based on MRI to noninvasively evaluate the HER2 status and further revealed prospects for predicting the DFS of patients with HER2-low-positive status. CLINICAL RELEVANCE STATEMENT: The MRI-based DLR model could noninvasively identify HER2-low-positive status, which is considered a novel prognostic predictor and therapeutic target. KEY POINTS: • The DLR model effectively distinguished the HER2 status of breast cancer patients, especially the HER2-low-positive status. • The DLR model was better than the traditional radiomics model or DSFR model in distinguishing HER2 expression. • The prediction score obtained using the model and lesion size were significant independent predictors of DFS.

Simple, sensitive, colorimetric detection of pyrophosphate via the analyte-triggered decomposition of metal-organic frameworks regulating their adaptive multi-color Tyndall effect.

This paper describes initially the application of the Tyndall effect (TE) of metal-organic framework (MOF) materials as a colorimetric signaling strategy for the sensitive detection of pyrophosphate ion (PPi). The used MOF NH2-MIL-101(Fe) was prepared with Fe3+ ions and fluorescent ligands of 2-amino terephthalic acid (NH2-BDC). The fluorescence of NH2-BDC in MOF is quenched due to the ligand-to-metal charge transfer effect, while the NH2-MIL-101(Fe) suspension shows a strong TE. In the presence of PPi analyte, the MOFs will undergo decomposition because of the competitive binding of Fe3+ by PPi over NH2-BDC, resulting in a significant decrease in the TE signal and fluorescence restoration from the released ligands. The results demonstrate that the new method only requires a laser pointer pen (for TE creation) and a smartphone (for portable quantitative readout) to detect PPi in a linear concentration range of 1.25-800 µM, with a detection limit of ~210 nM (3σ) which is ~38 times lower than that obtained from traditional fluorescence with a spectrophotometer (linear concentration range, 50-800 µM; detection limit, 8.15 µM). Moreover, the acceptable recovery of PPi in several real samples (i.e., pond water, black tea, and human serum and urine) ranges from 97.66 to 119.15%.

Ferrocene-Based Polymeric Nanoparticles Carrying Doxorubicin for Oncotherapeutic Combination of Chemotherapy and Ferroptosis.

Mono-chemotherapy has significant side effects and unsatisfactory efficacy, limiting its clinical application. Therefore, a combination of multiple treatments is becoming more common in oncotherapy. Chemotherapy combined with the induction of ferroptosis is a potential new oncotherapy. Furthermore, polymeric nanoparticles (NPs) can improve the antitumor efficacy and decrease the toxicity of drugs. Herein, a polymeric NP, mPEG-b-PPLGFc@Dox, is synthesized to decrease the toxicity of doxorubicin (Dox) and enhance the efficacy of chemotherapy by combining it with the induction of ferroptosis. First, mPEG-b-PPLGFc@Dox is oxidized by endogenous H2 O2 and releases Dox, which leads to an increase of H2 O2 by breaking the redox balance. The Fe(II) group of ferrocene converts H2 O2 into ·OH, inducing subsequent ferroptosis. Furthermore, glutathione peroxidase 4, a biomarker of ferroptosis, is suppressed and the lipid peroxidation level is elevated in cells incubated with mPEG-b-PPLGFc@Dox compared to those treated with Dox alone, indicating ferroptosis induction by mPEG-b-PPLGFc@Dox. In vivo, the antitumor efficacy of mPEG-b-PPLGFc@Dox is higher than that of free Dox. Moreover, the loss of body weight in mice treated mPEG-b-PPLGFc@Dox is lower than in those treated with free Dox, indicating that mPEG-b-PPLGFc@Dox is less toxic than free Dox. In conclusion, mPEG-b-PPLGFc@Dox not only has higher antitumor efficacy but it reduces the damage to normal tissue.

Diffusion basis spectrum imaging detects pathological alterations in substantia nigra and white matter tracts with early-stage Parkinson's disease.

OBJECTIVES: Using diffusion basis spectrum imaging (DBSI) to examine the microstructural changes in the substantia nigra (SN) and global white matter (WM) tracts of patients with early-stage PD. METHODS: Thirty-seven age- and sex-matched patients with early-stage PD and 22 healthy controls (HCs) were enrolled in this study. All participants underwent clinical assessments and diffusion-weighted MRI scans, analyzed by diffusion tensor imaging (DTI) and DBSI to assess the pathologies of PD in SN and global WM tracts. RESULTS: The lower DTI fraction anisotropy (FA) was seen in SN of PD patients (PD: 0.316 ± 0.034 vs HCs: 0.331 ± 0.019, p = 0.015). The putative cells marker-DBSI-restricted fraction (PD: 0.132 ± 0.051 vs HCs: 0.105 ± 0.039, p = 0.031) and the edema/extracellular space marker-DBSI non-restricted-fraction (PD: 0.150 ± 0.052 vs HCs: 0.122 ± 0.052, p = 0.020) were both significantly higher and the density of axons/dendrites marker-DBSI fiber-fraction (PD: 0.718 ± 0.073 vs HCs: 0.773 ± 0.071, p = 0.003) was significantly lower in SN of PD patients. DBSI-restricted fraction in SN was negatively correlated with HAMA scores (r = - 0.501, p = 0.005), whereas DTI-FA was not correlated with any clinical scales. In WM tracts, only higher DTI axial diffusivity (AD) among DTI metrics was found in multiple WM regions in PD, while lower DBSI fiber-fraction and higher DBSI non-restricted-fraction were detected in multiple WM regions. DBSI non-restricted-fraction in both left fornix (cres)/stria terminalis (r = -0.472, p = 0.004) and right posterior thalamic radiation (r = - 0.467, p = 0.005) was negatively correlated with MMSE scores. CONCLUSION: DBSI could potentially detect and quantify the extent of inflammatory cell infiltration, fiber/dendrite loss, and edema in both SN and WM tracts in patients with early-stage PD, a finding remains to be further investigated through more extensive longitudinal DBSI analysis. CLINICAL RELEVANCE STATEMENT: Our study shows that DBSI indexes can potentially detect early-stage PD's pathological changes, with a notable ability to distinguish between inflammation and edema. This implies that DBSI has the potential to be an imaging biomarker for early PD diagnosis. KEY POINTS: • Diffusion basis spectrum imaging detected higher restricted-fraction in Parkinson's disease, potentially reflecting inflammatory cell infiltration. • Diffusion basis spectrum imaging detected higher non-restricted-fraction and lower fiber-fraction in Parkinson's disease, indicating the presence of edema and/or dopaminergic neuronal/dendritic loss. • Diffusion basis spectrum imaging metrics correlated with non-motor symptoms, suggesting its potential diagnostic role to detect early-stage PD dysfunctions.

Optimizing CT and MRI criteria for differentiating intrahepatic mass-forming cholangiocarcinoma and hepatocellular carcinoma.

BACKGROUND: Accurate diagnosis of intrahepatic mass-forming cholangiocarcinoma (IMCC) is crucial with regard to the choice of patient management and treatment options. PURPOSE: To evaluate the feasibility and diagnostic performance of the LI-RADS M (LR-M) targetoid criteria on computed tomography (CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) in differentiating IMCC from hepatocellular carcinoma (HCC). MATERIAL AND METHODS: A total of 118 patients with IMCC and HCC were included who underwent CT and EOB-MRI examinations. Multivariate analysis was used to determine the strongest predictors differentiating IMCC from HCC. Using these predictors, a predictive model for differentiating IMCC from HCC was constructed and the performance of the model was confirmed using the receiver operating characteristic curve. RESULTS: Multivariate analyses revealed rim-like arterial phase hyperenhancement (rim APHE) on CT and rim APHE, delayed central enhancement (DCE), and targetoid hepatobiliary phase (HBP) on MRI as independent variables significantly differentiating IMCC from HCC. The multivariate logistic regression model incorporating the three variables on EOB-MRI was constructed with an area under the curve (AUC) of 0.946, sensitivity of 87.80%, specificity of 92.21%, and accuracy of 94.60%. Per the DeLong test, the multivariate logistic regression model showed significantly higher AUC than rim APHE on CT (0.946 vs. 0.871; P = 0.008) and MRI (0.946 vs. 0.876; P = 0.003), whereas rim APHE on CT and MRI did not differ significantly (P = 0.809). CONCLUSION: The multivariate logistic regression model based on rim APHE, DCE, and targetoid HBP on EOB-MRI can effectively distinguish IMCC from HCC and is superior to any other targetoid appearance criterion of LI-RADS on CT and EOB-MRI.

Evaluation of the Effects of Anti-PD-1 Therapy on Triple-Negative Breast Cancer in Mice by Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Imaging.

BACKGROUND: The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity. PURPOSE: To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the therapeutic efficacy of anti-programmed death-1 (PD-1) therapy in a mouse triple negative breast cancer (TNBC) model. STUDY TYPE: Prospective. ANIMAL MODEL: A total of 54 BALB/c mouse subcutaneous 4 T1 transplantation models of TNBC. FIELD STRENGTH/SEQUENCE: A 3.0-T; turbo spin echo (TSE) T2-weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm2 ) and T1-twist DCE acquisition series. ASSESSMENT: DKI and DCE-MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross-sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists. STATISTICAL TESTS: The two-tailed unpaired t-test, Mann-Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P < 0.05 was considered statistically significant. RESULTS: The apparent diffusion coefficient (ADC), mean diffusivity (MD), Ktrans and Kep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and Ve values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P = 0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r = -0.833; MD, r = 0.890) and the CD45 level (ADC, r = 0.720; MD, r = 0.718). The Ktrans and Kep values were significantly correlated with the CD31 level (Ktrans , r = 0.820; Kep , r = 0.683). DATA CONCLUSION: DKI and DCE-MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti-PD-1 therapy, respectively. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 4.

Fuhrman nuclear grade prediction of clear cell renal cell carcinoma: influence of volume of interest delineation strategies on machine learning-based dynamic enhanced CT radiomics analysis.

OBJECTIVE: To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning-based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT. METHODS: This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed. RESULTS: Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI. CONCLUSION: Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading. KEY POINTS: • Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.

Radiomics can differentiate high-grade glioma from brain metastasis: a systematic review and meta-analysis.

OBJECTIVE: (1) To evaluate the diagnostic performance of radiomics in differentiating high-grade glioma from brain metastasis and how to improve the model. (2) To assess the methodological quality of radiomics studies and explore ways of embracing the clinical application of radiomics. METHODS: Studies using radiomics to differentiate high-grade glioma from brain metastasis published by 26 July 2021 were systematically reviewed. Methodological quality and risk of bias were assessed using the Radiomics Quality Score (RQS) system and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, respectively. Pooled sensitivity and specificity of the radiomics model were also calculated. RESULTS: Seventeen studies combining 1,717 patients were included in the systematic review, of which 10 studies without data leakage suspicion were employed for the quantitative statistical analysis. The average RQS was 5.13 (14.25% of total), with substantial or almost perfect inter-rater agreements. The inclusion of clinical features in the radiomics model was only reported in one study, as was the case for publicly available algorithm code. The pooled sensitivity and specificity were 84% (95% CI, 80-88%) and 84% (95% CI, 81-87%), respectively. The performances of feature extraction from the volume of interest (VOI) or (semi) automatic segmentation in the radiomics models were superior to those of protocols employing region of interest (ROI) or manual segmentation. CONCLUSION: Radiomics can accurately differentiate high-grade glioma from brain metastasis. The adoption of standardized workflow to avoid potential data leakage as well as the integration of clinical features and radiomics are advised to consider in future studies. KEY POINTS: • The pooled sensitivity and specificity of radiomics for differentiating high-grade gliomas from brain metastasis were 84% and 84%, respectively. • Avoiding potential data leakage by adopting an intensive and standardized workflow is essential to improve the quality and generalizability of the radiomics model. • The application of radiomics in combination with clinical features in differentiating high-grade gliomas from brain metastasis needs further validation.

Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase.

OBJECTIVE: To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer. MATERIALS AND METHODS: This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions. The least absolute shrinkage and selection operator (LASSO) method was used to select predictive feature sets for the classifiers. Four models were developed for predicting TILs: (1) single enhanced phase radiomics models; (2) fusion enhanced multi-phase radiomics models; (3) fusion multi-sequence radiomics models; and (4) a combined radiomics-based clinical model. RESULTS: Image features extracted from the delayed phase MRI, especially DCE_Phase 6 (DCE_P6), demonstrated dominant predictive performances over features from other phases. The fusion multi-sequence radiomics model and combined radiomics-based clinical model achieved the highest predictive performances with areas under the curve (AUCs) of 0.934 and 0.950, respectively; however, the differences were not statistically significant. CONCLUSION: The DCE-MRI radiomics model, especially image features extracted from the delayed phases, can help improve the performance in predicting TILs. The radiomics nomogram is effective in predicting TILs in breast cancer. KEY POINTS: • Radiomics features extracted from DCE-MRI, especially delayed phase images, help predict TIL levels in breast cancer. • We developed a nomogram based on MRI to predict TILs in breast cancer that achieved the highest AUC of 0.950.

Dual-Drug Backboned Polyprodrug with a Predefined Drug Combination for Synergistic Chemotherapy.

The codelivery of drugs at specific optimal ratios to cancer cells is vital for combination chemotherapy. However, most of the current strategies are unable to coordinate the loading and release of drug combinations to acquire precise and controllable synergistic ratios. In this work, we designed an innovative dual-drug backboned and reduction-sensitive polyprodrug PEG-P(MTO-ss-CUR) containing the anticancer drugs mitoxantrone (MTO) and curcumin (CUR) at an optimal synergistic ratio to reverse drug resistance. Due to synchronous drug activation and polymer backbone degradation, drug release at the predefined ratio with a synergistic anticancer effect was demonstrated by in vitro and in vivo experiments. Therefore, the dual-drug delivery system developed in this work provides a novel and efficient strategy for combination chemotherapy.

Can Chest CT Features Distinguish Patients With Negative From Those With Positive Initial RT-PCR Results for Coronavirus Disease (COVID-19)?

OBJECTIVE. The purpose of this study was to explore the value of CT in the diagnosis of coronavirus disease (COVID-19) pneumonia, especially for patients who have negative initial results of reverse transcription-polymerase chain reaction (RT-PCR) testing. MATERIALS AND METHODS. Patients with COVID-19 pneumonia from January 19, 2020, to February 20, 2020, were included. All patients underwent chest CT and swab RT-PCR tests within 3 days. Patients were divided into groups with negative (seven patients) and positive (14 patients) initial RT-PCR results. The imaging findings in both groups were recorded and compared. RESULTS. Twenty-one patients with symptoms (nine men, 12 women; age range, 26-90 years) were evaluated. Most of the COVID-19 lesions were located in multiple lobes (67%) in both lungs (72%) in our study. The main CT features were ground-glass opacity (95%) and consolidation (72%) with a subpleural distribution (100%). Otherwise, 33% of patients had other lesions around the bronchovascular bundle. The other CT features included air bronchogram (57%), vascular enlargement (67%), interlobular septal thickening (62%), and pleural effusions (19%). Compared with that in the group with positive initial RT-PCR results, CT of the group with negative initial RT-PCR results was less likely to show pulmonary consolidation (p < 0.05). CONCLUSION. The less pulmonary consolidation found at CT, the greater is the possibility of negative initial RT-PCR results. Chest CT is important in the screening of patients in whom disease is clinically suspected, especially those who have negative initial RT-PCR results.

Clear cell renal cell carcinoma: the value of sex-specific abdominal visceral fat measured on CT for prediction of Fuhrman nuclear grade.

OBJECTIVE: To explore whether sex-specific abdominal visceral fat composition on CT can predict the Fuhrman nuclear grade of clear cell renal cell carcinoma (ccRCC). METHODS: One hundred seventy-one patients (123 males and 48 females) from four hospitals (multicentre group) and 159 patients (109 males and 50 females) from the cancer imaging archive (TCIA-KIRC group) with pathologically proven ccRCC (multicentre: 124 low grade and 47 high grade; TCIA-KIRC: 79 low grade and 80 high grade) were retrospectively included. Abdominal fat was segmented into subcutaneous fat area (SFA) and visceral fat area (VFA) on CT using ImageJ. The total fat area (TFA) and relative VFA (rVFA) were then calculated. Clinical characteristics (age, sex, waist circumference and maximum tumour diameter) were also assessed. Univariate and multivariate logistic regression analyses were performed to identify the association between general or sex-specific visceral fat composition and Fuhrman grade. RESULTS: Females with high-grade ccRCC from the multicentre group had a higher rVFA (42.4 vs 31.3, p = 0.001) than those with low-grade ccRCC after adjusting for age. There was no significant difference in males. The rVFA remained a stable and independent predictor for females high-grade ccRCC in both the univariate (multicentre: OR 1.205, 95% CI 1.074-1.352, p = 0.001; TCIA-KIRC: OR 1.171, 95% CI 1.016-1.349, p = 0.029) and multivariate (multicentre: OR 1.095, 95% CI 1.024-1.170, p = 0.003; TCIA-KIRC: OR 1.103, 95% CI 1.024-1.187, p = 0.010) models. CONCLUSIONS: Sex-specific visceral fat composition has different values for predicting high-grade ccRCC and could be used as an independent predictor for females with high-grade ccRCC. KEY POINTS: • Visceral fat measurement (rVFA) as an independent predictor for high-grade ccRCC had good predictive power in females, but not in males. • Sex-specific visceral fat composition was significantly associated with high-grade ccRCC in females only. • The rVFA could be considered one of the risk factors for high-grade ccRCC for females.

The association between the ApoE polymorphisms and the MRI-defined intracranial lesions in a cohort of southern China population.

BACKGROUND: The apolipoprotein E (APOE) ε4 allele is considered as a risk factor for Alzheimer's disease (AD). However, the association of APOE allele with MRI evidence of intracranial lesions has not been well understood. METHODS: Quantitative real-time PCR was performed to detect the APOE genotype; MRI was examined for intracranial lesions. Their association was evaluated in a cohort of 226 AD patients and 2607 healthy individuals in southern China. RESULTS: The frequencies of ε2, ε3, and ε4 alleles were 8.0%, 82.9%, and 9.1% in the whole study population. The frequency of APOE-ε4 allele was significantly higher in the AD subjects than that in the control group (14.4% vs 8.6%, P < 0.001). We found that brain atrophy occurred at a rate of 12.3% in ε4 allele group vs 8.5% in non-ε4 genotype group, with a significance of P = 0.008. Severe brain atrophy occurred at a rate of 1.0% in ε4 allele group vs 0.2% in non-ε4 genotype group (P = 0.011). The individuals carrying APOE ε4/ε4 had an odds ratio (OR) of 7.64 (P < 0.01) for developing AD, while the APOE ε3/ε4 gene carriers had an OR of 1.47 (P = 0.031) and the OR in APOE ε2/ε3 carriers is 0.81 (P = 0.372). Interestingly, we found that the risk of ε4/ε4 allele carrier developing AD was significantly higher in male (P < 0.001) than female (P = 0.478). CONCLUSION: Compared to ε2 and ε3 alleles, the presence of APOE-ε4 allele might increase the risk for AD in a dose-dependent manner in southern China. Moreover, the presence of APOE-ε4 allele results in a higher incidence of brain atrophy.

[Signal intensity-time curve and quantitative dynamic contrast-enhanced magnetic resonance imaging in differentiating neoplasms of uterus].

OBJECTIVE: To evaluate signal intensity-time (SI-Time) curve and quantitative dynamic contrast-enhanced 3.0T magnetic resonance imaging in diagnosis and differentiating neoplasm of uterus.
 METHODS: A total of 42 cases of uterine neoplasm (20 were malignant and 22 were benign) were evaluated in our study. All cases received dynamic contrast-enhanced scanning on 3.0T MRI. The raw data was processed by Siemens Tissue 4D software and the SI-Time curve was obtained and analyzed. Pharmacokinetic modeling of Tofts with a modeled vascular input function was used for calculating volume parameters: volume transfer constant (Ktrans), reverse volume transfer constant (Kep), the extravascular extracellular space volume per unit volume of tissue (Ve). The correlation of these parameters at each groups were investigated. The SI-Time curve and the data of perfusion parameters between the 2 groups were compared by T test.
 RESULTS: Among 20 malignant tumors, 12 were cervical carcinoma and 8 were endometrial cancer. Among the benign tumors, 13 were leiomyomas, 3 were endometrial polyp, 3 were endometrial hyperplasia, and 3 were adenomyosis. 59.1% cases of benign tumors belong to Type I curve and 65% cases of malignant tumors belong to Type II curve. There was significant difference in SI-Time curve between benign and malignant tumors (P=0.011). If Type I curve was used as diagnostic criteria for benign tumors, and Type II and III curve were for malignant tumors, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value were 90.0%, 59.1%, 66.7%, and 86.7%, respectively. Ve was 0.477 ± 0.143 in malignant and 0.614 ± 0.146 in control group with significant difference (P=0.004). Ve was 0.477 ± 0.143 in malignant and 0.589 0.176 in benign group with significant difference (P=0.004). Ktrans was (0.178 ± 0.067) min⁻¹ in malignant and (0.263 ± 0.111) min⁻¹ in control group with significant difference (P=0.003). Ktrans was (0.182 ± 0.096) min⁻¹ in benign and (0.263 ± 0.111) min⁻¹ in control group with significant difference (P=0.011). 
 CONCLUSION: The type of SI-Time curve and perfusion parameters were important for differentiating benign and malignant uterine tumors in dynamic enhanced MRI. These parameters provide a supplement for conventional morphological MR diagnosis.

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI.

PURPOSE: To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. METHODS: 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. RESULTS: The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ([Formula: see text]) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features [Formula: see text] achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). CONCLUSION: The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.

HRadNet: A Hierarchical Radiomics-Based Network for Multicenter Breast Cancer Molecular Subtypes Prediction.

Breast cancer is a heterogeneous disease, where molecular subtypes of breast cancer are closely related to the treatment and prognosis. Therefore, the goal of this work is to differentiate between luminal and non-luminal subtypes of breast cancer. The hierarchical radiomics network (HRadNet) is proposed for breast cancer molecular subtypes prediction based on dynamic contrast-enhanced magnetic resonance imaging. HRadNet fuses multilayer features with the metadata of images to take advantage of conventional radiomics methods and general convolutional neural networks. A two-stage training mechanism is adopted to improve the generalization capability of the network for multicenter breast cancer data. The ablation study shows the effectiveness of each component of HRadNet. Furthermore, the influence of features from different layers and metadata fusion are also analyzed. It reveals that selecting certain layers of features for a specified domain can make further performance improvements. Experimental results on three data sets from different devices demonstrate the effectiveness of the proposed network. HRadNet also has good performance when transferring to other domains without fine-tuning.

Nanoparticle-mediated blockade of CXCL12/CXCR4 signaling enhances glioblastoma immunotherapy: Monitoring early responses with MRI radiomics.

The limited therapeutic efficacy of checkpoint blockade immunotherapy against glioblastoma is closely related to the blood-brain barrier (BBB) and tumor immunosuppressive microenvironment, where the latter is driven primarily by tumor-associated myeloid cells (TAMCs). Targeting the C-X-C motif chemokine ligand-12/C-X-C motif chemokine receptor-4 (CXCL12/CXCR4) signaling orchestrates the recruitment of TAMCs and has emerged as a promising approach for alleviating immunosuppression. Herein, we developed an iRGD ligand-modified polymeric nanoplatform for the co-delivery of CXCR4 antagonist AMD3100 and the small-molecule immune checkpoint inhibitor BMS-1. The iRGD peptide facilitated superior BBB crossing and tumor-targeting abilities both in vitro and in vivo. In mice bearing orthotopic GL261-Luc tumor, co-administration of AMD3100 and BMS-1 significantly inhibited tumor proliferation without adverse effects. A reprogramming of immunosuppression upon CXCL12/CXCR4 signaling blockade was observed, characterized by the reduction of TAMCs and regulatory T cells, and an increased proportion of CD8+T lymphocytes. The elevation of interferon-γ secreted from activated immune cells upregulated PD-L1 expression in tumor cells, highlighting the synergistic effect of BMS-1 in counteracting the PD-1/PD-L1 pathway. Finally, our research unveiled the ability of MRI radiomics to reveal early changes in the tumor immune microenvironment following immunotherapy, offering a powerful tool for monitoring treatment responses. STATEMENT OF SIGNIFICANCE: The insufficient BBB penetration and immunosuppressive tumor microenvironment greatly diminish the efficacy of immunotherapy for glioblastoma (GBM). In this study, we prepared iRGD-modified polymeric nanoparticles, loaded with a CXCR4 antagonist (AMD3100) and a small-molecule checkpoint inhibitor of PD-L1 (BMS-1) to overcome physical barriers and reprogram the immunosuppressive microenvironment in orthotopic GBM models. In this nanoplatform, AMD3100 converted the "cold" immune microenvironment into a "hot" one, while BMS-1 synergistically counteracted PD-L1 inhibition, enhancing GBM immunotherapy. Our findings underscore the potential of dual-blockade of CXCL12/CXCR4 and PD-1/PD-L1 pathways as a complementary approach to maximize therapeutic efficacy for GBM. Moreover, our study revealed that MRI radiomics provided a clinically translatable means to assess immunotherapeutic efficacy.

Repression of YEATS2 induces cellular senescence in hepatocellular carcinoma and inhibits tumor growth.

Hepatocellular carcinoma (HCC) stands as the third leading cause of cancer-related fatalities globally. In this study, we observed a significant increase in the expression level of the YEATS2 gene in HCC patients, and it is negatively correlated with the patients' survival rate. While we have previously identified the association between YEATS2 and the survival of pancreatic cancer cells, the regulatory mechanisms and significance in HCC are still to be fully elucidated. Our study shows that knockdown (KD) of YEATS2 expression leads to DNA damage, which in turn results in an upregulation of γ-H2A.X expression and activation of the canonical senescence-related pathway p53/p21Cip1. Moreover, our transcriptomic analysis reveals that YEATS2 KD cells can enhance the expression of p21Cip1 via the c-Myc/miR-93-5p pathway, consequently fostering the senescence of HCC cells. The initiation of cellular senescence through dual-channel activation suggests that YEATS2 plays a pivotal regulatory role in the process of cell proliferation. Ultimately, our in vivo research utilizing a nude mouse tumor model revealed a notable decrease in both tumor volume and weight after the suppression of YEATS2 expression. This phenomenon is likely attributable to the attenuation of proliferative cell activity, coupled with a concurrent augmentation in the population of natural killer (NK) cells. In summary, our research results have supplemented the understanding of the regulatory mechanisms of HCC cell proliferation and indicated that targeting YEATS2 may potentially inhibit liver tumor growth.