RATIONALE AND OBJECTIVES:Nasopharyngeal carcinoma (NPC) is characterized by a distinctive virologic and immunologic profile, in which Epstein-Barr virus-driven immune infiltration coexists with immune escape. This complex interplay gives rise to pronounced immune heterogeneity across spatial, molecular, and temporal dimensions, which critically influences response to immunotherapy and clinical outcomes. However, conventional biopsy-based immune evaluation is limited by sampling bias, invasiveness, and restricted spatial representativeness, precluding comprehensive characterization of the global tumor immune landscape. Radiomics enables high-throughput quantitative extraction of structural, textural, and functional features from routine medical imaging, offering a noninvasive and whole-tumor approach to characterize imaging-derived surrogate phenotypes associated with the tumor immune microenvironment. This review aims to summarize the biologic basis of immune heterogeneity in NPC and to evaluate recent advances in radiomics for noninvasive immune characterization. MATERIALS AND METHODS:We conducted a comprehensive review of recent literature focusing on radiomics-based approaches for assessing the tumor immune microenvironment in NPC. Studies were analyzed from a multidimensional perspective, encompassing spatial characterization of immune-associated imaging patterns, molecular prediction of immune phenotypes such as programmed death-ligand 1 (PD-L1) expression, and predictive modeling of treatment response, together with emerging longitudinal approaches aimed at capturing immune evolution during therapy. RESULTS:Radiomics enables high-throughput quantitative extraction of structural, textural, and functional features from routine medical imaging, providing a noninvasive and whole-tumor approach to characterize imaging-derived surrogate phenotypes of the tumor immune microenvironment. Accumulating evidence demonstrates its value in capturing spatial immune heterogeneity, predicting molecular immune markers such as PD-L1 expression, and stratifying patients according to treatment response. Additionally, longitudinal radiomics analyses show promise in reflecting dynamic immune evolution during therapy. CONCLUSION:Radiomics represents a promising noninvasive tool for comprehensive characterization of immune heterogeneity in NPC. Emerging directions include multimodal imaging integration and deep learning-based virtual immune phenotyping. Addressing key challenges such as standardization, reproducibility, and clinical validation will be essential to enable robust clinical translation.

To investigate the feasibility of measuring Epicardial fat volume (EFV) on non-gated, non-contrast, and non-gated non-contrast computed tomography (CT) images. A total of 79 patients (male/female: 46/33, mean age: 57.91 ± 13.30 years old) who underwent triple-rule-out CT (TRO-CT) examinations were retrospectively enrolled, where electrocardiography (ECG)-gated contrast-enhanced coronary CT angiography (CCTA), non-gated non-contrast routine chest CT (RCCT), non-contrast coronary artery calcium scoring CT (CACS-CT), and non-gated CT pulmonary angiography (CTPA) and aortic CT angiography (ACTA) were simultaneously available. Three experienced radiologists manually measured the EFV on each image set. EFVs obtained on CACS-CT, CTPA, ACTA, or RCCT were compared with those obtained on CCTA, which were taken as the reference. Subgroup comparison was made for patients identified with (n = 36) and without coronary plaques (n = 43). Inter-observer agreements were analyzed using intraclass correlation coefficients (ICCs). Differences in EFVs were assessed using paired Wilcoxon signed-rank tests. For EFVs significantly deviating from the reference, correlation coefficients were calculated for the possibility of correction. The ICCs for all measurements were > 0.88. EFVs obtained on CACS-CT showed no significant differences from the reference (p = 0.899), while those on CTPA, ACTA or RCCT were significantly underestimated (all p < 0.05). Similar results of comparison were found for patients without plaques, even though EFVs obtained on CTPA were found comparable to the reference for patients identified with plaques (p = 0.187). Underestimated EFVs were fitted using linear functions with respect to those on CCTA, where excellent fitting results were found (all R2 > 0.97). EFVs obtained on non-contrast CT images (e.g., CACS-CT) were sufficiently accurate, while those obtained on non-gated CT images (e.g., CTPA and ACTA) or non-gated non-contrast CT images (e.g., RCCT) were significantly underestimated. Fortunately, all underestimations can be eliminated through simple correction functions.

Abstact: Inflammatory bowel disease (IBD) is a chronic relapsing inflammatory disorder in which sustained mucosal injury increases the risk of colorectal cancer. Despite advances in biologic therapies, durable disease control remains limited, highlighting the need for mechanistically informed oral interventions. Integrative transcriptomic analysis of clinical IBD samples highlighted concurrent oxidative-stress and neutrophil-associated programs linked to tight-junction disruption and mucosal barrier injury. Guided by this findings, we engineered manganese tetroxide nanoparticle-decorated graphdiyne (Mn peroxide, and hydroxyl radicals. Following oral administration, Mn 3 O 4 /GDY remained stable in the gastric environment, showed inflammation-associated intestinal retention, reduced neutrophil infiltration and myeloperoxidase-associated oxidative stress, and restored epithelial barrier integrity. In a murine colitis model, Mn 3 O 4 /GDY markedly alleviated disease severity, with greater improvements across several assessed endpoints than those observed with pristine GDY and the clinical drug 5-aminosalicylic acid. Mechanistically, the nanoplatform attenuated the ROS–neutrophil inflammatory positive-feedback loop, thereby promoting mucosal healing and restoring intestinal homeostasis. This work supports a redox-regulated oral nanomedicine strategy for IBD treatment.

To develop and evaluate an automated, multimodal Transformer model for preoperative prediction of lymphovascular invasion (LVI) in invasive breast cancer using contrast-enhanced MRI. A retrospective study analyzed 288 patients with pathologically confirmed invasive breast cancer who all underwent preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The study included an internal cohort (238 patients) and an external cohort (50 patients). Tumor regions of interest (ROI) were manually delineated by radiologists and automatic tumor segmentation was performed using ResUNet++. The output results were then processed with 4 mm boundary dilation, while radiomic features were extracted and radiologists assessed MRI features according to BI-RADS criteria. Single-modality and multi-modality models were constructed for comparison, with the multi-modal fusion network integrating enhanced images, radiomic features, and MRI features. Model differences were assessed using DeLong test, and interpretability analysis was performed using Grad-CAM and SHAP methods. Automated segmentation was robust (Dice 0.916 internal and 0.921 external). The two-stage multimodal classifier achieved the highest AUC, 0.873 internally and 0.845 externally, compared with the best single-modality Transformer at 0.801 internally and 0.762 externally. Integrating automated MRI segmentation with Transformer-based multimodal learning enables reliable preoperative LVI prediction and shows promising cross-center generalizability for clinical translation.

OBJECTIVE:To develop and externally validate an integrated model that combines multimodality CT-MRI deep learning with clinical and radiological features for noninvasive preoperative hepatocellular carcinoma (HCC) histologic grading, and to evaluate whether this integration outperforms single-modality imaging, the multimodality imaging model alone, and a clinical-radiological model. METHODS:In this multicentre retrospective study, 668 patients with pathologically confirmed HCC from three institutions (January 2010-December 2023) were included. Single-modality cohorts from Centre 1 (CT-only, n=283; MRI-only, n=135) were used for modality-specific pretraining via staged transfer learning. A total of 250 patients with paired preoperative CT and MRI were allocated to a training cohort (n=88), an internal validation cohort (n=57), and two independent external validation cohort (n=62, n=43). Single-modality multiphase CT (mpCT) and multisequence MRI (msMRI) models, a combined multimodality CT-MRI deep learning model (TL-CMDLM), a clinical-radiological signature model (CRSM), and an integrated hybrid fusion model (TL-HFM) combining the CT-MRI deep learning signature with clinical and radiological features were developed. Performance was assessed using the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement, and decision curve analysis. RESULTS:A total of 250 patients with paired CT and MRI (218 men; mean age, 57 ± 10 years) were evaluated. The integrated TL-HFM achieved the highest discrimination, with AUCs of 0.89, 0.88, and 0.90 in the internal validation cohort and two external validation cohort, respectively, and significantly outperformed both the multimodality CT-MRI deep learning model alone (TL-CMDLM: 0.82, 0.72, 0.81) and the clinical-radiological model (CRSM: 0.62, 0.73, 0.59) across all cohorts (all p < 0.05). The multimodality CT-MRI model in turn outperformed each best single-modality model (mpCT: 0.76, 0.58, 0.61; msMRI: 0.72, 0.62, 0.62). Decision curve analysis confirmed that the integrated TL-HFM provided the greatest net benefit across the internal validation and two external validation cohort. CONCLUSION:An integrated model combining multimodality CT-MRI deep learning with clinical and radiological features provided the most accurate noninvasive preoperative grading of HCC, outperforming single-modality imaging, multimodality imaging alone, and a clinical-radiological model, and delivered the greatest net clinical benefit across multicentre cohorts.

PURPOSE:To develop and validate a comprehensive model for predicting postoperative early recurrence of hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA)-enhanced MRI. METHODS:239 patients with HCC who underwent curative surgical resection were recruited from two centers between April 2017 and December 2022. Radiomics features were extracted from the region of interest (ROI) on preoperative Gd-EOB-DTPA-enhanced MR images, and consistency analysis was performed to select stable radiomics features. Significant variables in the univariate and multivariate logistic regression analysis were included in clinical-radiologic model. Nomograms were constructed by combining the best performing radiologic and clinical-radiologic characteristics. Recurrence-free survival (RFS) comparisons were conducted using the log-rank test based on high versus low model-derived scores. RESULTS:The radiomics model based on multiple phases MR outperformed all other radiomics models and had the best discrimination for early recurrence, with AUC of 0.799 and 0.743 in the training and validation sets, respectively. In the entire cohort, high-risk patients exhibited significantly lower RFS compared to low-risk patients. CONCLUSION:The nomogram integrating Gd-EOB-DTPA enhanced MRI radiomics features and clinical-radiologic characteristics demonstrate superior predictive performance with postoperative early recurrence in patients with HCC. The model can identify patients at high risk and provide support for individualized treatment planning.

Due to the prolonged MRI encoding process, respiratory motion can cause undesired artifacts and image blurring, degrading image quality and limiting clinical applications in abdominal and pulmonary imaging. In this work, we develop a two-stage respiratory motion-resolved radial MR image reconstruction pipeline using an interpretable deep unrolled network (MoraNet), enabling high-quality imaging under free-breathing conditions. Firstly, low-resolution images are reconstructed from the central region of successive golden-angle radial k-space to extract respiratory motion signals. The binned k-space data based on the respiratory signal are then used to reconstruct the motion-resolved high-resolution image for each motion state. The MoraNet applies nonuniform fast Fourier transform (NUFFT) to operate radial encoding and convolutional neural network (CNN) modules to conduct image regularizations. The MoraNet was trained on retrospectively acquired lung MRI images for both fully sampled and undersampled acquisitions. The performance of the proposed method was evaluated on digital CT/MRI breathing XCAT (CoMBAT) phantom data, QUASAR motion phantom data acquired from a 1.0T MRI scanner and volunteer chest data acquired from a 1.5T MRI scanner. The MoraNet pipeline was compared with motion-averaged reconstruction and a conventional compressed sensing (CS)-based method in terms of SSIM, RMSE and computation time. Simulation and experimental results demonstrated that the proposed network could provide accurate respiratory signal estimation and enable effective motion correction. Compared with the CS method, the MoraNet preserved better structural details with lower RMSE and higher SSIM values at acceleration factor of 4, and meanwhile took ten-fold faster inference time.

Breast cancer remains one of the most common and lethal malignancies among women worldwide. Although conventional treatment approaches—including surgery, radiotherapy, chemotherapy, and targeted therapy—have achieved substantial progress, clinical outcomes are still severely limited by issues such as drug resistance, recurrence, and metastasis. In this context, metal-based immunotherapy has emerged as a novel and highly promising strategy, gaining increasing attention for its unique advantages in enhancing anti-tumor immune responses and remodeling the tumor immune microenvironment. In recent years, mounting evidence has demonstrated that metal nanoparticles, metal–organic frameworks (MOFs), and metal complexes hold great potential in breast cancer immunotherapy. These agents exert immunotherapeutic effects through mechanisms such as immune activation, modulation of immunosuppressive cells, and synergistic enhancement of immune checkpoint blockade. Despite these encouraging developments, several critical challenges remain, including systemic toxicity, limited clinical translation, and insufficient understanding of their immunomodulatory mechanisms. This review provides a comprehensive summary of recent advances in metal-based immunotherapy for breast cancer, with a particular focus on the applications of metal nanoparticles, metal complexes, and metal-based nanocarriers. The mechanisms of action, therapeutic advantages, and existing limitations are thoroughly discussed, and future directions are proposed to facilitate further research and clinical translation in this emerging field.

Quantifying individual deviations in brain morphology from normative references is useful for understanding neurodiversity and facilitating personalized management of brain health. Here we report Chinese brain normative references using morphological imaging scans of 24,061 healthy volunteers from 105 sites, revealing later peak ages of lifespan neurodevelopmental milestones (1.2-8.9 years) than European/North American populations. We model individual brain deviation scores in 3,932 individuals with different neurological disorders from population references to evaluate three key aspects of brain health assessment using machine learning approaches: estimating disease propensity, predicting cognitive and physical outcomes and assessing treatment effects with distinct disability progression. The norm-deviation scores outperformed raw structural measures in these evaluations. Chinese-specific normative brain references may foster personalized diagnosis and prognosis in neurological diseases, enabling clinically applicable assessments of brain health.

Rationale and ObjectivesThis study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs).Material and methodsThis retrospective multi-centre study enroled patients with pathologically confirmed SRMs. Data from three centres were used as training set (n = 229), with data from one centre serving as an independent test set (n = 81). Univariate and multivariate logistic regression analyses were utilised to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model.ResultsThere were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists' assessment, the AUC of the three radiologists based on the clinical experience were 0.78 (95% CI:0.66,0.89), 0.65 (95% CI:0.53,0.76), and 0.68 (95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93 (95% CI:0.86,0.97), 0.87 (95% CI:0.78,0.95) and 0.87 (95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P < 0.001).ConclusionThe CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies.

Purpose:To explore the clinical and imaging features of rare site Kimura's disease (KD).Methods:Retrospective analysis was conducted on the clinical manifestations, laboratory examinations, and imaging features of five patients with rare site KD. All imaging data, including the location, quantity, size, uniformity, boundary, and enhanced appearance of the lesion were evaluated by two independent radiologists.Results:Of the five patients, four were asymptomatic, and one experienced localized skin itching. Four cases involved subcutaneous nodules in the upper arm, while one was in the inguinal region. The main manifestations were single (three cases) or multiple (two cases) subcutaneous nodules/masses, with three patients accompanied by local lymph node enlargement. Four patients exhibited elevated eosinophil counts in their peripheral blood. Four patients had lesions with vascular flow voids; in three of these, the lesions also showed prominent enhancement. Notably, the lesion in a 5-year-old did not show vascular flow voids but displayed significant enhancement. Additionally, two patients showed edema around the lesions.Conclusion:The presence of solitary or multiple subcutaneous nodules/masses in the upper arm or inguinal area, accompanied by lymph node enlargement, elevated eosinophils in the peripheral blood, and the observation of internal vascular within the lesion, can aid in the diagnosis of KD occurring in uncommon anatomical locations.

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.

Background:The effect of diagnosing Graves' ophthalmopathy (GO) through traditional measurement and observation in medical imaging is not ideal. This study aimed to develop and validate deep learning (DL) models that could be applied to the diagnosis of GO based on magnetic resonance imaging (MRI) and compare them to traditional measurement and judgment of radiologists. Methods:A total of 199 clinically verified consecutive GO patients and 145 normal controls undergoing MRI were retrospectively recruited, of whom 240 were randomly assigned to the training group and 104 to the validation group. Areas of superior, inferior, medial, and lateral rectus muscles and all rectus muscles on coronal planes were calculated respectively. Logistic regression models based on areas of extraocular muscles were built to diagnose GO. The DL models named ResNet101 and Swin Transformer with T1-weighted MRI without contrast as input were used to diagnose GO and the results were compared to the radiologist's diagnosis only relying on MRI T1-weighted scans. Results:Areas on the coronal plane of each muscle in the GO group were significantly greater than those in the normal group. In the validation group, the areas under the curve (AUCs) of logistic regression models by superior, inferior, medial, and lateral rectus muscles and all muscles were 0.897 [95% confidence interval (CI): 0.833-0.949], 0.705 (95% CI: 0.598-0.804), 0.799 (95% CI: 0.712-0.876), 0.681 (95% CI: 0.567-0.776), and 0.905 (95% CI: 0.843-0.955). ResNet101 and Swin Transformer achieved AUCs of 0.986 (95% CI: 0.977-0.994) and 0.936 (95% CI: 0.912-0.957), respectively. The accuracy, sensitivity, and specificity of ResNet101 were 0.933, 0.979, and 0.869, respectively. The accuracy, sensitivity, and specificity of Swin Transformer were 0.851, 0.817, and 0.898, respectively. The ResNet101 model yielded higher AUC than models of all muscles and radiologists (0.986 vs. 0.905, 0.818; P<0.001). Conclusions:The DL models based on MRI T1-weighted scans could accurately diagnose GO, and the application of DL systems in MRI may improve radiologists' performance in diagnosing GO and early detection.

To develop a nomogram based on contrast-enhanced CT (CECT) image features and clinical factors for preoperatively predicting the expression level of Ki67 in patients with hepatocellular carcinoma (HCC). One hundred eighty-three patients diagnosed with HCC were included in this study. All patients underwent CECT scans before surgeries or biopsies. The Ki67 expression was assessed by immunohistochemistry. The nomogram was constructed based on a combination of CECT image features and clinical factors which showed an independent association with Ki67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) were used to verify the performance of the nomogram. In multivariate logistic regression, bumpy margin, blurred margin, intra-tumoral vessels and α-fetoprotein (AFP) were identified as independent predictors of Ki67 expression (p < 0.05). Three CECT image features and one clinical factor were selected to construct the nomogram, which showed great discrimination ability in the training and validation cohort with AUCs of 0.932 and 0.870 respectively. The calibration curve and DCA indicated that the nomogram had positive clinical application. The nomogram, developed based on CECT image features and clinical factors in this study, provide a non-invasive method for accurately predicting the expression level of Ki67, and has the potential to support clinicians in making pretreatment decisions.

[Objective]To assess the microstructural involvement of gray matter in recovered COVID-19 patients us-ing Synthetic MRI.[Methods]This study was conducted in 29 recovered COVID-19 patients,including severe group(SG,n=11)and ordinary group(OG,n=18).Healthy volunteers matched by age,sex,BMI and years of education were select-ed as a healthy control group(HC=23 cases).Each subject underwent synthetic MRI to generate quantitative T1 and T2 maps,and the T1 and T2 maps were segmented into 90 regions of interest(ROIs)using automatic anatomical labeling(AAL)mapping.T1 and T2 values for each ROI were obtained by averaging all voxels within the ROIs.The T1 and T2 values of the 90 brain regions between the three groups were compared.[Results]Relative to HC,the SG had significantly higher T2 values in bilateral orbital superior frontal gyrus,bilateral parahippocampal gyrus,bilateral putamen,bilateral middle temporal gyrus,bilateral Inferior temporal gyrus,left orbital superior frontal gyrus,left orbital inferior frontal gyrus,left gyrus rectus,left anterior cingulate and paracingulate gyri,right median cingulate and paracingulate gyri,left posterior cingulate gyrus,and left supramarginal gyrus(P＜0.05);Relative to OG,SG showed significantly increased T2 values in the left rectus gyrus,left parahippocampal gyrus,bilateral middle temporal gyrus,and bilateral inferior temporal gyrus(P＜0.05).Relative to HC,the T1 values of SG were significantly increased in bilateral orbital superior frontal gyrus,left rec-tus gyrus,left anterior cingulate and paracingulate gyri,right posterior cingulate gyrus,left parahippocampal gyrus,left lingual gyrus,left putamen,left thalamus(P＜0.05);Relative to OG,the T1 values of SG were significantly higher in the right posterior cingulate gyrus,right calcarine fissure and surrounding cortex,and left putamen(P＜0.05).[Conclusions]Even after recovering from COVID-19,patients may still have persistent or delayed damage to their brain gray matter structure,which is correlated with the severity of the condition.SyMRI can serve as a sensitive tool to assess the extent of microstructural damage to the central nervous system,aiding in early diagnosis of the disease.

Multi-phase contrast-enhanced CT images can provide abundant and complementary tumor information, and thus radiologists often use multi-phase images to assist in segmenting and diagnosing liver tumors. However, the current multi-stage liver tumor segmentation methods are based on convolutional neural networks (CNNs), which make them ineffective in extracting global information during the multi-phase information fusion process. In this study, we propose a novel multi-phase liver tumor segmentation approach using delayed phase images to aid in portal vein phase tumor segmentation. The proposed method employs a Transformer structure to extract both global information and local information of tumors, which contributes to the precise segmentation of tumor boundaries. More importantly, we design a cross-phase aggregator (CFA), which facilitates the bidirectional interaction of cross-phase features to take full advantage of the complementary information from multi-phase images. A dataset of 164 multi-phase abdominal CT scans was collected with Institutional Review Board approval to evaluate the performance of the proposed approach. The experimental results showed that the proposed approach can better utilize multi-phase information and is superior to several state-of-the-art methods. Ablation study is performed to further validate the effectiveness of each module in the proposed model. The proposed method has the potential to assist radiologists to locate more accurate liver tumors and improve their diagnosis efficiency.

The early diagnosis of hepatocellular carcinomas (HCCs) remains challenging in the clinic. Primovist-enhanced magnetic resonance imaging (MRI) aids HCC diagnosis but loses sensitivity for tumors <2 cm. Therefore, developing advanced MRI contrast agents is imperative for improving the diagnostic accuracy of HCCs in very-early-stage. To address this challenge, PEGylated ultra-small iron oxide nanoparticles (PUSIONPs) are synthesized and employed as liver-specific T1 MRI contrast agents. Intravenous delivery produces simultaneous hyperintense HCC and hypointense hepatic parenchyma signals on T1 imaging, creating an extraordinarily high tumor-to-liver contrast. Systematic studies uncover PUSIONP distribution in hepatic parenchyma, HCC lesions at the organ, tissue, cellular, and subcellular levels, revealing endosomal confinement of PUSIONP without aggregation. By mimicking such situations, the dependency of relaxometric properties on local PUSIONP concentration is investigated, emphasizing the key role of different endosomal concentrations in liver and tumor cells for high tumor-to-liver contrast and clear tumor boundaries. These findings offer exceptional imaging capabilities for early HCC diagnosis, potentially benefiting real HCC patients.

Accurately predicting the pathologic response to neoadjuvant immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC) prior to surgery is crucial for guiding clinical decision-making and minimizing potential ineffective treatments and unnecessary toxicity. This study aimed to assess the effectiveness of utilizing quantitative changes observed in MRI post-neoadjuvant immunotherapy as a predictive tool for determining the pathologic response in resectable locally advanced HNSCC patients. Twenty patients with resectable locally advanced HNSCC recruited in the prospective phase lb clinical trial were included in the current retrospective analysis. In this current analysis, patients underwent contrast-enhanced MRI and diffusion-weighted MRI (DWI) scanning before neoadjuvant immunotherapy and radical resection of the tumor, respectively. Response to neoadjuvant immunotherapy was based on histopathological evaluation of the resected specimen. The primary tumor volume and the apparent diffusion coefficient (ADC) value were measured. Fisher’s exact test and the Mann-Whitney U-test were used to compare the two groups of treatment response (good response and poor response). The area under the receiver operating characteristic curve (ROC) was used to assess the ability of relative changes in ADC value and tumor volume in discriminating between different pathologic response groups. Good response was found in 35

Idiopathic pulmonary fibrosis (IPF) is a progressive, life-threatening disease with no early detection, few treatments, and dismal outcomes. Although collagen overdeposition is a hallmark of lung fibrosis, current research mostly focuses on the cellular aspect, leaving collagen, particularly its dynamic remodeling (i.e., degradation and turnover), largely unexplored. Here, using a collagen hybridizing peptide (CHP) that specifically binds unfolded collagen chains, we reveal vast collagen denaturation in human IPF lungs and delineate the spatiotemporal progression of collagen denaturation three-dimensionally within fibrotic lungs in mice. Transcriptomic analyses support that lung collagen denaturation is strongly associated with up-regulated collagen catabolism in mice and patients. We thus show that CHP probing differentiates remodeling responses to antifibrotics and highlights the resolution of established fibrosis by agents up-regulating collagen catabolism. We further develop a radioactive CHP that detects fibrosis in vivo in mice as early as 7 days postlung-injury (Ashcroft score: 2-3) by positron emission tomography (PET) imaging and ex vivo in clinical lung specimens. These findings establish collagen denaturation as a promising marker of fibrotic remodeling for the investigation, diagnosis, and therapeutic development of pulmonary fibrosis.