Carbonic anhydrase IX (CAIX) is an important target for imaging clear cell renal cell carcinoma (ccRCC), but existing probes show high gastrointestinal uptake. Here, a sulfur(VI) fluoride exchange (SuFEx)-modified PET probe, [68Ga]Ga-SF-DPI-4452, was developed to improve biodistribution. In vitro studies showed slightly lower CAIX affinity than [68Ga]Ga-DPI-4452 (IC50:7.53 vs 4.60 nM) but higher internalization (23% vs 7%). In OS-RC-2 tumor-bearing mice, both tracers displayed specific and high tumor uptake at 1 h postinjection ([68Ga]Ga-SF-DPI-4452:10.68 ± 2.25%ID/g vs [68Ga]Ga-DPI-4452:9.83 ± 5.86%ID/g, P > 0.05). Notably, [68Ga]Ga-SF-DPI-4452 significantly reduced gastrointestinal uptake, resulting in markedly improved tumor-to-background contrast (e.g., tumor-to-stomach ratio: 32.69 ± 15.51 vs 1.21 ± 0.34, P < 0.05). Biodistribution studies in mice and PET/CT imaging in cynomolgus monkeys further confirmed reduced gastrointestinal accumulation. Overall, SuFEx modification preserves tumor targeting while minimizing off-target uptake, supporting its potential for CAIX-targeted imaging and radionuclide therapy.

OBJECTIVE:To investigate the prognostic value of baseline 18F-fluorodeoxyglucose (18F-FDG) PET metabolic parameters, along with clinical and pathological characteristics, in predicting postoperative outcomes in patients with ALK-positive non-small cell lung cancer (NSCLC). METHODS:A retrospective analysis was conducted on patients at our institution with pathologically confirmed ALK-positive NSCLC. Baseline PET metabolic parameters, clinical characteristics, and pathological features were examined. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cutoff values for all parameters. Survival analyses,including Kaplan-Meier curves, the log-rank test, and Cox proportional hazards regression, were employed to assess disease-free survival (DFS) and identify independent prognostic indicators. RESULTS:The analysis included 78 participants with a median follow-up time of 38.5 months (95% CI: 28.4 - 48.6). The median DFS was 72.8 months (95% CI: 44.7 - 100.8). Univariate analysis revealed significant associations between DFS and several clinical (T stage, overall clinical stage, and CYFRA21-1), PET (SUVmax, SUVmean, SUVpeak, TLG, and MTV), and pathological (Ki-67 index, tumor spread through air spaces [STAS], and pleural invasion) factors (p < 0.05, for all). Multivariate Cox regression analysis identified the following independent predictors of DFS: SUVmax (HR = 16.152, p = 0.002), STAS (HR = 6.122, p = 0.040), T stage (HR = 2.588, p = 0.049), and preoperative CYFRA21-1(HR = 6.509, p = 0.028). CONCLUSION:The assessment of 18F-FDG PET metabolic parameters, pathological factors, and clinical characteristics provides independent prognostic information for postoperative outcomes in patients with ALK-positive NSCLC. These findings may help inform postoperative adjuvant treatment strategies.

This study aimed to evaluate the predictive value of 18F-FDG PET/CT for pathological complete response (pCR) in patients with resectable non-small cell lung cancer (NSCLC) receiving neoadjuvant nivolumab-based therapy. This retrospective study included 125 patients with stage II–III NSCLC who received three cycles of neoadjuvant nivolumab plus chemotherapy between June 2019 and October 2024. All patients underwent ¹⁸F-FDG PET/CT imaging at baseline and after neoadjuvant therapy prior to surgery. Metabolic parameters including SUVmax, SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG)were measured. The diagnostic performance of these parameters for predicting pCR was evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Multiple logistic regression was used to identify independent predictive factors. An XGBoost model integrating significant imaging and clinicopathological predictors was developed. The pCR rate was 38.4

Granulomatosis with polyangiitis is a systemic disorder that can affect nearly any organ system, with frequent involvement of the kidneys, lungs, skin, and eyes. Although some patients relapse months to years after treatment, relapsing more than a decade later, particularly with tracheal involvement, is rare. We report a case of granulomatosis with polyangiitis with PET/CT findings mimicking malignancy in the left lung and trachea.

Positron Emission Tomography (PET) images reflect the metabolic rate of tracers in different tissues of the human body, crucial for early cancer diagnosis and treatment. Accurate tumor segmentation is essential to aid clinicians in determining drug dosages. Due to the low resolution of PET images, prior information (such as CT, MRI or distance information) are often incorporated to assist PET segmentation. In this paper, we propose an automatic 3D PET tumor segmentation framework assisted by geodesic sequences. Specifically, considering the intrinsic characteristics of PET images, we first construct geodesic prior, which effectively enhances the contrast between the tumor and background while suppressing noise and the influence of other tissues. To address the need for seed points in the geodesic prior, an automatic marking strategy is designed that identifies all suspected lesion regions and uses their central points as a series of seeds to generate the corresponding geodesic sequences. Subsequently, we develop a three-branch network architecture to simultaneously process PET images, geodesic sequences, and background geodesic information. To enhance image features, a distance attention mechanism is introduced at the end of the network encoder to effectively measure the similarity between different geodesic features, refining the image features. Finally, the network incorporates spatial regularization and local PET intensity information into the activation function via the Soft Threshold Dynamics with Local Intensity Fitting (STDLIF) module, further improving segmentation accuracy. Experimental results demonstrate that, compared to existing state-of-the-art algorithms, the proposed method shows better segmentation performance on both clinical and public datasets.

OBJECTIVE:This study aimed to evaluate the prognostic value of serial PET/CT metabolic parameters and postoperative pathologic response separately, compare their predictive performance, and further assess their combined prognostic value in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunotherapy plus chemotherapy. METHODS:We retrospectively analyzed 219 NSCLC patients who received neoadjuvant immunotherapy plus chemotherapy, followed by surgery. Pre- and post-treatment PET/CT metabolic parameters assessed by iPERCIST and postoperative pathologic response were collected. Their associations with disease-free survival (DFS) were evaluated using ROC and Kaplan-Meier analyses. RESULTS:During a median follow-up of 24.9 months, 59 patients (26.9%) experienced recurrence. PET metabolic parameters, particularly post-treatment parameters and their percentage changes (ΔPET), demonstrated significant prognostic value for DFS. According to iPERCIST, patients achieving imaging complete metabolic response (iCMR) demonstrated superior DFS compared with non-iCMR patients (median DFS not reached vs. 44.9 mo, P=0.001). Similarly, patients achieving pathologic complete response (pCR) or major pathologic response (MPR) showed improved DFS (P=0.008 and 0.002, respectively). Combined stratification demonstrated that patients with both iCMR and pCR had the most favorable outcomes compared with other groups (P=0.003). CONCLUSIONS:Serial PET metabolic parameters and postoperative pathologic response hold significant prognostic value following neoadjuvant immunotherapy plus chemotherapy in NSCLC. The combination of serial PET metabolic parameters and postoperative pathologic response in a joint model improves prognostic prediction in NSCLC following neoadjuvant immunotherapy plus chemotherapy.

OBJECTIVE:To study the ability of the combination of 18F-FDG positron-emission tomography/computed tomography (PET/CT) metabolic parameters and the dynamic monitoring of molecular residual disease (MRD) in predicting the prognosis of non-small-cell lung cancer (NSCLC) after surgery. METHODS:The clinical data and disease-free survival (DFS) data of 157 NSCLC patients who underwent 18F-FDG PET/CT at 2 weeks before surgery and were regularly monitored for MRD after surgery were retrospectively analyzed. The correlation between PET metabolic parameters and the dynamic monitoring of MRD and the values of the two in predicting prognosis of NSCLC were analyzed. RESULTS:Survival analysis revealed that patients with elevated 18F-FDG PET metabolism had a worse prognosis, while MRD-positive patients had a significantly worse prognosis than MRD-negative patients. By combining of PET metabolic parameters and MRD status, the PET-high-metabolism + MRD-positive group had a worse prognosis than the PET-high-metabolism + MRD-negative group or the PET-low-metabolism + MRD-positive group and the PET-low-metabolism + MRD-negative group. The prognostic model established by tumor TNM staging, clinical data and PET metabolic parameters(comd 1 model) had a high predictive value (C-index: 0.759, 95 % CI: 0.683-0.835). After adding this model to the MRD detection results(comd 2 model), the prognostic accuracy of the model improved (C-index: 0.873, 95 % CI: 0.824-0.922). CONCLUSION:The prognostic model made up of Tumor TNM staging,18F-FDG PET metabolic parameters and clinical data can accurately predict recurrence in NSCLC patients after surgery. Incorporating the results of the dynamic monitoring of MRD detection into the model can significantly enhance its the prognostic accuracy.

A 62-year-old man with echocardiographic findings suggestive of cardiac amyloidosis and positive 99m Tc-pyrophosphate ( 99m Tc-PYP) scintigraphy was ultimately diagnosed with Fabry disease confirmed by genetic testing. This case highlights myocardial 99m Tc-PYP uptake in Fabry cardiomyopathy, expanding the differential diagnosis of non-amyloid cardiomyopathies associated with myocardial radiotracer retention.

PURPOSE:This study aimed to evaluate the role of various metabolic parameters derived from baseline 18 F-FDG PET/CT in predicting the prognosis of stage IV non-small cell lung cancer (NSCLC) patients scheduled to receive osimertinib treatment. PATIENTS AND METHODS:A retrospective analysis was conducted on 177 NSCLC patients (98 males, 79 females; mean age 58.5 ± 11.0 y) who underwent osimertinib therapy and 18 F-FDG PET/CT scanning before treatment. Clinical and PET/CT parameters were assessed, including age, sex, smoking history, brain metastasis, bone metastasis, CEA level, SUVmax, SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Receiver operating characteristic (ROC) curve analysis was used to calculate the optimal cutoff values for all parameters. Progression-free survival (PFS) was analyzed using log-rank tests, Kaplan-Meier curves, and Cox proportional hazard models to identify prognostic markers. RESULTS:The mean follow-up period was 15.24 ± 8.14 months. Univariate analysis revealed that SUVmax, SUVmean, MTV, and TLG were significantly associated with PFS, with cutoff values of 12.3, 8.57, 13.49 cm 3 , and 162.37, respectively. The hazard ratios were 1.776 ( P = 0.007), 2.155 ( P ＜0.001), 3.312 ( P < 0.001), and 3.370 ( P < 0.001), respectively. Multivariate survival analysis indicated that MTV was an independent prognostic factor for PFS (HR = 2.323; P = 0.012). CONCLUSIONS:Baseline PET/CT metabolic parameters before osimertinib treatment may help identify potential NSCLC patients who could derive clinical benefit, and baseline MTV of the primary tumor from 18 F-FDG PET/CT is a reliable prognostic indicator for PFS in stage IV NSCLC patients treated with osimertinib.

OBJECTIVE:This study investigates the predictive value of 18 F-FDG PET/CT metabolic parameters in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunotherapy plus chemotherapy. METHODS:We conducted a retrospective analysis of clinical data from 131 patients with pathologically confirmed NSCLC who were deemed resectable after 3 cycles of neoadjuvant immunotherapy plus chemotherapy. Pretreatment and post-treatment PET metabolic parameters were evaluated. CT assessments based on immune response evaluation criteria in solid tumors (iRECIST) were compared with PET/CT assessments using the response criteria in solid tumors (PERCIST). ROC curve analysis and Kaplan-Meier survival analysis, including univariate and Cox multivariate analyses, were employed to assess the prognostic value of PET metabolic parameters after treatment. RESULTS:The PET/CT assessment based on PERCIST showed high consistency with prognosis, while the CT assessment based on iRECIST demonstrated low consistency. Statistically significant differences were observed between the iRECIST and PERCIST criteria ( P <0.001). ROC curve analysis revealed significant differences in post-treatment PET metabolic parameters (postSUVmax, postSUVmean, postSUVpeak, postMTV, and postTLG) as well as the percentage changes in metabolic parameters before and after treatment(Δ) (ΔSUVmax, ΔSUVmean, ΔSUVpeak, ΔMTV, and ΔTLG) ( P <0.05). Optimal cutoff values enabled stratification into high-risk and low-risk groups. Univariate analysis showed significantly higher survival in the low-risk group for all parameters except ΔMTV ( P =0.311), while Cox multivariate analysis identified ΔSUVmax as the most predictive. CONCLUSIONS:The PERCIST is more accurate than iRECIST in evaluating prognosis for NSCLC neoadjuvant immunotherapy plus chemotherapy. PET metabolic parameters, particularly ΔSUVmax, effectively predict prognosis and support clinical decision-making.

ObjectiveThis study aimed to investigate the expression of serine protease inhibitor kazal type 1 (SPINK1) and its carcinogenic effect in oral tongue squamous cell carcinoma (OTSCC). Design: Initially, bioinformatics analysis was conducted using data from The Cancer Genome Atlas and Gene Expression Omnibus to compare SPINK1 mRNA expression between malignant and adjacent tissues. Subsequently, the impact of differential expression on survival and other clinical variables was examined. Additionally, histology microarray analysis was performed to assess SPINK1 protein expression in 35 cases of malignant and adjacent tissues. Finally, alterations in SPINK1 expression were evaluated to determine its biological phenotypes in OTSCC, including proliferation, apoptosis, invasion, and metastasis. Results: OTSCC tissues exhibit higher levels of SPINK1 compared to surrounding cancerous tissues. Notably, increased SPINK1 expression correlates with the pathological N stage and independently predicts overall survival among patients with OTSCC. Conclusion: Suppression of SPINK1 inhibited OTSCC cell proliferation, invasion, and motility while promoting apoptosis. These findings suggest that SPINK1 may serve as a prognostic biomarker as well as a potential therapeutic target for managing OTSCC.

ABSTRACT:Endometriosis is a chronic inflammatory estrogen-dependent benign disease. It is defined as the endometrium growing outside the uterine cavity and the myometrium. It usually has low FDG uptake but rarely occurs in the ureters. We reported a case of a 47-year-old woman's left ureteral nodule originally misdiagnosed as a ureteral malignant tumor by PET/CT and finally pathologically confirmed as endometriosis.

The PET/CT imaging technology combining positron emission tomography (PET) and computed tomography (CT) is the most advanced imaging examination method currently, and is mainly used for tumor screening, differential diagnosis of benign and malignant tumors, staging and grading. This paper proposes a method for breast cancer lesion segmentation based on PET/CT bimodal images, and designs a dual-path U-Net framework, which mainly includes three modules: encoder module, feature fusion module and decoder module. Among them, the encoder module uses traditional convolution for feature extraction of single mode image; The feature fusion module adopts collaborative learning feature fusion technology and uses Transformer to extract the global features of the fusion image; The decoder module mainly uses multi-layer perceptron to achieve lesion segmentation. This experiment uses actual clinical PET/CT data to evaluate the effectiveness of the algorithm. The experimental results show that the accuracy, recall and accuracy of breast cancer lesion segmentation are 95.67%, 97.58% and 96.16%, respectively, which are better than the baseline algorithm. Therefore, it proves the rationality of the single and bimodal feature extraction method combining convolution and Transformer in the experimental design of this article, and provides reference for feature extraction methods for tasks such as multimodal medical image segmentation or classification.

Background Aortic intramural hematoma (IMH) is one of the typical entities of acute aortic syndrome and probably accounts for 5–25% of all cases. The ulcer-like projections (ULP), which are described as a focal, blood-filled pouch protruding into the hematoma of the aortic wall, are regarded as one of the high-risk imaging features of IMH and may cause initial medical treatment failure and death. Case presentation We present a case report of an acute type B IMH patient with impaired renal function and newly developed ULP in the acute phase. The 18 F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging ( 18 F-FDG PET/MR) was performed to evaluate the condition of aortic hematoma. The 18 F-FDG focal uptake along the aortic wall of the hematoma was normal compared to the background (SUV max 2.17; SUV SVC 1.6; TBR 1.35). We considered the IMH stable in such cases and opted for medical treatment and watchful observation. Six months after discharge, the patient’s recovery was satisfactory, and aortic remodeling was ideal. Conclusions The 18 F-FDG PET/MR is a novel tool to evaluate the risk of IMH patients and thus provides information for therapy selection.

Despite mounting evidence for dietary protease benefits, the mechanisms beyond enhanced protein degradation are poorly understood. This study aims to thoroughly investigate the impact of protease addition on the growth performance, intestinal function, and microbial composition of weaned piglets. Ninety 28-day-old weaned pigs were randomly assigned to the following three experimental diets based on their initial body weight for a 28-day experiment: (1) control (CC), a basic diet with composite enzymes without protease; (2) negative control (NC), a diet with no enzymes; and (3) dietary protease (PR), a control diet with protease. The results show that dietary proteases significantly enhanced growth performance and boosted antioxidant capacity, increasing the total antioxidant capacity (T-AOC) levels (p < 0.05) while reducing malonaldehyde levels (p < 0.05). Additionally, protease addition reduced serum levels of inflammatory markers TNF-α, IL-1β, and IL-6 (p < 0.05), suppressed mRNA expression of pro-inflammatory factors in the jejunum (p < 0.01), and inhibited MAPK and NF-κB signaling pathways. Moreover, protease-supplemented diets improved intestinal morphology and barrier integrity, including zonula occludens protein 1(ZO-1), Occludin, and Claudin-1 (p < 0.05). Microbiota compositions were also significantly altered by protease addition with increased abundance of beneficial bacteria (Lachnospiraceae_AC2044_group and Prevotellaceae_UCG-001) (p < 0.05) and reduced harmful Terrisporobacter (p < 0.05). Further correlation analysis revealed a positive link between beneficial bacteria and growth performance and a negative association with inflammatory factors and intestinal permeability. In summary, dietary protease addition enhanced growth performance in weaned piglets, beneficial effects which were associated with improved intestinal barrier integrity, immunological response, and microbiota composition.

Objective. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood-brain barrier, leading to a relatively poor prognosis for patients with BMs. Therefore, early detection and treatment of BMs are highly important for improving patient prognosis. This study aimed to investigate the feasibility of a multimodal radiomics-based method using 3D neural networks trained on18F-FDG PET/CT images to predict BMs in NSCLC patients.Approach. We included 226 NSCLC patients who underwent18F-FDG PET/CT scans of areas, including the lung and brain, prior to EGFR-TKI therapy. Moreover, clinical data (age, sex, stage, etc) were collected and analyzed. Shallow lung features and deep lung-brain features were extracted using PyRadiomics and 3D neural networks, respectively. A support vector machine (SVM) was used to predict BMs. The receiver operating characteristic (ROC) curve and F1 score were used to assess BM prediction performance.Main result. The combination of shallow lung and shallow-deep lung-brain features demonstrated superior predictive performance (AUC = 0.96 ± 0.01). Shallow-deep lung-brain features exhibited strong significance (P < 0.001) and potential predictive performance (coefficient > 0.8). Moreover, BM prediction by age was significant (P < 0.05).Significance. Our approach enables the quantitative assessment of medical images and a deeper understanding of both superficial and deep tumor characteristics. This noninvasive method has the potential to identify BM-related features with statistical significance, thereby aiding in the development of targeted treatment plans for NSCLC patients.

BackgroundAccurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge.PurposeExisting deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings.MethodsIn this paper, we propose a few-shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high-order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours.ResultsWe compared our proposed method with state-of-the-art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators.ConclusionOur approach utilizes the output of few-shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples.

Advancements in precision medicine necessitate understanding drug clearance pathways, especially in organs like the liver and kidneys. Traditional techniques such as PET/CT pose radiation hazards, whereas optical imaging poses challenges in maintaining both depth penetration and high resolution. Moreover, very few longitudinal studies have been performed for drug candidates for different symptoms. Leveraging non-ionizing photoacoustic tomography for deep tissue imaging, we developed a spatiotemporally resolved clearance pathway tracking (SRCPT) method, providing unprecedented insights into drug clearance dynamics within vital organs. SRCPT addresses challenges like laser fluence attenuation, enabling dynamic visualization of drug clearance pathways and essential parameter extraction. We employed a novel frequency component selection based synthetic aperture focusing technique (FCS-SAFT) with respiratory-artifacts-free weighting factors to enhance three-dimensional imaging resolutions. Inspired by this, we investigated the clearance pathway of a clinical drug, mitoxantrone, revealing reduced liver clearance when hepatic function is impaired. Furthermore, immunoglobulin G clearance analysis revealed significant differences among mice with varying renal injury degrees. The accuracy of our method was validated using a double-labeled probe [68Ga]DFO-IRDye800CW, showing a strong positive correlation between SRCPT and PET. We believe that this powerful SRCPT promises precise mapping of drug clearance pathways and enhances diagnosis and treatment of liver and kidney-related diseases. The SRCPT method enables dynamic visualization of drug clearance pathways with high precision, particularly in cases of liver and kidney dysfunction, enhancing diagnosis and treatment outcomes.