Abstract Purpose 68Ga-pentixafor PET/MRI, which targets the C-X-C chemokine receptor type 4 (CXCR4), has been shown to significantly enhance lesion localization accuracy in Cushing’s disease (CD). However, a subgroup of CD tumors exhibits both reduced 68Ga-pentixafor uptake and low CXCR4 expression. In this study, we propose a CXCR4-based stratification of CD patients to evaluate the utility of this stratification for improving lesion localization accuracy, delineating clinical characteristics, predicting survival outcomes, and characterizing mutational features. Methods This retrospective study analyzed 138 patients with surgically and pathologically confirmed CD. Patient subsets underwent 68Ga-Pentixafor PET/MRI (n = 78), CXCR4 immunohistochemistry (n = 116), and targeted gene sequencing (n = 129). Follow-up data were available for 115 patients. Results The localization sensitivity and diagnostic accuracy of 68Ga-pentixafor PET/MRI reached 98.7% and 96.3% respectively, when combined with conventional contrast-enhanced MRI. The entire retrospective cohort (n = 138) was stratified into CXCR4-high (n = 95) and CXCR4-low (n = 43) groups using receiver operating characteristic (ROC) analysis. Compared with the CXCR4-high group, the CXCR4-low group exhibited a higher proportion of relapsed tumors (P = 0.005), a lower proportion of hypokalemia (P = 0.005), larger tumor diameter (P = 0.026) and volume (P = 0.013), lower ACTH staining intensity (P < 0.001), and worse progression-free survival (PFS) after 2.5 years (P = 0.041). The prevalence of tumors harboring ubiquitin-specific peptidase 8 (USP8) hotspot mutations was significantly lower in the CXCR4-low group (P = 0.04) among macroadenomas but not among microadenomas. 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Predictive value of C-X-C motif chemokine receptor 4-directed molecular imaging in patients with advanced adrenocortical carcinoma. Eur J Nucl Med Mol Imaging. 2024;51:3643–50. https://doi.org/10.1007/s00259-024-06800-z. Acknowledgements We thank Prof Haixia Cheng, MD, PhD, and Dr Xiaojia Liu, MD, PhD, from the Department of Pathology, Huashan Hospital, Fudan University, China, for performing the histologic analysis. We thank Prof Dr Wenting Rui from the Department of Radiology, Huashan Hospital, Fudan University, China, help review the Contrast-enhanced MRI scans. We thank Dr Shuhua Ren, from the Department of Nuclear Medicine and PET, Huashan Hospital, Fudan University, China, help review the PET/MRI scans. Funding This work is supported by Noncommunicable Chronic Diseases–National Science and Technology Major Project (2023ZD0517800 to Yue Wu); National Natural Science Funds of China (82371875 and 82572141 to Zengyi Ma, 82073640 to Nidan Qiao, U21A20389 to Yao Zhao, 82373119 to Qilin Zhang), CAMS Innovation Fund for Medical Sciences (2023-I2M-C&T-B-125 to Y. Wang); the National Hypothalamic-Pituitary Disorders Multi-disciplinary Research Consortium, the China Pituitary Adenoma Specialist Council (CPASC), CAMS Innovation Fund for Medical Sciences (2021-I2M-C&T A-025), the National Science Fund for Distinguished Young Scholars (81725011), Clinical Research Project supported by Huashan Hospital, Fudan University (2024-YN001) to Yao Zhao. Author information Authors and Affiliations Contributions All authors contributed to the study conception and design. Guarantors of integrity of entire study, B.Y. Y.L., Yanfei Wu, M.C., Y.Z., Yue Wu, Q.Z.; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, B.Y., Y.L., Yanfei Wu, M.C., S.Z., Yi Wang, F.X., Y.G., Z.Y., Y.Z., Z.Z., H.Y., Yue Wu, Q.Z.; clinical studies, B.Y., Y.L., Yanfei Wu, Z.M., N.Q., S.Z., Yi Wang, Y.G., Z.Y., Z.Z., H.Y., Yongfei Wang, Y.Z., Yue Wu, Q.Z.; experimental studies, B.Y., Yanfei Wu, M.C., F.X., Q.Z.; statistical analysis, B.Y., Y.L., M.C., Z.M., N.Q., F.X., Yue Wu, Q.Z.; and manuscript editing, B.Y., Y.L., Yanfei Wu, M.C., Y.Z., Yue Wu, Q.Z. Corresponding authors Ethics declarations Ethics approval This study involving human participants conforms to the principles of Huashan Hospital Ethics Committee and the Declaration of Helsinki in 1964. Animal-based research was not included in the current study. Consent to participate Written consent was waived because of the retrospective nature of the study. Consent to publish Not applicable. Competing interests Author Fang Xie is in editorial board of this journal. The authors have no other relevant financial or non-financial interests to disclose. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information Below is the link to the electronic supplementary material. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Yao, B., Liu, Y., Wu, Y. et al. The value of targeting CXCR4 with 68Ga-Pentixafor PET/MRI for Cushing’s disease: a retrospective cohort study. Eur J Nucl Med Mol Imaging 53, 4199–4210 (2026). https://doi.org/10.1007/s00259-026-07821-6 Received: Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s00259-026-07821-6

PURPOSE: The 18 kDa translocator protein (TSPO) has been a central molecular target for imaging inflammation in the preclinical and clinical research settings across a plethora of applications, including neuroinflammation, cardiovascular inflammation and cancer. Recently, we reported the development of [18F]LW223 as a third-generation TSPO positron emission tomography (PET) radiotracer with binding to human TSPO independent of the rs6971 genetic polymorphism. This study reports the first whole-body human analysis, including biodistribution and dosimetry calculations, following intravenous administration of [18F]LW223. METHODS: Whole-body PET images were acquired over 250 min after intravenous bolus injection of 184.3 ± 20.2 MBq of [18F]LW223 in healthy adult human volunteers. Volumes of interest (VOIs) in different source organs were manually delineated by three independent observers, then time-activity curves were generated for residency times calculations for subsequent quantification of radiation equivalent and effective doses using OLINDA/EXM 2.2 software. RESULTS: The radiotracer biodistribution in humans recapitulated known TSPO expression in various tissues. The main elimination route was found to be hepatobiliary, and the critical organ was the intestine. The cumulated radioactivity excreted by the kidneys was < 10% over the measurement period and no bone uptake suggestive of in vivo defluorination was observed in any of the study subjects. The effective dose ranged between 11.8 ± 0.9 and 12.5 ± 0.9 µSv/MBq. Inter-observer VOI variability had no impact on estimated organ and whole-body effective doses. CONCLUSION: [18F]LW223 is predominantly excreted by the hepatobiliary route with no evidence of in vivo defluorination but demonstrates marked uptake into tissues with known TSPO expression. It complies with radiation limits and guidelines recommended by regulatory authorities and is in line with previously reported [18F]-labelled radiotracers, such as [18F]fluorodeoxyglucose. [18F]LW223 is suitable for translation into human clinical studies.

PURPOSE: Mesothelin (MSLN), a 40 kDa glycoprotein normally confined to mesothelial cells, is overexpressed in several malignancies, including triple-negative breast cancer (TNBC). The anti-mesothelin single-domain antibody (sdAb, or “nanobody®”) DOTA-A1K2, previously validated for positron emission tomography (PET) imaging using site-specific 68Ga radiolabeling, may also serve as a radio-theranostic agent when labeled with 177Lu. Moreover, 161Tb has recently been proposed as an alternative to 177Lu that might provide additional efficacy due to the emission of Auger electrons. The aim of this study was to evaluate in vitro and in vivo the therapeutic effect of [177Lu]Lu-DOTA-A1K2 and [161Tb]Tb-DOTA-A1K2. METHODS: Biodistribution was assessed in mice from 1 to 168 h post-injection. Therapeutic efficacy was tested using MDA-MB-231 TNBC cells transfected or not with MSLN. Clonogenic assays were performed after 24 h incubation with either radiotracer. In vivo, efficacy was evaluated over 9 weeks after a single intravenous injection of 2, 5, 10, or 20 MBq. RESULTS: DOTA-A1K2 was successfully radiolabeled with both isotopes with RCP > 95%. In vitro, [161Tb]Tb-DOTA-A1K2 was significantly more potent than [177Lu]Lu-DOTA-A1K2 in inhibiting colony formation (p < 0.01). In vivo in mice, the radiotracers exhibited similar biodistribution profiles. The administration of 5, 10 or 20 MBq of either [177Lu]Lu-DOTA-A1K2 or [161Tb]Tb-DOTA-A1K2 inhibited tumor growth compared to controls (p < 0.01 vs. vehicle), while no effect was observed at 2 MBq (p = NS). However, no differences in efficacy were observed between the two isotopes at any dose. CONCLUSIONS: This work provides the first sdAb-based theranostic approach targeting mesothelin-positive tumors. In vivo in mice, both [177Lu]Lu-DOTA-A1K2 and [161Tb]Tb-DOTA-A1K2 accumulated in tumors. In vivo efficacy was found to be comparable, despite the superior in vitro efficacy of 161Tb. Further studies are warranted to clarify this discrepancy, which could potentially result from mesothelin shedding that prevents Auger electrons from reaching tumor cells. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-025-07723-z.

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The rapid advancements in PET technology, coupled with the need for accurate and efficient imaging, necessitate the development of robust and generalizable methods for CT-free attenuation and scatter correction (ASC). Deep learning offers a promising solution, but exhibits limited performance when tested in diverse clinical settings and varying imaging conditions. We propose a few-shot fine-tuning paradigm that enables efficient adaptation of models from a source domain to a new target domain. Our backbone network incorporates statistical modulation to extract domain-specific distribution information and employs pixel-wise factor scaling modeling to disentangle ASC factor maps from input images. On a large and diverse dataset of 1539 subjects across multiple tracers, scanners, and centers, we evaluate model performance under single-tracer training, multi-tracer joint training, and few-shot adaptation strategies. Although joint training demonstrates strong performance on known tracers, the proposed few-shot adaptation approach, CrossPET-Adapt, excels at adapting to unseen domains with minimal data, outperforming joint training. This method significantly reduces radiation exposure and data requirements, offering a rapid and robust solution for CT-free PET ASC in varied clinical environments.

Despite rapid advances in artificial intelligence (AI) capabilities, clinical integration remains limited, partly due to an incomplete understanding of physicians' real-world engagement and determinants of adoption. We conducted an international cross-sectional survey between June and September 2024 using a 30-item questionnaire translated into 13 languages, analysing 1049 complete responses from 50 countries and territories. Most respondents reported fundamental to advanced understanding of AI (86.5%) and believed it would improve clinical practice (80.2%), particularly in efficiency (53.5%), timeliness (52.0%), and effectiveness (44.0%). However, only 27.8% had used AI in practice, and 17.7% had received formal training. Mann-Whitney analyses showed that positive attitudes and formal training were associated with greater AI understanding, familiarity and usage (all p ≤ 0.008). In multivariable regression, physicians with formal training were more than three times as likely to use AI (adjusted OR 3.40; 95% CI 2.31-5.01; p < 0.001), and those working in institutions with AI technologies were more than eight times as likely (adjusted OR 8.43; 95% CI 5.74-12.34; p < 0.001). These findings indicate a persistent gap between awareness and adoption driven primarily by structural factors, particularly the absence of formal AI training and limited institutional investment in AI technologies.

Precision oncology faces critical challenges in interpreting complex cellular signals and predicting drug responses across heterogeneous cancer environments. Here, we present BioGDR, a multimodal interpretable deep learning framework that integrates structure-based predicted biological features, including differential gene expression and kinase inhibition profiles, eliminating the need for experimental measurements. By modeling tumor transcriptomic states through pathway-informed graph neural networks and employing a drug-guided attention strategy, BioGDR enables mechanistic insights into drug sensitivity across compound and cellular contexts. Comprehensive evaluations demonstrate that BioGDR outperforms existing methods in compound screening relevant to early-stage drug discovery and in predicting cell line sensitivity across heterogeneous cellular states characteristic of precision oncology, while analyses on clinical patient cohorts further confirm its practical utility and generalization capability. Experimental validation with a novel ALDH1B1 inhibitor confirms its ability to identify sensitive cell populations and reveal underlying mechanisms. This work establishes a robust, biologically informed framework that bridges preclinical drug development and clinical applications, advancing precision oncology through integrative, multimodal learning and interpretable mechanism analysis.