C-X-C motif chemokine receptor 4 (CXCR4) has emerged as a powerful imaging target in nuclear oncology. In this regard, the PET agent [68Ga]Ga-pentixafor is promising but still evolving. In this article, we provide an overview of potentially useful imaging applications for CXCR4-targeted imaging in hemato-oncology and guidance on scan interpretation by discussing diagnostic pitfalls. We also report on theranostic efforts and how the field has been expanded toward the imaging of solid tumors. As such, this review focuses on potential clinical integration of CXCR4-targeted molecular imaging across multiple disease entities, particularly in the field of hemato-oncology.

Abstract Background To validate the clinical utility of a previously identified circulating tumor DNA methylation marker (meth-ctDNA) panel for disease detection and survival outcomes, meth-ctDNA markers were compared to PSA levels and PSMA PET/CT findings in men with different stages of prostate cancer (PCa). Methods 122 PCa patients who underwent [⁶⁸Ga]Ga-PSMA-11 PET/CT and plasma sampling (03/2019–08/2021) were analyzed. cfDNA was extracted, and a panel of 8 individual meth-ctDNA markers was queried. PET scans were qualitatively and quantitatively assessed. PSA and meth-ctDNA markers were compared to PET findings, and their relative prognostic value was evaluated. Results PSA discriminated best between negative and tumor-indicative PET scans in all (AUC 0.77) and hormone-sensitive (hsPC) patients (0.737). In castration-resistant PCa (CRPC), the meth-ctDNA marker KLF8 performed best (AUC 0.824). CHST11 differentiated best between non- and metastatic scans (AUC 0.705) overall, KLF8 best in hsPC and CRPC (AUC 0.662, 0.85). Several meth-ctDNA markers correlated low to moderate with the tumor volume in all (5/8) and CRPC patients (6/8), while PSA levels correlated moderately to strongly with the tumor volume in all groups (all p < 0.001). CRPC overall survival was independently associated with LDAH and PSA (p = 0.0168, p < 0.001). Conclusion The studied meth-ctDNA markers are promising for the minimally-invasive detection and prognostication of CRPC but do not allow for clinical characterization of hsPC. Prospective studies are warranted for their use in therapy response and outcome prediction in CRPC and potential incremental value for PCa monitoring in PSA-low settings.

Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization. We trained a generative model on 99mTc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis. A blinded reader study was performed to assess the clinical validity and quality of the generated data. We investigated the added value of the generated data by augmenting an independent small single-center dataset with synthetic data and by training a deep learning model to detect abnormal uptake in a downstream classification task. We tested this model on 7,472 scans from 6,448 patients across four external sites in a cross-tracer and cross-scanner setting and associated the resulting model predictions with clinical outcomes. The clinical value and high quality of the synthetic imaging data were confirmed by four readers, who were unable to distinguish synthetic scans from real scans (average accuracy: 0.48

PSMA-targeted radioligand therapies (PSMA RLT) are an effective and safe option for metastatic castration-resistant prostate cancer, but responsive subtypes and their biomarkers are not fully defined. Plasma samples for cell-free DNA (cfDNA) analysis were collected from 17 patients undergoing [¹⁷⁷Lu]Lu-PSMA-I T. CfDNA underwent whole-genome sequencing to establish copy number variation (CNV) profiles and circulating-tumor DNA (ctDNA) levels and compared between prostate-specific antigen (PSA) response- and 1-year overall survival (1YOS) groups. Non-responders exhibited higher degrees of cfDNA CNV burden (P = 0.048) and higher ctDNA levels (P = 0.036) than responders. Both markers allowed for the differentiation of responses (AUC: 0.792, 0.806) and 1YOS (AUC: 0.778, 0.847). Unresponsive patients exhibited higher levels of cfDNA genomic instability and ctDNA levels, warranting genome-wide CNV profiling studies next to targeted approaches for mechanistic radiobiological insights and their value as response biomarkers for PSMA RLTs.

Radioligand therapy with 177 Lu-Prostate Specific Membrane Antigen is a very promising treatment option for patients with prostate cancer. In the past ten years it started to be used as one of the final options in patients with metastatic, castration resistant prostate cancer, after previously exploiting all the common treatments. In Slovakia, this treatment has been available since the beginning of the year 2020. In this time, we have been able to adopt many of the recommendations from the European guidelines and adapt the rest to the clinical and national legislative requirements. In the time period between February 2020 and June 2024, we treated 104 patients and administered total of 362 cycles of the radioligand therapy with 177 Lu-Prostate Specific Membrane Antigen. Although the numbers of our patients suitable for systemic evaluation are for now rather small, our result has so far showed the correlation with some of the largest multicentric trials. More patients will be included in the further evaluation with upcoming months, and more precise data will be available.

We report a case of a woman in her mid-30s who developed severe cardiac shock hours after giving birth to her second child with the need for extracorporeal haemodynamic support. Initially, postpartum cardiomyopathy was suspected, and high-urgency heart transplantation was considered. However, the endocrine work-up and imaging revealed pheochromocytoma as the cause for acute heart failure that was completely reversible. Notably, the patient also developed Sheehan's syndrome with pituitary necrosis and sustained hypopituitarism, most likely as a consequence of the haemodynamic failure during pheochromocytoma crisis. While pheochromocytoma crisis is already an extremely rare peripartum complication, the current case is-to the best of our knowledge-the first report of pheochromocytoma associated with Sheehan's syndrome. This case also highlights the clinical conundrum that pheochromocytomas can be easily overlooked in pregnancy due to non-specific symptoms and confusion with pregnancy-related hypertension or hypertension-associated other diseases. Appropriate case detection is important, especially in pregnant women with early onset of hypertension.

The chemotherapy regimen capecitabine/temozolomide (CAPTEM) is routinely used in neuroendocrine tumors (NET), with antitumor activity particularly demonstrated in pancreatic or high-grade neuroendocrine neoplasms (NEN). However, different dosing regimens are used, and the optimal schedule remains to be defined. This single-center retrospective analysis assessed the efficacy and safety of CAPTEM in patients with NEN using a schedule starting both compounds simultaneously (temozolomide on days 1-5 and capecitabine on days 1-14 of a 28-day cycle) rather than sequentially. The primary parameters of interest were response rates, progression-free survival (PFS), and toxicities following this treatment regimen, hereinafter referred to as TEMCAP. The study population comprised 40 patients, half of whom (n = 20) had pancreatic NEN, and 9 patients (22.5%) had pulmonary or thymic NETs. The most common histology was NET G3 (n = 15, 37.5%), and 8 patients (20.0%) had a neuroendocrine carcinoma (NEC). Most patients (77.5%) had at least one prior systemic therapy, and 16 patients (40.0%) prior chemotherapy. The median number of TEMCAP cycles was 6 (range 1-16). Median PFS for the highly heterogeneous population was 13.3 months, while the median overall survival was 31.9 months. In total, 14/36 patients (38.9%) exhibited a partial response, and the disease control rate was 75.0%. The safety profile of TEMCAP (at a below-target mean temozolomide dose of 118.85 mg/m(2)) in our cohort was remarkably good with no toxicities of grade 3 or 4. Taken together, the results of this analysis further support the use of temozolomide/capecitabine in NEN and prompt further assessment of our modified TEMCAP schedule.

Purpose: This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to compare its performance with biopsy-proven GG (bxGG) assessment. Materials and Methods: A total of 146 patients with PCa recruited in a pilot study of a prospective clinical trial (NCT02659527) were retrospectively included in the side study, all of whom underwent 68Ga-PSMA-11 integrated positron emission tomography (PET) / magnetic resonance (MR) before radical prostatectomy (RP) between May 2014 and April 2020. To establish a multiomics ML model, we quantified PET radiomics features, pathway-level genomics features from whole exome sequencing, and pathomics features derived from immunohistochemical staining of 11 biomarkers. Based on the multiomics dataset, five ML models were established and validated using 100-fold Monte Carlo cross-validation. Results: Among five ML models, the random forest (RF) model performed best in terms of the area under the curve (AUC). Compared to bxGG assessment alone, the RF model was superior in terms of AUC (0.87 vs 0.75), specificity (0.72 vs 0.61), positive predictive value (0.79 vs 0.75), and accuracy (0.78 vs 0.77) and showed slightly decreased sensitivity (0.83 vs 0.89) and negative predictive value (0.80 vs 0.81). Among the feature categories, bxGG was identified as the most important feature, followed by pathomics, clinical, radiomics and genomics features. The three important individual features were bxGG, PSA staining and one intensity-related radiomics feature. Conclusion: The findings demonstrate a superior assessment of the developed multiomics-based ML model in whole-mount GG compared to the current clinical baseline of bxGG. This enables personalized patient management by identifying high-risk PCa patients for RP.

C-X-C motif chemokine receptor 4 (CXCR4) is overexpressed in a multitude of cancers, including neoplasms of hematopoietic origin. This feature can be leveraged by a theranostic approach, which provides a read-out of the actual CXCR4 expression in vivo, followed by CXCR4-targeted radioligand therapy (RLT) exerting anti-cancer as well as myeloablative efficacy. In a recent meeting of hematooncology and nuclear medicine specialists, statements on the current clinical practice and future perspectives of this innovative concept were proposed and summarized in this opinion article. Experts concluded that i) CXCR4-directed [68Ga]Ga-PentixaFor PET/CT has the potential to improve imaging for patients with marginal zone lymphoma; ii) CXCR4-targeted RLT exerts anti-lymphoma efficacy and myeloablative effects in patients with advanced, treatment-refractory T-cell lymphomas; iii) prospective trials with CXCR4-based imaging and theranostics are warranted.

Purpose: The purpose of this study was to assess the ability of pretreatment PET parameters and peripheral blood biomarkers to predict progression-free survival (PFS) and overall survival (OS) in NSCLC patients treated with ICIT.Methods: We prospectively included 87 patients in this study who underwent pre-treatment [F-18]-FDG PET/CT. Organ-specific and total metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured using a semiautomatic software. Sites of organ involvement (SOI) were assessed by PET/CT. The log-rank test and Cox-regression analysis were used to assess associations between clinical, laboratory, and imaging parameters with PFS and OS. Time dependent ROC were calculated and model performance was evaluated in terms of its clinical utility.Results: MTV increased with the number of SOI and was correlated with neutrophil and lymphocyte cell count (Spearman's rho = 0.27 or 0.32; p =.02 or 0.003; respectively). Even after adjustment for known risk factors, such as PD-1 expression and neutrophil cell count, the MTV and the number of SOI were independent risk factors for progression (per 100 cm(3); adjusted hazard ratio [aHR]: 1.13; 95% confidence interval [95%CI]: 1.01-1.28; p =.04; single SOI vs. >= 4 SOI: aHR: 2.26, 95%CI: 1.04-4.94; p =.04). MTV and the number of SOI were independent risk factors for overall survival (per 100 cm(3) aHR: 1.11, 95%CI: 1.01-1.23; p =.03; single SOI vs. >= 4 SOI: aHR: 4.54, 95%CI: 1.64-12.58; p =.04). The combination of MTV and the number of SOI improved the risk stratification for PFS and OS (log-rank test p <.001; C-index: 0.64 and 0.67).Conclusion: The MTV and the number of SOI are simple imaging markers that provide complementary information to facilitate risk stratification in NSCLC patients scheduled for ICIT.

To evaluate the dosimetry and pharmacokinetics of the novel radiolabelled somatostatin receptor antagonist [177Lu]Lu-satoreotide tetraxetan in patients with advanced neuroendocrine tumours (NETs). This study was part of a phase I/II trial of [177Lu]Lu-satoreotide tetraxetan, administered at a median cumulative activity of 13.0 GBq over three planned cycles (median activity/cycle: 4.5 GBq), in 40 patients with progressive NETs. Organ absorbed doses were monitored at each cycle using patient-specific dosimetry; the cumulative absorbed-dose limits were set at 23.0 Gy for the kidneys and 1.5 Gy for bone marrow. Absorbed dose coefficients (ADCs) were calculated using both patient-specific and model-based dosimetry for some patients. In all evaluated organs, maximum [177Lu]Lu-satoreotide tetraxetan uptake was observed at the first imaging timepoint (4 h after injection), followed by an exponential decrease. Kidneys were the main route of elimination, with a cumulative excretion of 57–66

ObjectivesRadical prostatectomy (RP) is a common intervention in patients with localized prostate cancer (PCa), with nerve-sparing RP recommended to reduce adverse effects on patient quality of life. Accurate pre-operative detection of extraprostatic extension (EPE) remains challenging, often leading to the application of suboptimal treatment. The aim of this study was to enhance pre-operative EPE detection through multimodal data integration using explainable machine learning (ML).MethodsPatients with newly diagnosed PCa who underwent [68Ga]Ga-PSMA-11 PET/MRI and subsequent RP were recruited retrospectively from two time ranges for training, cross-validation, and independent validation. The presence of EPE was measured from post-surgical histopathology and predicted using ML and pre-operative parameters, including PET/MRI-derived features, blood-based markers, histology-derived parameters, and demographic parameters. ML models were subsequently compared with conventional PET/MRI-based image readings.ResultsThe study involved 107 patients, 59 (55%) of whom were affected by EPE according to postoperative findings for the initial training and cross-validation. The ML models demonstrated superior diagnostic performance over conventional PET/MRI image readings, with the explainable boosting machine model achieving an AUC of 0.88 (95% CI 0.87-0.89) during cross-validation and an AUC of 0.88 (95% CI 0.75-0.97) during independent validation. The ML approach integrating invasive features demonstrated better predictive capabilities for EPE compared to visual clinical read-outs (Cross-validation AUC 0.88 versus 0.71, p = 0.02).ConclusionML based on routinely acquired clinical data can significantly improve the pre-operative detection of EPE in PCa patients, potentially enabling more accurate clinical staging and decision-making, thereby improving patient outcomes.Critical relevance statementThis study demonstrates that integrating multimodal data with machine learning significantly improves the pre-operative detection of extraprostatic extension in prostate cancer patients, outperforming conventional imaging methods and potentially leading to more accurate clinical staging and better treatment decisions.Key PointsExtraprostatic extension is an important indicator guiding treatment approaches.Current assessment of extraprostatic extension is difficult and lacks accuracy.Machine learning improves detection of extraprostatic extension using PSMA-PET/MRI and histopathology.

Radical prostatectomy (RP) is a common first-line treatment for patients with localized prostate cancer (PCa), with nerve-sparing techniques recommended to minimize adverse effects on quality of life. Accurate pre-operative detection of extraprostatic extension (EPE) remains difficult, often resulting in suboptimal treatment choices. Machine learning (ML) has the potential to improve pre-operative EPE detection through the integration of multimodal data, but its clinical adoption is hindered by implementation complexities and explainability issues. This study conducted a post-hoc analysis of a prospective clinical trial (NCT02659527) involving patients who underwent [68Ga]Ga-PSMA-11 PET/MRI from 2014 to 2019. Only the clinically used parameters were collected, including PET-derived features, blood-based markers, histology-derived parameters, and patient demographics. ML models were developed using either only non-invasive features or a combination of non-invasive and invasive features for EPE detection and were compared to conventional PET image readings. Among the 77 patients included in the study, 44 (57%) had EPE based on post-surgical findings. The ML models outperformed traditional PET image readings in diagnostic accuracy, with the explainable boosting machine (EBM) model achieving an AUC of 0.88. The ML model incorporating invasive features showed superior predictive ability for EPE compared to visual clinical assessments (AUC 0.88 vs. 0.71, p 0.02), while the difference between the ML model with non-invasive features and clinical assessments was not significant (AUC 0.83 vs. 0.71, p 0.34). Explainable ML models utilizing routinely acquired clinical data can greatly enhance the pre-operative detection of EPE in PCa patients, potentially leading to more accurate clinical staging, better treatment decisions, and improved patient outcomes. Please click on the 'PDF' for the full abstract!

Background The rising global cancer burden has led to an increasing demand for imaging tests such as [F-18]fluorodeoxyglucose ([F-18]FDG)-PET-CT. To aid imaging specialists in dealing with high scan volumes, we aimed to train a deep learning artificial intelligence algorithm to classify [F-18]FDG-PET-CT scans of patients with lymphoma with or without hypermetabolic tumour sites. Methods In this retrospective analysis we collected 16 583 [F-18]FDG-PET-CTs of 5072 patients with lymphoma who had undergone PET-CT before or after treatment at the Memorial Sloa Kettering Cancer Center, New York, NY, USA. Using maximum intensity projection (MIP), three dimensional (3D) PET, and 3D CT data, our ResNet34-based deep learning model (Lymphoma Artificial Reader System [LARS]) for [F-18]FDG-PET-CT binary classification (Deauville 1-3 vs 4-5), was trained on 80% of the dataset, and tested on 20% of this dataset. For external testing, 1000 [F-18]FDG-PET-CTs were obtained from a second centre (Medical University of Vienna, Vienna, Austria). Seven model variants were evaluated, including MIP-based LARS-avg (optimised for accuracy) and LARS-max (optimised for sensitivity), and 3D PET-CT-based LARS-ptct. Following expert curation, areas under the curve (AUCs), accuracies, sensitivities, and specificities were calculated. Findings In the internal test cohort (3325 PET-CTs, 1012 patients), LARS-avg achieved an AUC of 0.949 (95% CI 0.942-0.956), accuracy of 0.890 (0.879-0.901), sensitivity of 0.868 (0.851-0.885), and specificity of 0.913 (0.899-0.925); LARS-max achieved an AUC of 0.949 (0.942-0.956), accuracy of 0.868 (0.858-0.879), sensitivity of 0.909 (0.896-0.924), and specificity of 0.826 (0.808-0.843); and LARS-ptct achieved an AUC of 0.939 (0.930-0.948), accuracy of 0.875 (0.864-0.887), sensitivity of 0.836 (0.817-0.855), and specificity of 0.915 (0.901-0.927). In the external test cohort (1000 PET-CTs, 503 patients), LARS-avg achieved an AUC of 0.953 (0.938-0.966), accuracy of 0.907 (0.888-0.925), sensitivity of 0.874 (0.843-0.904), and specificity of 0.949 (0.921-0.960); LARS-max achieved an AUC of 0.952 (0.937-0.965), accuracy of 0.898 (0.878-0.916), sensitivity of 0.899 (0.871-0.926), and specificity of 0.897 (0.871-0.922); and LARS-ptct achieved an AUC of 0.932 (0.915-0.948), accuracy of 0.870 (0.850-0.891), sensitivity of 0.827 (0.793-0.863), and specificity of 0.913 (0.889-0.937). Interpretation Deep learning accurately distinguishes between [F-18]FDG-PET-CT scans of lymphoma patients with and without hypermetabolic tumour sites. Deep learning might therefore be potentially useful to rule out the presence of metabolically active disease in such patients, or serve as a second reader or decision support tool. Copyright (c) 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Background The diagnosis of cardiac amyloidosis can be established non-invasively by scintigraphy using bone-avid tracers, but visual assessment is subjective and can lead to misdiagnosis. We aimed to develop and validate an artificial intelligence (AI) system for standardised and reliable screening of cardiac amyloidosis-suggestive uptake and assess its prognostic value, using a multinational database of Tc-99m-scintigraphy data across multiple tracers and scanners. Methods In this retrospective, international, multicentre, cross-tracer development and validation study, 16 241 patients with 19 401 scans were included from nine centres: one hospital in Austria (consecutive recruitment Jan 4, 2010, to Aug 19, 2020), five hospital sites in London, UK (consecutive recruitment Oct 1, 2014, to Sept 29, 2022), two centres in China (selected scans from Jan 1, 2021, to Oct 31, 2022), and one centre in Italy (selected scans from Jan 1, 2011, to May 23, 2023). The dataset included all patients referred to whole-body Tc-99m-scintigraphy with an anterior view and all Tc-99m-labelled tracers currently used to identify cardiac amyloidosis-suggestive uptake. Exclusion criteria were image acquisition at less than 2 h (Tc-99m-3,3-diphosphono-1,2-propanodicarboxylic acid, Tc-99m-hydroxymethylene diphosphonate, and Tc-99m-methylene diphosphonate) or less than 1 h (Tc-99m-pyrophosphate) after tracer injection and if patients' imaging and clinical data could not be linked. Ground truth annotation was derived from centralised core-lab consensus reading of at least three independent experts (CN, TT-W, and JN). An AI system for detection of cardiac amyloidosis-associated high-grade cardiac tracer uptake was developed using data from one centre (Austria) and independently validated in the remaining centres. A multicase, multireader study and a medical algorithmic audit were conducted to assess clinician performance compared with AI and to evaluate and correct failure modes. The system's prognostic value in predicting mortality was tested in the consecutively recruited cohorts using cox proportional hazards models for each cohort individually and for the combined cohorts. Findings The prevalence of cases positive for cardiac amyloidosis-suggestive uptake was 142 (2%) of 9176 patients in the Austrian, 125 (2%) of 6763 patients in the UK, 63 (62%) of 102 patients in the Chinese, and 103 (52%) of 200 patients in the Italian cohorts. In the Austrian cohort, cross-validation performance showed an area under the curve (AUC) of 1 center dot 000 (95% CI 1 center dot 000-1 center dot 000). Independent validation yielded AUCs of 0 center dot 997 (0 center dot 993-0 center dot 999) for the UK, 0 center dot 925 (0 center dot 871-0 center dot 971) for the Chinese, and 1 center dot 000 (0 center dot 999-1 center dot 000) for the Italian cohorts. In the multicase multireader study, five physicians disagreed in 22 (11%) of 200 cases (Fleiss' kappa 0 center dot 89), with a mean AUC of 0 center dot 946 (95% CI 0 center dot 924-0 center dot 967), which was inferior to AI (AUC 0 center dot 997 [0 center dot 991-1 center dot 000], p=0 center dot 0040). The medical algorithmic audit demonstrated the system's robustness across demographic factors, tracers, scanners, and centres. The AI's predictions were independently prognostic for overall mortality (adjusted hazard ratio 1 center dot 44 [95% CI 1 center dot 19-1 center dot 74], p<0 center dot 0001). Interpretation AI-based screening of cardiac amyloidosis-suggestive uptake in patients undergoing scintigraphy was reliable, eliminated inter-rater variability, and portended prognostic value, with potential implications for identification, referral, and management pathways. Copyright (c) 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

To improve reproducibility and predictive performance of PET radiomic features in multicentric studies by cycle-consistent generative adversarial network (GAN) harmonization approaches. GAN-harmonization was developed to harmonize whole-body PET scans to perform image style and texture translation between different centers and scanners. GAN-harmonization was evaluated by application to two retrospectively collected open datasets and different tasks. First, GAN-harmonization was performed on a dual-center lung cancer cohort (127 female, 138 male) where the reproducibility of radiomic features in healthy liver tissue was evaluated. Second, GAN-harmonization was applied to a head and neck cancer cohort (43 female, 154 male) acquired from three centers. Here, the clinical impact of GAN-harmonization was analyzed by predicting the development of distant metastases using a logistic regression model incorporating first-order statistics and texture features from baseline 18F-FDG PET before and after harmonization. Image quality remained high (structural similarity: left kidney ≥ 0.800, right kidney ≥ 0.806, liver ≥ 0.780, lung ≥ 0.838, spleen ≥ 0.793, whole-body ≥ 0.832) after image harmonization across all utilized datasets. Using GAN-harmonization, inter-site reproducibility of radiomic features in healthy liver tissue increased at least by ≥ 5 ± 14 ≥ 16 ± 7 ≥ 19 ± 5 ≥ 16 ± 8 ≥ 17 ± 6 ≥ 23 ± 14

Circulating-tumor DNA (ctDNA) and prostate-specific membrane antigen (PSMA) ligand positron-emission tomography (PET) enable minimal-invasive prostate cancer (PCa) detection and survival prognostication. The present study aims to compare their tumor discovery abilities and prognostic values. One hundred thirty men with confirmed PCa (70.5 ± 8.0 years) who underwent [68Ga]Ga-PSMA-11 PET/CT (184.8 ± 19.7 MBq) imaging and plasma sample collection (March 2019–August 2021) were included. Plasma-extracted cell-free DNA was subjected to whole-genome-based ctDNA analysis. PSMA-positive tumor lesions were delineated and their quantitative parameters extracted. ctDNA and PSMA PET/CT discovery rates were compared, and the prognostic value for overall survival (OS) was evaluated. PSMA PET discovery rates according to castration status and PSA ranges did differ significantly (P = 0.013, P < 0.001), while ctDNA discovery rates did not (P = 0.311, P = 0.123). ctDNA discovery rates differed between localized and metastatic disease (P = 0.013). Correlations between ctDNA concentrations and PSMA-positive tumor volume (PSMA-TV) were significant in all (r = 0.42, P < 0.001) and castration-resistant (r = 0.65, P < 0.001), however not in hormone-sensitive patients (r = 0.15, P = 0.249). PSMA-TV and ctDNA levels were associated with survival outcomes in the Logrank (P < 0.0001, P < 0.0001) and multivariate Cox regression analysis (P = 0.0023, P < 0.0001). These findings suggest that PSMA PET imaging outperforms ctDNA analysis in detecting prostate cancer across the whole spectrum of disease, while both modalities are independently highly prognostic for survival outcomes.

Abstract Background Patient-derived tumour organoids (PDOs) are highly advanced in vitro models for disease modelling, yet they lack vascularisation. To overcome this shortcoming, organoids can be inoculated onto the chorioallantoic membrane (CAM); the highly vascularised, not innervated extraembryonic membrane of fertilised chicken eggs. Therefore, we aimed to (1) establish a CAM patient-derived xenograft (PDX) model based on PDOs generated from the liver metastasis of a colorectal cancer (CRC) patient and (2) to evaluate the translational pipeline (patient – in vitro PDOs – in vivo CAM-PDX) regarding morphology, histopathology, expression of C-X-C chemokine receptor type 4 (CXCR4), and radiotracer uptake patterns. Results The main liver metastasis of the CRC patient exhibited high 2-[18F]FDG uptake and moderate and focal [68Ga]Ga-Pentixafor accumulation in the peripheral part of the metastasis. Inoculation of PDOs derived from this region onto the CAM resulted in large, highly viable, and extensively vascularised xenografts, as demonstrated immunohistochemically and confirmed by high 2-[18F]FDG uptake. The xenografts showed striking histomorphological similarity to the patient’s liver metastasis. The moderate expression of CXCR4 was maintained in ovo and was concordant with the expression levels of the patient’s sample and in vitro PDOs. Following in vitro re-culturing of CAM-PDXs, growth, and [68Ga]Ga-Pentixafor uptake were unaltered compared to PDOs before transplantation onto the CAM. Although [68Ga]Ga-Pentixafor was taken up into CAM-PDXs, the uptake in the baseline and blocking group were comparable and there was only a trend towards blocking. Conclusions We successfully established an in vivo CAM-PDX model based on CRC PDOs. The histomorphological features and target protein expression of the original patient’s tissue were mirrored in the in vitro PDOs, and particularly in the in vivo CAM-PDXs. The [68Ga]Ga-Pentixafor uptake patterns were comparable between in vitro, in ovo and clinical data and 2-[18F]FDG was avidly taken up in the patient’s liver metastasis and CAM-PDXs. We thus propose the CAM-PDX model as an alternative in vivo model with promising translational value for CRC patients. Graphical Abstract