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BackgroundSomatostatin-receptor (SSTR)-targeted PET/CT provides important clinical information in addition to standard imaging in meningioma patients. [18F]SiTATE is a novel, 18F-labeled SSTR-targeting peptide with superior imaging properties according to preliminary data. We provide the first [18F]SiTATE PET/CT data of a large cohort of meningioma patients.MethodsPatients with known or suspected meningioma undergoing [18F]SiTATE PET/CT were included. Uptake intensity (SUV) of meningiomas, non-meningioma lesions, and healthy organs were assessed using a 50% isocontour volume of interest (VOI) or a spherical VOI, respectively. Also, trans-osseous extension on PET/CT was assessed.ResultsA total of 107 patients with 117 [18F]SiTATE PET/CT scans were included. Overall, 231 meningioma lesions and 61 non-meningioma lesions (e.g., post-therapeutic changes) were analyzed. Physiological uptake was lowest in healthy brain tissue, followed by bone marrow, parotid, and pituitary (SUVmean 0.06 ± 0.04 vs. 1.4 ± 0.9 vs. 1.6 ± 1.0 vs. 9.8 ± 4.6; p < 0.001). Meningiomas showed significantly higher uptake than non-meningioma lesions (SUVmax 11.6 ± 10.6 vs. 4.0 ± 3.3, p < 0.001). Meningiomas showed significantly higher uptake than non-meningioma lesions (SUVmax 11.6±10.6 vs. 4.0±3.3, p<0.001). 93/231 (40.3%) meningiomas showed partial trans-osseous extension and 34/231 (14.7%) predominant intra-osseous extension. 59/231 (25.6%) meningioma lesions found on PET/CT had not been reported on previous standard imaging. ConclusionThis is the first PET/CT study using an 18F-labeled SSTR-ligand in meningioma patients: [18F]SiTATE provides extraordinary contrast in meningioma compared to healthy tissue and non-meningioma lesions, which leads to a high detection rate of so far unknown meningioma sites and osseous involvement. Having in mind the advantageous logistic features of 18F-labeled compared to 68Ga-labeled compounds (e.g., longer half-life and large-badge production), [18F]SiTATE has the potential to foster a widespread use of SSTR-targeted imaging in neuro-oncology.

Objectives The recently proposed standardized reporting and data system for somatostatin receptor (SSTR)–targeted PET/CT SSTR-RADS 1.0 showed promising first results in the assessment of diagnosis and treatment planning with peptide receptor radionuclide therapy (PRRT) in neuroendocrine tumors (NET). This study aimed to determine the intra- and interreader agreement of SSTR-RADS 1.0. Methods SSTR-PET/CT scans of 100 patients were independently evaluated by 4 readers with different levels of expertise according to the SSTR-RADS 1.0 criteria at 2 time points within 6 weeks. For each scan, a maximum of five target lesions were freely chosen by each reader (not more than three lesions per organ) and stratified according to the SSTR-RADS 1.0 criteria. Overall scan score and binary decision on PRRT were assessed. Intra- and interreader agreement was determined using the intraclass correlation coefficient (ICC). Results Interreader agreement using SSTR-RADS 1.0 for identical target lesions (ICC ≥ 0.91) and overall scan score (ICC ≥ 0.93) was excellent. The decision to state “functional imaging fulfills requirements for PRRT and qualifies patient as potential candidate for PRRT” also demonstrated excellent agreement among all readers (ICC ≥ 0.86). Intrareader agreement was excellent even among different experience levels when comparing target lesion–based scores (ICC ≥ 0.98), overall scan score (ICC ≥ 0.93), and decision for PRRT (ICC ≥ 0.88). Conclusion SSTR-RADS 1.0 represents a highly reproducible and accurate system for stratifying SSTR-targeted PET/CT scans with high intra- and interreader agreement. The system is a promising approach to standardize the diagnosis and treatment planning in NET patients. Key Points • SSTR-RADS 1.0 offers high reproducibility and accuracy. • SSTR-RADS 1.0 is a promising method to standardize diagnosis and treatment planning for patients with NET.

Purpose Response assessment in the treatment of metastatic sarcoma primarily depends on imaging, as no established clinical or serological biomarkers reliably predict survival outcomes. This study evaluates the utility of Response Evaluation Criteria in Solid Tumors (RECIST v1.1) in predicting overall survival (OS) in sarcoma patients with pulmonary metastases. Methods We selected consecutive study subjects from a prospective registry based on the following criteria: (1) available CT imaging at first diagnosis of pulmonary metastases from sarcoma, (2) available follow-up CT imaging within 16 weeks of systemic therapy initiation, (3) documentation of OS. Volumetric segmentation of up to 5 lung metastases was performed over time. Progressive disease (PD) was defined as increase of the unidimensional sum of lesions ≥ 20% or appearance of new metastases according to RECIST v1.1. Kaplan-Meier survival analyses were performed. P values < 0.05 were considered statistically significant. Results Ninety-two patients were included (median age: 58 years; 50% female). Average time of follow-up CT was 67 days after baseline imaging. Patients with PD on first follow-up imaging ( n  = 24; 26%) showed significantly shorter OS (13.9 months vs. 29.3 months; p  = 0.014). The unidimensional growth threshold of 20% proposed by RECIST did not stratify OS (14.6 months vs. 26.8 months, p  = 0.221). The appearance of new metastases ( n  = 16; 17%) indicated significantly shorter OS (7.8 months vs. 27.0 months; p  < 0.001) and was frequently observed even in patients with decreasing size of existing metastases ( n  = 7; 8%). Conclusion Imaging progression patterns of pulmonary metastatic sarcoma demonstrate distinct associations with OS, highlighting the need for sarcoma-specific adaptations to established response criteria.

Background Pseudoprogression (PsPD) is a rare response pattern to immune checkpoint inhibitor (ICI) therapy in oncology. This study aims to reveal imaging features of PsPD, and their association to other relevant findings. Methods Patients with PsPD who had at least three consecutive cross-sectional imaging studies at our comprehensive cancer center were retrospectively analyzed. Treatment response was assessed according to immune Response Evaluation Criteria in Solid Tumors (iRECIST). PsPD was defined as the occurrence of immune unconfirmed progressive disease (iUPD) without follow-up confirmation. Target lesions (TL), non-target lesions (NTL), new lesions (NL) were analyzed over time. Tumor markers and immune-related adverse events (irAE) were correlated. Results Thirty-two patients were included (mean age: 66.7 ± 13.6 years, 21.9% female) with mean baseline STL of 69.7 mm ± 55.6 mm. PsPD was observed in twenty-six patients (81.3%) at FU1, and no cases occurred after FU4. Patients with iUPD exhibited the following: TL increase in twelve patients, (37.5%), NTL increase in seven patients (21.9%), NL appearance in six patients (18.8%), and combinations thereof in four patients (12.5%). The mean and maximum increase for first iUPD in sum of TL was 19.8 and 96.8 mm (+ 700.8%). The mean and maximum decrease in sum of TL between iUPD and consecutive follow-up was − 19.1 mm and − 114.8 mm (-60.9%) respectively. The mean and maximum sum of new TL at first iUPD timepoint were 7.6 and 82.0 mm respectively. In two patients (10.5%), tumor-specific serologic markers were elevated at first iUPD, while the rest were stable or decreased among the other PsPD cases (89.5%). In fourteen patients (43.8%), irAE were observed. Conclusions PsPD occurred most frequently at FU1 after initiation of ICI treatment. The two most prevalent reasons for PsPD were TL und NTL progression, with an increase in TL diameter commonly below + 100%. In few cases, PsPD was observed even if tumor markers were rising compared to baseline. Our findings also suggest a correlation between PsPD and irAE. These findings may guide decision-making of ICI continuation in suspected PsPD.

Objectives CD19-specific chimeric antigen receptor T-cell therapy (CART) has emerged as effective treatment for relapsed or refractory (r/r) lymphoma. The maximum distance (Dmax) of lymphoma lesions holds potential as prognostic imaging biomarker in lymphoma treated with conventional therapies, but evidence in the context of CART remains scarce and further studies are needed to clarify its clinical relevance. We evaluated Dmax at baseline imaging as a potential prognostic tool for assessment of metabolic and overall response, progression-free survival (PFS) and overall survival (OS). Material & methods Consecutive r/r lymphoma patients with (PET/)CT imaging at baseline (BL) before lymphodepletion and subsequent CAR T-cell transfusion were included. Dmax was measured in cm at BL. Patients were divided by tertiles into three equal sized groups according to Dmax. Ann Arbor stages were calculated at baseline and the sum of product diameters (SPD) was used to represent tumor burden (TB). Overall response according to Lugano criteria and the Deauville score were determined at day 90 PET/CT imaging. Results Thirty-nine patients met the inclusion criteria. Median Dmax was 40.0 cm (IQR: 16.4–70.3 cm) at BL. Median TB decreased from BL with 4,095 mm 2 to 770 mm 2 at FU imaging. Median TB at BL was significantly higher in the Dmax intermediate and high group compared to the Dmax low group ( p  = 0.005) with 7,222 mm 2 (IQR: 3,355–11,941 mm 2 ), 4,649 mm 2 (IQR: 2,376–10,406 mm 2 ) and 1,739 mm 2 (IQR: 715–7,402 mm 2 ), respectively. Dmax intermediate and high group showed significantly higher Ann Arbor stages ( p  < 0.001). The survival analysis revealed a significantly ( p  = 0.030) shorter PFS in the Dmax high group compared to the other patients (91 vs. 364 days), but no relevant differences in OS ( p  = 0.151). Conclusions Patients with high Dmax showed a shorter PFS, but no significant differences in OS. Dmax is a useful parameter for characterizing tumor dissemination, which could also be incorporated into scores due to its interval scale.

Recent immunotherapeutic approaches have evolved as powerful treatment options with high anti-tumour responses involving the patient’s own immune system. Passive immunotherapy applies agents that enhance existing anti-tumour responses, such as antibodies against immune checkpoints. Active immunotherapy uses agents that direct the immune system to attack tumour cells by targeting tumour antigens. Active cellular-based therapies are on the rise, most notably chimeric antigen receptor T cell therapy, which redirects patient-derived T cells against tumour antigens. Approved treatments are available for a variety of solid malignancies including melanoma, lung cancer and haematologic diseases. These novel immune-related therapeutic approaches can be accompanied by new patterns of response and progression and immune-related side-effects that challenge established imaging-based response assessment criteria, such as Response Evaluation Criteria in Solid tumours (RECIST) 1.1. Hence, new criteria have been developed. Beyond morphological information of computed tomography (CT) and magnetic resonance imaging, positron emission tomography (PET) emerges as a comprehensive imaging modality by assessing (patho-)physiological processes such as glucose metabolism, which enables more comprehensive response assessment in oncological patients. We review the current concepts of response assessment to immunotherapy with particular emphasis on hybrid imaging with 18 F-FDG-PET/CT and aims at describing future trends of immunotherapy and additional aspects of molecular imaging within the field of immunotherapy.

This study aimed to evaluate the predictive and monitoring role of somatostatin receptor (SSTR) positron emission tomography-computed tomography (PET/CT) and clinical parameters in patients with neuroendocrine liver metastases (NELM) from pancreatic neuroendocrine tumors (pNET) receiving capecitabine and temozolomide (CAPTEM). This retrospective study included twenty-two patients with pNET and NELM receiving CAPTEM who underwent pre- and post-therapeutic Ga-DOTATATE/-TOC PET/CT. Imaging (including standardized uptake value [SUV] of target lesions [NELM and pNET], normal spleen and liver) and clinical (Chromogranin A [CgA], Ki-67) parameters were assessed. Treatment outcome was evaluated as response according to RECIST 1.1, progression free survival (PFS) and overall survival (OS). The median PFS (mPFS) was 7 months. Responders had a significantly longer mPFS compared to non-responders (10 . 4 months p = 0.022). Median OS (mOS) was 33 months (mOS: responders = 80 months, non-responders = 24 months p = 0.182). Baseline imaging showed higher SUV in responders, including absolute SUV, tumor-to-spleen (T/S), and tumor-to-liver (T/L) ratios (p < 0.02). All SUV parameters changed only in the responders during follow-up. Univariable Cox regression analysis identified baseline Tmax/Smean ratio and percentage change in size of pNETs as significant factors associated with PFS. A baseline Tmax/Smean ratio < 1.5 was associated with a shorter mPFS (10 . 4 months, (p < 0.05)). Prognostic factors for OS included age, percentage change in CgA and in T/S ratios in univariable Cox regression. SSTR-PET/CT can be useful for predicting response and survival outcomes in pNET patients receiving CAPTEM: Higher baseline SUV values, particularly Tmax/Smean ratios of liver metastases were associated with better response and prolonged PFS.

Background To investigate a non-invasive radiomics-based machine learning algorithm to differentiate upper urinary tract urothelial carcinoma (UTUC) from renal cell carcinoma (RCC) prior to surgical intervention. Methods Preoperative computed tomography venous-phase datasets from patients that underwent procedures for histopathologically confirmed UTUC or RCC were retrospectively analyzed. Tumor segmentation was performed manually, and radiomic features were extracted according to the International Image Biomarker Standardization Initiative . Features were normalized using z-scores, and a predictive model was developed using the least absolute shrinkage and selection operator (LASSO). The dataset was split into a training cohort (70%) and a test cohort (30%). Results A total of 236 patients [30.5% female, median age 70.5 years (IQR: 59.5–77), median tumor size 5.8 cm (range: 4.1–8.2 cm)] were included. For differentiating UTUC from RCC, the model achieved a sensitivity of 88.4% and specificity of 81% (AUC: 0.93, radiomics score cutoff: 0.467) in the training cohort. In the validation cohort, the sensitivity was 80.6% and specificity 80% (AUC: 0.87, radiomics score cutoff: 0.601). Subgroup analysis of the validation cohort demonstrated robust performance, particularly in distinguishing clear cell RCC from high-grade UTUC (sensitivity: 84%, specificity: 73.1%, AUC: 0.84) and high-grade from low-grade UTUC (sensitivity: 57.7%, specificity: 88.9%, AUC: 0.68). Limitations include the need for independent validation in future randomized controlled trials (RCTs). Conclusions Machine learning-based radiomics models can reliably differentiate between RCC and UTUC in preoperative CT imaging. With a suggested performance benefit compared to conventional imaging, this technology might be added to the current preoperative diagnostic workflow. Clinical trial number Local ethics committee no. 20–179

Purpose To assess the eligibility of patients with advanced or recurrent solid malignancies presented to a molecular tumor board (MTB) at a large precision oncology center for inclusion in trials with the endpoints objective response rate (ORR) or duration of response (DOR) based on Response Evaluation Criteria in Solid Tumors (RECIST version 1.1). Methods Prospective patients with available imaging at the time of presentation in the MTB were included. Imaging data was reviewed for objectifiable measurable disease (MD) according to RECIST v1.1. Additionally, we evaluated the patients with MD for representativeness of the identified measurable lesion(s) in relation to the overall tumor burden. Results 262 patients with different solid malignancies were included. 177 patients (68%) had MD and 85 (32%) had non-measurable disease (NMD) at the time point of MTB presentation in accordance with RECIST v1.1. MD was not representative of the overall tumor burden in eleven patients (6%). The main reasons for NMD were lesions with longest diameter shorter than 10 mm (22%) and non-measurable peritoneal carcinomatosis (18%). Colorectal cancer and malignant melanoma displayed the highest rates of MD (> 75%). In contrast, gastric cancer, head and neck malignancies, and ovarian carcinoma had the lowest rates of MD (< 55%). In case of MD, the measurable lesions were representative of the overall tumor burden in the vast majority of cases (94%). Conclusion Approximately one third of cancer patients with advanced solid malignancies are not eligible for treatment response assessment in trials with endpoints ORR or DOR at the time of MTB presentation. The rate of patients eligible for trials with imaging endpoints differs significantly based on the underlying malignancy and should be taken under consideration during the planning of new precision oncology trials.