Body composition impacts outcome of bronchoscopic lung volume reduction in patients with severe emphysema: a fully automated CT-based analysis.

Chronic Obstructive Pulmonary Disease (COPD) is characterized by progressive and irreversible airflow limitation, with individual body composition influencing disease severity. Severe emphysema worsens symptoms through hyperinflation, which can be relieved by bronchoscopic lung volume reduction (BLVR). To investigate how body composition, assessed through CT scans, impacts outcomes in emphysema patients undergoing BLVR. Fully automated CT-based body composition analysis (BCA) was performed in patients with end-stage emphysema receiving BLVR with valves. Post-interventional muscle and adipose tissues were quantified, body size-adjusted, and compared to baseline parameters. Between January 2015 and December 2022, 300 patients with severe emphysema underwent endobronchial valve treatment. Significant improvements were seen in outcome parameters, which were defined as changes in pulmonary function, physical performance, and quality of life (QoL) post-treatment. Muscle volume remained stable (1.632 vs. 1.635 for muscle bone adjusted ratio (BAR) at baseline and after 6 months respectively), while bone adjusted adipose tissue volumes, especially total and pericardial adipose tissue, showed significant increase (2.86 vs. 3.00 and 0.16 vs. 0.17, respectively). Moderate to strong correlations between bone adjusted muscle volume and weaker correlations between adipose tissue volumes and outcome parameters (pulmonary function, QoL and physical performance) were observed. Particularly after 6-month, bone adjusted muscle volume changes positively corresponded to improved outcomes (ΔForced expiratory volume in 1 s [FEV1], r = 0.440; ΔInspiratory vital capacity [IVC], r = 0.397; Δ6Minute walking distance [6MWD], r = 0.509 and ΔCOPD assessment test [CAT], r = -0.324; all p < 0.001). Group stratification by bone adjusted muscle volume changes revealed that groups with substantial muscle gain experienced a greater clinical benefit in pulmonary function improvements, QoL and physical performance (ΔFEV1%, 5.5 vs. 39.5; ΔIVC%, 4.3 vs. 28.4; Δ6MWDm, 14 vs. 110; ΔCATpts, -2 vs. -3.5 for groups with ΔMuscle, BAR% < -10 vs. > 10, respectively). BCA results among patients divided by the minimal clinically important difference for forced expiratory volume of the first second (FEV1) showed significant differences in bone-adjusted muscle and intramuscular adipose tissue (IMAT) volumes and their respective changes after 6 months (ΔMuscle, BAR% -5 vs. 3.4 and ΔIMAT, BAR% -0.62 vs. 0.60 for groups with ΔFEV1 ≤ 100 mL vs > 100 mL). Altered body composition, especially increased muscle volume, is associated with functional improvements in BLVR-treated patients.

Elexacaftor/tezacaftor/ivacaftor influences body composition in adults with cystic fibrosis: a fully automated CT-based analysis.

A poor nutritional status is associated with worse pulmonary function and survival in people with cystic fibrosis (pwCF). CF transmembrane conductance regulator modulators can improve pulmonary function and body weight, but more data is needed to evaluate its effects on body composition. In this retrospective study, a pre-trained deep-learning network was used to perform a fully automated body composition analysis on chest CTs from 66 adult pwCF before and after receiving elexacaftor/tezacaftor/ivacaftor (ETI) therapy. Muscle and adipose tissues were quantified and divided by bone volume to obtain body size-adjusted ratios. After receiving ETI therapy, marked increases were observed in all adipose tissue ratios among pwCF, including the total adipose tissue ratio (+ 46.21%, p < 0.001). In contrast, only small, but statistically significant increases of the muscle ratio were measured in the overall study population (+ 1.63%, p = 0.008). Study participants who were initially categorized as underweight experienced more pronounced effects on total adipose tissue ratio (p = 0.002), while gains in muscle ratio were equally distributed across BMI categories (p = 0.832). Our findings suggest that ETI therapy primarily affects adipose tissues, not muscle tissue, in adults with CF. These effects are primarily observed among pwCF who were initially underweight. Our findings may have implications for the future nutritional management of pwCF.

Fully automated MR-based virtual biopsy of primary CNS lymphomas.

Background: Primary central nervous system lymphomas (PCNSL) pose a challenge as they may mimic gliomas on magnetic resonance imaging (MRI) imaging, compelling precise differentiation for appropriate treatment. This study focuses on developing an automated MRI-based workflow to distinguish between PCNSL and gliomas. Methods: MRI examinations of 240 therapy-naive patients (141 males and 99 females, mean age: 55.16 years) with cerebral gliomas and PCNSLs (216 gliomas and 24 PCNSLs), each comprising a non-contrast T1-weighted, fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted sequence were included in the study. HD-GLIO, a pre-trained segmentation network, was used to generate segmentations automatically. To validate the segmentation efficiency, 237 manual segmentations were prepared (213 gliomas and 24 PCNSLs). Subsequently, radiomics features were extracted following feature selection and training of an XGBoost algorithm for classification. Results: The segmentation models for gliomas and PCNSLs achieved a mean Sørensen-Dice coefficient of 0.82 and 0.80 for whole tumors, respectively. Three classification models were developed in this study to differentiate gliomas from PCNSLs. The first model differentiated PCNSLs from gliomas, with an area under the curve (AUC) of 0.99 (F1-score: 0.75). The second model discriminated between high-grade gliomas and PCNSLs with an AUC of 0.91 (F1-score: 0.6), and the third model differentiated between low-grade gliomas and PCNSLs with an AUC of 0.95 (F1-score: 0.89). Conclusions: This study serves as a pilot investigation presenting an automated virtual biopsy workflow that distinguishes PCNSLs from cerebral gliomas. Prior to clinical use, it is necessary to validate the results in a prospective multicenter setting with a larger number of PCNSL patients.

AI-Assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Adult Trauma Patients.

Background: This study aimed to evaluate the impact of an AI-assisted fracture detection program on radiology residents' performance in pediatric and adult trauma patients and assess its implications for residency training. Methods: This study, conducted retrospectively, included 200 radiographs from participants aged 1 to 95 years (mean age: 40.7 ± 24.5 years), encompassing various body regions. Among these, 50% (100/200) displayed at least one fracture, totaling one hundred thirty-five fractures, assessed by four radiology residents with different experience levels. A machine learning algorithm was employed for fracture detection, and the ground truth was established by consensus among two experienced senior radiologists. Fracture detection accuracy, reporting time, and confidence were evaluated with and without AI support. Results: Radiology residents' sensitivity for fracture detection improved significantly with AI support (58% without AI vs. 77% with AI, p < 0.001), while specificity showed minor improvements (77% without AI vs. 79% with AI, p = 0.0653). AI stand-alone performance achieved a sensitivity of 93% with a specificity of 77%. AI support for fracture detection significantly reduced interpretation time for radiology residents by an average of approximately 2.6 s (p = 0.0156) and increased resident confidence in the findings (p = 0.0013). Conclusion: AI support significantly enhanced fracture detection sensitivity among radiology residents, particularly benefiting less experienced radiologists. It does not compromise specificity and reduces interpretation time, contributing to improved efficiency. This study underscores AI's potential in radiology, emphasizing its role in training and interpretation improvement.

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer.

PURPOSE: The aim of this study was to investigate the potential of multiparametric 18F-FDG PET/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients. MATERIALS AND METHODS: A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric 18F-FDG PET/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language. RESULTS: Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91 % and specificity of 92 %. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83 %, specificity of 67 % and an AUC of 0.82. CONCLUSION: M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET/MRI data. KEY POINTS: · Radiomics analysis based on multiparametric PET/MRI enables prediction of the metastatic status of cervical cancers. · Prediction of M-stage is superior to N-stage. · Multiparametric PET/MRI displays a valuable platform for radiomics analyses .

Delayed 68Ga-FAPI-46 PET/MR imaging confirms ongoing fibroblast activation in patients after acute myocardial infarction.

Purpose of the Report: Combined cardiac 68Ga-Fibroblast-Activation Protein-alpha inhibitor (FAPI) positron-emission tomography (PET) and cardiac magnetic resonance imaging (MRI) constitute a novel diagnostic tool in patients for the assessment of myocardial damage after an acute myocardial infarction (AMI). Purpose of this pilot study was to evaluate simultaneous Ga-68-FAPI-46-PET/MR imaging in the delayed phase after AMI. Material and Methods: Eleven patients underwent hybrid 68Ga-FAPI-46 PET/MRI post AMI. Standardized uptake values and fibroblast activation volume (FAV) were calculated and correlated with serum biomarkers and MRI parameters. Results: Significant 68Ga-FAPI-46 uptake could be demonstrated in 11 (100 %) patients after a mean period of 30.9 ± 22.0 days. FAV significantly exceeded the infarction size in MRI and showed a good correlation to MRI parameters as well as to serum biomarkers of myocardial damage. Conclusions: 68Ga-FAPI-46 PET/MRI offers molecular and morphological imaging of affected myocardium after AMI. This study demonstrates ongoing fibroblast activation in a delayed phase after AMI and generates hypotheses for future studies while aiming for a better understanding of myocardial remodeling following ischemic tissue damage.

Impact of CT and MRI in the diagnostic workup of malignant triton tumour-a monocentric analysis and review of the literature.

OBJECTIVES: Malignant triton tumours (MTTs) are rare but aggressive subtypes of malignant peripheral nerve sheath tumours (MPNSTs) with a high recurrence rate and 5-year survival of 14%. Systematic imaging data on MTTs are scarce and mainly based on single case reports. Therefore, we aimed to identify typical CT and MRI features to improve early diagnosis rates of this uncommon entity. METHODS: A systematic review on literature published until December 2022 on imaging characteristics of MTTs was performed. Based on that, we conducted a retrospective, monocentric analysis of patients with histopathologically proven MTTs from our department. Explorative data analysis was performed. RESULTS: Initially, 29 studies on 34 patients (31.42 ± 22.6 years, 12 female) were evaluated: Literature described primary MTTs as huge, lobulated tumours (108 ± 99.3 mm) with central necrosis (56% [19/34]), low T1w (81% [17/21]), high T2w signal (90% [19/21]) and inhomogeneous enhancement on MRI (54% [7/13]). Analysis of 16 patients (48.9 ± 13.8 years; 9 female) from our institution revealed comparable results: primary MTTs showed large, lobulated masses (118 mm ± 64.9) with necrotic areas (92% [11/12]). MRI revealed low T1w (100% [7/7]), high T2w signal (100% [7/7]) and inhomogeneous enhancement (86% [6/7]). Local recurrences and soft-tissue metastases mimicked these features, while nonsoft-tissue metastases appeared unspecific. CONCLUSIONS: MTTs show characteristic features on CT and MRI. However, these do not allow a reliable differentiation between MTTs and other MPNSTs based on imaging alone. Therefore, additional histopathological analysis is required. ADVANCES IN KNOWLEDGE: This largest published systematic analysis on MTT imaging revealed typical but unspecific imaging features that do not allow a reliable, imaging-based differentiation between MTTs and other MPNSTs. Hence, additional histopathological analysis remains essential.

The importance of educational tools and a new software solution for visualizing and quantifying report correction in radiology training.

A novel software, DiffTool, was developed in-house to keep track of changes made by board-certified radiologists to preliminary reports created by residents and evaluate its impact on radiological hands-on training. Before (t0) and after (t2-4) the deployment of the software, 18 residents (median age: 29 years; 33% female) completed a standardized questionnaire on professional training. At t2-4 the participants were also requested to respond to three additional questions to evaluate the software. Responses were recorded via a six-point Likert scale ranging from 1 ("strongly agree") to 6 ("strongly disagree"). Prior to the release of the software, 39% (7/18) of the residents strongly agreed with the statement that they manually tracked changes made by board-certified radiologists to each of their radiological reports while 61% were less inclined to agree with that statement. At t2-4, 61% (11/18) stated that they used DiffTool to track differences. Furthermore, we observed an increase from 33% (6/18) to 44% (8/18) of residents who agreed to the statement "I profit from every corrected report". The DiffTool was well accepted among residents with a regular user base of 72% (13/18), while 78% (14/18) considered it a relevant improvement to their training. The results of this study demonstrate the importance of providing a time-efficient way to analyze changes made to preliminary reports as an additive for professional training.

Fibroblast Activation Protein α-Directed Imaging and Therapy of Solitary Fibrous Tumor.

Fibroblast activation protein α (FAPα) is expressed at high levels in several types of tumors. Here, we report the expression pattern of FAPα in solitary fibrous tumor (SFT) and its potential use as a radiotheranostic target. Methods: We analyzed FAPα messenger RNA and protein expression in biopsy samples from SFT patients using immunohistochemistry and multiplexed immunofluorescence. Tracer uptake and detection efficacy were assessed in patients undergoing clinical 68Ga-FAPα inhibitor (FAPI)-46 PET,18F-FDG PET, and contrast-enhanced CT. 90Y-FAPI-46 radioligand therapy was offered to eligible patients with progressive SFT. Results: Among 813 patients and 126 tumor entities analyzed from the prospective observational MASTER program of the German Cancer Consortium, SFT (n = 34) had the highest median FAPα messenger RNA expression. Protein expression was confirmed in tumor biopsies from 29 of 38 SFT patients (76%) in an independent cohort. Most cases showed intermediate to high FAPα expression by immunohistochemistry (24/38 samples, 63%), which was located primarily on the tumor cell surface. Nineteen patients who underwent 68Ga-FAPI-46 PET imaging demonstrated significantly increased tumor uptake, with an SUVmax of 13.2 (interquartile range [IQR], 10.2), and an improved mean detection efficacy of 94.5% (SEM, 4.2%), as compared with 18F-FDG PET (SUVmax, 3.2 [IQR, 3.1]; detection efficacy, 77.3% [SEM, 5.5%]). Eleven patients received a total of 34 cycles (median, 3 cycles [IQR, 2 cycles]) of 90Y-FAPI-46 radioligand therapy, which resulted in disease control in 9 patients (82%). Median progression-free survival was 227 d (IQR, 220 d). Conclusion: FAPα is highly expressed by SFT and may serve as a target for imaging and therapy. Further studies are warranted to define the role of FAPα-directed theranostics in the care of SFT patients.

Systematic evaluation of human soft tissue attenuation correction in whole-body PET/MR: Implications from PET/CT for optimization of MR-based AC in patients with normal lung tissue.

BACKGROUND: Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons. PURPOSE: The overall quality of magnetic resonance (MR)-based AC in whole-body PET/MRI was evaluated in direct comparison to computed tomography (CT)-based AC serving as reference. The quantitative impact of isolated tissue classes in the MR-AC was systematically investigated to identify potential optimization needs and strategies. METHODS: Data of n = 60 whole-body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC-models based on the CT data for each patient, simulating variations of MR-AC. The original continuous CT-AC (CT-org) is referred to as reference. A pseudo MR-AC (CT-mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR-AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated. RESULTS: The overall performance of MR-AC is in good agreement compared to CT-AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT-AC (myocardium -15%, bone tissue -14%, and lungs ±20%). Using single-valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT-AC to < ±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over- or undercorrections in PET signal compared to CT-AC (±5%). CONCLUSIONS: The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment-based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT-AC reference.

Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

OBJECTIVES: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose. METHODS: On a retrospective training cohort of 1507 CT localizers randomly selected from calcium scoring and angiography scans and acquired between 2010 and 2017, optimized scan ranges were delimited by two radiologists in consensus. A neural network was trained to reproduce the scan ranges and was tested on two randomly selected and independent validation cohorts: an internal cohort of 233 CT localizers (January 2018-June 2020) and an external cohort from a nearby hospital of 298 CT localizers (July 2020-December 2020). Localizers where a bypass surgery was visible were excluded. The effective radiation dose to the patient was simulated using a Monte Carlo simulation. Scan ranges of radiographers, radiologists, and the network were compared using an equivalence test; likewise, the reduction in effective dose was tested using a superior test. RESULTS: The network replicated the radiologists' scan ranges with a Dice score of 96.5 ± 0.02 (p < 0.001, indicating equivalence). The generated scan ranges resulted in an effective dose reduction of 10.0% (p = 0.002) in the internal cohort and 12.6% (p < 0.001) in the external cohort compared to the scan ranges delimited by radiographers in clinical routine. CONCLUSIONS: Automatic delimitation of the scan range can result in a radiation dose reduction to the patient. CLINICAL RELEVANCE STATEMENT: Fully automated delimitation of the scan range using a deep neural network enables a significant reduction in radiation exposure during CT coronary angiography compared to manual examination planning. It can also reduce the workload of the radiographers. KEY POINTS: • Scan range delimitation for coronary computed tomography angiography could be performed with high accuracy by a deep neural network. • Automated scan ranges showed a high agreement of 96.5% with the scan ranges of radiologists. • Using a Monte Carlo simulation, automated scan ranges reduced the effective dose to the patient by up to 12.6% (0.9 mSv) compared to the scan ranges of radiographers in clinical routine.

Prediction of therapy response of breast cancer patients with machine learning based on clinical data and imaging data derived from breast [18F]FDG-PET/MRI.

PURPOSE: To evaluate if a machine learning prediction model based on clinical and easily assessable imaging features derived from baseline breast [18F]FDG-PET/MRI staging can predict pathologic complete response (pCR) in patients with newly diagnosed breast cancer prior to neoadjuvant system therapy (NAST). METHODS: Altogether 143 women with newly diagnosed breast cancer (54 ± 12 years) were retrospectively enrolled. All women underwent a breast [18F]FDG-PET/MRI, a histopathological workup of their breast cancer lesions and evaluation of clinical data. Fifty-six features derived from positron emission tomography (PET), magnetic resonance imaging (MRI), sociodemographic / anthropometric, histopathologic as well as clinical data were generated and used as input for an extreme Gradient Boosting model (XGBoost) to predict pCR. The model was evaluated in a five-fold nested-cross-validation incorporating independent hyper-parameter tuning within the inner loops to reduce the risk of overoptimistic estimations. Diagnostic model-performance was assessed by determining the area under the curve of the receiver operating characteristics curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Furthermore, feature importances of the XGBoost model were evaluated to assess which features contributed most to distinguish between pCR and non-pCR. RESULTS: Nested-cross-validation yielded a mean ROC-AUC of 80.4 ± 6.0% for prediction of pCR. Mean sensitivity, specificity, PPV, and NPV of 54.5 ± 21.3%, 83.6 ± 4.2%, 63.6 ± 8.5%, and 77.6 ± 8.1% could be achieved. Histopathological data were the most important features for classification of the XGBoost model followed by PET, MRI, and sociodemographic/anthropometric features. CONCLUSION: The evaluated multi-source XGBoost model shows promising results for reliably predicting pathological complete response in breast cancer patients prior to NAST. However, yielded performance is yet insufficient to be implemented in the clinical decision-making process.

What Role Does PET/MRI Play in Musculoskeletal Disorders?

Musculoskeletal disorders of nononcological origin are one of the most frequent reasons for consultation. Patients suffering from musculoskeletal disorders also consult more than once for the same reason. This results in multiple clinical follow-ups after several radiological and serum examinations, the main ones including X-rays targeting the painful anatomical region and inflammatory serum parameters. As part of their work up, patients suffering from musculoskeletal disorders often require multisequence, multi-parameter MRI. PET/MRI is a promising imaging modality for their diagnosis, with the added advantage of being able to be performed in a single visit. PET/MRI is particularly useful for diagnosing osteomyelitis, spondylodiscitis, arthritis, many pediatric pathologies, and a wide range of other musculoskeletal pathologies. PET/MRI is already used to diagnose malignant bone tumors such as osteosarcoma. However, current knowledge of the indications for PET/MRI in nononcological musculoskeletal disorders is based on studies involving only a few patients. This review focuses on the usefulness of PET/MRI for diagnosing nononcological musculoskeletal disorders.

Recurrent prostate cancer: combined role for MRI and PSMA-PET in 68Ga-PSMA-11 PET/MRI.

OBJECTIVES: To investigate the specific strengths of MRI and PET components in 68Ga-PSMA-11 PET/MRI for staging of patients with biochemically recurrent prostate cancer (PCa). METHODS: Patients with biochemical recurrence of PCa and contrast-enhanced whole-body 68Ga-PSMA-11 PET/MRI including a dedicated pelvic multiparametric MRI were included in this retrospective study. Imaging datasets of MRI and PET were evaluated separately regarding local PCa recurrence (Tr), pelvic lymph node metastases (N1), distant lymph node metastases (M1a), bone metastases (M1b), and soft tissue metastases (M1c) according to PROMISE version 1. Data evaluation was performed patient- and region-/lesion-based. Cox regression revealed a PSA of 1.69 ng/mL as a cut-off for subgroup analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were evaluated for each image component. Differences in staging accuracy were assessed using the Wilcoxon and McNemar test. RESULTS: Altogether 102 patients (mean aged 68 ± 8 years, median PSA 1.33 ng/mL) were included. PCa was found in 70/102 (68%) patients. Accuracy of MRI in the detection of Tr, N1, M + , M1a, and M1b was 100%, 79%, 90%, 97%, and 95% for PSA < 1.69 ng/mL and 100%, 87%, 87%, 91%, and 96% for PSA > 1.69 ng/mL. Accuracy of 68Ga-PSMA-11 PET was 93%, 97%, 93%, 98%, and 100% for PSA < 1.69 ng/mL and 87%, 91%, 96%, 100%, and 96% for PSA > 1.69 ng/mL. CONCLUSIONS: Combined assessment of 68Ga-PSMA-11 PET/MRI improves tumor localization in men with biochemical recurrence. The MRI detected local recurrence of PCa more often whereas 68 Ga-PSMA-11 PET detected lymph node metastases more often, especially for PSA < 1.69 ng/mL. CLINICAL RELEVANCE STATEMENT: This study gives a scientific baseline to improve the understanding and reading of 68Ga-PSMA-11 PET/MRI imaging in patients with biochemically recurrent PCa by showing the specific strength of each imaging component. KEY POINTS: • Combining the individual modality strengths of 68Ga-PSMA-11 PET/MRI improves tumor localization in men with biochemical recurrence of prostate cancer. • MRI component of 68 Ga-PSMA-11 PET/MRI shows its strength in detecting local recurrence of prostate cancer, especially at PSA < 1.69 ng/mL. • 68 Ga-PSMA-11 PET component shows its strength in detecting local and distant lymph node metastases, especially at PSA < 1.69 ng/mL.

68Ga-Labeled Fibroblast Activation Protein Inhibitor (68Ga-FAPI) PET for Pancreatic Adenocarcinoma: Data from the 68Ga-FAPI PET Observational Trial.

The fibroblast activation protein (FAP) is highly expressed on carcinoma-associated fibroblasts in the stroma of pancreatic cancer and thus is a promising target for imaging and therapy. Preliminary data on PET imaging with radiolabeled FAP inhibitors (FAPIs) demonstrate superior tumor detection. Here we assess the accuracy of FAP-directed PET in patients with pancreatic cancer. Methods: Of 64 patients with suspected or proven pancreatic cancer, 62 (97%) were included in the data analysis of the 68Ga-FAPI PET observational trial (NCT04571086). All of these patients underwent contrast-enhanced CT, and 38 patients additionally underwent 18F-FDG PET. The primary study endpoint was the association of 68Ga-FAPI PET uptake intensity and histopathologic FAP expression. Secondary endpoints were detection rate, diagnostic performance, interreader reproducibility, and change in management. Datasets were interpreted by 2 masked readers. Results: The primary endpoint was met: The association between 68Ga-FAPI SUVmax and histopathologic FAP expression was significant (Spearman r, 0.48; P = 0.04). For histopathology-validated lesions, 68Ga-FAPI PET showed high sensitivity and positive predictive values (PPVs) on per-patient (sensitivity, 100%; PPV, 96.3%) and per-region (sensitivity, 100%; PPV, 97.0%) bases. In a head-to-head comparison versus 18F-FDG or contrast-enhanced CT, 68Ga-FAPI detected more tumor on a per-lesion (84.7% vs. 46.5% vs. 52.9%), per-patient (97.4% vs. 73.7% vs. 92.1%), or per-region (32.6% vs. 18.8% vs. 23.7%) basis, respectively. 68Ga-FAPI PET readers showed substantial overall agreement on the basis of the Fleiss κ: primary κ, 0.77 (range, 0.66-0.88). Minor and major changes in clinical management occurred in 5 patients (8.4%) after 68Ga-FAPI PET. Conclusion: We confirmed an association of 68Ga-FAPI PET SUVmax and histopathologic FAP expression in pancreatic cancer patients. Additionally, we found high detection rate and diagnostic accuracy, superior to those of 18F-FDG PET/CT. 68Ga-FAPI might become a powerful diagnostic tool for pancreatic cancer work-up.

BOA: A CT-Based Body and Organ Analysis for Radiologists at the Point of Care.

PURPOSE: The study aimed to develop the open-source body and organ analysis (BOA), a comprehensive computed tomography (CT) image segmentation algorithm with a focus on workflow integration. METHODS: The BOA combines 2 segmentation algorithms: body composition analysis (BCA) and TotalSegmentator. The BCA was trained with the nnU-Net framework using a dataset including 300 CT examinations. The CTs were manually annotated with 11 semantic body regions: subcutaneous tissue, muscle, bone, abdominal cavity, thoracic cavity, glands, mediastinum, pericardium, breast implant, brain, and spinal cord. The models were trained using 5-fold cross-validation, and at inference time, an ensemble was used. Afterward, the segmentation efficiency was evaluated on a separate test set comprising 60 CT scans. In a postprocessing step, a tissue segmentation (muscle, subcutaneous adipose tissue, visceral adipose tissue, intermuscular adipose tissue, epicardial adipose tissue, and paracardial adipose tissue) is created by subclassifying the body regions. The BOA combines this algorithm and the open-source segmentation software TotalSegmentator to have an all-in-one comprehensive selection of segmentations. In addition, it integrates into clinical workflows as a DICOM node-triggered service using the open-source Orthanc research PACS (Picture Archiving and Communication System) server to make the automated segmentation algorithms available to clinicians. The BCA model's performance was evaluated using the Sørensen-Dice score. Finally, the segmentations from the 3 different tools (BCA, TotalSegmentator, and BOA) were compared by assessing the overall percentage of the segmented human body on a separate cohort of 150 whole-body CT scans. RESULTS: The results showed that the BCA outperformed the previous publication, achieving a higher Sørensen-Dice score for the previously existing classes, including subcutaneous tissue (0.971 vs 0.962), muscle (0.959 vs 0.933), abdominal cavity (0.983 vs 0.973), thoracic cavity (0.982 vs 0.965), bone (0.961 vs 0.942), and an overall good segmentation efficiency for newly introduced classes: brain (0.985), breast implant (0.943), glands (0.766), mediastinum (0.880), pericardium (0.964), and spinal cord (0.896). All in all, it achieved a 0.935 average Sørensen-Dice score, which is comparable to the one of the TotalSegmentator (0.94). The TotalSegmentator had a mean voxel body coverage of 31% ± 6%, whereas BCA had a coverage of 75% ± 6% and BOA achieved 93% ± 2%. CONCLUSIONS: The open-source BOA merges different segmentation algorithms with a focus on workflow integration through DICOM node integration, offering a comprehensive body segmentation in CT images with a high coverage of the body volume.

Determining body height and weight from thoracic and abdominal CT localizers in pediatric and young adult patients using deep learning.

In this retrospective study, we aimed to predict the body height and weight of pediatric patients using CT localizers, which are overview scans performed before the acquisition of the CT. We trained three commonly used networks (EfficientNetV2-S, ResNet-18, and ResNet-34) on a cohort of 1009 and 1111 CT localizers of pediatric patients with recorded body height and weight (between January 2013 and December 2019) and validated them in an additional cohort of 116 and 127 localizers (acquired in 2020). The best-performing model was then tested in an independent cohort of 203 and 225 CT localizers (acquired between January 2021 and March 2023). In addition, a cohort of 1401 and 1590 localizers from younger adults (acquired between January 2013 and December 2013) was added to the training set to determine if it could improve the overall accuracy. The EfficientNetV2-S using the additional adult cohort performed best with a mean absolute error of 5.58 ± 4.26 cm for height and 4.25 ± 4.28 kg for weight. The relative error was 4.12 ± 4.05% for height and 11.28 ± 12.05% for weight. Our study demonstrated that automated estimation of height and weight in pediatric patients from CT localizers can be performed.

Quantification performance of silicon photomultiplier-based PET for small 18F-, 68Ga- and 124I-avid lesions in the context of radionuclide therapy planning.

PURPOSE: The aim of this study was to investigate conditions for reliable quantification of sub-centimeter lesions with low18F,68Ga, and124I uptake using a silicon photomultiplier-based PET/CT system. METHODS: A small tumor phantom was investigated under challenging but clinically realistic conditions resembling prostate and thyroid cancer lymph node metastases (6 spheres with 3.7-9.7 mm in diameter, 9 different activity concentrations ranging from about 0.25-25 kBq/mL, and a signal-to-background ratio of 20). Radionuclides with different positron branching ratios and prompt gamma coincidence contributions were investigated. Maximum-, contour-, and oversize-based partial volume effect (PVE) correction approaches were applied. Detection and quantification performance were estimated, considering a ±30 % deviation between imaged-derived and true activity concentrations as acceptable. A standard and a prolonged acquisition time and two image reconstruction algorithms (time-of-flight with/without point spread function modelling) were analyzed. Clinical data were evaluated to assess agreement of PVE-correction approaches indicating lesion quantification validity. RESULTS: The smallest 3.7-mm sphere was not visible. If the lesions were clearly observed, quantification was, except for a few cases, acceptable using contour- or oversized-based PVE-corrections. Quantification accuracy did not substantially differ between 18F, 68Ga, and 124I. No systematic differences between the analyzed reconstruction algorithms or shorter and larger acquisition times were observed. In the clinical evaluation of 20 lesions, an excellent statistical agreement between oversize- and contour-based PVE-corrections was observed. CONCLUSIONS: At the lower end of size (<10 mm) and activity concentration ranges of lymph-node metastases, quantification with reasonable accuracy is possible for 18F, 68Ga, and 124I, possibly allowing pre-therapeutic lesion dosimetry and individualized radionuclide therapy planning.

Oncologic Staging with 68Ga-FAPI PET/CT Demonstrates a Lower Rate of Nonspecific Lymph Node Findings Than 18F-FDG PET/CT.

Nonspecific lymph node uptake on 18F-FDG PET/CT imaging is a significant pitfall for tumor staging. Fibroblast activation protein α expression on cancer-associated fibroblasts and some tumor cells is less sensitive to acute inflammatory stimuli, and fibroblast activation protein-directed PET may overcome this limitation. Methods: Eighteen patients from our prospective observational study underwent 18F-FDG and 68Ga fibroblast activation protein inhibitor (FAPI) PET/CT scans within a median of 2 d (range, 0-22 d). Lymph nodes were assessed on histopathology and compared with SUV measurements. Results: On a per-patient basis, lymph nodes were rated malignant in 10 (56%) versus 7 (39%) patients by 18F-FDG PET/CT versus 68Ga-FAPI PET/CT scans, respectively, with a respective accuracy of 55% versus 94% for true lymph node metastases. Five of 6 (83%) false-positive nodes on the 18F-FDG PET/CT scans were rated true negative by the 68Ga-FAPI PET/CT scans. On a per-lesion basis, tumor detection rates were similar (85/89 lesions, 96%). Conclusion: 68Ga-FAPI PET/CT imaging demonstrated higher accuracy for true nodal involvement and therefore has the potential to replace 18F-FDG PET/CT imaging for cancer staging.

International EANM-SNMMI-ISMRM consensus recommendation for PET/MRI in oncology.

PREAMBLE: The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional non-profit medical association that facilitates communication worldwide between individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. The merged International Society for Magnetic Resonance in Medicine (ISMRM) is an international, nonprofit, scientific association whose purpose is to promote communication, research, development, and applications in the field of magnetic resonance in medicine and biology and other related topics and to develop and provide channels and facilities for continuing education in the field.The ISMRM was founded in 1994 through the merger of the Society of Magnetic Resonance in Medicine and the Society of Magnetic Resonance Imaging. SNMMI, ISMRM, and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine and/or magnetic resonance imaging. The SNMMI, ISMRM, and EANM will periodically define new guidelines for nuclear medicine practice to help advance the science of nuclear medicine and/or magnetic resonance imaging and to improve the quality of service to patients throughout the world. Existing practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated. Each practice guideline, representing a policy statement by the SNMMI/EANM/ISMRM, has undergone a thorough consensus process in which it has been subjected to extensive review. The SNMMI, ISMRM, and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging and magnetic resonance imaging requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guideline by those entities not providing these services is not authorized. These guidelines are an educational tool designed to assist practitioners in providing appropriate care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, the SNMMI, the ISMRM, and the EANM caution against the use of these guidelines in litigation in which the clinical decisions of a practitioner are called into question. The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by the physician or medical physicist in light of all the circumstances presented. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the guidelines. The practice of medicine includes both the art and the science of the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment. Therefore, it should be recognized that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources, and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.