Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography.

OBJECTIVE: Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. In this work, we developed a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset. Approach: Signal-to-noise ratio was extended into a multivariate space where each image was treated as a separate information channel. The general definition was applied to contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term, characterizing image quality within each image, and covariance weighted CNR (Covar-CNR), characterizing contrast relative to covariance between images. The metrology was demonstrated using experimental data from an investigational photon-counting CT scanner. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, 8 mg/mL) was imaged with variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance was calculated between CNR terms and image acquisition variables. A multivariate regression was fit to experimental data. Main Results: Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77±30.64) than threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45±2.49) across all conditions. Analysis of variance found each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms. Significance: In this work, we described a theoretical framework to extend the signal-to-noise ratio to a multivariate form to characterize images independently and provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT. .

Development of a separability index for task specific characterization of spectral computed tomography.

PURPOSE: In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions. METHOD: Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated. RESULTS: Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability. CONCLUSION: The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

Spatial Atlas for Mapping Vascular Microcalcification Using 18F-NaF PET/CT: Application in Hyperphosphatemic Familial Tumoral Calcinosis.

BACKGROUND: Vascular calcification causes significant morbidity and occurs frequently in diseases of calcium/phosphate imbalance. Radiolabeled sodium fluoride positron emission tomography/computed tomography has emerged as a sensitive and specific method for detecting and quantifying active microcalcifications. We developed a novel technique to quantify and map total vasculature microcalcification to a common space, allowing simultaneous assessment of global disease burden and precise tracking of site-specific microcalcifications across time and individuals. METHODS: To develop this technique, 4 patients with hyperphosphatemic familial tumoral calcinosis, a monogenic disorder of FGF23 (fibroblast growth factor-23) deficiency with a high prevalence of vascular calcification, underwent radiolabeled sodium fluoride positron emission tomography/computed tomography imaging. One patient received serial imaging 1 year after treatment with an IL-1 (interleukin-1) antagonist. A radiolabeled sodium fluoride-based microcalcification score, as well as calcification volume, was computed at all perpendicular slices, which were then mapped onto a standardized vascular atlas. Segment-wise mCSmean and mCSmax were computed to compare microcalcification score levels at predefined vascular segments within subjects. RESULTS: Patients with hyperphosphatemic familial tumoral calcinosis had notable peaks in microcalcification score near the aortic bifurcation and distal femoral arteries, compared with a control subject who had uniform distribution of vascular radiolabeled sodium fluoride uptake. This technique also identified microcalcification in a 17-year-old patient, who had no computed tomography-defined calcification. This technique could not only detect a decrease in microcalcification score throughout the patient treated with an IL-1 antagonist but it also identified anatomic areas that had increased responsiveness while there was no change in computed tomography-defined macrocalcification after treatment. CONCLUSIONS: This technique affords the ability to visualize spatial patterns of the active microcalcification process in the peripheral vasculature. Further, this technique affords the ability to track microcalcifications at precise locations not only across time but also across subjects. This technique is readily adaptable to other diseases of vascular calcification and may represent a significant advance in the field of vascular biology.

Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography.

OBJECTIVE: Different methods can be used to condition imaging systems for clinical use. The purpose of this study was to assess how these methods complement one another in evaluating a system for clinical integration of an emerging technology, photon-counting computed tomography (PCCT), for thoracic imaging. METHODS: Four methods were used to assess a clinical PCCT system (NAEOTOM Alpha; Siemens Healthineers, Forchheim, Germany) across 3 reconstruction kernels (Br40f, Br48f, and Br56f). First, a phantom evaluation was performed using a computed tomography quality control phantom to characterize noise magnitude, spatial resolution, and detectability. Second, clinical images acquired using conventional and PCCT systems were used for a multi-institutional reader study where readers from 2 institutions were asked to rank their preference of images. Third, the clinical images were assessed in terms of in vivo image quality characterization of global noise index and detectability. Fourth, a virtual imaging trial was conducted using a validated simulation platform (DukeSim) that models PCCT and a virtual patient model (XCAT) with embedded lung lesions imaged under differing conditions of respiratory phase and positional displacement. Using known ground truth of the patient model, images were evaluated for quantitative biomarkers of lung intensity histograms and lesion morphology metrics. RESULTS: For the physical phantom study, the Br56f kernel was shown to have the highest resolution despite having the highest noise and lowest detectability. Readers across both institutions preferred the Br56f kernel (71% first rank) with a high interclass correlation (0.990). In vivo assessments found superior detectability for PCCT compared with conventional computed tomography but higher noise and reduced detectability with increased kernel sharpness. For the virtual imaging trial, Br40f was shown to have the best performance for histogram measures, whereas Br56f was shown to have the most precise and accurate morphology metrics. CONCLUSION: The 4 evaluation methods each have their strengths and limitations and bring complementary insight to the evaluation of PCCT. Although no method offers a complete answer, concordant findings between methods offer affirmatory confidence in a decision, whereas discordant ones offer insight for added perspective. Aggregating our findings, we concluded the Br56f kernel best for high-resolution tasks and Br40f for contrast-dependent tasks.

Skeletal involvement in Erdheim-Chester disease: Multimodality imaging features and association with the BRAFV600E mutation.

OBJECTIVE: The aim of this study was to characterize the distribution of skeletal involvement in Erdheim-Chester disease (ECD) by using radiography, computed tomography (CT), 18F-FDG positron emission tomography/computed tomography (PET/CT), and bone scans, as well as looking for associations with the BRAFV600E mutation. MATERIAL AND METHODS: Prospective study of 50 consecutive patients with biopsy-confirmed ECD who had radiographs, CT, 18F-FDG PET/CT, and Tc-99m MDP bone scans. At least two experienced radiologists with expertise in the relevant imaging studies analyzed the images. Summary statistics were expressed as the frequency with percentages for categorical data. Fisher's exact test, as well as odds ratios (OR) with 95 % confidence intervals (CI), were used to link imaging findings to BRAFV600E mutation. The probability for co-occurrence of bone involvement at different locations was calculated and graphed as a heat map. RESULTS: All 50 cases revealed skeletal involvement at different regions of the skeleton. The BRAFV600E mutation, which was found in 24 patients, was correlated with femoral and tibial involvement on 18F-FDG PET/CT and bone scan. The appearance of changes on the femoral, tibial, fibular, and humeral involvement showed correlation with each other based on heat maps of skeletal involvement on CT. CONCLUSION: This study reports the distribution of skeletal involvement in a cohort of patients with ECD. CT is able to detect the majority of ECD skeletal involvement. Considering the complementary nature of information from different modalities, imaging of ECD skeletal involvement is optimized by using a multi-modality strategy.

Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports.

OBJECTIVE: Radiology reporting is an essential component of clinical diagnosis and decision-making. With the advent of advanced artificial intelligence (AI) models like GPT-4 (Generative Pre-trained Transformer 4), there is growing interest in evaluating their potential for optimizing or generating radiology reports. This study aimed to compare the quality and content of radiologist-generated and GPT-4 AI-generated radiology reports. METHODS: A comparative study design was employed in the study, where a total of 100 anonymized radiology reports were randomly selected and analyzed. Each report was processed by GPT-4, resulting in the generation of a corresponding AI-generated report. Quantitative and qualitative analysis techniques were utilized to assess similarities and differences between the two sets of reports. RESULTS: The AI-generated reports showed comparable quality to radiologist-generated reports in most categories. Significant differences were observed in clarity (p = 0.027), ease of understanding (p = 0.023), and structure (p = 0.050), favoring the AI-generated reports. AI-generated reports were more concise, with 34.53 fewer words and 174.22 fewer characters on average, but had greater variability in sentence length. Content similarity was high, with an average Cosine Similarity of 0.85, Sequence Matcher Similarity of 0.52, BLEU Score of 0.5008, and BERTScore F1 of 0.8775. CONCLUSION: The results of this proof-of-concept study suggest that GPT-4 can be a reliable tool for generating standardized radiology reports, offering potential benefits such as improved efficiency, better communication, and simplified data extraction and analysis. However, limitations and ethical implications must be addressed to ensure the safe and effective implementation of this technology in clinical practice. CLINICAL RELEVANCE STATEMENT: The findings of this study suggest that GPT-4 (Generative Pre-trained Transformer 4), an advanced AI model, has the potential to significantly contribute to the standardization and optimization of radiology reporting, offering improved efficiency and communication in clinical practice. KEY POINTS: • Large language model-generated radiology reports exhibited high content similarity and moderate structural resemblance to radiologist-generated reports. • Performance metrics highlighted the strong matching of word selection and order, as well as high semantic similarity between AI and radiologist-generated reports. • Large language model demonstrated potential for generating standardized radiology reports, improving efficiency and communication in clinical settings.

Virtual monoenergetic imaging in photon-counting CT of the head and neck.

PURPOSE: Advantages of virtual monoenergetic images (VMI) have been reported for dual energy CT of the head and neck, and more recently VMIs derived from photon-counting (PCCT) angiography of the head and neck. We report image quality metrics of VMI in a PCCT angiography dataset, expanding the anatomical regions evaluated and extending observer-based qualitative methods further than previously reported. METHODS: In a prospective study, asymptomatic subjects underwent contrast enhanced PCCT of the head and neck using an investigational scanner. Image sets of low, high, and full spectrum (Threshold-1) energies; linear mix of low and high energies (Mix); and 23 VMIs (40-150 keV, 5 keV increments) were generated. In 8 anatomical locations, SNR and radiologists' preferences for VMI energy levels were measured using a forced-choice rank method (4 observers) and ratings of image quality using visual grading characteristic (VGC) analysis (2 observers) comparing VMI to Mix and Threshold-1 images. RESULTS: Fifteen subjects were included (7 men, 8 women, mean 57 years, range 46-75). Among all VMIs, SNRs varied by anatomic location. The highest SNRs were observed in VMIs. Radiologists preferred 50-60 keV VMIs for vascular structures and 75-85 keV for all other structures. Cumulative ratings of image quality averaged across all locations were higher for VMIs with areas under the curve of VMI vs Mix and VMI vs Threshold-1 of 0.67 and 0.68 for the first reader and 0.72 and 0.76 for the second, respectively. CONCLUSION: Preferred keV level and quality ratings of VMI compared to mixed and Threshold-1 images varied by anatomical location.

Impact of the confluence of cardiac motion and high spatial resolution on performance of ECG-gated imaging with an investigational photon-counting CT system: A phantom study.

PURPOSE: Photon-counting CT (PCCT) has higher spatial resolution that conventional EID CT which improves imaging of stationary coronary plaques and stents.. In this work, we evaluated the relationship between higher spatial resolution and motion acquisition on an investigational PCCT system. METHODS: An investigational photon-counting CT scanner (Siemens CounT) with ECG gating was used to image a coronary tree phantom with models of healthy, stenotic, and stented arteries using a motion simulator. Images were acquired with matched clinical parameters at rest and 60 beats per minute. An additional set of high dose stationary images were averaged to generate a motion-free, reduced noise reference. Scans were completed at standard (0.5 mm2) and high-resolution (0.25 mm2). Motion images were reconstructed at multiple phases. Regions of interest were drawn around vessels and segmented. Percentage difference from the reference standard was evaluated for vessel diameter and circularity. Mutual information between the reference and stationary and motion datasets was used as a measure of volumetric similarity. RESULTS: The stenotic vessel showed the most variation from the reference when compared to healthy or stented vessels. Compared to standard resolution, high-resolution images had lower bias for diameter (-0.012 ± 0.19% vs -0.052 ± 0.14%) and lower variability for circularity (-0.13 ± 0.138% vs -0.12 ± 0.144%). Both differences were found to be statistically significant. High-resolution images had a slightly lower mutual information (1.28) than standard resolution (1.31). CONCLUSION: The higher spatial resolution enabled by photon-counting CT can be harnessed for cardiac imaging as the benefits of high spatial resolution acquisitions remain relevant in the presence of motion.

Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma.

Our review shows that AI-based analysis of lymphoma whole-body FDG-PET/CT can inform all phases of clinical management including staging, prognostication, treatment planning, and treatment response evaluation. We highlight advancements in the role of neural networks for performing automated image segmentation to calculate PET-based imaging biomarkers such as the total metabolic tumor volume (TMTV). AI-based image segmentation methods are at levels where they can be semi-automatically implemented with minimal human inputs and nearing the level of a second-opinion radiologist. Advances in automated segmentation methods are particularly apparent in the discrimination of lymphomatous vs non-lymphomatous FDG-avid regions, which carries through to automated staging. Automated TMTV calculators, in addition to automated calculation of measures such as Dmax are informing robust models of progression-free survival which can then feed into improved treatment planning.

Emerging Role of 18F-NaF PET/Computed Tomographic Imaging in Osteoporosis: A Potential Upgrade to the Osteoporosis Toolbox.

Osteoporosis is a metabolic bone disorder that leads to a decline in bone microarchitecture, predisposing individuals to catastrophic fractures. The current standard of care relies on detecting bone structural change; however, these methods largely miss the complex biologic forces that drive these structural changes and response to treatment. This review introduces sodium fluoride (18F-NaF) positron emission tomography/computed tomography (PET/CT) as a powerful tool to quantify bone metabolism. Here, we discuss the methods of 18F-NaF PET/CT, with a special focus on dynamic scans to quantify parameters relevant to bone health, and how these markers are relevant to osteoporosis.

Multi-Scale Temporal Imaging: From Micro- and Meso- to Macro-scale-time Nuclear Medicine.

Time provides a common frame of reference for understanding different processes of change. Within the context of medical imaging, time has three different time scales to be considered: (i) microtime, (ii) mesotime, and (iii) macrotime, respectively, which span a single imaging session, distinct imaging sessions within a short period, and scans with large time gaps spanning months of even years. There has commonly been greater emphasis on the microtime and mesotime scales in both clinical practice and research, with less focus on questions that are at the macrotime scale.

Applications of Artificial Intelligence in 18F-Sodium Fluoride Positron Emission Tomography/Computed Tomography:: Current State and Future Directions.

This review discusses the current state of artificial intelligence (AI) in 18F-NaF-PET/CT imaging and the potential applications to come in diagnosis, prognostication, and improvement of care in patients with bone diseases, with emphasis on the role of AI algorithms in CT bone segmentation, relying on their prevalence in medical imaging and utility in the extraction of spatial information in combined PET/CT studies.

Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities.

Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, has tremendous potential to advance the diagnosis of RDs. Patient advocacy groups must be active stakeholders in the AI ecosystem if we are to avoid potential issues related to the implementation of AI into health care. AI medical devices must not only be RD-aware at each stage of their conceptualization and life cycle but also should be trained on diverse and augmented datasets representative of the end-user population including RDs. Inability to do so leads to potential harm and unsustainable deployment of AI-based medical devices (AIMDs) into clinical practice.

Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions).

Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.

Applications of artificial intelligence in orthopaedic surgery.

The practice of medicine is rapidly transforming as a result of technological breakthroughs. Artificial intelligence (AI) systems are becoming more and more relevant in medicine and orthopaedic surgery as a result of the nearly exponential growth in computer processing power, cloud based computing, and development, and refining of medical-task specific software algorithms. Because of the extensive role of technologies such as medical imaging that bring high sensitivity, specificity, and positive/negative prognostic value to management of orthopaedic disorders, the field is particularly ripe for the application of machine-based integration of imaging studies, among other applications. Through this review, we seek to promote awareness in the orthopaedics community of the current accomplishments and projected uses of AI and ML as described in the literature. We summarize the current state of the art in the use of ML and AI in five key orthopaedic disciplines: joint reconstruction, spine, orthopaedic oncology, trauma, and sports medicine.

Evaluation of Coronary Plaques and Stents with Conventional and Photon-counting CT: Benefits of High-Resolution Photon-counting CT.

PURPOSE: To compare the performance of energy-integrating detector (EID) CT, photon-counting detector CT (PCCT), and high-resolution PCCT (HR-PCCT) for the visualization of coronary plaques and reduction of stent artifacts in a phantom model. MATERIALS AND METHODS: An investigational scanner with EID and PCCT subsystems was used to image a coronary artery phantom containing cylindrical probes simulating different plaque compositions. The phantom was imaged with and without coronary stents using both subsystems. Images were reconstructed with a clinical cardiac kernel and an additional HR-PCCT kernel. Regions of interest were drawn around probes and evaluated for in-plane diameter and a qualitative comparison by expert readers. A linear mixed-effects model was used to compare the diameter results, and a Shrout-Fleiss intraclass correlation coefficient was used to assess consistency in the reader study. RESULTS: Comparing in-plane diameter to the physical dimension for nonstented and stented phantoms, measurements of the HR-PCCT images were more accurate (nonstented: 4.4% ± 1.1 [standard deviation], stented: -9.4% ± 4.6) than EID (nonstented: 15.5% ± 4.0, stented: -19.5% ± 5.8) and PCCT (nonstented: 19.4% ± 2.5, stented: -18.3% ± 4.4). Our analysis of variance found diameter measurements to be different across image groups for both nonstented and stented cases (P < .001). HR-PCCT showed less change on average in percent stenosis due to the addition of a stent (-5.5%) than either EID (+90.5%) or PCCT (+313%). For both nonstented and stented phantoms, observers rated the HR-PCCT images as having higher plaque conspicuity and as being the image type that was least impacted by stent artifacts, with a high level of agreement (interclass correlation coefficient = 0.85). CONCLUSION: Despite increased noise, HR-PCCT images were able to better visualize coronary plaques and reduce stent artifacts compared with EID or PCCT reconstructions.Keywords: CT-Spectral Imaging (Dual Energy), Phantom Studies, Cardiac, Physics, Technology Assessment© RSNA, 2021.

A Clinically Driven Task-Based Comparison of Photon Counting and Conventional Energy Integrating CT for Soft Tissue, Vascular, and High-Resolution Tasks.

Photon-counting CT detectors are the next step in advancing CT system development and will replace the current energy integrating detectors (EID) in CT systems in the near future. In this context, the performance of PCCT was compared to EID CT for three clinically relevant tasks: abdominal soft tissue imaging, where differentiating low contrast features is important; vascular imaging, where iodine detectability is critical; and, high-resolution skeletal and lung imaging. A multi-tiered phantom was imaged on an investigational clinical PCCT system (Siemens Healthineers) across different doses using three imaging modes: macro and ultra-high resolution (UHR) PCCT modes and EID CT. Images were reconstructed using filtered backprojection and soft tissue (B30f), vascular (B46f), or high-resolution (B70f; U70f for UHR) kernels. Noise power spectra, task transfer functions, and detectability index were evaluated. For a soft tissue task, PCCT modes showed comparable noise and resolution with improved contrast-to-noise ratio. For a vascular task, PCCT modes showed lower noise and improved iodine detectability. For a high resolution task, macro mode showed lower noise and comparable resolution while UHR mode showed higher noise but improved spatial resolution for both air and bone. PCCT offers competitive advantages to EID CT for clinical tasks.

A deep-learning based artificial intelligence (AI) approach for differentiation of clear cell renal cell carcinoma from oncocytoma on multi-phasic MRI.

PURPOSE: To investigate the diagnostic performance of a deep convolutional neural network for differentiation of clear cell renal cell carcinoma (ccRCC) from renal oncocytoma. METHODS: In this retrospective study, 74 patients (49 male, mean age 59.3) with 243 renal masses (203 ccRCC and 40 oncocytoma) that had undergone MR imaging 6 months prior to pathologic confirmation of the lesions were included. Segmentation using seed placement and bounding box selection was used to extract the lesion patches from T2-WI, and T1-WI pre-contrast, post-contrast arterial and venous phases. Then, a deep convolutional neural network (AlexNet) was fine-tuned to distinguish the ccRCC from oncocytoma. Five-fold cross validation was used to evaluate the AI algorithm performance. A subset of 80 lesions (40 ccRCC, 40 oncocytoma) were randomly selected to be classified by two radiologists and their performance was compared to the AI algorithm. Intra-class correlation coefficient was calculated using the Shrout-Fleiss method. RESULTS: Overall accuracy of the AI system was 91% for differentiation of ccRCC from oncocytoma with an area under the curve of 0.9. For the observer study on 80 randomly selected lesions, there was moderate agreement between the two radiologists and AI algorithm. In the comparison sub-dataset, classification accuracies were 81%, 78%, and 70% for AI, radiologist 1, and radiologist 2, respectively. CONCLUSION: The developed AI system in this study showed high diagnostic performance in differentiation of ccRCC versus oncocytoma on multi-phasic MRIs.

CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1.

Purpose To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. Materials and Methods In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive. Each target node was measured using RECIST 1.1 criteria before and after merging or splitting, followed by volumetric segmentation. To compare RECIST 1.1 with volume, a single-dimension hypothetical diameter (HD) was determined from the nodal volume. The nodes were divided into three groups: (a) one-target merged (one target node merged with other nodes); (b) two-target merged (two neighboring target nodes merged); and (c) split node (a conglomerate node cleaved into smaller fragments). Bland-Altman analysis and t test were applied to compare RECIST 1.1 with HD. On the basis of the RECIST 1.1 concept, we compared response category changes between RECIST 1.1 and HD. Results The data set consisted of 30 merged nodes (19 one-target merged and 11 two-target merged) and 20 split nodes (mean age for all 50 included patients, 50 years ± 7 [standard deviation]; 38 men). RECIST 1.1, volumetric, and HD measurements indicated an increase in size in all one-target merged nodes. While volume and HD indicated an increase in size for nodes in the two-target merged group, RECIST 1.1 showed a decrease in size in all two-target merged nodes. Although volume and HD demonstrated a decrease in size of all split nodes, RECIST 1.1 indicated an increase in size in 60% (12 of 20) of the nodes. Discrepancy of the response categories between RECIST 1.1 and HD was observed in 5% (one of 19) in one-target merged, 82% (nine of 11) in two-target merged, and 55% (11 of 20) in split nodes. Conclusion RECIST 1.1 does not optimally reflect size changes when lymph nodes merge or split. Keywords: CT, Lymphatic, Tumor Response Supplemental material is available for this article. © RSNA, 2021.

18Fluorodeoxyglucose-positron emission tomography/computed tomography for differentiation of renal tumors in hereditary kidney cancer syndromes.

PURPOSE: To assess differences in FDG-PET/CT uptake among four subtypes of renal tumors: clear cell RCC (ccRCC), papillary type I and II RCC (pRCC), and oncocytoma. METHODS: This retrospective study investigated 33 patients with 98 hereditary renal tumors. Lesions greater than 1 cm and patients with a timeframe of less than 18 months between preoperative imaging and surgery were considered. FDG-PET/CT images were independently reviewed by two nuclear medicine physicians, blinded to clinical information. Volumetric lesion SUVmean was measured and used to calculate a target-to-background ratio respective to liver (TBR). The Shrout-Fleiss intra-class correlation coefficient was used to assess reliability between readers. A linear mixed effects model, accounting for within-patient correlations, was used to compare TBR values of primary renal lesions with and without distant metastasis. RESULTS: The time interval between imaging and surgery for all tumors had a median of 77 (Mean: 139; Range: 1-512) days. Intra-class reliability of mean TBR resulted in a mean κ score of 0.93, indicating strong agreement between the readers. The mixed model showed a significant difference in mean TBR among the subtypes (p < 0.0001). Pairwise comparison showed significant differences between pRCC type II and ccRCC (p < 0.0001), pRCC type II and pRCC type I (p = 0.0001), and pRCC type II and oncocytoma (p = 0.0016). Furthermore, a significant difference in FDG uptake was present between primary pRCC type II renal lesions with and without distant metastasis (p = 0.023). CONCLUSION: pRCC type II lesions demonstrated significantly higher FDG activity than ccRCC, pRCC type I, or oncocytoma. These findings indicate that FDG may prove useful in studying the metabolic activity of renal neoplasms, identifying lesions of highest clinical concern, and ultimately optimizing active surveillance, and personalizing management plans.