Positron Emission Tomography And Computed Tomography Images Research Articles

MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images

In clinical diagnosis, positron emission tomography and computed tomography (PET-CT) images containing complementary information are fused. Tumor segmentation based on multi-modal PET-CT images is an important part of clinical diagnosis and treatment. However, the existing current PET-CT tumor segmentation methods mainly focus on positron emission tomography (PET) and computed tomography (CT) feature fusion, which weakens the specificity of the modality. In addition, the information interaction between different modal images is usually completed by simple addition or concatenation operations, but this has the disadvantage of introducing irrelevant information during the multi-modal semantic feature fusion, so effective features cannot be highlighted. To overcome this problem, this paper propose a novel Multi-modal Fusion and Calibration Networks (MFCNet) for tumor segmentation based on three-dimensional PET-CT images. First, a Multi-modal Fusion Down-sampling Block (MFDB) with a residual structure is developed. The proposed MFDB can fuse complementary features of multi-modal images while retaining the unique features of different modal images. Second, a Multi-modal Mutual Calibration Block (MMCB) based on the inception structure is designed. The MMCB can guide the network to focus on a tumor region by combining different branch decoding features using the attention mechanism and extracting multi-scale pathological features using a convolution kernel of different sizes. The proposed MFCNet is verified on both the public dataset (Head and Neck cancer) and the in-house dataset (pancreas cancer). The experimental results indicate that on the public and in-house datasets, the average Dice values of the proposed multi-modal segmentation network are 74.14% and 76.20%, while the average Hausdorff distances are 6.41 and 6.84, respectively. In addition, the experimental results show that the proposed MFCNet outperforms the state-of-the-art methods on the two datasets.

Prevalence, Distribution, and Concomitance of Whole-Spine Ossification of the Posterior Longitudinal Ligament and Ossification of the Ligament Flavum in South Koreans: A Whole-Spine-CT-Based Cross-Sectional Study.

No studies assessing the prevalence of ossification of the spinal ligament were conducted using whole-spine positron emission tomography and computed tomography (PET-CT) in healthy Koreans. We aimed to determine the prevalence of ossification of the posterior longitudinal ligament (OPLL) and ossification of the ligament flavum (OLF) in healthy Koreans using whole-body PET-CT. We reviewed whole-body PET-CT images captured during general health checkups at the General Health Promotion Center of our institution from January 2015 to 2020. OPLL and OLF were identified by the presence of heterotopic ossification in the posterior longitudinal ligament and ligament flavum on axial and sagittal PET-CT images. A total of 1,934 adults (1,645 men, 289 women) were included. The mean age was 48.05 years (range, 28-86 years). Among the 1,934 patients, 173 had OPLL (8.9%). The most commonly involved cervical vertebra levels arranged according to frequency were C4, C5, C3, and C6. OLF was observed in 125 patients (6.5%). The most commonly involved thoracic levels were T10, T11, and T5. The prevalence of OPLL and OLF was the highest in patients aged 60-69 years. Among the C-OPLL patients, 15.1% had T-OPLL, 5.0% had L-OPLL, and 25.8% had T-OLF. Our study revealed the prevalence of OPLL and OLF in healthy Korean subjects. It was consistent with that in other Asian countries. The presence of OPLL and OLF at most locations correlated with the presence or absence of spinal ossification at other locations.

PET-CT in Clinical Adult Oncology—IV. Gynecologic and Genitourinary Malignancies

Concurrently acquired positron emission tomography and computed tomography (PET-CT) is an advanced imaging modality with diverse oncologic applications, including staging, therapeutic assessment, restaging and longitudinal surveillance. This series of six review articles focuses on providing practical information to providers and imaging professionals regarding the best use and interpretative strategies of PET-CT for oncologic indications in adult patients. In this fourth article of the series, the more common gynecological and adult genitourinary malignancies encountered in clinical practice are addressed, with an emphasis on Food and Drug Administration (FDA)-approved and clinically available radiopharmaceuticals. The advent of new FDA-approved radiopharmaceuticals for prostate cancer imaging has revolutionized PET-CT imaging in this important disease, and these are addressed in this report. However, [18F]F-fluoro-2-deoxy-d-glucose (FDG) remains the mainstay for PET-CT imaging of gynecologic and many other genitourinary malignancies. This information will serve as a guide for the appropriate role of PET-CT in the clinical management of gynecologic and genitourinary cancer patients for health care professionals caring for adult cancer patients. It also addresses the nuances and provides guidance in the accurate interpretation of FDG PET-CT in gynecological and genitourinary malignancies for imaging providers, including radiologists, nuclear medicine physicians and their trainees.

Diffuse large B-cell lymphoma segmentation in PET-CT images via hybrid learning for feature fusion.

Diffuse large B-cell lymphoma (DLBCL) is an aggressive type of lymphoma with high mortality and poor prognosis that especially has a high incidence in Asia. Accurate segmentation of DLBCL lesions is crucial for clinical radiation therapy. However, manual delineation of DLBCL lesions is tedious and time-consuming. Automatic segmentation provides an alternative solution but is difficult for diffuse lesions without the sufficient utilization of multimodality information. Our work is the first study focusing on positron emission tomography and computed tomography (PET-CT) feature fusion for the DLBCL segmentation issue. We aim to improve the fusion performance of complementary information contained in PET-CT imaging with a hybrid learning module in the supervised convolutional neural network. First, two encoder branches extract single-modality features, respectively. Next, the hybrid learning component utilizes them to generate spatial fusion maps which can quantify the contribution of complementary information. Such feature fusion maps are then concatenated with specific-modality (i.e., PET and CT) feature maps to obtain a representation of the final fused feature maps in different scales. Finally, the reconstruction part of our network creates a prediction map of DLBCL lesions by integrating and up-sampling the final fused feature maps from encoder blocks in different scales. The ability of our method was evaluated to detect foreground and segment lesions in three independent body regions (nasopharynx, chest, and abdomen) of a set of 45 PET-CT scans. Extensive ablation experiments compared our method to four baseline techniques for multimodality fusion (input-level (IL) fusion, multichannel (MC) strategy, multibranch (MB) strategy, and quantitative weighting (QW) fusion). The results showed that our method achieved a high detection accuracy (99.63% in the nasopharynx, 99.51% in the chest, and 99.21% in the abdomen) and had the superiority in segmentation performance with the mean dice similarity coefficient (DSC) of 73.03% and the modified Hausdorff distance (MHD) of 4.39mm, when compared with the baselines (DSC: IL: 53.08%, MC: 63.59%, MB: 69.98%, and QW: 72.19%; MHD: IL: 12.16mm, MC: 6.46mm, MB: 4.83mm, and QW: 4.89mm). A promising segmentation method has been proposed for the challenging DLBCL lesions in PET-CT images, which improves the understanding of complementary information by feature fusion and may guide clinical radiotherapy. The statistically significant analysis based on P-value calculation has indicated a degree of significant difference between our proposed method and other baselines (almost metrics: P<0.05). This is a preliminary research using a small sample size, and we will collect data continually to achieve the larger verification study.

Accuracy of positron emission tomography and computed tomography (PET/CT) in detecting nodal metastasis according to histology of non-small cell lung cancer.

Positron emission tomography and computed tomography (PET-CT) is the non-invasive gold standard method for determining the oncological stage of patient with diagnosis of lung cancer. A correct preoperative staging is significant because only patients who do not have a history of regional or distant disease are those who will benefit from a surgical treatment. However, due to the different values of the PET-CT in terms of sensitivity and specificity to evaluate the mediastinal lymph node involvement, it is often necessary to perform a surgical mediastinal sampling through a cervical video mediastinoscopy (VM). Patient's risk factors which could modify the results of the PET scan, performing differences between non-invasive staging and the lymph node sampling due to VM are not yet clearly established in the literature. This knowledge will allow to identify in whom a surgical staging by sampling the mediastinal lymph nodes is needed. We included 234 patients with diagnosis of lung cancer who underwent a mediastinal lymph node staging by PET-CT images and histopathological results of mediastinal sampling by VM, analyzing the sensitivity and specificity of this non-invasive imaging study. We also analyzed variables that could modify the results of PET-CT, such as tumor type, location of the tumor and patient's history. We showed that those PET-CT presented an overall sensitivity and specificity of 93.8 and 62.7%, respectively, with negative and positive predictive values of 95.05 and 57.1%, respectively. The false-positive rate was 25% (57 of 234 patients). Analyzing risk factors involved in this false-positive rate (n = 57), we found that the only statistically significant factor that could explain these results was the histology of squamous carcinoma (p < 0.03). In this group of patients, it is essential to perform a mediastinal lymph node biopsy to know the real state of lymph node involvement.

Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer.

The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications (e.g., detection and segmentation) requires combining the sensitivity of PET to detect abnormal regions with anatomical localization from CT. Current methods for PET-CT image analysis either process the modalities separately or fuse information from each modality based on knowledge about the image analysis task. These methods generally do not consider the spatially varying visual characteristics that encode different information across the different modalities, which have different priorities at different locations. For example, a high abnormal PET uptake in the lungs is more meaningful for tumor detection than physiological PET uptake in the heart. Our aim is to improve fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis. Our CNN first encodes modality-specific features and then uses them to derive a spatially varying fusion map that quantifies the relative importance of each modality's features across different spatial locations. These fusion maps are then multiplied with the modality-specific feature maps to obtain a representation of the complementary multi-modality information at different locations, which can then be used for image analysis. We evaluated the ability of our CNN to detect and segment multiple regions (lungs, mediastinum, tumors) with different fusion requirements using a dataset of PET-CT images of lung cancer. We compared our method to baseline techniques for multi-modality image fusion (fused inputs (FS), multi-branch (MB) techniques, and multichannel (MC) techniques) and segmentation. Our findings show that our CNN had a significantly higher foreground detection accuracy (99.29%, p < 0:05) than the fusion baselines (FS: 99.00%, MB: 99.08%, TC: 98.92%) and a significantly higher Dice score (63.85%) than recent PET-CT tumor segmentation methods.

3D FULLY CONVOLUTIONAL NETWORKS FOR CO-SEGMENTATION OF TUMORS ON PET-CT IMAGES.

Positron emission tomography and computed tomography (PET-CT) dual-modality imaging provides critical diagnostic information in modern cancer diagnosis and therapy. Automated accurate tumor delineation is essentially important in computer-assisted tumor reading and interpretation based on PET-CT. In this paper, we propose a novel approach for the segmentation of lung tumors that combines the powerful fully convolutional networks (FCN) based semantic segmentation framework (3D-UNet) and the graph cut based co-segmentation model. First, two separate deep UNets are trained on PET and CT, separately, to learn high level discriminative features to generate tumor/non-tumor masks and probability maps for PET and CT images. Then, the two probability maps on PET and CT are further simultaneously employed in a graph cut based co-segmentation model to produce the final tumor segmentation results. Comparative experiments on 32 PET-CT scans of lung cancer patients demonstrate the effectiveness of our method.

IMPROVING TUMOR CO-SEGMENTATION ON PET-CT IMAGES WITH 3D CO-MATTING.

Positron emission tomography and computed tomography (PET-CT) plays a critically important role in modern cancer therapy. In this paper, we focus on automated tumor delineation on PET-CT image pairs. Inspired by co-segmentation model, we develop a novel 3D image co-matting technique making use of the inner-modality information of PET and CT for matting. The obtained co-matting results are then incorporated in the graph-cut based PET-CT co-segmentation framework. Our comparative experiments on 32 PET-CT scan pairs of lung cancer patients demonstrate that the proposed 3D image co-matting technique can significantly improve the quality of cost images for the co-segmentation, resulting in highly accurate tumor segmentation on both PET and CT scan pairs.

Central nervous system PET-CT imaging reveals regional impairments in pediatric patients with Wolfram syndrome.

Wolfram syndrome (WFS) is inherited as an autosomal recessive disease with main clinical features of diabetes mellitus, optic atrophy, diabetes insipidus and deafness. However, various neurological defects may also be detected. The aim of this study was to evaluate aspects of brain structure and function using PET-CT (positron emission tomography and computed tomography) and MRI (magnetic resonance imaging) in pediatric patients with WFS. Regional changes in brain glucose metabolism were measured using standardized uptake values (SUVs) based on images of (18F) fluorodeoxyglucose (FDG) uptake in 7 WFS patients aged 10.1–16.0 years (mean 12.9±2.4) and in 20 healthy children aged 3–17.9 years (mean 12.8±4.1). In all patients the diagnosis of WFS was confirmed by DNA sequencing of the WFS1 gene. Hierarchical clustering showed remarkable similarities of glucose uptake patterns among WFS patients and their differences from the control group. SUV data were subsequently standardized for age groups <13 years old and>13 years old to account for developmental differences. Reduced SUVs in WFS patients as compared to the control group for the bilateral brain regions such as occipital lobe (−1.24±1.20 vs. −0.13±1.05; p = 0.028) and cerebellum (−1.11±0.69 vs. −0.204±1.00; p = 0.036) were observed and the same tendency for cingulate (−1.13±1.05 vs. −0.15±1.12; p = 0.056), temporal lobe (−1.10±0.98 vs. −0.15±1.10; p = 0.057), parietal lobe (−1.06±1.20 vs. −0.08±1.08; p = 0.058), central region (−1.01±1.04 vs. −0.09±1.06; p = 0.060), basal ganglia (−1.05±0.74 vs. −0.20±1.07; p = 0.066) and mesial temporal lobe (−1.06±0.82 vs. −0.26±1.08; p = 0.087) was also noticed. After adjusting for multiple hypothesis testing, the differences in glucose uptake were non-significant. For the first time, regional differences in brain glucose metabolism among patients with WFS were shown using PET-CT imaging.

Efficient PET-CT image retrieval using graphs embedded into a vector space.

Combined positron emission tomography and computed tomography (PET-CT) produces functional data (from PET) in relation to anatomical context (from CT) and it has made a major contribution to improved cancer diagnosis, tumour localisation, and staging. The ability to retrieve PET-CT images from large archives has potential applications in diagnosis, education, and research. PET-CT image retrieval requires the consideration of modality-specific 3D image features and spatial contextual relationships between features in both modalities. Graph-based retrieval methods have recently been applied to represent contextual relationships during PET-CT image retrieval. However, accurate methods are computationally complex, often requiring offline processing, and are unable to retrieve images at interactive rates. In this paper, we propose a method for PET-CT image retrieval using a vector space embedding of graph descriptors. Our method defines the vector space in terms of the distance between a graph representing a PET-CT image and a set of fixed-sized prototype graphs; each vector component measures the dissimilarity of the graph and a prototype. Our evaluation shows that our method is significantly faster (≈800× speedup, p <; 0.05) than retrieval using the graph-edit distance while maintaining comparable precision (5% difference, p > 0.05).

Designing user interfaces to enhance human interpretation of medical content-based image retrieval: application to PET-CT images

Content-based image retrieval (CBIR) in medicine has been demonstrated to improve evidence-based diagnosis, education, and teaching. However, the low clinical adoption of CBIR is partially because the focus of most studies has been the development of feature extraction and similarity measurement algorithms with limited work on facilitating better understanding of the similarity between complex volumetric and multi-modality medical images. In this paper, we present a method for defining user interfaces (UIs) that enable effective human user interpretation of retrieved images. We derived a set of visualisation and interaction requirements based on the characteristics of modern volumetric medical images. We implemented a UI that visualised multiple views of a single image, displayed abstractions of image data, and provided access to supplementary non-image data. We also defined interactions for refining the search and visually indicating the similarities between images. We applied the UI for the retrieval of multi-modality positron emission tomography and computed tomography (PET-CT) images. We conducted a user survey to evaluate the capabilities of our UI. Our proposed method obtained a high rating ( ≥ 4 out of 5) in the majority of survey questions. In particular, the survey responses indicated the UI presented all the information necessary to understand the retrieved images, and did so in an intuitive manner. Our proposed UI design improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The implementation of CBIR UIs designed to assist human interpretation could facilitate wider adoption of medical CBIR systems.

Comparison of standardized uptake values obtained from 18f fluorodeoxyglucose positron emission tomography/computed tomography imaging performed with 2d and 3D modes in oncological cases

We investigated the usability of standardized uptake values (SUV) obtained from both two- and three-dimensional (2D and 3D) positron-emission tomography and computed tomography (PET-CT) imaging, and compared the images obtained from these techniques in terms of image quality, lesion detectability, and the presence of artifacts. Image data from 100 patients, who had undergone two PET imagings obtained in 2D and 3D mode after a low dose CT, were evaluated prospectively. Subjective analysis of 2D and 3D images was performed by two readers evaluating the following criteria: overall image quality, detectability of each identified lesion, and the presence of artifacts. The lesions recognized by the readers were also analyzed quantitatively by measuring SUV values. There was a significant difference between the SUVs obtained in 2D and 3D modes. Regardless if the first scan was performed in 2D or 3D mode, the values obtained from 3D imaging were significantly lower than those obtained from 2D imaging (mean SUV(max) was 10.48±7.57 for 2D, and 9.66±6.93 for 3D, P < 0.001). Visual analysis did not reveal significant differences regarding lesion detectability between two modes. In oncological PET-CT applications, SUV values are significantly lower in 3D compared with 2D mode. Thus when serial scanning is needed to evaluate response to therapy in the same patient, the imaging modality should be taken into account and performed with the same method to avoid misinterpretation. Additionally, 3D PET-CT imaging can be used instead of 2D PET-CT due to its shorter scanning time without loss of lesion detectability.

Positron Emission Tomography in Staging Early Lung Cancer

Among patients with early-stage non-small cell lung cancer (NSCLC), preoperative imaging tests are important in defining surgical candidates. To assess whether whole-body positron emission tomography and computed tomography (PET-CT) plus cranial imaging correctly upstages cancer in more patients with NSCLC than does conventional staging plus cranial imaging. Randomized clinical trial with recruitment from June 2004 to August 2007. The centralized, computer-generated, variable block size randomization scheme was stratified by treatment center and cancer stage. Participants, health care providers, and outcome assessors were not blinded to imaging modality assignment. 8 hospitals and 5 PET-CT centers in academic institutions. Eligible patients were older than 18 years; had histologic or cytologic proof of stage I, II, or IIIA NSCLC on the basis of chest radiography and thoracic CT; and had a tumor considered to be resectable. PET-CT or conventional staging (abdominal CT and bone scan). All patients also had cranial imaging using CT or magnetic resonance imaging. The primary outcome was correct upstaging, thereby avoiding stage-inappropriate surgery. Secondary outcomes were incorrect upstaging and incorrect understaging. 170 patients were assigned to PET-CT and 167 to conventional staging. Eight patients (3 who had PET-CT and 5 who had conventional staging) did not have planned surgery. Disease was correctly upstaged in 23 of 167 PET-CT recipients and 11 of 162 conventional staging recipients (13.8% vs. 6.8%; difference, 7.0 percentage points [95% CI, 0.3 to 13.7 percentage points]), thereby sparing these patients from surgery. Disease was incorrectly upstaged in 8 PET-CT recipients and 1 conventional staging recipient (4.8% vs. 0.6%; difference, 4.2 percentage points [CI, 0.5 to 8.6 percentage points]), and it was incorrectly understaged in 25 and 48 patients, respectively (14.9% vs. 29.6%; difference, 14.7 percentage points [CI, 5.7 to 23.4 percentage points]). At 3 years, 52 patients who had PET-CT and 57 patients who had conventional staging had died. The relatively small sample and the fact that some patients did not have planned surgery limited the ability to determine precise differences in clinical outcomes that were attributable to testing strategies. Preoperative staging with PET-CT and cranial imaging identifies more patients with mediastinal and extrathoracic disease than conventional staging, thereby sparing more patients from stage-inappropriate surgery, but the strategy also incorrectly upstaged disease in more patients.

Repetitive Noninvasive Monitoring of HSV1-tk-Expressing T Cells Intravenously Infused into Nonhuman Primates Using Positron Emission Tomography and Computed Tomography with 18 F-FEAU

Adoptive transfer of antigen-specific cytotoxic T lymphocytes (CTLs) has been successfully used to treat patients with different types of cancer. However, the long-term spatial-temporal dynamics of the distribution of systemically infused CTLs remains largely unknown. Noninvasive imaging of adoptively transferred CTLs using molecular-genetic reporter imaging with positron emission tomography and computed tomography (PET-CT) represents an innovative approach to understanding the long-term migratory patterns and therapeutic potential of adoptively transferred T cells. Here we report the application of repetitive PET-CT imaging with [18F]fluoro-5-ethyl-1-beta-D-arabinofuranosyluracil (18F-FEAU) in two nonhuman primates demonstrating that autologous polyclonal macaque T lymphocytes activated and transduced with a retroviral vector encoding for the sr39 mutant herpes simplex virus 1 thymidine kinase (sr39HSV1-tk) reporter gene can be detected after intravenous infusion in discrete lymphoid organs and in sites of inflammation. This study represents a proof of principle and supports the application of 18F-FEAU PET-CT imaging for monitoring the distribution of intravenously administered sr39HSV1-tk gene-transduced CTLs in humans.

Deferring Planned Neck Dissection Following Chemoradiation for Stage IV Head and Neck Cancer: The Utility of PET‐CT

To determine whether combined positron emission tomography and computed tomography (PET-CT) may be of value in deferring planned neck dissections for patients with advanced head and neck squamous cell carcinoma (HNSCC). Observational study of patients with de novo cervical > or =N2 regional spread of HNSCC in a tertiary care academic medical center. Forty-three patients were identified who underwent post-treatment PET-CT within 6 months of completing neoadjuvant chemotherapy combined with radiation therapy (CRT). The PET-CT was "positive" if the radiologist recommended tissue sampling or resection of cervical lymph nodes, or if there was progressive neck disease in the setting of distant metastatic disease. Patients who had positive PET-CT underwent confirmatory biopsy given clinical suspicion for regional cervical metastasis without distant disease. Patients with negative PET-CT were followed clinically and radiographically for a minimum of 5 months (median 18.1 months) after CRT. Ten (22%) of the 43 post-treatment PET-CT studies were positive. Seven of the 10 PET-CT scans (70% of positives) were true-positive given histologically-confirmed residual viable tumor or progressive disease including disease in the neck. The 3 remaining studies (30% of positives) were false-positive PET-CT results, given resolution of fluorodeoxyglucose (FDG) avidity on subsequent imaging or tissue sampling demonstrating absence of viable tumor cells. Of the 33 patients with negative PET-CTs in the neck, 1 patient had absence of FDG-avidity in the setting of malignant disease in the neck (3% false negatives); otherwise, patients with an initially negative PET-CT scan had no recurrences during the study (97% true negatives). This corresponds to a sensitivity of 87.5% (7/8), a specificity of 91% (32/35), a positive predictive value of 70% (7/10), a negative predictive value of 97% (32/33), and accuracy of 91% (39/43) for PET-CT scans in the detection of cervical metastatic disease after CRT. Overall, 37 (86%) of 43 patients were spared neck dissection using this technology without evidence of recurrent disease in the neck at extended follow-up. Our results suggest that planned neck dissection after CRT for HNSCC may be deferred in favor of serial PET-CT imaging, and that sampling of areas of suspicious FDG-avid uptake can be rationally considered prior to therapeutic neck dissection. These data also suggest that negative PET-CT scans are highly reliable for the absence of residual cervical nodal disease.

Standardized Uptake Values of Normal Organs on 18F-Fluorodeoxyglucose Positron Emission Tomography and Computed Tomography Imaging

Standardized uptake values (SUVs) of normal organs were evaluated by 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography and computed tomography (PET-CT) scanning. Seventy patients (38 men and 32 women) with no non-physiological 18F-FDG uptake participated in the study. All patients fasted for at least 4 h before PET-CT imaging and their fasting blood glucose levels were within the normal range. Image acquisition was performed after intravenous administration of 18F-FDG and images were obtained from the vertex to the upper thigh region. The SUVs of various organs were determined from the transverse views. The uptake of 18F-FDG was highest in the cerebrum, cerebellum, myocardium, tonsils, liver and spleen in both sexes. Having knowledge of the physiological uptake of 18F-FDG and normal organ SUVs is required for the correct interpretation of whole-body 18F-FDG-PET-CT studies.

Diagnostic Utility of Positron Emission Tomography‐Computed Tomography for Predicting Malignancy in Cystic Neck Masses in Adults

Combined positron emission tomography and computed tomography (PET-CT) is used for the diagnostic evaluation and staging of squamous cell carcinoma of the head and neck (SCCHN). By superimposing anatomic localization of CT with the physiologic data of PET, occult primary and metastatic neoplasms might be identified. Because the diagnostic algorithm for cystic neck masses in adults often overlaps with the work-up of cancer of unknown primary site, we evaluated the utility of PET-CT scans to identify the presence of malignancy and the location of primary tumor. Single-institution retrospective case review series. We reviewed the PET-CT imaging of cystic neck masses occurring in five patients over 40 years of age with significant risk factors for SCCHN and correlated this information with histopathology. In each patient in our series, the PET portion of a combined PET-CT was misleading, whereas the CT examination was more suggestive of the correct pathology. Ultimately, clinical judgment and endoscopic evaluation, guided by CT findings, were most valuable for distinguishing malignant versus benign processes and identifying the primary tumor sites. PET-CT may not be a reliable modality for identifying malignancy in adults with suspicious cystic neck masses. A thorough clinical evaluation by an experienced head and neck surgeon, in conjunction with contrast-enhanced CT, may be sufficient to facilitate the optimal management of such patients.