Added value of FDG-PET for detection of progressive supranuclear palsy.

BACKGROUND: Diagnostic criteria for progressive supranuclear palsy (PSP) include midbrain atrophy in MRI and hypometabolism in [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) as supportive features. Due to limited data regarding their relative and sequential value, there is no recommendation for an algorithm to combine both modalities to increase diagnostic accuracy. This study evaluated the added value of sequential imaging using state-of-the-art methods to analyse the images regarding PSP features. METHODS: The retrospective study included 41 PSP patients, 21 with Richardson's syndrome (PSP-RS), 20 with variant PSP phenotypes (vPSP) and 46 sex- and age-matched healthy controls. A pretrained support vector machine (SVM) for the classification of atrophy profiles from automatic MRI volumetry was used to analyse T1w-MRI (output: MRI-SVM-PSP score). Covariance pattern analysis was applied to compute the expression of a predefined PSP-related pattern in FDG-PET (output: PET-PSPRP expression score). RESULTS: The area under the receiver operating characteristic curve for the detection of PSP did not differ between MRI-SVM-PSP and PET-PSPRP expression score (p≥0.63): about 0.90, 0.95 and 0.85 for detection of all PSP, PSP-RS and vPSP. The MRI-SVM-PSP score achieved about 13% higher specificity and about 15% lower sensitivity than the PET-PSPRP expression score. Decision tree models selected the MRI-SVM-PSP score for the first branching and the PET-PSPRP expression score for a second split of the subgroup with normal MRI-SVM-PSP score, both in the whole sample and when restricted to PSP-RS or vPSP. CONCLUSIONS: FDG-PET provides added value for PSP-suspected patients with normal/inconclusive T1w-MRI, regardless of PSP phenotype and the methods to analyse the images for PSP-typical features.

Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance.

PURPOSE: Deep convolutional neural networks (CNN) are promising for automatic classification of dopamine transporter (DAT)-SPECT images. Reporting the certainty of CNN-based decisions is highly desired to flag cases that might be misclassified and, therefore, require particularly careful inspection by the user. The aim of the current study was to design and validate a CNN-based system for the identification of uncertain cases. METHODS: A network ensemble (NE) combining five CNNs was trained for binary classification of [123I]FP-CIT DAT-SPECT images as "normal" or "neurodegeneration-typical reduction" with high accuracy (NE for classification, NEfC). An uncertainty detection module (UDM) was obtained by combining two additional NE, one trained for detection of "reduced" DAT-SPECT with high sensitivity, the other with high specificity. A case was considered "uncertain" if the "high sensitivity" NE and the "high specificity" NE disagreed. An internal "development" dataset of 1740 clinical DAT-SPECT images was used for training (n = 1250) and testing (n = 490). Two independent datasets with different image characteristics were used for testing only (n = 640, 645). Three established approaches for uncertainty detection were used for comparison (sigmoid, dropout, model averaging). RESULTS: In the test data from the development dataset, the NEfC achieved 98.0% accuracy. 4.3% of all test cases were flagged as "uncertain" by the UDM: 2.5% of the correctly classified cases and 90% of the misclassified cases. NEfC accuracy among "certain" cases was 99.8%. The three comparison methods were less effective in labelling misclassified cases as "uncertain" (40-80%). These findings were confirmed in both additional test datasets. CONCLUSION: The UDM allows reliable identification of uncertain [123I]FP-CIT SPECT with high risk of misclassification. We recommend that automatic classification of [123I]FP-CIT SPECT images is combined with an UDM to improve clinical utility and acceptance. The proposed UDM method ("high sensitivity versus high specificity") might be useful also for DAT imaging with other ligands and for other binary classification tasks.

Prostate-specific membrane antigen radioguided surgery with negative histopathology: an in-depth analysis.

PURPOSE: To identify reasons for negative histopathology of specimens from prostate-specific membrane antigen (PSMA) radioguided surgery (PSMA-RGS) in recurrent prostate cancer (PCa) after prostatectomy. METHODS: Of 302 patients who underwent PSMA-RGS, 17 (5.6%) demonstrated a negative histopathology. Preoperative data, PSMA PET, PSMA SPECT, and follow-up information were analyzed retrospectively to differentiate true/false positive (TP/FP) from true/false negative (TN/FN) lesions. RESULTS: The median prostate-specific antigen at PET was 0.4 ng/ml (interquartile range [IQR] 0.3-1.2). Twenty-five index lesions (median short axis 7 mm, IQR 5-8; median long-axis 12 mm, IQR 8-17) had a median SUVmax of 4 (IQR 2.6-6; median PSMA expression score 1, IQR 1-1). Six lesions were TP, twelve were FP, one was TN, and six remained unclear. All TP lesions were in the prostatic fossa or adjacent to the internal iliac arteries. Three suspected local recurrences were FP. All FP lymph nodes were located at the distal external iliac arteries or outside the pelvis. A low PSMA-expressing TN node was identified next to a common iliac artery. Unclear lesions were located next to the external iliac arteries or outside the pelvis. CONCLUSION: In most cases with a negative histopathology from PSMA-RGS, lesions were FP on PSMA PET. Unspecific uptake should be considered in low PSMA-expressing lymph nodes at the distal external iliac arteries or outside the pelvis, especially if no PSMA-positive lymph nodes closer to the prostatic fossa are evident. Rarely, true positive metastases were missed by surgery or histopathology.

Removing outliers from the normative database improves regional atrophy detection in single-subject voxel-based morphometry.

PURPOSE: Single-subject voxel-based morphometry (VBM) compares an individual T1-weighted MRI to a sample of normal MRI in a normative database (NDB) to detect regional atrophy. Outliers in the NDB might result in reduced sensitivity of VBM. The primary aim of the current study was to propose a method for outlier removal ("NDB cleaning") and to test its impact on the performance of VBM for detection of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). METHODS: T1-weighted MRI of 81 patients with biomarker-confirmed AD (n = 51) or FTLD (n = 30) and 37 healthy subjects with simultaneous FDG-PET/MRI were included as test dataset. Two different NDBs were used: a scanner-specific NDB (37 healthy controls from the test dataset) and a non-scanner-specific NDB comprising 164 normal T1-weighted MRI from 164 different MRI scanners. Three different quality metrics based on leave-one-out testing of the scans in the NDB were implemented. A scan was removed if it was an outlier with respect to one or more quality metrics. VBM maps generated with and without NDB cleaning were assessed visually for the presence of AD or FTLD. RESULTS: Specificity of visual interpretation of the VBM maps for detection of AD or FTLD was 100% in all settings. Sensitivity was increased by NDB cleaning with both NDBs. The effect was statistically significant for the multiple-scanner NDB (from 0.47 [95%-CI 0.36-0.58] to 0.61 [0.49-0.71]). CONCLUSION: NDB cleaning has the potential to improve the sensitivity of VBM for the detection of AD or FTLD without increasing the risk of false positive findings.

No impact of attenuation and scatter correction on the interpretation of dopamine transporter SPECT in patients with clinically uncertain parkinsonian syndrome.

PURPOSE: The benefit from attenuation and scatter correction (ASC) of dopamine transporter (DAT)-SPECT for the detection of nigrostriatal degeneration in clinical routine is still a matter of debate. The current study evaluated the impact of ASC on visual interpretation and semi-quantitative analysis of DAT-SPECT in a large patient sample. METHODS: One thousand seven hundred forty consecutive DAT-SPECT with 123I-FP-CIT from clinical routine were included retrospectively. SPECT images were reconstructed iteratively without and with ASC. Attenuation correction was based on uniform attenuation maps, scatter correction on simulation. All SPECT images were categorized with respect to the presence versus the absence of Parkinson-typical reduction of striatal 123I-FP-CIT uptake by three independent readers. Image reading was performed twice to assess intra-reader variability. The specific 123I-FP-CIT binding ratio (SBR) was used for automatic categorization, separately with and without ASC. RESULTS: The mean proportion of cases with discrepant categorization by the same reader between the two reading sessions was practically the same without and with ASC, about 2.2%. The proportion of DAT-SPECT with discrepant categorization without versus with ASC by the same reader was 1.66% ± 0.50% (1.09-1.95%), not exceeding the benchmark of 2.2% from intra-reader variability. This also applied to automatic categorization of the DAT-SPECT images based on the putamen SBR (1.78% discrepant cases between without versus with ASC). CONCLUSION: Given the large sample size, the current findings provide strong evidence against a relevant impact of ASC with uniform attenuation and simulation-based scatter correction on the clinical utility of DAT-SPECT to detect nigrostriatal degeneration in patients with clinically uncertain parkinsonian syndrome.

Brain MRI in Progressive Supranuclear Palsy with Richardson's Syndrome and Variant Phenotypes.

BACKGROUND: Brain magnetic resonance imaging (MRI) is used to support the diagnosis of progressive supranuclear palsy (PSP). However, the value of visual descriptive, manual planimetric, automatic volumetric MRI markers and fully automatic categorization is unclear, particularly regarding PSP predominance types other than Richardson's syndrome (RS). OBJECTIVES: To compare different visual reading strategies and automatic classification of T1-weighted MRI for detection of PSP in a typical clinical cohort including PSP-RS and (non-RS) variant PSP (vPSP) patients. METHODS: Forty-one patients (21 RS, 20 vPSP) and 46 healthy controls were included. Three readers using three strategies performed MRI analysis: exclusively visual reading using descriptive signs (hummingbird, morning-glory, Mickey-Mouse), visual reading supported by manual planimetry measures, and visual reading supported by automatic volumetry. Fully automatic classification was performed using a pre-trained support vector machine (SVM) on the results of atlas-based volumetry. RESULTS: All tested methods achieved higher specificity than sensitivity. Limited sensitivity was driven to large extent by false negative vPSP cases. Support by automatic volumetry resulted in the highest accuracy (75.1% ± 3.5%) among the visual strategies, but performed not better than the midbrain area (75.9%), the best single planimetric measure. Automatic classification by SVM clearly outperformed all other methods (accuracy, 87.4%), representing the only method to provide clinically useful sensitivity also in vPSP (70.0%). CONCLUSIONS: Fully automatic classification of volumetric MRI measures using machine learning methods outperforms visual MRI analysis without and with planimetry or volumetry support, particularly regarding diagnosis of vPSP, suggesting the use in settings with a broad phenotypic PSP spectrum. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy.

BACKGROUND: To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS). OBJECTIVES: To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice. METHODS: This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern. RESULTS: Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample. CONCLUSIONS: Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes.

PURPOSE: Deep convolutional neural networks (CNN) provide high accuracy for automatic classification of dopamine transporter (DAT) SPECT images. However, CNN are inherently black-box in nature lacking any kind of explanation for their decisions. This limits their acceptance for clinical use. This study tested layer-wise relevance propagation (LRP) to explain CNN-based classification of DAT-SPECT in patients with clinically uncertain parkinsonian syndromes. METHODS: The study retrospectively included 1296 clinical DAT-SPECT with visual binary interpretation as "normal" or "reduced" by two experienced readers as standard-of-truth. A custom-made CNN was trained with 1008 randomly selected DAT-SPECT. The remaining 288 DAT-SPECT were used to assess classification performance of the CNN and to test LRP for explanation of the CNN-based classification. RESULTS: Overall accuracy, sensitivity, and specificity of the CNN were 95.8%, 92.8%, and 98.7%, respectively. LRP provided relevance maps that were easy to interpret in each individual DAT-SPECT. In particular, the putamen in the hemisphere most affected by nigrostriatal degeneration was the most relevant brain region for CNN-based classification in all reduced DAT-SPECT. Some misclassified DAT-SPECT showed an "inconsistent" relevance map more typical for the true class label. CONCLUSION: LRP is useful to provide explanation of CNN-based decisions in individual DAT-SPECT and, therefore, can be recommended to support CNN-based classification of DAT-SPECT in clinical routine. Total computation time of 3 s is compatible with busy clinical workflow. The utility of "inconsistent" relevance maps to identify misclassified cases requires further investigation.

Impact of age and sex correction on the diagnostic performance of dopamine transporter SPECT.

PURPOSE: The specific binding ratio (SBR) of 123I-FP-CIT (FP-CIT) in the putamen decreases with age by about 5% per decade and most likely is about 10% higher in females. However, the clinical utility of age and sex correction of the SBR is still a matter of debate. This study tested the impact of age and sex correction on the diagnostic performance of the putamen SBR in three independent patient samples. METHODS: Research sample: 207 healthy controls (HC) and 438 Parkinson's disease (PD) patients. Clinical sample A: 183 patients with neurodegenerative parkinsonian syndrome (PS) and 183 patients with non-neurodegenerative PS from one site. Clinical sample B: 84 patients with neurodegenerative PS and 38 patients with non-neurodegenerative PS from another site. Correction for age and sex of the putamen SBR was based on linear regression in the HC or non-neurodegenerative PS, separately in each sample. The area under the ROC curve (AUC) was used as performance measure. RESULTS: The putamen SBR was higher in females compared to males (PPMI: 14%, p < 0.0005; clinical sample A: 7%, p < 0.0005; clinical sample B: 6%, p = 0.361). Age-related decline of the putamen SBR ranged between 3.3 and 10.4% (p ≤ 0.019). In subjects ≥ 50 years, age and sex explained < 10% of SBR between-subjects variance. Correction of the putamen SBR for age and sex resulted in slightly decreased AUC in the PPMI sample (0.9955 versus 0.9969, p = 0.025) and in clinical sample A (0.9448 versus 0.9519, p = 0.057). There was a small, non-significant AUC increase in clinical sample B (0.9828 versus 0.9743, p = 0.232). CONCLUSION: These findings do not support age and sex correction of the putaminal FP-CIT SBR in the diagnostic workup of parkinsonian syndromes. This most likely is explained by the fact that the proportion of between-subjects variance caused by age and sex is considerably below the symptom threshold of about 50% reduction in neurodegenerative PS.

The Role of the Striatum in Learning to Orthogonalize Action and Valence: A Combined PET and 7 T MRI Aging Study.

Pavlovian biases influence instrumental learning by coupling reward seeking with action invigoration and punishment avoidance with action suppression. Using a probabilistic go/no-go task designed to orthogonalize action (go/no-go) and valence (reward/punishment), recent studies have shown that the interaction between the two is dependent on the striatum and its key neuromodulator dopamine. Using this task, we sought to identify how structural and neuromodulatory age-related differences in the striatum may influence Pavlovian biases and instrumental learning in 25 young and 31 older adults. Computational modeling revealed a significant age-related reduction in reward and punishment sensitivity and marked (albeit not significant) reduction in learning rate and lapse rate (irreducible noise). Voxel-based morphometry analysis using 7 Tesla MRI images showed that individual differences in learning rate in older adults were related to the volume of the caudate nucleus. In contrast, dopamine synthesis capacity in the dorsal striatum, assessed using [18F]-DOPA positron emission tomography in 22 of these older adults, was not associated with learning performance and did not moderate the relationship between caudate volume and learning rate. This multiparametric approach suggests that age-related differences in striatal volume may influence learning proficiency in old age.

Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.

PURPOSE: This study investigated the potential of deep convolutional neural networks (CNN) for automatic classification of FP-CIT SPECT in multi-site or multi-camera settings with variable image characteristics. METHODS: The study included FP-CIT SPECT of 645 subjects from the Parkinson's Progression Marker Initiative (PPMI), 207 healthy controls, and 438 Parkinson's disease patients. SPECT images were smoothed with an isotropic 18-mm Gaussian kernel resulting in 3 different PPMI settings: (i) original (unsmoothed), (ii) smoothed, and (iii) mixed setting comprising all original and all smoothed images. A deep CNN with 2,872,642 parameters was trained, validated, and tested separately for each setting using 10 random splits with 60/20/20% allocation to training/validation/test sample. The putaminal specific binding ratio (SBR) was computed using a standard anatomical ROI predefined in MNI space (AAL atlas) or using the hottest voxels (HV) analysis. Both SBR measures were trained (ROC analysis, Youden criterion) using the same random splits as for the CNN. CNN and SBR trained in the mixed PPMI setting were also tested in an independent sample from clinical routine patient care (149 with non-neurodegenerative and 149 with neurodegenerative parkinsonian syndrome). RESULTS: Both SBR measures performed worse in the mixed PPMI setting compared to the pure PPMI settings (e.g., AAL-SBR accuracy = 0.900 ± 0.029 in the mixed setting versus 0.957 ± 0.017 and 0.952 ± 0.015 in original and smoothed setting, both p < 0.01). In contrast, the CNN showed similar accuracy in all PPMI settings (0.967 ± 0.018, 0.972 ± 0.014, and 0.955 ± 0.009 in mixed, original, and smoothed setting). Similar results were obtained in the clinical sample. After training in the mixed PPMI setting, only the CNN provided acceptable performance in the clinical sample. CONCLUSIONS: These findings provide proof of concept that a deep CNN can be trained to be robust with respect to variable site-, camera-, or scan-specific image characteristics without a large loss of diagnostic accuracy compared with mono-site/mono-camera settings. We hypothesize that a single CNN can be used to support the interpretation of FP-CIT SPECT at many different sites using different acquisition hardware and/or reconstruction software with only minor harmonization of acquisition and reconstruction protocols.

Impact of plasma glucose level on the pattern of brain FDG uptake and the predictive power of FDG PET in mild cognitive impairment.

PURPOSE: Increased blood glucose level (BGL) has been reported to cause alterations of FDG uptake in the brain that mimic Alzheimer's disease (AD), even within the "acceptable" range ≤ 160 mg/dl. The aim of this study was (i) to confirm this in a large sample of well-characterized normal control (NC) subjects, and (ii) to analyze its impact on the prediction of AD dementia (ADD) in mild cognitive impairment (MCI). METHODS: The study included NCs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that were cognitively stable for ≥36 months after PET (n = 87, 74.2 ± 5.3 y), and ADNI MCIs with ≥36 months follow-up if not progressed to ADD earlier (n = 323, 71.1 ± 7.1 y). Seventy-three of the MCIs had progressed to ADD within 36 months. In the NCs, parenchyma-scaled FDG uptake was tested for clusters of correlation with BGL on the family-wise, error-corrected 5% level. In the MCIs, ROC analysis was used to assess the power of FDG uptake in a predefined AD-typical region for prediction of ADD. ROC analysis was repeated after correcting mean FDG uptake in the AD-typical region for BGL based on linear regression in the NCs. RESULTS: In the NCs, BGL (59-149 mg/dl) was negatively correlated with FDG uptake in a cluster comprising the occipital cortex and precuneus but sparing the posterior cingulate, independent of amyloid-ß and ApoE4 status. In the MCIs, FDG uptake in the AD-typical region provided an area of 0.804 under the ROC curve for prediction of ADD. Correcting FDG uptake in the AD-typical region for BGL (55-189 mg/dl) did not change predictive performance (area = 0.808, p = 0.311). CONCLUSIONS: Increasing BGL is associated with relative reduction of FDG uptake in the posterior cortex even in the "acceptable" range ≤ 160 mg/dl. The BGL-associated pattern is similar to the typical AD pattern, but not identical. BGL-associated variability of regional FDG uptake has no relevant impact on the power of FDG PET for prediction of MCI-to-ADD progression.

The asphericity of the metabolic tumour volume in NSCLC: correlation with histopathology and molecular markers.

PURPOSE: Asphericity (ASP) is a tumour shape descriptor based on the PET image. It quantitates the deviation from spherical of the shape of the metabolic tumour volume (MTV). In order to identify its biological correlates, we investigated the relationship between ASP and clinically relevant histopathological and molecular signatures in non-small-cell lung cancer (NSCLC). METHODS: The study included 83 consecutive patients (18 women, aged 66.4 ± 8.9 years) with newly diagnosed NSCLC in whom PET/CT with 18F-FDG had been performed prior to therapy. Primary tumour resection specimens and core biopsies were used for basic histopathology and determination of the Ki-67 proliferation index. EGFR status, VEGF, p53 and ALK expression were obtained in a subgroup of 44 patients. The FDG PET images of the primary tumours were delineated using an automatic algorithm based on adaptive thresholding taking into account local background. In addition to ASP, SUVmax, MTV and some further descriptors of shape and intratumour heterogeneity were assessed as semiquantitative PET measures. RESULTS: SUVmax, MTV and ASP were associated with pathological T stage (Kruskal-Wallis, p = 0.001, p < 0.0005 and p < 0.0005, respectively) and N stage (p = 0.017, p = 0.003 and p = 0.002, respectively). Only ASP was associated with M stage (p = 0.026). SUVmax, MTV and ASP were correlated with Ki-67 index (Spearman's rho = 0.326/p = 0.003, rho = 0.302/p = 0.006 and rho = 0.271/p = 0.015, respectively). The latter correlations were considerably stronger in adenocarcinomas than in squamous cell carcinomas. ASP, but not SUVmax or MTV, showed a tendency for a significant association with the extent of VEGF expression (p = 0.058). In multivariate Cox regression analysis, ASP (p < 0.0005) and the presence of distant metastases (p = 0.023) were significantly associated with progression-free survival. ASP (p = 0.006), the presence of distant metastases (p = 0.010), and Ki-67 index (p = 0.062) were significantly associated with overall survival. CONCLUSION: The ASP of primary NSCLCs on FDG PET images is associated with tumour dimensions and molecular markers of proliferation and angiogenesis.

Imaging of Tumor Metabolism Using Positron Emission Tomography (PET).

Molecular imaging employing PET/CT enables in vivo visualization, characterization, and measurement of biologic processes in tumors at a molecular and cellular level. Using specific metabolic tracers, information about the integrated function of multiple transporters and enzymes involved in tumor metabolic pathways can be depicted, and the tracers can be directly applied as biomarkers of tumor biology. In this review, we discuss the role of F-18-fluorodeoxyglucose (FDG) as an in vivo glycolytic marker which reflects alterations of glucose metabolism in cancer cells. This functional molecular imaging technique offers a complementary approach to anatomic imaging such as computed tomography (CT) and magnetic resonance imaging (MRI) and has found widespread application as a diagnostic modality in oncology to monitor tumor biology, optimize the therapeutic management, and guide patient care. Moreover, emerging methods for PET imaging of further biologic processes relevant to cancer are reviewed, with a focus on tumor hypoxia and aberrant tumor perfusion. Hypoxic tumors are associated with poor disease control and increased resistance to cytotoxic and radiation treatment. In vivo imaging of hypoxia, perfusion, and mismatch of metabolism and perfusion has the potential to identify specific features of tumor microenvironment associated with poor treatment outcome and, thus, contribute to personalized treatment approaches.

Dual time point imaging for F18-FDG-PET/CT does not improve the accuracy of nodal staging in non-small cell lung cancer patients.

OBJECTIVES: To analyze the diagnostic performance of dual time point imaging (DTPI) for pre-therapeutic lymph node (LN) staging in non-small cell lung cancer (NSCLC). METHODS: This was a retrospective analysis of 47 patients with NSCLC who had undergone DTPI by PET (early + delayed) using F18-fluorodeoxyglucose (FDG). PET raw data were reconstructed iteratively (point spread function + time-of-flight). LN uptake in PET was assessed visually (four-step score) and semi-quantitatively (SUVmax, SUVmean, ratios LN/primary, LN/liver, and LN/mediastinal blood pool). DTPI analyses included retention indices (RIs), Δ-ratios and changes in visual score. Histology or cytology served as standards of reference. Accuracy was determined based on ROC analyses. RESULTS: Thirty-six of 155 LNs were malignant. DTPI accuracy was low for all measures (visual assessment, 24.5%; RI SUVmax, 68.4%; RI SUVmean, 65.8%; Δ-ratios, 63.9-76.1%) and significantly inferior to early PET. Accuracies of early (range, 86.5-92.9%) and delayed PET (range, 85.2-92.9%) were comparable. At early PET, accuracy of the visual score (92.9%) was similar or superior to semi-quantitative analyses (range, 86.5-92.3%). CONCLUSIONS: Using a modern PET/CT device and novel image reconstruction, neither additional delayed PET nor DTPI analyses improved the accuracy of PET-based LN staging. Dedicated visual assessment criteria performed very well. KEY POINTS: • DTPI did not improve accuracy of PET-based LN staging in NSCLC. • Analyzed SUV ratios were not superior to LN SUVmax or SUVmean. • A four-step visual score may allow highly accurate, standardized LN assessment.

Preserved brain metabolic activity at the age of 96 years.

Loss of brain tissue becomes notable to cerebral magnetic resonance imaging (MRI) at age 30 years, and progresses more rapidly from mid 60s. The incidence of dementia increases exponentially with age, and is all too frequent in the oldest old (≥ 90 years of age), the fastest growing age group in many countries. However, brain pathology and cognitive decline are not inevitable, even at extremely old age (den Dunnen et al., 2008).

Assessment of bone marrow inflammation in patients with myelofibrosis: an 18F-fluorodeoxyglucose PET/CT study.

PURPOSE: Myelofibrosis is a haematopoietic stem cell neoplasm characterized by bone marrow inflammation, reactive marrow fibrosis and extramedullary haematopoiesis. The aim of this study was to determine if (18)F-FDG PET/CT can be used to noninvasively visualize and quantify the extent and activity of bone marrow involvement. METHODS: In 30 patients, the biodistribution of (18)F-FDG was analysed by measuring the standardized uptake value in the bone marrow compartment and spleen. Imaging findings were compared with laboratory, cytogenetic and histopathological data. RESULTS: Retention of (18)F-FDG was observed in bone marrow and spleen. Bone marrow involvement varied, ranging from mildly increased uptake in the central skeleton to extensive uptake in most parts of the skeleton. The extent of bone marrow involvement decreased over time from initial diagnosis (r s = -0.43, p = 0.019). Metabolic activity of the bone marrow decreased as the histopathological grade of fibrosis increased (r s = -0.37, p = 0.04). There was a significant positive correlation between the metabolic activity of the bone marrow and that of the spleen (p = 0.04). CONCLUSION: (18)F-FDG PET/CT is as a promising technique for the quantitation of bone marrow inflammation in myelofibrosis. Our data indicate that the intensity of bone marrow (18)F-FDG uptake decreases as bone marrow fibrosis increases. Further evaluation in prospective studies is required to determine the potential clinical impact and prognostic significance of PET.

Increased evidence for the prognostic value of primary tumor asphericity in pretherapeutic FDG PET for risk stratification in patients with head and neck cancer.

PURPOSE: In a previous study, we demonstrated the first evidence that the asphericity (ASP) of pretherapeutic FDG uptake in the primary tumor provides independent prognostic information in patients with head and neck cancer. The aim of this work was to confirm these results in an independent patient group examined at a different site. METHODS: FDG-PET/CT was performed in 37 patients. The primary tumor was delineated by an automatic algorithm based on adaptive thresholding. For the resulting ROIs, the metabolically active part of the tumor (MTV), SUVmax, SUVmean, total lesion glycolysis (TLG) and ASP were computed. Univariate Cox regression with respect to progression free survival (PFS) and overall survival (OS) was performed. For survival analysis, patients were divided in groups of high and low risk according to the parameter cut-offs defined in our previous work. In a second step, the cut-offs were adjusted to the present data. Univariate and multivariate Cox regression was performed for the pooled data consisting of the current and the previously described patient group (N = 68). In multivariate Cox regression, clinically relevant parameters were included. RESULTS: Univariate Cox regression using the previously published cut-off values revealed TLG (hazard ratio (HR) = 3) and ASP (HR = 3) as significant predictors for PFS. For OS MTV (HR = 2.7) and ASP (HR = 5.9) were significant predictors. Using the adjusted cutoffs MTV (HR = 2.9/3.3), TLG (HR = 3.1/3.3) and ASP (HR = 3.1/5.9) were prognostic for PFS/OS. In the pooled data, multivariate Cox regression revealed a significant prognostic value with respect to PFS/OS for MTV (HR = 2.3/2.1), SUVmax (HR = 2.1/2.5), TLG (HR = 3.5/3.6), and ASP (HR = 3.4/4.4). CONCLUSIONS: Our results confirm the independent prognostic value of ASP of the pretherapeutic FDG uptake in the primary tumor in patients with head and neck cancer. Moreover, these results demonstrate that ASP can be determined unambiguously across different sites.

Magnetic resonance enterography for assessment of intestinal graft-versus-host disease after allogeneic stem cell transplantation.

OBJECTIVES: To determine the diagnostic performance of MR enterography (MRE) for detection and grading of gastrointestinal graft-versus-host disease (GI GvHD) after hematopoietic stem cell transplantation (SCT). METHODS: Forty-one patients with known GvHD or suspected GvHD underwent MRE and GI endoscopy with multi-level biopsies. MRE images were reviewed for presence of intestinal wall inflammation. Clinical grading of GI GvHD was performed. Histopathological evaluation (HPE) served as the reference standard. RESULTS: Overall, MRE demonstrated a per-patient sensitivity of 81.5 % for detection of GI GvHD. The most common findings were intestinal wall thickening (81.5 % of GvHD patients), luminal stenosis (81.5 %), mural contrast enhancement (70.4 %), and ascites (59.3 %). These findings were also observed in other conditions than GvHD. The most frequently involved intestinal segment was the sigmoid colon (63.0 %), followed by the ileum (59.3 %) and the jejeunum (51.9 %). The number of involved segments (r s =0.54, p =0.009) correlated significantly with clinical severity as determined by GvHD grading. CONCLUSIONS: After allogeneic stem cell transplantation, MRE may (1) contribute to detection and localization of GI GvHD, and (2) add information indicating the clinical severity of disease, but findings are unspecific. False negative results may be observed not only in low-grade GI GvHD. KEY POINTS: • Magnetic resonance enterography (MRE) allows for detection of GI GvHD • Common findings are wall thickening, stenosis, mural contrast enhancement, and ascites • The extent of GI involvement on MRE correlates with clinical severity of GvHD • Involvement of sigmoid colon and small intestine is common • Findings are unspecific and also observed in other conditions, e.g. infectious enteritis.