FDG-PET Imaging Research Articles

PSAT288 Mucosa-associated Lymphoid Tissue Thyroid Lymphoma Presenting as a Rapidly Growing Neck Mass in an Asymptomatic Male Patient

Abstract A 77-year-old male was referred to endocrinology for a 2-month history of a rapidly enlarging painless left neck mass. His only other complaint was unintentional weight loss of 10 pounds over 12-months. On exam patient had a firm, smooth, nontender 2 cm thyroid/neck mass extending below left clavicle. No abnormalities were detected for right thyroid gland. There was no palpable lymphadenopathies or hepatosplenomegaly noted. No signs of tracheal, esophageal, or neck vein compression were noted. He did not have any history of radiation exposure, family history of thyroid-related disorders, or malignancy. Laboratory test including complete blood count, comprehensive metabolic profile and TSH were within normal limits. TPO antibody was positive. Ultrasound revealed a 7.7×4.1×4 cm hypoechoic spongiform mass replacing the left lobe of the thyroid, as well as scattered sub centimeter nodules. Contrasted CT of the neck confirmed 6.5×5.2×3.9 cm left thyroid mass causing mild rightward deviation of the trachea and extending into the superior mediastinum and no lymphadenopathy. FNA of the mass revealed presence of crushed lymphoid cells without thyroid follicular cells, which raised the possibility of chronic lymphocytic thyroiditis. FDG- PET scan showed intense, heterogeneous FDG uptake in the left neck mass (max SUV=10.7), likely arising from the left thyroid lobe concerning for thyroid malignancy without evidence of distant metastases. The patient underwent a left thyroid nodule core biopsy with subsequent left thyroid hemithyroidectomy. The pathology stains of the excised left thyroid lobe core biopsy showed extra nodal zone lymphoma of mucosa- associated lymphoid tissue (MALT Lymphoma) with extensive plasmocytic differentiation. Surgical path confirmed extra nodal zone lymphoma of mucosa-associated lymphoid tissue (MALT Lymphoma) with extensive plasmocytic differentiation and foci of chronic lymphocytic thyroiditis. Immunochemistry studies supported a diagnosis of plasma cell neoplasm or the plasma cell component of a B-cell lymphoproliferative disorder. Short-term follow-up FDG-PET and CT imaging demonstrated post-surgical changes without evidence of recurrence. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m.

Early Reduction of Glucose Consumption Is a Biomarker of Kinase Inhibitor Efficacy Which Can Be Reversed with GLUT1 Overexpression in Lung Cancer Cells.

Small molecule inhibitors that target oncogenic driver kinases are an important class of therapies for non-small cell lung cancer (NSCLC) and other malignancies. However, these therapies are not without their challenges. Each inhibitor works on only a subset of patients, the pharmacokinetics of these inhibitors is variable, and these inhibitors are associated with significant side effects. Many of these inhibitors lack non-invasive biomarkers to confirm pharmacodynamic efficacy, and our understanding of how these inhibitors block cancer cell growth remains incomplete. Limited clinical studies suggest that early (< 2weeks after start of therapy) changes in tumor glucose consumption, measured by [18F]FDG PET imaging, can predict therapeutic efficacy, but the scope of this strategy and functional relevance of this inhibition of glucose consumption remains understudied. Here we demonstrate that early inhibition of glucose consumption as can be measured clinically with [18F]FDG PET is a consistent phenotype of efficacious targeted kinase inhibitors and is necessary for the subsequent inhibition of growth across models of NSCLC. We tested nine NSCLC cell lines (A549, H1129, H1734, H1993, H2228, H3122, H460, HCC827, and PC9 cells) and ten targeted therapies (afatinib, buparlisib, ceritinib, cabozantinib, crizotinib, dovitinib, erlotinib, ponatinib, trametinib, and vemurafenib) across concentrations ranging from 1.6nM to 5µM to evaluate whether these inhibitors block glucose consumption at 24-h post-drug treatment and cell growth at 72-h post-drug treatment. We overexpressed the facilitative glucose transporter SLC2A1 (GLUT1) to test the functional connection between blocked glucose consumption and cell growth after treatment with a kinase inhibitor. A subset of these inhibitors and cell lines were studied in vivo. Across the nine NSCLC cell lines, ten targeted therapies, and a range of inhibitor concentrations, whether a kinase inhibitor blocked glucose consumption at 24-h post-drug treatment strongly correlated with whether that inhibitor blocked cell growth at 72-h post-drug treatment in cell culture. These results were confirmed in vivo with [18F]FDG PET imaging. GLUT1 overexpression blocked the kinase inhibitors from limiting glucose consumption and cell growth. Our results demonstrate that the early inhibition of lung cancer glucose consumption in response to a kinase inhibitor is a strong biomarker of and is often required for the subsequent inhibition of cell growth. Early inhibition of glucose consumption may provide complementary information to other biomarkers in determining whether a drug will effectively limit tumor growth.

Adjustment for theAge- and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism.

The online version contains supplementary material available at 10.1007/s43657-022-00079-6.

Prospective Evaluation of Interim FDG-PET Imaging during Chemoradiation for Anal SCC

<h3>Purpose/Objective(s)</h3> De-escalation of therapy for anal squamous cell carcinoma is under investigation in multiple prospective randomized trials. Imaging metrics from (F-18) fluorodeoxyglucose positron emission tomography (PET) before and during chemoradiation may serve as a biomarker to tailor treatment and aid in appropriate patient and dose selection. <h3>Materials/Methods</h3> From June 2012 to July 2021, 56 patients with anal canal cancer were enrolled in two prospective studies (30 and 26 patients, respectively) of PET prior to and during CRT after receiving ∼30 Gy. PET metrics of the primary site were calculated for the pre-treatment and interim images, including maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) based on 40% SUVmax (MTV40, TLG40) or SUV 2.5 (MTV2.5, TLG2.5) thresholds. Associations between absolute pre-treatment and interim PET metrics and freedom from local/regional recurrence (FFLR) were investigated using the Cox proportional hazards method. Local/regional recurrence was defined as primary or nodal (in-field) recurrence. No adjustment was made for multiple comparisons. <h3>Results</h3> Forty-four patients were eligible for analysis. Patients with non-squamous cell histology (adenocarcinoma, melanoma) (3 patients), recurrent anal cancer (1 patient), metastatic disease (1 patient), and incomplete studies due to treatment toxicity or patient choice (7 patients) were excluded. Median follow-up was 2.4 years. Clinical stage was T3 or T4 in 43% of patients, and 52% were node positive. Seven patients have expired and disease recurrence patterns following treatment included local/regional (5 patients) and distant disease (7 patients). On univariate analysis, age, T and N stage, duration of treatment, and HIV status did not significantly correlate with FFLR. TLG2.5 (1.05 [0.99-1.11]) was marginally associated with FFLR. No pretreatment metrics or other interim metrics were significantly associated with FFLR. Multivariate analysis was not conducted given the small number of events (N=5). <h3>Conclusion</h3> In this expanded pilot study, characteristics of PET acquired during treatment of anal canal cancer were not significantly associated with FFLR. Given the limited number of FFLR events, definitive conclusions about the utility of interim PET cannot be drawn. The correlation of various PET metrics with FFLR warrants further investigation to determine if interim PET as an imaging biomarker can be used to personalize treatment, potentially in the setting of dose de-escalation.

Development of a new risk scoring system based on spleen involvement and the lymphocyte/monocyte ratio for follicular lymphoma patients

BackgroundThis study aimed to retrospectively evaluate a large Chinese cohort's baseline characters and develop a new predictive scoring system. Methods: A total of 491 FL patients were reviewed, and 337newly diagnosed 1–3a FL patients from January 2010 to October 2020 were enrolled in our retrospective cohort. Results: Progression-free survival (PFS) was assessed as the first endpoint; the 2-year and 5-year PFS were 81.4% and 67.8%, respectively. A new risk scoring system (SLMR) was established after the univariable and multivariable analyses, and four independent factors such as β2-MG, LDH, LMR, the number of nodal areas ≥ 5, and spleen involvement were detected by FDG-PET imaging were included. The SLMR was compared with the existing models using discrimination and calibration, and k-fold cross-validation was performed for internal validation. Our cohort classified patients into three groups (low-, intermediate- and high) according to the SLMR and showed the best model performance, especially for FL3a and received rituximab maintenance after induction therapy patients.

Gastroenteropancreatic neuroendocrine neoplasm imaging: standard reporting templates.

Standardized reporting in radiology has an established role in numerous disease processes, with added benefits in oncology of reduced variability, and generation of a thorough and pertinent report with a focused and relevant conclusion. Many radiologists are not familiar with the imaging patterns of neuroendocrine neoplasm (NEN) spread and recurrence. This paper will present standardized CT, MRI, and PET templates for reporting gastroenteropancreatic (GEP) NENs and explain the rationale for including specific pertinent positive and negative findings, at various stages of disease management, based on site of origin. Basic templates for initial and follow-up anatomic and molecular GEP NEN imaging were created with input from the multidisciplinary Society of Abdominal Radiology (SAR) Neuroendocrine Tumor Disease Focused Panel (NET-DFP). The templates were further modified and finalized after several iterations. Four main report templates were generated for (i) initial anatomic CT or MR imaging studies, (ii) follow-up anatomic CT or MR imaging studies, (iii) initial Somatostatin Receptor (SSTR) or FDG PET imaging studies, and (iv) follow-up SSTR or FDG PET imaging studies. Each study template was formatted to allow its integration into a dictation software directly and be modified as needed, with internalized instructions indicating where a drop-down menu or macro may be used to personalize the template as necessary. These templates were created through a combination of multidisciplinary expert opinion discussion supported by literature review and provide basic structured reporting standards for GEP NEN anatomic and molecular imaging studies.

Potential novel imaging targets of inflammation in cardiac sarcoidosis

Cardiac sarcoidosis (CS) is an inflammatory disease with high morbidity and mortality, with a pathognomonic feature of non-caseating granulomatous inflammation. While 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a well-established modality to image inflammation and diagnose CS, there are limitations to its specificity and reproducibility. Imaging focused on the molecular processes of inflammation including the receptors and cellular microenvironments present in sarcoid granulomas provides opportunities to improve upon FDG-PET imaging for CS. This review will highlight the current limitations of FDG-PET imaging for CS while discussing emerging new nuclear imaging molecular targets for the imaging of cardiac sarcoidosis.

Localization of myocardial FDG uptake for prognostic risk stratification in corticosteroid-naïve cardiac sarcoidosis

BackgroundThe localization of myocardial 18F-fluorodeoxyglucose (FDG) uptake affecting long-term clinical outcomes has not been elucidated in patients with corticosteroid-naïve cardiac sarcoidosis (CS). ObjectivesThis study sought to investigate the localization of myocardial FDG uptake on positron emission tomography (PET) and myocardial perfusion abnormality to predict adverse events (AEs) for a long-term follow-up in patients with corticosteroid-naïve CS. MethodsConsecutive 90 patients with clinical suspicion of CS who underwent FDG-PET imaging to assess for inflammation were enrolled. AEs were defined as a composite of sustained ventricular tachycardia (VT), heart transplantation, and all-cause death, which were ascertained by medical records, defibrillator interrogation, and telephone interviews. ResultsOf 90 patients, 42 patients (mean age 62.9 ± 12.0 years; 76.2% females) were confirmed active cardiac involvement. Over a median follow-up of 4.9 years, 15 patients with CS experienced AEs including 6 sustained ventricular tachycardias (VT) and 9 deaths. Cox proportional-hazards model after adjustment for left ventricular systolic dysfunction revealed that FDG uptake in the right ventricle (RV) or basal anterolateral area of the left ventricle (LV) with myocardial perfusion abnormality was predictive of AEs. ConclusionsFDG uptake in the RV or basal anterolateral area of the LV with myocardial perfusion abnormality provides long-term prognostic risk stratification in patients with corticosteroid-naïve CS.

Simulated Reduced-Count Whole-Body FDG PET: Evaluation in Children and Young Adults Imaged on a Digital PET Scanner.

BACKGROUND. Digital PET scanners with increased sensitivity may allow shorter scan acquisition times or reductions in administered radiopharmaceutical activities. OBJECTIVE. The purpose of this study was to evaluate in children and young adults the impact of shorter simulated acquisition times on the quality of whole-body FDG PET images obtained using a digital PET/CT system. METHODS. This retrospective study included 27 children and young adults (nine male and 18 female patients) who underwent clinically indicated whole-body FDG PET/CT examinations performed using a 25-cm axial FOV PET/CT system at 90 s per bed position (expressed hereafter as seconds per bed). Raw list-mode data were reprocessed to simulate acquisition times of 60, 55, 50, 45, 40, and 30 s/bed. Three radiologists independently reviewed reconstructed images and assigned Likert scores for lesion conspicuity, normal structure conspicuity, image quality, and image noise. A separate observer recorded the SUVmax, SUVmean, and SD of the SUV (SUVSD) for liver, thigh, and the most FDG-avid lesion. The SUVSD/SUVmean (the SUVSD divided by the SUVmean) was calculated as a surrogate of image noise. ANOVA, the Friedman test, and the Dunn test were used to compare qualitative measures (combining reader scores) and SUV measurements. RESULTS. The mean patient age was 10.8 ± 8.3 (SD) years, mean BMI was 18.7 ± 2.9, and mean administered FDG activity was 4.44 ± 0.37 MBq/kg (0.12 ± 0.01 mCi/kg). No qualitative measure showed a significant difference versus 90 s/bed for the simulated acquisition at 60 s/bed (all p > .05). Significant differences (all p < .05) versus 90 s/bed were observed for lesion conspicuity at at most 40 s/bed, conspicuity of normal structures and overall image quality at at most 45 s/bed, and image noise at at most 55 s/bed. SUVmean was not significantly different from 90 s/bed for any site for any reduced-count simulation (all p > .05). SUVSD/SUVmean and SUVmax showed gradual increases with decreasing acquisition times and were significantly different from 90 s/bed only for liver at 60 s/bed (for SUVmax: 1.00 ± 0.00 vs 1.05 ± 0.03, p = .02; for SUVSD/SUVmean: 0.09 ± 0.02 vs 0.11 ± 0.02, p = .04). CONCLUSION. Favorable findings for the simulated acquisition at 60 s/bed suggest that, in children and young adults who undergo imaging performed using a 25-cm FOV digital PET scanner, acquisition time or administered FDG activity may be decreased by approximately 33% from the clinical standard without significantly impacting image quality. CLINICAL IMPACT. A 25-cm axial FOV digital scanner may allow FDG PET/CT examinations to be performed with reduced radiation exposure or faster scan acquisition times.

Principal component analysis of texture features derived from FDG PET images of melanoma lesions

BackgroundThe clinical utility of radiomics is hampered by a high correlation between the large number of features analysed which may result in the “bouncing beta” phenomenon which could in part explain why in a similar patient population texture features identified and/or cut-off values of prognostic significance differ from one study to another. Principal component analysis (PCA) is a technique for reducing the dimensionality of large datasets containing highly correlated variables, such as texture feature datasets derived from FDG PET images, increasing data interpretability whilst at the same time minimizing information loss by creating new uncorrelated variables that successively maximize variance. Here, we report on PCA of a texture feature dataset derived from 123 malignant melanoma lesions with a significant range in lesion size using the freely available LIFEx software.ResultsThirty-eight features were derived from all lesions. All features were standardized. The statistical assumptions for carrying out PCA analysis were met. Seven principal components with an eigenvalue > 1 were identified. Based on the “elbow sign” of the Scree plot, only the first five were retained. The contribution to the total variance of these components derived using Varimax rotation was, respectively, 30.6%, 23.6%, 16.1%, 7.4% and 4.1%. The components provided summarized information on the locoregional FDG distribution with an emphasis on high FDG uptake regions, contrast in FDG uptake values (steepness), tumour volume, locoregional FDG distribution with an emphasis on low FDG uptake regions and on the rapidity of changes in SUV intensity between different regions.ConclusionsPCA allowed to reduce the dataset of 38 features to a set of 5 uncorrelated new variables explaining approximately 82% of the total variance contained within the dataset. These principal components may prove more useful for multiple regression analysis considering the relatively low numbers of patients usually included in clinical trials on FDG PET texture analysis. Studies assessing the superior differential diagnostic, predictive or prognostic value of principal components derived using PCA as opposed to the initial texture features in clinical relevant settings are warranted.

Risk of recurrence after chemoradiotherapy identified by multimodal MRI and 18F-FDG-PET/CT in locally advanced cervical cancer

Background and purposeMRI, applying dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) sequences, and 18F-fluorodeoxyglucose (18F-FDG) PET/CT provide information about tumor aggressiveness that is unexploited in treatment of locally advanced cervical cancer (LACC). We investigated the potential of a multimodal combination of imaging parameters for classifying patients according to their risk of recurrence. Materials and methodsEighty-two LACC patients with diagnostic MRI and FDG-PET/CT, treated with chemoradiotherapy, were collected. Thirty-eight patients with MRI only were included for validation of MRI results. Endpoints were survival (disease-free, cancer-specific, overall) and tumor control (local, locoregional, distant). Ktrans, reflecting vascular function, apparent diffusion coefficient (ADC), reflecting cellularity, and standardized uptake value (SUV), reflecting glucose uptake, were extracted from DCE-MR, DW-MR and FDG-PET images, respectively. By applying an oxygen consumption and supply-based method, ADC and Ktrans parametric maps were voxel-wise combined into hypoxia images that were used to determine hypoxic fraction (HF). ResultsHF showed a stronger association with outcome than the single modality parameters. This association was confirmed in the validation cohort. Low HF identified low-risk patients with 95% precision. Based on the 50th SUV-percentile (SUV50), patients with high HF were divided into an intermediate- and high-risk group with high and low SUV50, respectively. This defined a multimodality biomarker, HF/SUV50. HF/SUV50 increased the precision of detecting high-risk patients from 41% (HF alone) to 57% and showed prognostic significance in multivariable analysis for all endpoints. ConclusionMultimodal combination of MR- and FDG-PET/CT-images improves classification of LACC patients compared to single modality images and clinical factors.

Distinct cerebral cortical perfusion patterns in idiopathic normal-pressure hydrocephalus.

The aims of the study are to evaluate idiopathic normal-pressure hydrocephalus (INPH)-related cerebral blood flow (CBF) abnormalities and to investigate their relation to cortical thickness in INPH patients. We investigated cortical CBF utilizing surface-based early-phase 18 F-florbetaben (E-FBB) PET analysis in two groups: INPH patients and healthy controls. All 39 INPH patients and 20 healthy controls were imaged with MRI, including three-dimensional volumetric images, for automated surface-based cortical thickness analysis across the entire brain. A subgroup with 37 participants (22 INPH patients and 15 healthy controls) that also underwent 18 F-fluorodeoxyglucose (FDG) PET imaging was further analyzed. Compared with age- and gender-matched healthy controls, INPH patients showed statistically significant hyperperfusion in the high convexity of the frontal and parietal cortical regions. Importantly, within the INPH group, increased perfusion correlated with cortical thickening in these regions. Additionally, significant hypoperfusion mainly in the ventrolateral frontal cortex, supramarginal gyrus, and temporal cortical regions was observed in the INPH group relative to the control group. However, this hypoperfusion was not associated with cortical thinning. A subgroup analysis of participants that also underwent FDG PET imaging showed that increased (or decreased) cerebral perfusion was associated with increased (or decreased) glucose metabolism in INPH. A distinctive regional relationship between cerebral cortical perfusion and cortical thickness was shown in INPH patients. Our findings suggest distinct pathophysiologic mechanisms of hyperperfusion and hypoperfusion in INPH patients.

P11.54.A Factors effecting early diagnosis in neurolymphomatosis

Abstract Background Neurolymphomatosis is defined as infiltration of the peripheral nervous system by non-Hodgkin lymphoma (NHL). Given its rarity, correct diagnosis is often delayed. Here we assessed diagnostic work-up and factors affecting timely diagnosis at a tertiary referral center. Material and Methods The quality control database of the Section of Neuro-Oncology at Yale Cancer Center was searched for neurolymphomatosis cases seen between 2000 and 2021. Clinical, radiological and pathological findings were collected. Factors affecting the interval from symptom onset to diagnosis were investigated in univariate analysis using SPSS version 27. P-values &amp;lt;0.05 were considered statistically significant. Results We identified 22 cases of neurolymphomatosis (primary n=7, secondary n=15) with secondary disease diagnosed a median of 16 months (range: 4-144) after NHL. Patients presented with painful polyneuropathy (n=16), painless polyneuropathy (n=4), cranial neuropathy (n=3), and autonomic neuropathy (n=1). Diagnosis was nerve biopsy-based (n=11) or resulted from integration of imaging findings (n=11) with NHL history (n=5) or detection of NHL in the cerebrospinal fluid (n=6), respectively. Lesions localized to nerve roots (n=16), peripheral nerves (n=12), plexus (n=10), cranial (n=3) or autonomic (n=1) nerves. Histologically, most cases were classified as diffuse-large B-cell lymphoma (n=18). Median interval from symptom onset to diagnosis was 3 months (range: 1-12). Secondary neurolymphomatosis (p=0.02) and FDG-PET imaging (p&amp;lt;0.01) were associated with an earlier diagnosis. Conclusion Diagnosis of neurolymphomatosis requires a high degree of clinical suspicion with subsequent integration of clinical, radiological and pathological findings. FDG-PET, the imaging gold standard, and a known NHL history correlated with an earlier diagnosis.

Can Dynamic Whole-Body FDG PET Imaging Differentiate between Malignant and Inflammatory Lesions?

Background: Investigation of the clinical feasibility of dynamic whole-body (WB) [18F]FDG PET, including standardized uptake value (SUV), rate of irreversible uptake (Ki), and apparent distribution volume (Vd) in physiologic tissues, and comparison between inflammatory/infectious and cancer lesions. Methods: Twenty-four patients were prospectively included to undergo dynamic WB [18F]FDG PET/CT for clinically indicated re-/staging of oncological diseases. Parametric maps of Ki and Vd were generated using Patlak analysis alongside SUV images. Maximum parameter values (SUVmax, Kimax, and Vdmax) were measured in liver parenchyma and in malignant or inflammatory/infectious lesions. Lesion-to-background ratios (LBRs) were calculated by dividing the measurements by their respective mean in the liver tissue. Results: Seventy-seven clinical target lesions were identified, 60 malignant and 17 inflammatory/infectious. Kimax was significantly higher in cancer than in inflammatory/infections lesions (3.0 vs. 2.0, p = 0.002) while LBRs of SUVmax, Kimax, and Vdmax did not differ significantly between the etiologies: LBR (SUVmax) 3.3 vs. 2.9, p = 0.06; LBR (Kimax) 5.0 vs. 4.4, p = 0.05, LBR (Vdmax) 1.1 vs. 1.0, p = 0.18). LBR of inflammatory/infectious and cancer lesions was higher in Kimax than in SUVmax (4.5 vs. 3.2, p < 0.001). LBRs of Kimax and SUVmax showed a strong correlation (Spearman’s rho = 0.83, p < 0.001). Conclusions: Dynamic WB [18F]FDG PET/CT is feasible in a clinical setting. LBRs of Kimax were higher than SUVmax. Kimax was higher in malignant than in inflammatory/infectious lesions but demonstrated a large overlap between the etiologies.

Bone marrow segmentation and radiomics analysis of [18F]FDG PET/CT images for measurable residual disease assessment in multiple myeloma

Background and objectivesThe last few years have been crucial in defining the most appropriate way to quantitatively assess [18F]FDG PET images in Multiple Myeloma (MM) patients to detect persistent tumor burden. The visual evaluation of images complements the assessment of Measurable Residual Disease (MRD) in bone marrow samples by multiparameter flow cytometry (MFC) or next-generation sequencing (NGS). The aim of this study was to quantify MRD by analyzing quantitative and texture [18F]FDG PET features. MethodsWhole body [18F]FDG PET of 39 patients with newly diagnosed MM were included in the database, and visually evaluated by experts in nuclear medicine. A segmentation methodology of the skeleton from CT images and an additional manual segmentation tool were proposed, implemented in a software solution including a graphical user interface. Both the compact bone and the spinal canal were removed from the segmentation to obtain only the bone marrow mask. SUV metrics, GLCM, GLRLM, and NGTDM parameters were extracted from the PET images and evaluated by Mann-Whitney U-tests and Spearman ρ rank correlation as valuable features differentiating PET+/PET- and MFC+/MFC- groups. Seven machine learning algorithms were applied for evaluating the classification performance of the extracted features. ResultsQuantitative analysis for PET+/PET- differentiating demonstrated to be significant for most of the variables assessed with Mann-Whitney U-test such as Variance, Energy, and Entropy (p-value = 0.001). Moreover, the quantitative analysis with a balanced database evaluated by Mann-Whitney U-test revealed in even better results with 19 features with p-values < 0.001. On the other hand, radiomics analysis for MFC+/MFC- differentiating demonstrated the necessity of combining MFC evaluation with [18F]FDG PET assessment in the MRD diagnosis. Machine learning algorithms using the image features for the PET+/PET- classification demonstrated high performance metrics but decreasing for the MFC+/MFC- classification. ConclusionsA proof-of-concept for the extraction and evaluation of bone marrow radiomics features of [18F]FDG PET images was proposed and implemented. The validation showed the possible use of these features for the image-based assessment of MRD.

18F]FDG-PET/CT radiomics for the identification of genetic clusters in pheochromocytomas and paragangliomas

ObjectivesBased on germline and somatic mutation profiles, pheochromocytomas and paragangliomas (PPGLs) can be classified into different clusters. We investigated the use of [18F]FDG-PET/CT radiomics, SUVmax and biochemical profile for the identification of the genetic clusters of PPGLs.MethodsIn this single-centre cohort, 40 PPGLs (13 cluster 1, 18 cluster 2, 9 sporadic) were delineated using a 41% adaptive threshold of SUVpeak ([18F]FDG-PET) and manually (low-dose CT; ldCT). Using PyRadiomics, 211 radiomic features were extracted. Stratified 5-fold cross-validation for the identification of the genetic cluster was performed using multinomial logistic regression with dimensionality reduction incorporated per fold. Classification performances of biochemistry, SUVmax and PET(/CT) radiomic models were compared and presented as mean (multiclass) test AUCs over the five folds. Results were validated using a sham experiment, randomly shuffling the outcome labels.ResultsThe model with biochemistry only could identify the genetic cluster (multiclass AUC 0.60). The three-factor PET model had the best classification performance (multiclass AUC 0.88). A simplified model with only SUVmax performed almost similarly. Addition of ldCT features and biochemistry decreased the classification performances. All sham AUCs were approximately 0.50.ConclusionPET radiomics achieves a better identification of PPGLs compared to biochemistry, SUVmax, ldCT radiomics and combined approaches, especially for the differentiation of sporadic PPGLs. Nevertheless, a model with SUVmax alone might be preferred clinically, weighing model performances against laborious radiomic analysis. The limited added value of radiomics to the overall classification performance for PPGL should be validated in a larger external cohort.Key Points• Radiomics derived from [18F]FDG-PET/CT has the potential to improve the identification of the genetic clusters of pheochromocytomas and paragangliomas.• A simplified model with SUVmaxonly might be preferred clinically, weighing model performances against the laborious radiomic analysis.• Cluster 1 and 2 PPGLs generally present distinctive characteristics that can be captured using [18F]FDG-PET imaging. Sporadic PPGLs appear more heterogeneous, frequently resembling cluster 2 PPGLs and occasionally resembling cluster 1 PPGLs.

Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI.

Objective: To investigate the role of prediction microvascular invasion (mVI) in hepatocellular carcinoma (HCC) by 18F-FDG PET image texture analysis and hybrid criteria combining PET/CT and multi-parameter MRI. Materials and methods: Ninety-seven patients with HCC who received the examinations of MRI and 18F-FDG PET/CT were retrospectively included in this study and were randomized into training and testing cohorts. The lesion image texture features of 18F-FDG PET were extracted using MaZda software. The optimal predictive texture features of mVI were selected, and the classification procedure was conducted. The predictive performance of mVI by radiomics classier in training and testing cohorts was respectively recorded. Next, the hybrid model was developed by integrating the 18F-FDG PET image texture, metabolic parameters, and MRI parameters to predict mVI through logistic regression. Furthermore, the diagnostic performance of each time was recorded. Results: The 18F-FDG PET image radiomics classier showed good predicted performance in both training and testing cohorts to discriminate HCC with/without mVI, with an AUC of 0.917 (95% CI: 0.824–0.970) and 0.771 (95% CI: 0.578, 0.905). The hybrid model, which combines radiomics classier, SUVmax, ADC, hypovascular arterial phase enhancement pattern on contrast-enhanced MRI, and non-smooth tumor margin, also yielded better predictive performance with an AUC of 0.996 (95% CI: 0.939, 1.000) and 0.953 (95% CI: 0.883, 1.000). The differences in AUCs between radiomics classier and hybrid classier were significant in both training and testing cohorts (DeLong test, both p < 0.05). Conclusion: The radiomics classier based on 18F-FDG PET image texture and the hybrid classier incorporating 18F-FDG PET/CT and MRI yielded good predictive performance, which might provide a precise prediction of HCC mVI preoperatively.

Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments

BackgroundCurrent radiological assessments of 18fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging data in diffuse large B-cell lymphoma (DLBCL) can be time consuming, do not yield real-time information regarding disease burden and organ involvement, and hinder the use of FDG-PET to potentially limit the reliance on invasive procedures (e.g. bone marrow biopsy) for risk assessment.MethodsOur aim is to enable real-time assessment of imaging-based risk factors at a large scale and we propose a fully automatic artificial intelligence (AI)-based tool to rapidly extract FDG-PET imaging metrics in DLBCL. On availability of a scan, in combination with clinical data, our approach generates clinically informative risk scores with minimal resource requirements. Overall, 1268 patients with previously untreated DLBCL from the phase III GOYA trial (NCT01287741) were included in the analysis (training: n = 846; hold-out: n = 422).ResultsOur AI-based model comprising imaging and clinical variables yielded a tangible prognostic improvement compared to clinical models without imaging metrics. We observed a risk increase for progression-free survival (PFS) with hazard ratios [HR] of 1.87 (95% CI: 1.31–2.67) vs 1.38 (95% CI: 0.98–1.96) (C-index: 0.59 vs 0.55), and a risk increase for overall survival (OS) (HR: 2.16 (95% CI: 1.37–3.40) vs 1.40 (95% CI: 0.90–2.17); C-index: 0.59 vs 0.55). The combined model defined a high-risk population with 35% and 42% increased odds of a 4-year PFS and OS event, respectively, versus the International Prognostic Index components alone. The method also identified a subpopulation with a 2-year Central Nervous System (CNS)-relapse probability of 17.1%.ConclusionOur tool enables an enhanced risk stratification compared with IPI, and the results indicate that imaging can be used to improve the prediction of central nervous system relapse in DLBCL. These findings support integration of clinically informative AI-generated imaging metrics into clinical workflows to improve identification of high-risk DLBCL patients.Trial RegistrationRegistered clinicaltrials.gov number: NCT01287741.Graphical

Regional Characterization of the Gottingen Minipig Brain by [18 F]FDG Dynamic Pet Modeling

Purpose To determine the best kinetic model to be applied on dynamic brain [18 F]FDG PET images by characterizing the regional brain glucose metabolism of normal Göttingen minipigs.MethodsNine Göttingen minipigs were scanned with a clinical PET/CT tomograph, starting from the injection of an intravenous bolus of [18 F]FDG, for about 25 min. Dynamic images were reconstructed and nine brain regions of interest (ROI), plus a vascular region, were defined and time-activity curves (TAC) were determined.Three kinetic models were considered for fitting with experimental TACs: one-tissue compartment model 1TC, two-tissue irreversible compartment model 2TCi and two-tissue reversible model 2TC. Akaike Information Criterion was considered to evaluate the goodness of each model fitting. Regional and global kinetic parameter values were evaluated, in addition to the partition coefficient, net influx rate and retention index (RI).ResultsBoth 2TCi and 2TC models turned out to be good choices for the next analysis. Parameter values were very similar between the different brain regions, with similar values to when the brain as a whole is considered (kinetic parameters mean values, from 2TCi model: K1 = 1.0 ml/g/min, k2 = 0.49 min− 1, k3 = 0.034 min− 1, K1/k2 = 2.14ml/g, Ki =0.069 ml/g/min; from 2TC model: K1 = 1.10 ml/g/min, k2 = 0.54 min− 1, k3 = 0.058 min− 1, k4 = 0.039 min− 1, K1/k2 = 2.18 ml/g, Ki = 0.10 ml/g/min; RI mean ± sd: 0.147 ± 0.037 min− 1), with the exception of the cerebellum (mean values from the 2TCi model: K1 = 0.52 ml/g/min, k2 = 0.56 min− 1, k3 = 0.025 min− 1, K1/k2 = 0.98ml/g, Ki=0.022 ml/g/min; from 2TC model: K1 = 0.54 ml/g/min, k2 = 0.61 min− 1, k3 = 0.044 min− 1, k4 = 0.038 min− 1, K1/k2 = 0.95ml/g, Ki=0.032 ml/g/min; RI mean ± sd: 0.071 ± 0.018 min− 1).ConclusionThe two-tissue model is able to describe the regional brain metabolism in Göttingen minipigs. Compared to the 2TCi model, in the 2TC model the k4 micro-parameter was also evaluated. This led to adjustments of the other microparameters, especially k3 and consequently the net influx rate Ki. For healthy minipigs, the glucose metabolism was similar in all of the brain regions analyzed, with the exception of the cerebellum, where the FDG uptake was lower.

Patterns of hypometabolism in frontal lobe epilepsy originating in different frontal regions.

ObjectivesAnalysis of FDG‐PET imaging commonly shows that hypometabolism extends into extra‐epileptogenic zones (extra‐EZ). This study investigates the distribution patterns of hypometabolism in frontal lobe epilepsy (FLE) originating in different frontal regions.MethodsSixty‐four patients with FLE were grouped by EZ localization according to Brodmann areas (BAs): Group 1 (the frontal motor and premotor area), BAs 4, 6, and 8; Group 2 (the inferior frontal gyrus and opercular area), BAs 44, 45, and 47; Group 3 (the dorsal prefrontal area), BAs 9, 10, 11, and 46; and Group 4 (the medial frontal and anterior cingulate gyrus), BAs 32 and 24. Regions of extra‐EZ hypometabolism were statistically analyzed between FLE groups and healthy controls. Correlation analysis was performed to identify relationships between the intensity of hypometabolism and clinical characteristics.ResultsSignificant hypometabolism in the ipsilateral (Groups 1 and 4) or bilateral (Groups 2 and 3) anterior insulae was found. Groups 1 and 4 presented with limited distribution of extra‐EZ hypometabolism, whereas Groups 2 and 3 showed widely distributed extra‐EZ hypometabolism in the rectus gyrus, cingulate gyrus, and other regions. Additionally, the intensity of hypometabolism was correlated with epilepsy duration in Groups 2 and 3.ConclusionsAll FLE groups showed hypometabolism in the anterior insula. In addition, distinct patterns of extra‐EZ hypometabolism were identified for each FLE group. This quantitative FDG‐PET analysis expanded our understanding of the topography of epileptic networks and can guide EZ localization in the future.