18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC). METHODS: A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology. The PET/CT signatures were constructed by classic radiomic, handcrafted-feature-based model and KSTM self-learning-based model. The clinical nomogram was constructed by independent risk factors for PM. Lastly, the PET/CT signatures, clinical nomogram, and experts' diagnoses were fused using evidential reasoning to establish the MMF model. RESULTS: The MMF model showed excellent performance in both cohorts (area under the curve [AUC] 94.16% and 90.84% in training and testing), and demonstrated better prediction accuracy than clinical nomogram or experts' diagnoses (net reclassification improvement p < 0.05). The MMF model also had satisfactory generalization ability, even in mucinous adenocarcinoma and signet ring cell carcinoma which have poor uptake of 18F-FDG (AUC 97.98% and 89.71% in training and testing). CONCLUSIONS: The 18F-FDG PET/CT radiomics-based MMF model may have significant clinical implications in predicting PM in AGC, revealing that it is necessary to combine the information from different modalities for comprehensive prediction of PM.

Multimodality radiomics analysis based on [18F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis.

OBJECTIVES: To determine whether radiomics models developed from 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT combined with multisequence MRI could contribute to predicting the progression-free survival (PFS) of nasopharyngeal carcinoma (NPC) patients. METHODS: One hundred thirty-two NPC patients who underwent both PET/CT and MRI scanning were retrospectively enrolled (88 vs. 44 for training vs. testing). For each modality/sequence (i.e., PET, CT, T1, T1C, and T2), 1906 radiomics features were extracted from the primary tumor volume. Univariate Cox model and correlation analysis were used for feature selection. A multivariate Cox model was used to establish radiomics signature. Prognostic performances of 5 individual modality models and 12 multimodality models (3 integrations × 4 fusion strategies) were assessed by the concordance index (C-index) and log-rank test. A clinical-radiomics nomogram was built to explore the clinical utilities of radiomics signature, which was evaluated by discrimination, calibration curve, and decision curve analysis (DCA). RESULTS: The radiomics signatures of individual modalities showed limited prognostic efficacy with a C-index of 0.539-0.664 in the testing cohort. Different fusion strategies exhibited a slight difference in predictive performance. The PET/CT and MRI integrated model achieved the best performance with a C-index of 0.745 (95% CI, 0.619-0.865) in the testing cohort (log-rank test, p < 0.05). Clinical-radiomics nomogram further improved the prognosis, which also showed satisfactory discrimination, calibration, and net benefit. CONCLUSIONS: Multimodality radiomics analysis by combining PET/CT with multisequence MRI could potentially improve the efficacy of PFS prediction for NPC patients. KEY POINTS: • Individual modality radiomics models showed limited performance in prognosis evaluation for NPC patients. • Combined PET, CT and multisequence MRI radiomics signature could improve the prognostic efficacy. • Multilevel fusion strategies exhibit comparable performance but feature-level fusion deserves more attention.

Development and evaluation of a radiomics model of resting 13N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease.

Background: Coronary angiography (CAG) is usually performed in patients with coronary heart disease (CHD) to evaluate the coronary artery stenosis. However, patients with iodine allergy and renal dysfunction are not suitable for CAG. We try to develop a radiomics machine learning model based on rest 13N-ammonia (13N-NH3) positron emission tomography (PET) myocardial perfusion imaging (MPI) to predict coronary stenosis. Methods: Eighty-four patients were included with the inclusion criteria: adult patients; suspected CHD; resting MPI and CAG were performed; and complete data. Coronary artery stenosis >75% were considered to be significant stenosis. Patients were randomly divided into a training group and a testing group with a ratio of 1:1. Myocardial blood flow (MBF), perfusion defect extent (EXT), total perfusion deficit (TPD), and summed rest score (SRS) were obtained. Myocardial static images of the left ventricular (LV) coronary segments were segmented, and radiomics features were extracted. In the training set, the conventional parameter (MPI model) and radiomics (Rad model) models were constructed using the machine learning method and were combined to construct a nomogram. The models' performance was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, decision analysis curve (DCA), and calibration curves. Testing and subgroup analysis were performed. Results: MPI model was composed of MBF and EXT, and Rad model was composed of 12 radiomics features. In the training set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.795/0.778/0.937/0.511, 0.912/0.825/0.760/0.936 and 0.911/0.865/0.924/0.766 respectively. In the testing set, the AUC/accuracy/sensitivity/specificity of the MPI model, Rad model, and the nomogram were 0.798/0.722/0.659/0.841, 0.887/0.810/0.744/0.932 and 0.900/0.849/0.854/0.841 respectively. The AUC of Rad model and nomogram were significantly higher than that of MPI model. The DCA curve also showed that the clinical net benefit of the Rad model and nomogram was similar but greater than that of MPI model. The calibration curve showed good agreement between the observed and predicted values of the Rad model. In the subgroup analysis of Rad model, there was no significant difference in AUC between subgroups. Conclusions: The Rad model is more accurate than the MPI model in predicting coronary stenosis. This noninvasive technique could help improve risk stratification and had good generalization ability.

Clinical and imaging features preoperative evaluation of histological grade and microvascular infiltration of hepatocellular carcinoma.

BACKGROUND: To predict the histological grade and microvascular invasion (MVI) in patients with HCC. METHODS: A retrospective analysis was conducted on 175 patients who underwent MRI enhancement scanning (from September 2016.9 to October 2020). They were divided into MVI positive, MVI negative, Grade-high and Grade-low groups. RESULTS: The AFP of 175 HCC patients distributed in MVI positive and negative groups, Grade-low and Grade-high groups were statistically significant (P = 0.002 and 0.03, respectively). Multiple HCC lesions were more common in MVI positive and Grade-high groups. Correspondingly, more single lesions were found in MVI negative and Grade-low groups (P = 0.005 and 0.019, respectively). Capsule on MRI was more common in MVI negative and Grade-high groups, and the difference was statistically significant (P = 0.02 and 0.011, respectively). There were statistical differences in the distribution of three MRI signs: artistic rim enhancement, artistic peripheral enhancement, and tumor margin between MVI positive and MVI negative groups (P = 0.001, < 0.001, and < 0.001, respectively). Tumor hypointensity on HBP was significantly different between MVI positive and negative groups (P < 0.001). CONCLUSIONS: Our research shows that preoperative enhanced imaging can be used to predict MVI and tumor differentiation grade of HCC. The prognosis of MVI-negative group was better than that of MVI-positive group.

Radiosynthesis and biological evaluation of 18F-labeled bispecific heterodimer targeted dual gastrin-releasing peptide receptor and prostate-specific membrane antigen for prostate cancer imaging.

OBJECTIVE: Approximately 5% of prostatic primary tumors and 15% of metastatic tumors were found to be prostate-specific membrane antigen (PSMA)-negative. Targeting gastrin-releasing peptide receptor (GRPR) has been shown to complement patients with PSMA-negative prostate cancer (PCa). Based on previous findings, simultaneously targeting PSMA and GRPR imaging may improve the diagnosis of PCa. In this study, we report the radiosynthesis and biological evaluation of a bispecific heterodimer of NOTA-GRPR-PSMA that targeted both PSMA and GRPR for extended PCa imaging. METHODS: NOTA-GRPR-PSMA was labeled using the Al18F-chelating one-step method. The competitive combination experiment and specific binding assay were performed in vitro using 22Rv1 (PSMA+) and PC-3 (GRPR+) cells. To determine the distribution and specificity in vivo, biodistribution and micro-PET/computed tomography of [18F]AlF-GRPR-PSMA were performed on mice bearing 22Rv1 or PC-3 tumors. RESULTS: [18F]AlF-GRPR-PSMA had a radiochemical purity of over 98% and demonstrated high stability in vivo and in vitro, with a LogD of -1.2 ± 0.05. Cell uptake and inhibition studies of [18F]AlF-GRPR-PSMA in 22Rv1 and PC-3 cells revealed bispecific GRPR and PSMA bindings. According to the biodistribution study and PET imaging, [18F]AlF-GRPR-PSMA was mainly excreted through the kidney. Tumor uptake was high in 22Rv1 tumor (10.1 ± 0.4 %ID/g) and moderate in PC-3 tumor (2.1 ± 0.6 %ID/g) 2 h p.i., whereas blocking studies significantly decreased the tumor uptake of 22Rv1 and PC-3. CONCLUSION: [18F]AlF-GRPR-PSMA has the potential to simultaneously target PSMA and GRPR for PCa imaging.

Feasibility of perfusion and early-uptake 18F-FDG PET/CT in primary hepatocellular carcinoma: a dual-input dual-compartment uptake model.

PURPOSE: PET enables a concurrent evaluation of perfusion status and metabolic activity. We aimed to evaluate the feasibility of perfusion and early-uptake 18F-FDG PET/CT in hepatocellular carcinoma (HCC) using a dual-input dual-compartment uptake model. MATERIALS AND METHODS: Data from 5 min dynamic PET/CT and conventional PET/CT scans were retrospectively collected from 17 pathologically diagnosed HCCs. Parameters such as hepatic arterial blood flow (Fa), portal vein blood flow (Fv), total blood flow (F), hepatic arterial perfusion index (HPI), portal vein perfusion index (PPI), blood volume (BV), extracellular mean transit time (MTT) and intracellular uptake rate (Ki) were calculated. Fa, HPI, MTT and Ki images were generated and used to identify HCC. RESULTS: Compared with the surrounding liver tissue, HCCs showed significant increases in Fa, HPI, Ki and the maximum standard uptake value (SUVmax) (all P < 0.001) and significant reductions in Fv (P < 0.05) and PPI (P < 0.001). F, BV and MTT (all P > 0.05) did not differ significantly between HCCs and the surrounding liver tissue. Perfusion and early-uptake PET/CT increased the positivity rate of HCCs from 52.9% with conventional PET/CT alone to 88.2% with the combined method (P < 0.05). CONCLUSIONS: Perfusion and early-uptake PET/CT are feasible for diagnosing HCC and provide added functional information to enhance diagnostic performance.

Preoperative prediction of regional lymph node metastasis of colorectal cancer based on 18F-FDG PET/CT and machine learning.

PURPOSE: To establish and validate a regional lymph node (LN) metastasis prediction model of colorectal cancer (CRC) based on 18F-FDG PET/CT and radiomic features using machine-learning methods. METHODS: A total of 199 colorectal cancer patients underwent pre-therapy diagnostic 18F-FDG PET/CT scans and CRC radical surgery. The Chang-Gung Image Texture Analysis toolbox (CGITA) was used to extract 70 PET radiomic features reflecting 18F-FDG uptake heterogeneity of tumors. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomic signature score (Rad-score). The training set was used to establish five machine-learning prediction models and the test set was used to test the efficacy of the models. The effectiveness of the models was compared by ROC analysis. RESULTS: The CRC patients were divided into a training set (n = 144) and a test set (n = 55). Two radiomic features were selected to build the Rad-score. Five machine-learning algorithms including logistic regression, support vector machine (SVM), random forest, neural network and eXtreme gradient boosting (XGBoost) were used to established models. Among the five machine-learning models, logistic regression (AUC 0.866, 95% CI 0.808-0.925) and XGBoost (AUC 0.903, 95% CI 0.855-0.951) models performed the best. In the training set, the AUC of these two models were significantly higher than that of the LN metastasis status reported by 18F-FDG PET/CT for differentiating positive and negative regional LN metastases in CRC (all p < 0.05). Good efficacy of the above two models was also achieved in the test set. We created a nomogram based on the logistic regression model that visualized the results and provided an easy-to-use method for predicting regional LN metastasis in patients with CRC. CONCLUSION: In this study, five machine-learning models for preoperative prediction of regional LN metastasis of CRC based on 18F-FDG PET/CT and PET-based radiomic features were successfully developed and validated. Among them, the logistic regression and XGBoost models performed the best, with higher efficacy than 18F-FDG PET/CT in both the training and test sets.

Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images.

OBJECTIVES: To develop a radiomics signature to predict locoregional recurrence (LR) and distant metastasis (DM), as extracted from pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/X-ray computed tomography (PET/CT) images in locally advanced nasopharyngeal carcinoma (NPC). MATERIALS AND METHODS: Eighty-five patients with Stage III-IVB NPC underwent pretreatment [18F]FDG PET/CT scans and received radiotherapy or chemoradiotherapy. 53 of them achieved disease control, and 32 of them failed after treatment (15: LR, 17: DM). A total of 114 radiomic features were extracted from PET/CT images. For univariate analysis, Wilcoxon test and Chi-square test were used to compare median values of features between different treatment outcomes and predict the risk of treatment failure, respectively. For multivariate analysis, all features were grouped into clusters based on Pearson correlation using hierarchical clustering, and the representative feature of each cluster was chosen by the Relief algorithm. Then sequential floating forward selection (SFFS) coupled with a support vector machine (SVM) classifier were used to derive the optimized feature set in terms of the area under receiver operating characteristic (ROC) curve (AUC). The performance of the model was evaluated by leave-one-out-cross-validation, fivefold cross-validation, tenfold cross-validation. RESULTS: Twenty features had significant differences between disease control and treatment failure. NPC patients with values of Compactness1, Compactness2, Coarseness_NGTDM or SGE_GLGLM above the median as well as patients with values of Irregularity, RLN_GLRLM or GLV_GLSZM below the median, showed a significant (p < 0.05) higher risk of treatment failure (about 50% vs. 25%). The derived radiomics signature consisted of 5 features with the highest AUC value of 0.8290 (sensitivity: 0.8438, specificity: 0.7736) using leave-one-out-cross-validation. CONCLUSION: Locoregional recurrence (LR) and DM of locally advanced NPC can be predicted using radiomics analysis of pretreatment [18F]FDG PET/CT. The SFFS feature selection coupled with SVM classifier can derive the optimized feature set with correspondingly highest AUC value for pretreatment prediction of LR and/or DM of NPC.

Development and validation of an ultrasound-based nomogram for preoperative prediction of cervical central lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: Preoperative prediction of central lymph node metastasis (CLNM) holds significant value in determining a patient's suitability for surgical resection and the need for adjuvant treatment, thereby contributing to better therapeutic strategies. This study aimed to build and confirm a nomogram that integrates ultrasound (US) characteristics with clinical features to predict CLNM in patients with papillary thyroid carcinoma (PTC) preoperatively. METHODS: The prediction model was set up with a training dataset that included 512 patients with histopathologically confirmed PTC. The least absolute shrinkage and selection operator (LASSO) regression method was applied to select US features in the development cohort. The patients' US characteristics and clinical features were incorporated into a multivariate logistic regression analysis to develop the nomogram. The clinical feasibility, calibration, and discriminatory ability of the nomogram were evaluated in an independent validation cohort of 306 patients. RESULTS: Age, sex, tumor size, multiple tumors, and US-based CLNM status were included as independent predictors in the personalized nomogram. The nomogram showed good calibration and discrimination in the training and validation datasets. The addition of the BRAF V600E mutation status did not improve the performance of the nomogram. The decision curve analysis showed the nomogram to have clinical feasibility. CONCLUSIONS: A nomogram that integrates US characteristics with patients' clinical features was built. This US-based nomogram can be expediently applied to promote the personalized preoperative prediction of CLNM and to develop surgical strategies, such as tailored central compartment neck dissection, in patients with PTC.

Radiosynthesis and preclinical evaluation of [18F]FEM as a potential novel PET probe for tumor imaging.

In the 21st century, the incidence and mortality of cancer, one of the most challenging diseases in the world, have rapidly increased. The purpose of this study was to develop 2-(2-[18F]fluoroethoxy)ethyl 4-methylbenzenesulfonate ([18F]FEM) as a positron emission tomography (PET) agent for tumor imaging. In this study, [18F]FEM was synthesized with a good radiochemical yield (45.4 ± 5.8%), high specific radioactivity (over 25 GBq/µmol), and commendable radiochemical purity (over 99%). The octanol/water partition coefficient of [18F]FEM was 1.44 ± 0.04. The probe demonstrated good stability in vitro (phosphate-buffered saline (PBS) and mouse serum (MS)), and binding specificity to five different tumor cell lines (A549, PC-3, HCC827, U87, and MDA-MB-231). PET imaging of tumor-bearing mice showed that [18F]FEM specifically accumulated at the tumor site of the five different tumor cell lines. The average tumor-to-muscle (T/M) ratio was over 2, and the maximum T/M values reached about 3.5. The biodistribution and dynamic PET imaging showed that most probes were metabolized by the liver, whereas a small part was metabolized by the kidney. Moreover, dynamic brain images and quantitative data showed [18F]FEM can quickly cross the blood brain barrier (BBB) and quickly fade out, thereby suggesting it may be a promising candidate probe for the imaging of brain tumors. The presented results demonstrated that [18F]FEM is a promising probe for tumor PET imaging.

Expert Consensus on clinical application of FDG PET/CT in infection and inflammation.

To further promote the clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in infection and inflammation and standardize the diagnostic process, the experts in relevant fields in China carried out discussion and formed the Expert Consensus on the clinical application of FDG PET/CT in infection and inflammation. This consensus is intended to provide a reference for imaging physicians to select a reasonable diagnostic plan. However, it should be noted that it couldn't include or solve all the problems in clinical operation. Imaging physicians and technicians should develop a comprehensive and reasonable diagnostic procedure according to their professional knowledge, clinical experience and currently available medical resources when facing specific patients.

Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma.

PURPOSE: This work aims to identify intratumoral habitats with distinct heterogeneity based on 2-deoxy-2-[18F]fluro-D-glucose positron emission tomography (PET)/X-ray computed tomography (CT) imaging, and to develop a subregional radiomics approach to predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC). PROCEDURES: In total, 128 NPC patients (85 vs. 43 for primary vs. validation cohorts) who underwent pre-treatment PET/CT scan were enrolled retrospectively. Each tumor was partitioned into several phenotypically consistent subregions based on individual- and population-level clustering. For each subregion, 202 radiomics features were extracted to construct imaging biomarker for prognosis via Cox's proportional hazard model combined with forward stepwise feature selection. Relevance of imaging biomarkers and clinicopathological factors were assessed by multivariate Cox regression analysis and Spearman's correlation analysis. To investigate whether imaging biomarkers could provide complementary prognosis information beyond existing predictors, a scoring system was further developed for risk stratification and compared with AJCC staging system. RESULTS: Three subregions (denoted as S1, S2, and S3) were discovered with distinct PET/CT imaging characteristics in the two cohorts. The prognostic performance of imaging biomarker S3 outperformed the whole tumor (C-index, 0.69 vs. 0.58; log-rank test, p < 0.001 vs. p = 0.552). Imaging biomarker S3 and AJCC stage were identified as independent predictors (p = 0.011 and 0.042, respectively) after adjusting for clinicopathological factors. The scoring system outperformed the traditional AJCC staging system (log-rank test, p < 0.0001 vs. p = 0.0002 in primary cohort and p = 0.0021 vs. p = 0.0277 in validation cohort, respectively). CONCLUSIONS: Subregional radiomics analysis of PET/CT imaging has the potential to predict PFS in patients with NPC, which also provides complementary prognostic information for traditional predictors.

Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.

PURPOSE: To identify optimal machine learning methods for radiomics-based differentiation of local recurrence versus inflammation from post-treatment nasopharyngeal positron emission tomography/X-ray computed tomography (PET/CT) images. PROCEDURES: Seventy-six nasopharyngeal carcinoma (NPC) patients were enrolled (41/35 local recurrence/inflammation as confirmed by pathology). Four hundred eighty-seven radiomics features were extracted from PET images for each patient. The diagnostic performance was investigated for 42 cross-combinations derived from 6 feature selection methods and 7 classifiers. Of the original cohort, 70 % was applied for feature selection and classifier development, and the remaining 30 % used as an independent validation set. The diagnostic performance was evaluated using area under the ROC curve (AUC), test error, sensitivity, and specificity. Furthermore, the performance of the radiomics signatures against routine features was statistically compared using DeLong's method. RESULTS: The cross-combination fisher score (FSCR) + k-nearest neighborhood (KNN), FSCR + support vector machines with radial basis function kernel (RBF-SVM), FSCR + random forest (RF), and minimum redundancy maximum relevance (MRMR) + RBF-SVM outperformed others in terms of accuracy (AUC 0.883, 0.867, 0.892, 0.883; sensitivity 0.833, 0.864, 0.831, 0.750; specificity 1, 1, 0.873, 1) and reliability (test error 0.091, 0.136, 0.150, 0.136). Compared with conventional metrics, the radiomics signatures showed higher AUC values (0.867-0.892 vs. 0.817), though the differences were not statistically significant (p = 0.462-0.560). CONCLUSION: This study identified the most accurate and reliable machine learning methods, which could enhance the application of radiomics methods in the precision of diagnosis of NPC.

Positron-emission tomography for hepatocellular carcinoma: Current status and future prospects.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality worldwide. Various imaging modalities provide important information about HCC for its clinical management. Since positron-emission tomography (PET) or PET-computed tomography was introduced to the oncologic setting, it has played crucial roles in detecting, distinguishing, accurately staging, and evaluating local, residual, and recurrent HCC. PET imaging visualizes tissue metabolic information that is closely associated with treatment. Dynamic PET imaging and dual-tracer have emerged as complementary techniques that aid in various aspects of HCC diagnosis. The advent of new radiotracers and the development of immuno-PET and PET-magnetic resonance imaging have improved the ability to detect lesions and have made great progress in treatment surveillance. The current PET diagnostic capabilities for HCC and the supplementary techniques are reviewed herein.

Synthesis and Preclinical Evaluation of the Fibrin-Binding Cyclic Peptide 18F-iCREKA: Comparison with Its Contrasted Linear Peptide.

Purpose: Cys-Arg-Glu-Lys-Ala (CREKA) is a pentapeptide which can target fibrin-fibronectin complexes. Our previous study has built a probe called iCREKA which was based on CREKA and has proved the feasibility and specificity of iCREKA by the fluorescence experiment. The purpose of this study is to achieve the 18F-labeled iCREKA and make preclinical evaluation of the 18F-iCREKA with comparison of its contrasted linear peptide (LP). Methods: CREKA, LP, and iCREKA were labeled by the Al18F labeling method, respectively. These 18F-labeled peptides were evaluated by the radiochemistry, binding affinity, in vitro stability, in vivo stability, micro-PET imaging, and biodistribution tests. Results: 18F-NOTA-iCREKA was stable both in vitro and in vivo. However, 18F-NOTA-CREKA and 18F-NOTA-LP were both unstable. The FITC or 18F-labeled iCREKA could be abundantly discovered only in matrix metalloproteinases- (MMPs-) 2/9 highly expressed U87MG cells, while the FITC or 18F-labeled LP could also be abundantly discovered in MMP-2/9 lowly expressed Caov3 cells. Biodistribution and micropositron emission tomography (PET) imaging revealed that the U87MG xenografts showed a higher uptake of 18F-NOTA-iCREKA than 18F-NOTA-LP while the Caov3 xenografts showed very low uptake of both 18F-NOTA-iCREKA and 18F-NOTA-LP. The tumor-to-muscle (T/M) ratio of 18F-NOTA-iCREKA (9.93 ± 0.42) was obviously higher than 18F-NOTA-LP (2.69 ± 0.35) in U87MG xenografts. Conclusions: The novel CREKA-based probe 18F-NOTA-iCREKA could get a high uptake in U87MG cells and high T/M ratio in U87MG mice. It was more stable and specific than the 18F-NOTA-LP.

Synthesis and Evaluation of 18F-Labeled Peptide for Gonadotropin-Releasing Hormone Receptor Imaging.

The gonadotropin-releasing hormone (GnRH) receptor is overexpressed in the majority of tumors of the human reproductive system. The purpose of this study was to develop an 18F-labeled peptide for tumor GnRH receptor imaging. In this study, the GnRH (pGlu1-His2-Trp3-Ser4-Tyr5-Gly6-Leu7-Arg8-Pro9-Gly10-NH2) peptide analogues FP-d-Lys6-GnRH (FP = 2-fluoropropanoyl) and NOTA-P-d-Lys6-GnRH (P = ethylene glycol) were designed and synthesized. The IC50 values of FP-d-Lys6-GnRH and NOTA-P-d-Lys6-GnRH were 2.0 nM and 56.2 nM, respectively. 4-Nitrophenyl-2-[18F]fluoropropionate was conjugated to the ε-amino group of the d-lysine side chain of d-Lys6-GnRH to yield the new tracer [18F]FP-d-Lys6-GnRH with a decay-corrected yield of 8 ± 3% and a specific activity of 20-100 GBq/µmol (n=6). Cell uptake studies of [18F]FP-d-Lys6-GnRH in GnRH receptor-positive PC-3 cells and GnRH receptor-negative CHO-K1 cells indicated receptor-specific accumulation. Biodistribution and PET studies in nude mice bearing PC-3 xenografted tumors showed that [18F]FP-d-Lys6-GnRH was localized in tumors with a higher uptake than in surrounding muscle and heart tissues. Furthermore, the metabolic stability of [18F]FP-d-Lys6-GnRH was determined in mouse blood and PC-3 tumor homogenates at 1 h after tracer injection. The presented results indicated a potential of the novel tracer [18F]FP-d-Lys6-GnRH for tumor GnRH receptor imaging.

Gummatous Syphilis Mimicking Malignant Bone Tumor on FDG PET/CT.

A 47-year-old woman suffered worsening pain in the waist and numbness in the right thigh for 1 month. MRI was performed to determine the cause, which detected an osteolytic lesion in the T12 vertebral body, suggestive of possible bone metastasis. FDG PET/CT scan was undertaken to detect the primary tumor, which only showed the same isolated lesion in the T12 without any other abnormal hypermetabolic lesion. The pathology following vertebrectomy revealed granulomatous infection. The diagnosis of osseous syphilis was eventually made following a subsequent positive Treponema pallidum serological test.

Radiomics Analysis of PET and CT Components of PET/CT Imaging Integrated with Clinical Parameters: Application to Prognosis for Nasopharyngeal Carcinoma.

PURPOSE: To investigate the prognostic performance of radiomics features, as extracted from positron emission tomography (PET) and X-ray computed tomography (CT) components of baseline 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT images and integrated with clinical parameters, in patients with nasopharyngeal carcinoma (NPC). PROCEDURES: One hundred twenty-eight NPC patients (85 vs. 43 for training vs. validation), containing a subset of 86 patients with local-regional advanced stage, were enrolled. All patients underwent pretreatment PET/CT scans (mean follow-up time 24 ± 14 months). Three thousand two hundred seventy-six radiomics features extracted from PET or CT components and 13 clinical parameters were used to predict progression-free survival (PFS). Univariate analysis with Benjamini-Hochberg false discovery rate (FDR) correction was first used to screen significant features, and redundant features with Spearman's correlation > 0.8 were further eliminated. Then, seven multivariate models involving PET features and/or CT features and/or clinical parameters (denoted as clinical, PET, CT, clinical + PET, clinical + CT, PET + CT and clinical + PET + CT) were constructed by forward stepwise multivariate Cox regression. Model performance was evaluated by concordance index (C-index). RESULTS: Sixty patients encountered events (28 recurrences, 17 metastases, and 15 deaths). Six clinical parameters, 3 PET features, and 14 CT features in training cohort and 4 clinical parameters, 10 PET features, and 4 CT features in subset of local-regional advanced stage were significantly associated with PFS. Combining PET and/or CT features with clinical parameters showed equal or higher prognostic performance than models with PET or CT or clinical parameters alone (C-index 0.71-0.76 vs. 0.67-0.73 and 0.62-0.75 vs. 0.54-0.75 for training and validation cohorts, respectively), while the prognostic performance was significantly improved in local-regional advanced cohort (C-index 0.67-0.84 vs. 0.64-0.77, p value 0.001-0.059). CONCLUSION: Radiomics features extracted from the PET and CT components of baseline PET/CT images provide complementary prognostic information and improved outcome prediction for NPC patients compared with use of clinical parameters alone.

A Rare Case of Neutrophil-Rich, ALK-Negative Anaplastic Large Cell Lymphoma in the Lung Mimicking a Pulmonary Abscess on 18F-FDG PET/CT.

A 21-year-old man complained of cough, fever, and hemoptysis for 15 days. Peripheral neutrophil cell (33.8 × 10/L) was markedly increased, and a mass in the left lung was detected by chest radiography. F-FDG PET/CT was referred for characterizing the lesion and found a large mass with multiple cavities in the left lung, which had markedly high uptake of F-FDG, mimicking pulmonary abscess. Surprisingly, the lesion was eventually proved to be neutrophil-rich anaplastic large cell lymphoma. After 4 cycles' chemotherapy, the lesion shrank significantly.