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Objective:To evaluate the value of 18F-fluorodeoxyglucose positron emission computed tomography ( 18F-FDG PET/CT) myocardial metabolic image quality by the ratio of the maximum standard uptake value (M/B) of left ventricular myocardium to cardiac blood pool. Methods:The clinical data of 145 non diabetes patients with coronary heart disease who were admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2016 to August 2022 were retrospectively selected. All patients received intravenous injection of 18F-FDG for myocardial glucose metabolism imaging. According to the visual (qualitative) analysis, the image was divided into three categories: the best, the second best and the worst. Group A was the best image quality group, while Group B was the second best and the worst image quality group. The left ventricular myocardium, cardiac blood pool and descending aorta were semi-quantitatively analyzed, and M/B was calculated. Results:All 145 patients underwent image fusion analysis. Image visual (qualitative) analysis showed that the myocardial metabolic image quality of 111 patients was evaluated as the best (111 patients in Group A), 19 patients as the second best, and 15 patients as the worst (34 patients in Group B). The SUVmax value of the descending aorta in the two groups was lower than that of the left ventricle, with statistically significant difference (all P<0.05). The M/B value of group A was higher than that of group B, with statistically significant difference ( P<0.05). Conclusions:For the evaluation of image quality and the reliability of image interpretation, M/B value may be a good evaluation index, which reduces the interference of image quality evaluation due to high 18F-FDG uptake in some heart blood pools (background), and improves the accuracy of evaluation of myocardial viability.
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Objective:To investigate the predictive value of F-2-fluoro-2-deoxy-D-glucose ( 18F-FDG) PET-CT metabolic parameters for the recurrence of type 1 autoimmune pancreatitis (AIP). Methods:Eighty-six patients with type 1 AIP who met the International Consensus Diagnostic Criteria (ICDC) and underwent 18F-FDG PET-CT before interventional treatment at the PLA General Hospital between May 2009 and June 2021 were included and divided into recurrence group ( n=43) and no-recurrence group ( n=43) according to whether they recurred after treatment. The standard uptake value (SUV)≥2.5 fixed threshold was used to outline the pancreatic lesion volume of interest (VOI) in three dimensions, and the three-dimensional diameter of the lesion, maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), peak standardized uptake value (SUVpeak), metabolic tumor volume (MTV), total lesion glycolysis (TLG), target-to-bench ratio (TBR) and standardized uptake value ratio (SUVR) were measured to compare the clinical characteristics, biochemical indices and treatment of the two groups; univariate and multifactorial regression analysis were used to examine 18F-FDG PET/CT visual indices of pancreatic lesions and extra-pancreatic involved organs as well as metabolic parameters in the two groups. A recurrence prediction model was constructed and its predictive efficacy was assessed. Results:The proportion of patients receiving glucocorticoid maintenance therapy was significantly higher in the no-recurrence group than in the recurrence group (58% vs 23.3%), and the serum IgG4 levels before treatment were significantly higher in the recurrence group [(15 309±11 724) mg/L vs (8 816±7 169) mg/L]. The results of univariate analysis showed that the proportion of extra-pancreatic salivary gland involvement and VOI, SUVmax, SUVpeak, SUVR, TBR, MTV, and TLG were significantly higher in the recurrence group than in the no-recurrence group, and the differences were statistically significant (all P values <0.05); the results of multivariate analysis showed that VOI ( OR=1.012, 95% CI 1.001-1.023 ), SUV max ( OR=1.398, 95% CI 1.029-1.899), SUV peak ( OR=1.408, 95% CI1.002-1.978), SUVR ( OR=1.977, 95% CI1.036-3.771) and MTV ( OR=1.012, 95% CI1.000- 1.022) in the recurrence group were significantly higher than those in the no-recurrence group, and all differences were statistically significant (all P values <0.05). The prediction model was constructed by multifactorial binary logistic regression analysis of SUVR>2, MTV>36 cm 3, and IgG4>11 400 mg/L, which had an AUC of 0.800 (95% CI 0.704-0.897), sensitivity of 81.4% (95% CI 0.661-0.911), specificity of 74.4% (95% CI 0.585-0.860), and prediction accuracy of 77.9%. Conclusions:18F-FDG PET/CT metabolic parameters can be used as predictors of type 1 AIP recurrence; a multiparameter model constructed based on metabolic parameters SUVR, MTV and IgG4 has a good predictive efficacy for predicting type 1 AIP recurrence.
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Objective:To investigate the application value of dual-phase 18F-fluorocholine (FCH) PET/CT imaging in uremic hyperparathyroidism (uHPT). Methods:Twenty patients (10 males, 10 females, age: (46.8±12.3) years) who were diagnosed with uHPT and underwent neck ultrasound and dual-phase (5, 45 min) 18F-FCH PET/CT imaging at Affiliated Hospital of Southwest Medical University between December 2019 and March 2022 were retrospectively analyzed. Patients underwent parathyroidectomy within 1 month after PET/CT imaging. The sensitivity of neck ultrasound and dual-phase 18F-FCH PET/CT imaging for the diagnosis of hyperfunctioning parathyroid glands were compared based on the surgical results. The early- and late-phase 18F-FCH PET/CT images were compared visually and quantitatively, and the difference of SUV max between parathyroid hyperplasia and parathyroid adenoma was compared. The correlations between SUV max and important laboratory parameters and the volume of lesions measured on CT were tested. Fisher exact test, paired t test, independent-sample t test and Spearman rank correlation analysis were used for statistical analysis. Results:A total of 69 masses were removed in 20 patients with uHPT, and 55 parathyroid hyperplasia and 10 parathyroid adenomas were identified by pathology. Dual-phase 18F-FCH PET/CT imaging (87.69%, 57/65) was more sensitive than neck ultrasound (56.92%, 37/65) for the diagnosis of hyperfunction of the parathyroid gland ( P=0.001). The early imaging detected more lesions than late imaging (57 vs 49) respectively, which showing higher sensitivity (87.69%(57/65) vs 75.38%(49/65); P<0.001). The SUV max(5.75±2.21 vs 4.08±1.51) and the corresponding parathyroid-to-thyroid ratio (2.99±0.99 vs 3.57±1.30) were both significantly different between early and late imaging ( t values: 8.28, 4.33, both P<0.001). There were no significant differences between parathyroid hyperplasia and parathyroid adenoma in SUV max(early imaging: 5.08±2.27 vs 6.58±2.24; t=-1.90, P=0.063; late imaging: 3.89±1.54 vs 4.93±1.04; t=-1.94, P=0.059). The sum of SUV max of all lesions in early imaging was not correlated with preoperative serum parathyroid hormone (PTH) or Ca or P or lesion size ( rs values: from -0.22 to 0.06, all P>0.05). Conclusions:Dual-phase 18F-FCH PET/CT imaging has high sensitivity in the diagnosis of uHPT, and early and late imaging shows advantages in different aspects, with good preoperative localization ability. Therefore, for patients with uHPT, it is recommended to complete the dual-phase 18F-FCH PET/CT examination before surgery.
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Objective:To evaluate the value of integrated PET/MR in assessing myocardial viability in ischemic heart disease.Methods:A total of 39 patients (28 males, 11 females; age (60.1±12.0) years) diagnosed with ischemic heart disease in Xuanwu Hospital, Capital Medical University were retrospectively enrolled from September 2020 to December 2021. All patients underwent cardiac 13N-NH 3·H 2O and 18F-FDG PET/MR examinations. Late gadolinium enhancement (LGE) sequence was included in MRI scan. PET and MRI images were analyzed and myocardial viability of each myocardial segment was evaluated according to the American Heart Association (AHA) 17 segment method. The extent of left ventricular infarcted myocardium was measured based on PET and MRI images. Weighted Kappa test was used to evaluate the agreement of PET and MRI in assessing myocardial viability. The extent of infarcted myocardium measured by PET and MRI was compared by paired- t test, and Pearson correlation analysis was used to assess the correlation between them. Results:There was a moderate agreement between PET and MRI in assessing myocardial viability ( Kappa=0.532, P<0.001), with the agreement rate of 69.83%(463/663). There was no significant difference but strong correlation between the extents of infarcted myocardium measured by PET and MRI ((23.89±14.23)% vs (23.55±11.90)%; t=-0.24, P=0.809; r=0.79, P<0.001). In segments with normal perfusion and metabolism on PET, 22.52% (100/444) showed abnormal enhancement on MRI. On the other hand, 39.89% (73/183) of the segments classified as non-viable on MRI showed normal or viable on PET. Conclusion:Integrated PET/MR is able to take full advantage of the complementary nature of PET and MRI, achieving the comprehensive and accurate evaluation of myocardial viability.
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Objective:To investigate the value of pre-therapy 18F-FDG PET/CT radiomic models in differentiating epidermal growth factor receptor (EGFR) exon 19 deletion from exon 21 L858R missense in patients with non-small cell lung cancer (NSCLC). Methods:A total of 172 patients with EGFR mutant NSCLC (54 males, 118 females, age: (56.2±12.5) years) in the Fourth Hospital of Hebei Medical University between January 2015 and November 2019 were retrospectively included. Exon 19 mutation was found in 75 patients and exon 21 mutation was identified in 97 patients. The patients were divided into training set ( n=121) and validation set ( n=51) in a 7∶3 ratio by using random number table. The LIFEx 4.00 package was used to extract texture features of PET/CT images of lesions. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature screening. Three machine learning models, namely logistic regression (LR), random forest (RF), and support vector machine (SVM) models, were constructed based on the selected optimal feature subsets. The ROC curve analysis was performed to assess the predictive performance of those models. Finally, decision curve analysis (DCA) was used to evaluate the clinical value of the models. Results:Nine radiomics features, including 6 PET features (histogram (HISTO)_Kurtosis, SHAPE_Sphericity, gray level run length matrix (GLRLM)_ low gray-level run emphasis (LGRE), GLRLM_ run length non-uniformity (RLNU), neighborhood grey level different matrix (NGLDM)_Contrast, gray level zone length matrix (GLZLM)_ short-zone low gray-level emphasis (SZLGE)), and 3 CT features (gray level co-occurrence matrix (GLCM)_Correlation, GLRLM_ run percentage (RP), NGLDM_Contrast), were screened by LASSO algorithm. Three machine learning models had similar predictive performance in the training and validation sets: AUCs for the RF model were 0.79, 0.77, and those for the SVM model were 0.76, 0.75, for the LR model were 0.77, 0.75. The DCA showed that the 3 machine learning models had good net benefits and clinical values in predicting EGFR mutation subtypes.Conclusion:18F-FDG PET/CT radiomics provide a non-invasive method for the identification of EGFR exon 19 deletion and exon 21 L858R missense mutations in patients with NSCLC, which may help the clinical decision-making and the formulation of individualized treatment plan.
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Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly ravaged the world and infected hundreds of millions of people since its outbreak. Early diagnosis of COVID-19 is conducive to the control of virus transmission and the timely treatment of patients. Among the imaging techniques, chest CT is an important basis for the diagnosis and evaluation of COVID-19. 18F-FDG PET is not generally recommended as a routine diagnostic tool for COVID-19, but it plays an important role in the assessment of SARS-CoV-2-related events based on the characteristic of whole-body multi-systemic scan and functional imaging diagnosis. In this paper, the application of 18F-FDG PET in COVID-19 diagnosis, prognostic evaluation and long-term sequelae evaluation, and clinical performance of 18F-FDG PET after COVID-19 vaccine are summarized on the basis of literature research and clinical reports analysis. Furthermore, the application and development direction of other new molecular probes for nuclear medicine in COVID-19 are prospected.
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Objective:To investigate the value of machine learning model based on 18F-FDG PET/CT radiomics features in preoperative differential diagnosis of gastric cancer (GC) and primary gastric lymphoma (PGL). Methods:A total of 155 patients with GC (104 males, 51 females; age (59.3±12.8) years) and 82 patients with PGL (40 males, 42 females; age (56.8±14.6) years) who underwent 18F-FDG PET/CT imaging before treatment from January 2012 to December 2020 in Tianjin Medical University Cancer Institute and Hospital were included in this retrospective study. Patients were randomly divided into training set and test set by using Python3.7.1 software. Volumes of interest (VOIs) in PET and CT images were drawn and two-dimensional and three-dimensional radiomics features were extracted. Two machine learning models, including multi-layer perceptron (MLP) and support vector machine (SVM), were established based on CT radiomics features alone, PET radiomics features alone and PET/CT radiomics features to differentiate GC and PGL, respectively. The predictive performance of each model was evaluated by ROC curve analysis. Results:There were 166 patients in training set and 71 patients in test set. Generally, SVM machine learning model based on PET/CT radiomics features showed a trend to be superior to MLP machine learning model in the differential diagnosis of GC and PGL (PET-SVM: AUC=0.88, 95% CI: 0.83-0.94); PET/CT-MLP: AUC=0.80, 95% CI: 0.73-0.87; z=1.15, P=0.337). The AUC of PET/CT-SVM machine learning model was significantly higher than that of CT-SVM machine learning model (CT-SVM: AUC=0.74, 95% CI: 0.67-0.81; z=2.28, P=0.022). Conclusion:Machine learning model based on 18F-FDG PET/CT radiomics features is expected to be a non-invasive, effective tool for preoperative differential diagnosis of GC and PGL.
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Objective:To assess the predictive efficacy of 18F-FDG PET/CT-based radiomics models for the mutation status of Kirsten rats sarcoma viral oncogene homolog (KRAS) in patients with non-small cell lung cancer (NSCLC). Methods:From January 2016 to January 2021, the 18F-FDG PET/CT images and KRAS testing of 258 NSCLC patients (180 males, 78 females; age: 33-91 years) in the First Affiliated Hospital of the Air Force Military Medical University were retrospectively analyzed. Patients were randomly divided into training set ( n=180) and validation set ( n=78) in the ratio of 7∶3. Tumor lesions on PET and CT images were drawn respectively, and the radiomics features of PET and CT lesions were extracted. The radiomics features were screened by least absolute shrinkage and selection operator (LASSO). CT radiomics score (RS) model, PET/CT RS model and composite models of PET/CT RS combined with screened clinical information were eventually developed. ROC curves were used to assess the predictive efficacy of these models. Results:The CT RS model included 4 radiomics features and the PET/CT RS model included 4 CT radiomics features and 8 PET radiomics features. The CT RS model and the PET/CT RS model both had significant differences in RS between KRAS mutant and wild-type patients in the training set and validation set ( z values: from -8.30 to -4.10, all P<0.001). In predicting KRAS mutations, the composite model of PET/CT RS combined with age showed AUCs of 0.879 and 0.852 in the training and validation sets respectively, which were higher than those of the CT RS model (0.813 and 0.770) and the PET/CT RS model (0.858 and 0.834). The accuracy of the composite model of PET/CT RS combined with age were 81.67%(147/180) and 79.49%(62/78) in the training set and validation set respectively, which were also higher than those of the CT RS model (75.00%(135/180) and 74.36%(58/78)) and the PET/CT RS model (78.89%(142/180) and 78.21%(61/78)). Conclusion:Models based on radiomics features can predict KRAS gene mutation status, and the composite model combining PET/CT RS and age can improve the prediction performance.
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Objective:To explore the value of 18F-FDG PET/CT combined with pro-gastrin-releasing peptide (ProGRP) and neuron-specific enolase (NSE) in diagnosis and differential diagnosis of stageⅠA small cell lung cancer (SCLC). Methods:From June 2017 to October 2021, 113 patients (75 males, 38 females; age 32-79 years) with stageⅠA lung cancer (70 with adenocarcinoma, 25 with squamous cell carcinoma, 18 with SCLC; patients with adenocarcinoma and squamous cell carcinoma were combined into non-SCLC (NSCLC) group) and 30 patients with benign pulmonary nodule (21 males, 9 females; age 37-77 years) from the Affiliated Qingdao Central Hospital of Qingdao University were retrospectively analyzed. All patients were examined by 18F-FDG PET/CT and serum tumor markers associated with lung cancer. Differences of the clinical, imaging and tumor markers data among different groups were analyzed by χ2 test, Fisher exact test and Kruskal-Wallis rank sum test. Independent risk factors were analyzed by logistic regression analysis and ROC curve analysis was used to analyze the value of different predictive factors in diagnosis and differential diagnosis of SCLC. Results:There were significant differences in SUV max, lobulation sign, spiculation sign, calcification, pleural traction sign, ProGRP, NSE and carcinoembryonic antigen (CEA) among SCLC, NSCLC and benign nodules groups ( H values: 14.06-20.54, χ2 values: 8.16-14.95, all P<0.05), in which lobulation sign of SCLC was more than that of benign nodules (12/18 vs 26.7%(8/30); χ2=7.41, P=0.007), spiculation sign (2/18 vs 51.6%(49/95); χ2=10.01, P=0.002) and pleural traction sign (1/18 vs 35.8%(34/95); χ2=6.47, P=0.011) were less than those of NSCLC, SUV max was higher than that of benign nodules (7.4(5.8, 9.0) vs 2.3(1.4, 5.1); H=51.82, P<0.001), ProGRP was higher than that of NSCLC and benign nodules (64.0(40.1, 84.8) vs 38.7(26.9, 47.6), 36.7(29.1, 40.5) ng/L; H values: 36.13, 43.96, P values: 0.002, 0.001) and NSE was higher than that of benign nodules (12.4(10.9, 14.5) vs 7.4(5.4, 11.8) μg/L; H=40.53, P=0.001). When differentiated SCLC from NSCLC, spiculation sign (odds ratio ( OR)=0.043, 95% CI: 0.004-0.450, P=0.009) and ProGRP ( OR=1.083, 95% CI: 1.035-1.133, P<0.001) were independent risk factors for SCLC, and the AUC of the two factors combination was 0.875, with the sensitivity and specificity of 14/18 and 84.2%(80/95). When differentiated SCLC from benign nodules, SUV max( OR=2.706, 95% CI: 1.099-6.662, P=0.030), ProGRP ( OR=1.165, 95% CI: 1.009-1.344, P=0.038) and NSE ( OR=1.639, 95% CI: 1.016-2.645, P=0.043) were independent risk factors for SCLC, and the AUC of the three factors combination was 0.985, with the sensitivity and specificity of 17/18 and 96.7%(29/30). Conclusion:18F-FDG PET/CT combined with tumor markers ProGRP and NSE is helpful to improve the diagnosis and differential diagnosis of stage ⅠA SCLC.
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Objective:To explore the uptake characteristics and temporal changes of 68Ga-fibroblast activation protein inhibitors (FAPIs) and 18F-FDG in the anastomotic site of reconstructed digestive tracts after radical surgery for gastrointestinal adenocarcinoma. Methods:A cohort of 43 patients (28 males, 15 females; age range 28-79 years) who underwent radical surgery for gastrointestinal adenocarcinoma and underwent 18F-FDG PET/CT follow-up between November 2020 and June 2022 in the First Affiliated Hospital of the Air Force Medical University was prospectively included. One week after the 18F-FDG PET/CT examination, 68Ga-FAPI-04 PET/CT imaging was performed. ROIs were drawn on the PET images at the highest uptake level of anastomotic sites of reconstructed digestive tract and abdominal wall incisions, and SUV max and target-to-background ratio (TBR) were determined. χ2 test, one-way analysis of variance, Kruskal-Wallis rank sum test (Bonferroni correction) and Wilcoxon signed-rank test were supplied. Results:There were 86 surgical wounds (13 gastric-intestinal anastomotic sites, 14 esophagus-intestinal anastomotic sites, 16 intestinal-intestinal anastomotic sites, and 43 abdominal wall incisions) included. In 68Ga-FAPI-04 PET imaging, SUV max of gastric-intestinal anastomotic sites was higher than that of abdominal wall incisions, with a statistically significant difference (adjusted P=0.014). The TBR did not show statistically significant differences among different types of surgical wounds ( H=3.88, P=0.275). In 18F-FDG PET imaging, SUV max of gastric-intestinal, esophagus-intestinal, and intestinal-intestinal anastomotic sites were all higher than that of abdominal wall incisions, with statistically significant differences (adjusted all P<0.001). There were no statistically significant differences in TBR among different types of surgical wounds ( H=3.02, P=0.388). In 68Ga-FAPI-04 PET imaging, the TBR of all types of anastomotic sites exhibited a decreasing trend with increasing postoperative time. Except for intestinal-intestinal anastomotic sites, the differences in TBR between < 0.5-year and ≥ 1.5-year groups were statistically significant for other types of surgical wounds (adjusted P<0.05). In 18F-FDG PET imaging, the TBR of abdominal wall incisions showed a decreasing trend with increasing postoperative time. However, the TBR of other types of surgical wounds did not show a decreasing trend, and the differences in TBR among different time groups were not statistically significant ( H values: 0.53-2.75, P values: 0.252-0.768). In comparing the two PET imaging agents, for all surgical wounds within the <0.5-year and 0.5-1.5-year groups, the 68Ga-FAPI-04 TBR was consistently higher than the 18F-FDG TBR ( z values: -3.17 and -2.55, P values: 0.002 and 0.011). However, in the ≥1.5-year group, the TBR values tended to be consistent, and the differences were not statistically significant ( z=-0.70, P=0.485). Conclusions:The 18F-FDG uptake in the anastomotic sites of reconstructed digestive tracts reaches a low level under half a year after surgery and does not significantly change over time, while the 68Ga-FAPIs uptake remains relatively high within the first 1.5 years after surgery but decreases over time. These patterns suggest that clinical attention should be paid to the differential diagnosis of anastomotic inflammation or fibrosis, which resulting in agent uptake and local tumor recurrence.
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Objective:To investigate the clinical utility of 68Ga-labeled fibroblast activation protein inhibitor (FAPI) PET/CT in the detection of primary and metastatic gastric signet-ring cell carcinoma (GSRCC) and compared the results with those of 18F-FDG PET/CT. Methods:A total of 21 patients (10 males, 11 females, average age 52 years) with primary and metastatic GSRCC who underwent 68Ga-FAPI and 18F-FDG PET/CT at the First Affiliated Hospital of Xiamen University from June 2020 to May 2022 were retrospectively analyzed. Pathological results of surgery and (or) biopsy were used as the " gold standard" for final diagnosis. In cases whose surgery or tissue biopsies were not available, clinical and radiographic follow-up results were used as the reference standards. Wilcoxon signed-rank test was used to compare the SUV max of 18F-FDG and 68Ga-FAPI. McNemar χ2 test was used to compare the detection rate between 18F-FDG and 68Ga-FAPI PET/CT. Results:68Ga-FAPI PET/CT showed higher SUV max than 18F-FDG in primary tumors (5.3(2.4, 15.7) vs 2.4(1.8, 2.5); z=2.31, P=0.021), local recurrences (7.8(6.0, 8.9) vs 2.4(1.9, 3.4); z=2.20, P=0.028), lymph nodes metastases (7.7(4.5, 12.2) vs 2.4(1.9, 3.6); z=6.01, P<0.001) and bone/visceral metastases (6.7(5.3, 11.1) vs 2.4(2.0, 3.4); z=11.36, P<0.001). Regarding diagnostic accuracy, 68Ga-FAPI PET/CT showed higher sensitivities than 18F-FDG for primary tumors (7/9 vs 2/9; χ2=3.20, P=0.063) and local recurrences (7/7 vs 2/7; χ2=3.20, P=0.063). It also demonstrated higher lesion detection rates than 18F-FDG for suspicious lymph node metastases (86%(65/76) vs 32%(24/76); χ2=31.37, P<0.001) and bone/visceral metastases (99%(184/185) vs 39%(73/185); χ2=107.08, P<0.001). Conclusions:68Ga-FAPI PET/CT showed higher tumor uptake and lesion detection rate than 18F-FDG in the primary and metastatic GSRCC. 68Ga-FAPI PET/CT demonstrates good diagnostic performance for tumor detection, staging, and restaging of GSRCC, which is helpful to further guide clinical treatment strategy.
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Objective:To investigate the prognostic value of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in patients with extensive-stage small cell lung cancer (ES-SCLC).Methods:From March 2019 to June 2020, 69 patients (55 males, 14 females, age: 38-87 years) with ES-SCLC who underwent pretreatment 18F-FDG PET/CT in First Affiliated Hospital of Zhengzhou University were retrospectively enrolled. The variables including gender, age, smoking, weight loss, liver metastasis, bone metastasis, malignant effusion, SUV max of the primary tumor, whole-body MTV (wbMTV) and whole-body TLG (wbTLG) (including wbMTV 40%, wbTLG 40%, wbMTV 2.5 and wbTLG 2.5) were analyzed. The predictors of overall survival (OS) were analyzed by using Kaplan-Meier method (log-rank test). Results:Of 69 ES-SCLC patients, 43(62%) died and 26(38%) were still alive by the end of follow-up, with a median survival time of 15.0(95% CI: 11.7-18.3) months. Univariate analysis revealed that age ( χ2=4.53, P=0.033), bone metastasis ( χ2=18.05, P<0.001), liver metastasis ( χ2=27.94, P<0.001), wbMTV 2.5 ( χ2=3.98, P=0.046), and wbTLG 2.5( χ2=5.80, P=0.016) were significant predictors of OS. Conclusion:wbMTV 2.5 and wbTLG 2.5 are assosciated with OS and may provide some reference value for predicting the prognosis of ES-SCLC patients.
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Objective:To explore the prognostic value of 18F-FDG PET-based radiomics features by machine learning in older patients(≥60 years) with diffuse large B-cell lymphoma (DLBCL). Methods:A total of 166 older patients (88 males, 78 females, age: 60-93 years) with DLBCL who underwent pre-therapy 18F-FDG PET/CT from March 2011 to November 2019 were enrolled in the retrospective study. There were 115 patients in training cohort and 51 patients in validation cohort. The lesions in PET images were manually drawn and the obtained radiomics features from patients in training cohort were selected by the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (Xgboost), and then classified by support vector machine (SVM) to build radiomics signatures (RS) for predicting overall survival (OS). A multi-parameter model was constructed by using Cox proportional hazard model and assessed by concordance index (C-index). Results:A total of 1 421 PET radiomics features were extracted and 10 features were selected to build RS. The univariate Cox regression analysis showed that RS was a predictor of OS (hazard ratio ( HR)=5.685, 95% CI: 2.955-10.939; P<0.001). The multi-parameter model that incorporated RS, metabolic metrics, and clinical risk factors, exhibited significant prognostic superiority over the clinical model, PET-based model, and the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) in terms of OS (training cohort: C-index: 0.752 vs 0.737 vs 0.739 vs 0.688; validation cohort: C-index: 0.845 vs 0.798 vs 0.844 vs 0.775). Conclusions:RS can be used as a survival predictor for older patients(≥60 years) with DLBCL. Furthermore, the multi-parameter model incorporating RS is able to successfully predict prognosis.
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Objective:To investigate the prognostic value of metabolic parameters measured by 18F-FDG PET/CT in patients with primary advanced cutaneous malignant melanoma (CMM). Methods:A retrospective analysis was comprised of 42 patients with advanced CMM (15 males and 27 females; median age: 60.0 years) from Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School between June 2014 and December 2019. All patients were initially diagnosed by pathology, and underwent 18F-FDG PET/CT imaging. 18F-FDG PET/CT parameters including SUV max, SUV mean, total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) of metastatic lesions were measured. ROC curve analysis was performed to obtain the optimal cut-off values of those metabolic parameters for predicting progression-free survival (PFS) and over-all survival (OS). Patients were divided into different groups based on their metabolic parameters (≥cut-off values or <cut-off values), and Kaplan-Meier survival curve and log-rank test were used to analyze the OS/PFS differences between 2 groups. The independent prognostic risk factors of PFS and OS were screened by univariate analysis and Cox proportional risk model. Results:The median follow-up time of 42 patients with advanced CMM was 26.3 months, with 32 patients suffered from disease progression and 21 patients died, and the median survival was 33.1 months. The optimal cut-off values for PFS/OS were 4.63/4.77 for SUV max, 3.31/3.31 for SUV mean, 8.22 cm 3/22.32 cm 3 for TMTV, and 18.22 g/51.37 g for TLG, respectively. Kaplan-Meier analysis and log-rank test showed that patients with SUV max ≥4.63 or SUV mean ≥3.31 suffered from poorer PFS ( χ2 values: 7.12, 5.42, P values: 0.008, 0.020), meanwhile, patients with SUV max ≥4.77 or TMTV≥22.32 cm 3 or TLG≥51.37 g suffered from poorer OS ( χ2 value: 4.73, 5.60, 6.31, P values: 0.030, 0.018, 0.012). Multivariate analysis demonstrated that SUV max was significant predictor for both PFS (hazard risk ( HR)=3.03(95% CI: 1.23-7.45), P=0.016) and OS ( HR=3.62(95% CI: 1.19-11.00), P=0.023), while TMTV and TLG were significant predictors for OS ( HR: 2.87(95% CI: 1.20-6.87), 3.34(95% CI: 1.39-8.05); P values: 0.018, 0.007). Conclusions:Baseline 18F-FDG PET/CT metabolic parameters have certain value in prediction of prognosis in patients with advanced CMM. SUV max of metastatic lesions is an independent prognostic risk factor for both PFS and OS, and TMTV and TLG are independent prognostic risk factors for OS in patients with advanced CMM.
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Objective:To explore the added value of T 1-weighted stack-of-stars volumetric interpolated body examination (StarVIBE) sequence on PET/MR image quality. Methods:A retrospective analysis was performed on 60 patients (42 males, 18 females; age 11-86 (58±12) years) who underwent 18F-FDG PET/MR examination and with positive PET results in Ruijin Hospital, Shanghai Jiao Tong University School of Medicine from April 2020 to April 2021. All patients completed StarVIBE sequence collection, and volumetric interpolated body examination (VIBE) sequence was used as control. StarVIBE and VIBE sequence images were evaluated independently using five-point method by two physicians. The evaluation was carried out from six aspects: lesion display, lesion boundary display, vascular around lesions display, fusion level with PET image, image artifact and overall image quality. Wilcoxon signed rank test was used to compare the image quality of the two sequences, and Kappa test was performed to assess the consistency of the image quality scores between the two physicians. Results:There were 26 cases with cervical lesions, 14 cases with chest lesions, 7 cases with abdomen lesions and 13 cases with pelvic lesions. The scores of lesion display (4.0(3.8, 4.5) vs 3.5(3.0, 4.0)), lesion boundary display (4.0(4.0, 4.0) vs 3.0(3.0, 3.5)), vascular around lesions display (5.0(4.0, 5.0) vs 4.0(3.5, 4.5)), fusion level with PET image (5.0(5.0, 5.0) vs 4.5(4.0, 5.0)), image artifact (4.5(4.0, 5.0) vs 4.5(4.0, 5.0)) and overall image quality (5.0(4.0, 5.0) vs 4.0(4.0, 4.0)) of StarVIBE sequences were better than those of VIBE sequences ( z values: 3.77-6.54, all P<0.001). On the vascular around the lesions display, the scores of StarVIBE were significantly better than those of VIBE sequence in the neck (5.0(4.5, 5.0) vs 3.0(2.7, 3.5); z=4.49, P<0.001) and chest (4.5(4.3, 4.7) vs 4.0(3.6, 4.3); z=3.10, P=0.002). As for image quality, the scores of StarVIBE were also significantly better than those of VIBE in neck (5.0(4.5, 5.0) vs 4.0(3.7, 4.5); z=4.36, P<0.001) and chest (5.0(5.0, 5.0) vs 4.0(4.0, 4.5); z=3.02, P=0.003). In abdominal lesions, the score of StarVIBE was higher than that of VIBE in blood vessels (4.5(3.5, 5.0) vs 4.0(3.5, 4.5); z=2.07, P=0.038), and there was no difference between score of overall image quality (4.0(3.7, 4.5) vs 4.0(3.5, 4.5); z=0.27, P=0.785). The score of overall image quality of pelvic StarVIBE sequence was better than that of VIBE sequence (5.0(4.5, 5.0) vs 4.0(4.0, 4.5); z=2.12, P=0.034). Kappa value of image quality score between two physicians was 0.554, indicating moderate consistency. Conclusion:In whole-body PET/MR imaging, StarVIBE sequence can significantly improve the image quality of cervical, thoracic and pelvic lesions when comparing with VIBE sequence.
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Objective:To evaluate the value of radiomic fusion features combined with extreme gradient boosting (XGBoost) machine learning models based on 18F-FDG PET/CT images in the prediction of human epidermal growth factor receptor 2 (HER2) expression status in breast cancer. Methods:18F-FDG PET/CT images of 210 patients with primary breast cancer (all females; age 52(46, 60) years; 95 HER2-positive and 115 HER2-negative) in Tianjin Medical University Cancer Institute and Hospital between January 2012 and December 2019 were retrospectively analyzed. About 70% of the HER2-positive and HER2-negative groups were randomly selected using Python 3.7.1 software as a training set ( n=147; 67 HER2-positive and 80 HER2-negative, age: 52(46, 60) years vs 55(45, 62) years) and 30% as a test set ( n=63; 28 HER2-positive and 35 HER2-negative, age: 54(43, 65) years vs 52(45, 61) years). After tumor segmentation on CT and PET images being finished, CT and PET radiomic features were extracted respectively. PET/CT fusion features (including PET/CT splicing features and PET/CT mean features) were obtained through post-processing. The support vector machine (SVM) model and XGBoost model were established. The selected features were input to predict the expression status of HER2 in primary breast cancer lesions, and the prediction efficiency of the model was evaluated by ROC curve. The Delong test was used to compare the predictive effectiveness of different models and radiomic features, and the calibration curve of the machine learning model with the highest prediction efficiency was plotted. Results:Compared with SVM model, XGBoost model had better prediction performance ( z values: 2.26-3.54, P values: 0.016-0.040) when four kinds of radiomic features (CT features, PET features, PET/CT splicing features and PET/CT mean features) were input. ROC curve analysis showed that PET/CT mean features with XGBoost machine learning model had the best performance in predicting the expression status of HER2, and the maximum AUC was 0.83 (95% CI: 0.73-0.93), which was superior to CT features (0.75(95% CI: 0.63-0.88); z=3.57, P=0.027), PET features (0.73(95% CI: 0.60-0.86); z=2.64, P=0.034) and PET/CT splicing features (0.74(95% CI: 0.60-0.87); z=2.49, P=0.037). Conclusion:XGBoost machine learning model based on PET/CT radiomics fusion features is expected to predict HER2 expression status in patients with breast cancer.
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Objective:To investigate the predictive values of 18F-FDG PET/CT image feature and metabolic parameters for the malignant potential of gastrointestinal stromal tumor (GIST). Methods:From March 2014 to June 2020, the 18F-FDG PET/CT imaging and surgical pathological data of 35 patients with GIST (27 males, 8 females; age 44-84 years) from Union Hospital, Tongji Medical College, Huazhong University of Science and Technology and Zhongnan Hospital of Wuhan University were analyzed retrospectively. Patients were divided into ring-shaped uptake group and other uptake patterns group according to 18F-FDG PET/CT image feature. Fisher′s exact test was used to analyze the differences of tumor necrosis and National Institutes of Health (NIH) risk classification (short for NIH classification) between different image feature groups. Mann-Whitney U test was used to analyze the differences of SUV max , metabolic parameters at different thresholds (2.5, 40%, 50%) of SUV max (metabolic tumor volume (MTV; MTV 2.5, MTV 40%, MTV 50%) and total lesion glycolysis (TLG; TLG 2.5, TLG 40%, TLG 50%)) between different clinicopathological features (lesion location, tumor diameter, mitotic count, Ki-67, necrosis, image feature, NIH classification) groups. Spearman rank correlation analysis was used to explore the correlation between clinicopathological features and metabolic parameters. ROC curve analysis was used to distinguish NIH classification of different metabolic parameters. Delong test was used to compared differences between different AUCs. Results:Of 35 GIST patients, 11(31.4%) were ring-shaped uptake and 24(68.6%) were other uptake patterns, and the differences of necrosis (7/11 vs 12.5%(3/24); P=0.004) and NIH classification (11/11 vs 25.0%(6/24); P<0.001) between the two groups were significant. There were significant differences of metabolic parameters between different groups of tumor diameter, mitotic count, necrosis, image feature, NIH classification ( z values: from -4.70 to -2.09, all P<0.05), while there were no significant differences of Ki-67 ( z values: from -0.83 to -0.71, all P>0.05). Metabolic parameters were correlated with mitotic count, tumor diameter, necrosis, image feature and NIH classification ( rs values: 0.36-0.81, all P<0.05), while was not correlated with Ki-67 ( rs values: 0.12-0.14, all P>0.05). The differences of AUCs between SUV max and MTV 2.5, TLG 2.5, TLG 40%, TLG 50%were significant (0.752, 0.856, 0.856, 0.882, 0.886; z values: 1.96-2.12, all P<0.05). Conclusions:The NIH classification of GIST with ring-shaped uptake on 18F-FDG PET/CT is higher and more prone to necrosis. The 18F-FDG PET/CT metabolic parameters based on different thresholds of SUV max have certain significance for the prediction of NIH classification of GIST, and may be superior to SUV max.
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Objective:To explore the predictive value of 18F-FDG PET/CT metabolic parameters combined with inflammatory markers for the medium-term efficacy of chemotherapy in patients with primary gastrointestinal diffuse large B cell lymphoma (PGI-DLBCL). Methods:From April 2011 to May 2020, 67 patients (37 males, 30 females, age: 28-85 years) with PGI-DLBCL examined by 18F-FDG PET/CT before chemotherapy in Changhai Hospital, Navy Medical University were retrospectively analyzed. All patients were treated with cyclophosphamide+ doxorubicin+ vincristine+ prednisone (CHOP) or rituximab+ CHOP (R-CHOP) regimens, and the medium-term efficacy was evaluated after 2-4 cycles of chemotherapy. The effect outcome was divided into complete remission (CR) group and non-CR (NCR) group based on the Lugano lymphoma response evaluation criteria. Mann-Whitney U test was used to compare the differences of SUV max, peak of SUV (SUV peak), metabolic tumor volume (MTV), total lesion glycolysis (TLG), platelet/lymphocyte ratio (PLR) and neutrophil/lymphocyte ratio (NLR) between two groups. The independent risk factors of NCR were analyzed by multivariate logistic regression and the binary logistic regression model was established according to the results. The model was tested with external validation data ( n=15). Results:Of 67 PGI-DLBCL patients, 28(41.8%) were CR and 39(58.2%) were NCR. SUV peak, MTV, TLG, PLR and NLR in NCR group (17.3(12.3, 28.1), 73.8(42.9, 141.7) cm 3, 887.5(300.9, 2 075.3) g, 203.9(155.7, 297.1), 3.9(3.0, 4.9)) were significantly higher than those in CR group (9.5(6.2, 15.2), 11.3(4.7, 23.2) cm 3, 85.2(35.5, 214.6) g, 149.3(102.8, 173.1), 2.2(1.8, 4.6); z values: from -6.41 to -2.33, all P<0.05). The logistic regression model was as follows: P=1/(1+ e - x), x=0.100×MTV+ 0.024×PLR-8.064. The prediction accuracy for NCR risk was 86.57%(58/67), with the accuracy of 13/15 tested by external validation data. Conclusion:MTV combined with PLR has a good predictive value for medium-term efficacy of CHOP/R-CHOP chemotherapy in patients with PGI-DLBCL.
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Objective:To investigate the clinical manifestations and 18F-FDG PET/CT imaging features of hilar tumor pulmonary infarction. Methods:From July 2016 to June 2021, 49 patients (40 males, 9 females; age 32-81 years) with hilar tumor pulmonary infarction who underwent PET/CT and enhanced CT in the second Hospital of Shandong University and Shandong Cancer Hospital and Institute, Shandong First Medical University were retrospectively enrolled. All patients were diagnosed by imaging follow-up or pathology. Clinical features and 18F-FDG PET/CT imaging features were analyzed. Results:A total of 108 infarcts were found in 49 patients by 18F-FDG PET/CT. Small cell carcinoma was the most common hilar tumor (67.35%, 33/49). The most common clinical manifestations of hilar tumor pulmonary infarction were cough (69.39%, 34/49) and hemoptysis (34.69%, 17/49). Pulmonary infarction was mainly multiple (69.39%, 34/49), and multiple lung lobes might be involved. The CT morphology of infarcts was wedge-shaped (46.30%, 50/108) or patchy (53.70%, 58/108), and the density was mainly bubble consolidation (61.11%, 66/108). There were 91 (84.26%, 91/108) infarcts showing FDG hypermetabolism, with the SUV max of 1.48-6.62, and the hypermetabolism mode was rim sign (36.11%, 39/108) or heterogeneous hypermetabolism (48.15%, 52/108). Nineteen patients (38.78%, 19/49) were complicated with pulmonary vein involvement, and 26 patients (53.06%, 26/49) had ipsilateral pleural effusion. Conclusions:Hilar tumor pulmonary infarction is characterized by cough. It is helpful for the diagnosis of hilar tumor pulmonary infarction in patients with hilar tumor when wedge-shaped, bubble consolidation, rim sign and heterogeneous hypermetabolism lesions are found in 18F-FDG PET/CT images.
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Objective:To explore whether the specific synaptic vesicle glycoprotein 2A (SV2A) targeted imaging agent ( R)-4-(3-fluoro-5-(fluoro- 18F)phenyl)-1-((3-methylpyridin-4-yl)methyl)pyrrolidin-2-one ( 18F-SDM-8) can be used to detect epileptic foci. Methods:Twenty male Sprague-Dawley (SD) rats (8-9 weeks) were injected with 1.2 μl of kainic acid (16 rats in the epilepsy group) or saline (4 rats in the control group) into the right hippocampus. 18F-SDM-8 and 18F-FDG mircoPET/CT imaging were respectively performed at 1-2 d (acute phase), 6-7 d (incubation period) and 45-60 d (chronic phase) after the seizure. Asymmetric index (AI) was used to evaluate the epileptic foci identify ability of 18F-SDM-8. Paired t test, Mann-Whitney U test and Pearson correlation analysis were used to analyze data. Results:In the three periods of 18F-SDM-8 imaging, the differences of AI of hippocampus between the epilepsy group and control group were statistically significant ( z values: from -2.64 to 2.67, all P<0.05). Both imaging agents had asymmetric uptake in the epilepsy group (right was lower than left), and the decrease in the medial right temporal lobe was the most significant. The pathological staining results were consistent with the imaging results. In the chronic phase of the epilepsy group, the differences of 18F-SDM-8 SUV mean (right versus left) in each brain area of interest were statistically significant ( t value: from -33.40 to -5.60, all P<0.05). The asymmetric uptake of the two imaging agents in the hippocampus had a better correlation ( r=0.97, P=0.001), and the AI of 18F-SDM-8 ((34.2±8.4)%) in this area was 1.4 times higher than that of 18F-FDG ((24.6±4.7)%). Conclusions:18F-SDM-8 PET is a promising method to test the level of SV2A. It can reflect the changes of SV2A in the rat epilepsy model induced by intrahippocampal injection of kainic acid, and improve the sensitivity of molecular imaging for epileptic foci.