Search

Background and objective In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value. Methods Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups. Results Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66). Conclusion The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients.

Objective：The current study aims to evaluate the correlation between inflammatory markers and 18F⁃fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters in patients with small cell lung cancer (SCLC) at baseline. Methods：A total of 56 SCLC patients, who underwent 18F ⁃ FDG PET/CT and did not receive any treatment in Department of Nuclear Medicine, the First Affiliated Hospital of Nanjing Medical University between January 2014 and December 2019, were analyzed retrospectively. The inflammatory markers［neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), monocyte/lymphocyte ratio (MLR) and systemic immune⁃inflammation index (SII) ], clinical data and metabolic parameters of primary tumor［maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), TLG (total lesion glycolysis) ］within one week before PET/CT detection were collected to analyze the correlation by Spearman’s rho test. Results：The slight positive correlations were found between inflammatory markers (NLR, PLR, MLR, SII) and partial metabolic parameters［TLG (rNLR=0.309, rPLR=0.304, rMLR=0.271, rSII=0.362), MTV (rNLR=0.354, rPLR=0.341, rMLR=0.290, rSII=0.411) ］of primary tumor in 56 patients with SCLC (all P < 0.05) . The SUVmax and SUVmean were not found to be correlated with these hematological parameters (all P > 0.05) . The neutrophils, NLR, MLR, SII, MTV and TLG in extensive stage were higher than those in limited stage, and the lymphocytes in extensive stage were lower than those in limited stage (all P < 0.05), but SUVmax, SUVmean, PLR, monocytes and platelets were not found differences between extensive and limited stage (all P > 0.05) . Conclusion：Baseline NLR, MLR, SII of SCLC not only reflect the systemic metabolism, but also reflect the local inflammation of tumor. It may be necessary to receive whole⁃ body PET/CT for staging to adjust treatment strategy in those patients with high NLR or MLR, SII. [ABSTRACT FROM AUTHOR]

Copyright of Imaging Science & Photochemistry is the property of Imaging Science & Photochemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective. To evaluate the prognostic value of preoperative delayed diuretic 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging in patients with bladder cancer. Methods. The clinical data and preoperative delayed diuretic 18F-FDG PET/CT images of 104 bladder cancer patients who received radical cystectomy (RC) from 2016 to 2018 at Renji Hospital, Shanghai Jiao Tong University School of Medicine were retrospectively analyzed. The diagnostic ability and optimal boundary values of maximum standardized uptake value (SUVmax), tumor metabolic volume (MTV) and total lesion glycolysis (TLG) were obtained by receiver operating characteristic curve (ROC) analysis. Patients were divided into high SUVmax group (n=53) and low SUVmax group (n=51), high MTV group (n=52)and low MTV group (n=52), high TLG group (n=67) and low TLG group (n=37). The correlation between PET/CT metabolic parameters and clinical data was analyzed by chi-square test. Survival curves were drawn by Kaplan-Meier method and tested by Log-rank method. The prognostic value of metabolic parameters was evaluated by Cox regression model. Results. MTV (P=0.000) and TLG (P=0.000) had diagnostic abilities for postoperative recurrence of bladder cancer. Preoperative treatment (P=0.033) and T stage of tumor (P=0.035) were correlated with MTV, while neutrophil-lymphocyte ratio (NLR) (P=0.026), hemoglobin (P=0.004), albumin (P=0.021), preoperative treatment (P=0.015) and T stage (P=0.015) were related to TLG. Multivariate Cox regression analysis showed that lymph node metastasis (HR=2.433, P=0.016) and TLG (HR=4.499, P=0.000) were independent prognostic factors of progression-free survival (PFS), while preoperative treatment (HR=0.220, P=0.014) and TLG (HR=7.107,P=0.013) were independent predictors of overall survival (OS). Conclusion. Delayed diuretic 18F-FDG PET/CT before RC can predict the postoperative survival. [ABSTRACT FROM AUTHOR]

Objective To investigate the relation between 18F-FDG PET/CT metabolic parameters and the clinicopathological characteristics of colorectal cancer (CRC) patients. Methods We included 358 CRC patients who met the admission criteria. 18F-FDG PET/CT scans were performed before any treatment. Maximum standard uptake, average standard uptake, tumor metabolic volume (MTV) and total glycolysis (TLG) of the primary lesion of CRC were measured and calculated. The correlation of metabolic parameters with lesion location, pathological profile and TNM staging were investigated through Mann-Whitney U test and Kruskal-Wallis test. Results The MTV and TLG values in the primary foci of colon cancer were the highest, followed by sigmoid cancer and the lowest in rectal cancer, with statistically significant differences between the groups (P < 0.001). SUVmax values of poorly/poorly-moderately differentiated colorectal cancer were higher than those of moderately/well differentiated colorectal cancer (P=0.028). MTV values of primary mucinous adenocarcinoma were higher than those of primary adenocarcinoma (P=0.023). MTV and TLG values were higher in T4 lesions than those in T2-3 lesions (P=0.0001, P=0.007); SUVmax and SUVmean values of sigmoid carcinoma with distant metastasis were lower than those without distant metastasis(P=0.015, P=0.011). Conclusion The metabolic parameters of CRC are correlated with the lesion location, pathological features and TNM staging, which could partly reflect the pathological characteristics and heterogeneity of CRC.

Tumor-node-metastasis (TNM) staging system is the most important basis for making therapeutic decisions and predicting prognosis of lung cancer patients. The metabolic parameters including standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) measured by 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG PET/CT) associate with tumor aggressiveness and can provide additional prognostic information. A new staging methodology combines the conventional cTNM with the metabolic tumor burden quantified from the PET images is a better predictor of overall survival with superior stratifying power may help oncologists to make better treatment plans. 18F-FDG PET/CT image texture analysis, as an emerging research tool, is used to quantify the spatial heterogeneity of radioactive uptake in tumors, thereby to explore the biological characteristics of the tumor. This article reviews developments in evaluating the 18F-FDG PET/CT metabolic parameters and its role as a prognostic factor for non-small cell lung cancer.

Objective: To investigate the prognostic value of 18 F-FDG-PET/CT metabolic parameters and blood inflammatory markers for advanced non-small cell lung cancer (NSCLC, stage Ⅳ/ⅢB) treated with first-line chemotherapy combined with immunotherapy and construct a nomogram prediction model for NSCLC., Methods: We retrospectively analyzed the metabolic parameters (SUVmax, MTV and TLG) and blood markers of inflammation (NLR, DNLR, PLR and SII) in 105 patients with advanced NSCLC receiving chemotherapy combined with baseline 18 F-FDG-PET/CT prior to immunotherapy from March, 2019 to June, 2021. ROC curve was used to calculate the best cut-off points for grouping, and univariate and multivariate COX regression analyses were performed to screen the independent predictors of prognosis for a combined diagnostic analysis. The effective biomarkers were included in the prediction model, and the nomogram model was constructed using the cph function in the rms function package of R language software., Results: The patients were followed up for a median of 17.5 months, and their median progression-free survival (PFS) was 16 months with a median overall survival (OS) of 13.6 months. A high PLR (≥151.050) and a high TLG (≥101.940) were significant independent prognostic factors for PFS, and a high SII (≥941.385) and a high TLG (≥101.940) were independent prognostic factors for OS. The nomogram combining PET and blood markers of inflammation showed a good performance for prognostic prediction (with C-index of 0.682 for PFS and of 0.727 for OS) and good fitting of the calibration curve. The clinical decision curve showed good clinical utility of the nomogram., Conclusion: The baseline PET/CT metabolic parameters and blood inflammatory markers are associated with PFS and OS of patients with advanced NSCLC receiving first-line chemotherapy, and the constructed nomogram based on these parameters has a good performance for prognostic prediction in these patients.

Tumor-node-metastasis (TNM) staging system is the most important basis for making therapeutic decisions and predicting prognosis of lung cancer patients. The metabolic parameters including standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) measured by 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG PET/CT) associate with tumor aggressiveness and can provide additional prognostic information. A new staging methodology combines the conventional cTNM with the metabolic tumor burden quantified from the PET images is a better predictor of overall survival with superior stratifying power may help oncologists to make better treatment plans. 18FFDG PET/CT image texture analysis, as an emerging research tool, is used to quantify the spatial heterogeneity of radioactive uptake in tumors, thereby to explore the biological characteristics of the tumor. This article reviews developments in evaluating the 18F-FDG PET/CT metabolic parameters and its role as a prognostic factor for non-small cell lung cancer. [ABSTRACT FROM AUTHOR]

Background: In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value., Methods: Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups., Results: Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66)., Conclusions: The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients.

Objective To investigate the value of 18 F-FDG PET/CT metabolic parameters and metabolic heterogeneity for predicting the expression of human epidermal growth factor receptor 2 (HER2) in patients with gastric cancer. Methods A total of 45 patients with gastric cancer confirmed by surgical pathology between September 2016 and May 2021 were enrolled in this study.All the patients underwent 18 F-FDG PET/CT examination before surgery.The maximum standardized uptake value (SUVmax),metabolic tumor volume (MTV),and total lesion glycolysis (TLG) of primary gastric cancer were measured,and the linear regression slope of MTV corresponding to different SUVmax thresholds (40% SUVmax and 80% SUVmax) was calculated.The absolute value of the slope was deemed to represent the metabolic heterogeneity of primary gastric cancer,termed the heterogeneity index (HI).Univariate and multivariate Logistic regression analyses were conducted to evaluate the correlations of 18 F-FDG PET/CT metabolic parameters and HI with HER2 expression. Results The 45 patients included 10 with positive HER2 expression and 35 with negative result.The MTV ( P =0.043) and HI ( P =0.048) were lower in the patients with positive HER2 expression than in the patients with negative HER2 expression.The MTV and HI had the optimal thresholds of 12.10 cm 3 and 3.71,respectively,which respectively showed the accuracy of 62.2% and 57.8% for predicting HER2 expression.The univariate Logistic regression showed that the tumor differentiation degree,MTV,and HI were correlated with HER2 expression,while the multivariate Logistic regression showed that only the tumor differentiation degree ( OR =20.130,95% CI =1.843-219.860, P =0.014) was an independent predictor for HER2 expression.A further stratified analysis of the tumor differentiation degree showed that HER2 expression only varied among different MTV threshold groups in patients with moderately/well differentiated gastric cancer ( P =0.031). Conclusions MTV and HI were associated with HER2 expression in gastric cancer,whereas neither played an independent predictive role.Therefore,these factors should be combined with clinicopathological characteristics of patients to jointly guide treatment decisions.

Objective To investigate the effects of subclinical hypothyroidism （SCH） on blood glucose and metabolic parameters in type 2 diabetes （T2DM). Methods A total of 973 T2DM patients were recruited， and the clinical data， biochemical and thyroid function parameters were measured. One hundred and forty eight T2DM patients were diagnosed as SCH （SCHgroup） and152 euthyroid patients were selected as control group （NSCH group） . Results （1） The prevalence of SCH in type 2 diabetes was 15.3% （148/973） . （2） Compared with the NSCH group， the prevalence of overweight/obesity was significantly higher in SCH group （54.1%vs35.5%， <0.01） as well as hypertension （64.9%vs52%， <0.05). There was no significant difference in HbA1c control and prevalence of dyslipidemia and hyperuricemia between two groups. Conclusion SCH may have some adverse effects on blood pressure and BMI in T2DM. [ABSTRACT FROM AUTHOR]

Tumor-node-metastasis (TNM) staging system is the most important basis for making therapeutic decisions and predicting prognosis of lung cancer patients. The metabolic parameters including standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) measured by ¹⁸F-fluorodeoxyglucose-positron emission tomography (¹⁸F-FDG PET/CT) associate with tumor aggressiveness and can provide additional prognostic information. A new staging methodology combines the conventional cTNM with the metabolic tumor burden quantified from the PET images is a better predictor of overall survival with superior stratifying power may help oncologists to make better treatment plans. ¹⁸F-FDG PET/CT image texture analysis, as an emerging research tool, is used to quantify the spatial heterogeneity of radioactive uptake in tumors, thereby to explore the biological characteristics of the tumor. This article reviews developments in evaluating the ¹⁸F-FDG PET/CT metabolic parameters and its role as a prognostic factor for non-small cell lung cancer.
.