Repeated dynamic [18F]FDG PET/CT imaging using a high-sensitivity PET/CT scanner for assessing non-small cell lung cancer patients undergoing induction immuno-chemotherapy followed by hypo-fractionated chemoradiotherapy and consolidative immunotherapy: report from a prospective observational study (GASTO-1067).

OBJECTIVE: The study aims to investigate the role of dynamic [18F]FDG PET/CT imaging by high-sensitivity PET/CT scanner for assessing patients with locally advanced non-small cell lung cancer (LA-NSCLC) who undergo induction immuno-chemotherapy, followed by concurrent hypo-fractionated chemoradiotherapy (hypo-CCRT) and consolidative immunotherapy. METHODS: Patients with unresectable LA-NSCLC are prospectively recruited. Dynamic [18F]FDG PET/CT scans are conducted at four timepoints: before treatment (Baseline), after induction immuno-chemotherapy (Post-IC), during hypo-CCRT (Mid-hypo-CCRT) and after hypo-CCRT (Post-hypo-CCRT). The primary lung tumors (PTs) are manually delineated, and the metabolic features, including the Patlak-Ki (Ki), maximum SUV (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have been evaluated. The expressions of CD3, CD8, CD68, CD163, CD34 and Ki67 in primary lung tumors at baseline are assayed by immunohistochemistry. The levels of blood lymphocytes at four timepoints are analyzed with flow cytometry. RESULTS: Fifteen LA-NSCLC patients are enrolled between December 2020 and December 2022. Baseline Ki of primary tumor yields the highest AUC values of 0.722 and 0.796 for predicting disease progression and patient death, respectively. Patients are classified into the High FDG Ki group (n = 8, Ki > 2.779 ml/min/100 g) and the Low FDG Ki group (n = 7, Ki ≤ 2.779 ml/min/100 g). The High FDG Ki group presents better progression-free survival (P = 0.01) and overall survival (P = 0.025). The High FDG Ki group exhibits more significant reductions in Ki after hypo-CCRT compared to the Low FDG Ki group. Patients with a reduction in Ki > 73.1% exhibit better progression-free survival than those with a reduction ≤ 73.1% in Ki (median: not reached vs. 7.33 months, P = 0.12). The levels of CD3+ T cells (P = 0.003), CD8+ T cells (P = 0.002), CD68+ macrophages (P = 0.071) and CD163+ macrophages (P = 0.012) in primary tumor tissues are higher in the High FDG Ki group. The High FDG Ki group has higher CD3+CD8+ lymphocytes in blood at baseline (P = 0.108), post-IC (P = 0.023) and post-hypo-CCRT (P = 0.041) than the Low FDG Ki group. CONCLUSIONS: The metabolic features in the High FDG Ki group significantly decrease during the treatment, particularly after induction immuno-chemotherapy. The Ki value of primary tumor shows significant relationship with the treatment response and survival in LA-NSCLC patients by the combined immuno-chemoradiotherapy regimen. TRIAL REGISTRATION: ClinicalTrials.gov. NCT04654234. Registered 4 December 2020.

Patlak-Ki derived from ultra-high sensitivity dynamic total body [18F]FDG PET/CT correlates with the response to induction immuno-chemotherapy in locally advanced non-small cell lung cancer patients.

PURPOSE: This study aimed to investigate the predictive value of metabolic features in response to induction immuno-chemotherapy in patients with locally advanced non-small cell cancer (LA-NSCLC), using ultra-high sensitivity dynamic total body [18F]FDG PET/CT. METHODS: The study analyzed LA-NSCLC patients who received two cycles of induction immuno-chemotherapy and underwent a 60-min dynamic total body [18F]FDG PET/CT scan before treatment. The primary tumors (PTs) were manually delineated, and their metabolic features, including the Patlak-Ki, Patlak-Intercept, maximum SUV (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were evaluated. The overall response rate (ORR) to induction immuno-chemotherapy was evaluated according to RECIST 1.1 criteria. The Patlak-Ki of PTs was calculated from the 20-60 min frames using the Patlak graphical analysis. The best feature was selected using Laplacian feature importance scores, and an unsupervised K-Means method was applied to cluster patients. ROC curve was used to examine the effect of selected metabolic feature in predicting tumor response to treatment. The targeted next generation sequencing on 1021 genes was conducted. The expressions of CD68, CD86, CD163, CD206, CD33, CD34, Ki67 and VEGFA were assayed through immunohistochemistry. The independent samples t test and the Mann-Whitney U test were applied in the intergroup comparison. Statistical significance was considered at P < 0.05. RESULTS: Thirty-seven LA-NSCLC patients were analyzed between September 2020 and November 2021. All patients received two cycles of induction chemotherapy combined with Nivolumab/ Camrelizumab. The Laplacian scores showed that the Patlak-Ki of PTs had the highest importance for patient clustering, and the unsupervised K-Means derived decision boundary of Patlak-Ki was 2.779 ml/min/100 g. Patients were categorized into two groups based on their Patlak-Ki values: high FDG Patlak-Ki (H-FDG-Ki, Patlak-Ki > 2.779 ml/min/100 g) group (n = 23) and low FDG Patlak-Ki (L-FDG-Ki, Patlak-Ki ≤ 2.779 ml/min/100 g) group (n = 14). The ORR to induction immuno-chemotherapy was 67.6% (25/37) in the whole cohort, with 87% (20/23) in H-FDG-Ki group and 35.7% (5/14) in L-FDG-Ki group (P = 0.001). The sensitivity and specificity of Patlak-Ki in predicting the treatment response were 80% and 75%, respectively [AUC = 0.775 (95%CI 0.605-0.945)]. The expression of CD3+/CD8+ T cells and CD86+/CD163+/CD206+ macrophages were higher in the H-FDG-Ki group, while Ki67, CD33+ myeloid cells, CD34+ micro-vessel density (MVD) and tumor mutation burden (TMB) were comparable between the two groups. CONCLUSIONS: The total body [18F]FDG PET/CT scanner performed a dynamic acquisition of the entire body and clustered LA-NSCLC patients into H-FDG-Ki and L-FDG-Ki groups based on the Patlak-Ki. Patients with H-FDG-Ki demonstrated better response to induction immuno-chemotherapy and higher levels of immune cell infiltration in the PTs compared to those with L-FDG-Ki. Further studies with a larger patient cohort are required to validate these findings.

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes.

PURPOSE: This study aimed to quantitatively assess [18F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [18F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC. METHODS: The 60-min dynamic total-body [18F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry. RESULTS: A total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUVmean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUVmean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3+/CD8+) and macrophages (CD68+/CD163+) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups. CONCLUSION: The dynamic total-body [18F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUVmean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [18F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy.

Correction: COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling.

[This corrects the article DOI: 10.2196/31726.].

COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling.

BACKGROUND: COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public's conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public's vaccine awareness through sentiment-based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines. OBJECTIVE: In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved. METHODS: We collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter's application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment-based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns. RESULTS: Overall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign. CONCLUSIONS: To the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment-based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign.

Advantages and Challenges of Total-Body PET/CT at a Tertiary Cancer Center: Insights from Sun Yat-sen University Cancer Center.

In recent decades, researchers worldwide have directed their efforts toward enhancing the quality of PET imaging. The detection sensitivity and image resolution of conventional PET scanners with a short axial field of view have been constrained, leading to a suboptimal signal-to-noise ratio. The advent of long-axial-field-of-view PET scanners, exemplified by the uEXPLORER system, marked a significant advancement. Total-body PET imaging possesses an extensive scan range of 194 cm and an ultrahigh detection sensitivity, and it has emerged as a promising avenue for improving image quality while reducing the administered radioactivity dose and shortening acquisition times. In this review, we elucidate the application of the uEXPLORER system at the Sun Yat-sen University Cancer Center, including the disease distribution, patient selection workflow, scanning protocol, and several enhanced clinical applications, along with encountered challenges. We anticipate that this review will provide insights into routine clinical practice and ultimately improve patient care.

M1 stage subdivisions based on 18F-FDG PET-CT parameters to identify locoregional radiotherapy for metastatic nasopharyngeal carcinoma.

Purpose: To establish a risk classification of de novo metastatic nasopharyngeal carcinoma (mNPC) patients based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) radiomics parameters to identify suitable candidates for locoregional radiotherapy (LRRT). Methods: In all, 586 de novo mNPC patients who underwent 18F-FDG PET-CT prior to palliative chemotherapy (PCT) were involved. A Cox regression model was performed to identify prognostic factors for overall survival (OS). Candidate PET-CT parameters were incorporated into the PET-CT parameter score (PPS). Recursive partitioning analysis (RPA) was applied to construct a risk stratification system. Results: Multivariate Cox regression analyses revealed that total lesion glycolysis of locoregional lesions (LRL-TLG), the number of bone metastases (BMs), metabolic tumor volume of distant soft tissue metastases (DSTM-MTV), pretreatment Epstein-Barr virus DNA (EBV DNA), and liver involvement were independent prognosticators for OS. The number of BMs, LRL-TLG, and DSTM-MTV were incorporated as the PPS. Eligible patients were divided into three stages by the RPA-risk stratification model: M1a (low risk, PPSlow + no liver involvement), M1b (intermediate risk, PPSlow + liver involvement, PPShigh + low EBV DNA), and M1c (high risk, PPShigh + high EBV DNA). PCT followed by LRRT displayed favorable OS rates compared to PCT alone in M1a patients (p < 0.001). No significant survival difference was observed between PCT plus LRRT and PCT alone in M1b and M1c patients (p > 0.05). Conclusions: The PPS-based RPA stratification model could identify suitable candidates for LRRT. Patients with stage M1a disease could benefit from LRRT.

Value of baseline and end of chemotherapy 18F-FDG PET/CT in pediatric patients with Burkitt lymphoma.

The aim of this study was to analyze whether the baseline metabolic parameters of 18F-FDG PET/CT in pediatric patients with Burkitt lymphoma (BL) can predict treatment response and prognosis. We retrospectively analyzed 68 pediatric patients with BL who underwent PET/CT before treatment. PET images were analyzed semi-quantitatively by measuring the maximum standardized uptake (SUVmax), total metabolic tumor volume (tMTV), and total lesion glycolysis (TLG). Survival curves were plotted according to the Kaplan-Meier method. Univariate and multivariate Cox proportional hazards regression models were used to assess the relation between potential variables and outcomes. tMTV and TLG were significantly lower in patients with complete response compared with those with partial response at the end of treatment. PET metabolic parameters (tMTV and TLG) were the independent prognostic values for outcome. TMTV and TLG were significantly connected with treatment response and prognosis in pediatric with BL.

Preoperative AFU Is a Useful Serological Prognostic Predictor for Colorectal Liver Oligometastasis Patients Undergoing Hepatic Resection.

Background: Preoperative alpha-l-fucosidase (AFU) has been used as a diagnostic biomarker for several cancers, but its role as a prognostic predictor in colorectal cancer liver oligometastasis (CLOM) patients after radical surgery has not been well defined. This study aimed to investigate the prognostic significance of preoperative serum AFU for CLOM patients after hepatic resection. Methods: A retrospective data set was collected to evaluate the prognostic value of preoperative AFU in CLOM patients after radical hepatic resection. A total of 269 patients with histopathologically confirmed CLOM were enrolled. The optimal cut-off value of preoperative AFU was determined using X-tile software. Univariate and multivariate analyses were used to identify the prognostic significance of preoperative serum AFU. Results: The X-tile software showed that the optimal cut-off value of preoperative AFU was set at 30.8 U/L. Patients with preoperative AFU≤30.8 and >30.8 were classified into high and low AFU groups, respectively. Female patients and those with a single liver metastasis had a higher tendency to have a preoperative AFU≤30.8 U/L; patients with lower clinical risk score (CRS) were more likely to have AFU >30.8 U/L than patients with higher CRS. The results showed that preoperative AFU was an independent prognostic factor for overall survival (OS) (P=0.041). Patients with a preoperative AFU≤30.8 U/L had a lower OS rate than those with AFU>30.8 U/L. Furthermore, for patients with lower CRS scores (0-2), the tendency clearly showed that patients with higher preoperative AFU had a better prognosis (P=0.029). Conclusions: Higher preoperative serum AFU can predict better survival in CLOM patients after hepatic resection, especially for CLOM patients with lower CRS scores.