OBJECTIVE: This multicenter randomized phase III trial evaluated whether locoregional control of patients with LAHNSCC could be improved by fluorodeoxyglucose-positron emission tomography (FDG-PET)-guided dose-escalation while minimizing the risk of increasing toxicity using a dose-redistribution and scheduled adaptation strategy.
METHODS: Patients with T3-4-N0-3-M0 LAHNSCC were randomly assigned (1:1) to either receive a dose distribution ranging from 64-84 Gy/35 fractions with adaptation at the 10thfraction (rRT) or conventional 70 Gy/35 fractions (cRT). Both arms received concurrent three-cycle 100 mg/m2cisplatin. Primary endpoints were 2-year locoregional control (LRC) and toxicity. Primary analysis was based on the intention-to-treat principle.
RESULTS: Due to slow accrual, the study was prematurely closed (at 84 %) after randomizing 221 eligible patients between 2012 and 2019 to receive rRT (N = 109) or cRT (N = 112). The 2-year LRC estimate difference of 81 % (95 %CI 74-89 %) vs. 74 % (66-83 %) in the rRT and cRT arm, respectively, was not found statistically significant (HR 0.75, 95 %CI 0.43-1.31,P=.31). Toxicity prevalence and incidence rates were similar between trial arms, with exception for a significant increased grade ≥ 3 pharyngolaryngeal stenoses incidence rate in the rRT arm (0 versus 4 %,P=.05). In post-hoc subgroup analyses, rRT improved LRC for patients with N0-1 disease (HR 0.21, 95 %CI 0.05-0.93) and oropharyngeal cancer (0.31, 0.10-0.95), regardless of HPV.
CONCLUSIONS: Adaptive and dose redistributed radiotherapy enabled dose-escalation with similar toxicity rates compared to conventional radiotherapy. While FDG-PET-guided dose-escalation did overall not lead to significant tumor control or survival improvements, post-hoc results showed improved locoregional control for patients with N0-1 disease or oropharyngeal cancer treated with rRT.

More than half of patients with tuberous sclerosis complex (TSC) suffer from drug-resistant epilepsy (DRE), and resection surgery is the most effective way to control intractable epilepsy. Precise preoperative localization of epileptogenic tubers among all cortical tubers determines the surgical outcomes and patient prognosis. Models for preoperatively predicting epileptogenic tubers using 18F-FDG PET images are still lacking, however. We developed noninvasive predictive models for clinicians to predict the epileptogenic tubers and the outcome (seizure freedom or no seizure freedom) of cortical tubers based on 18F-FDG PET images.
Forty-three consecutive TSC patients with DRE were enrolled, and 235 cortical tubers were selected as the training set. Quantitative indices of cortical tubers on 18F-FDG PET were extracted, and logistic regression analysis was performed to select those with the most important predictive capacity. Machine learning models, including logistic regression (LR), linear discriminant analysis (LDA), and artificial neural network (ANN) models, were established based on the selected predictive indices to identify epileptogenic tubers from multiple cortical tubers. A discriminating nomogram was constructed and found to be clinically practical according to decision curve analysis (DCA) and clinical impact curve (CIC). Furthermore, testing sets were created based on new PET images of 32 tubers from 7 patients, and follow-up outcome data from the cortical tubers were collected 1, 3, and 5 years after the operation to verify the reliability of the predictive model. The predictive performance was determined by using receiver operating characteristic (ROC) analysis.
PET quantitative indices including SUVmean, SUVmax, volume, total lesion glycolysis (TLG), third quartile, upper adjacent and standard added metabolism activity (SAM) were associated with the epileptogenic tubers. The SUVmean, SUVmax, volume and TLG values were different between epileptogenic and non-epileptogenic tubers and were associated with the clinical characteristics of epileptogenic tubers. The LR model achieved the better performance in predicting epileptogenic tubers (AUC = 0.7706; 95% CI 0.70-0.83) than the LDA (AUC = 0.7506; 95% CI 0.68-0.82) and ANN models (AUC = 0.7425; 95% CI 0.67-0.82) and also demonstrated good calibration (Hosmer‒Lemeshow goodness-of-fit p value = 0.7). In addition, DCA and CIC confirmed the clinical utility of the nomogram constructed to predict epileptogenic tubers based on quantitative indices. Intriguingly, the LR model exhibited good performance in predicting epileptogenic tubers in the testing set (AUC = 0.8502; 95% CI 0.71-0.99) and the long-term outcomes of cortical tubers (1-year outcomes: AUC = 0.7805, 95% CI 0.71-0.85; 3-year outcomes: AUC = 0.8066, 95% CI 0.74-0.87; 5-year outcomes: AUC = 0.8172, 95% CI 0.75-0.87).
The 18F-FDG PET image-based LR model can be used to noninvasively identify epileptogenic tubers and predict the long-term outcomes of cortical tubers in TSC patients.

The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an 18F-FDG PET-driven approach to allow early identification-for intensification therapy-of diffuse large B-cell lymphoma patients with a high risk of relapse. Besides, some works have reported the prognostic value of baseline PET radiomics features (RFs). This work investigated the added value of such biomarkers on survival of patients involved in the GAINED protocol. Methods: Conventional PET features and RFs were computed from 18F-FDG PET at baseline and extracted using different volume definitions (patient level, largest lesion, and hottest lesion). Clinical features and the consolidation treatment information were also considered in the model. Two machine-learning pipelines were trained with 80% of patients and tested on the remaining 20%. The training was repeated 100 times to highlight the test set variability. For the 2-y progression-free survival (PFS) outcome, the pipeline included a data augmentation and an elastic net logistic regression model. Results for different feature groups were compared using the mean area under the curve (AUC). For the survival outcome, the pipeline included a Cox univariate model to select the features. Then, the model included a split between high- and low-risk patients using the median of a regression score based on the coefficients of a penalized Cox multivariate approach. The log-rank test P values over the 100 loops were compared with a Wilcoxon signed-ranked test. Results: In total, 545 patients were included for the 2-y PFS classification and 561 for survival analysis. Clinical features alone, consolidation features alone, conventional PET features, and RFs extracted at patient level achieved an AUC of, respectively, 0.65 ± 0.07, 0.64 ± 0.06, 0.60 ± 0.07, and 0.62 ± 0.07 (0.62 ± 0.07 for the largest lesion and 0.54 ± 0.07 for the hottest). Combining clinical features with the consolidation features led to the best AUC (0.72 ± 0.06). Adding conventional PET features or RFs did not improve the results. For survival, the log-rank P values of the model involving clinical and consolidation features together were significantly smaller than all combined-feature groups (P < 0.007). Conclusion: The results showed that a concatenation of multimodal features coupled with a simple machine-learning model does not seem to improve the results in terms of 2-y PFS classification and PFS prediction for patient treated according to the GAINED protocol.

Conventional whole-body static 18F-FDG PET imaging provides a semiquantitative evaluation of overall glucose metabolism without insight into the specific transport and metabolic steps. Here we demonstrate the ability of total-body multiparametric 18F-FDG PET to quantitatively evaluate glucose metabolism using macroparametric quantification and assess specific glucose delivery and phosphorylation processes using microparametric quantification for studying recovery from coronavirus disease 2019 (COVID-19). Methods: The study included 13 healthy subjects and 12 recovering COVID-19 subjects within 8 wk of confirmed diagnosis. Each subject had a 1-h dynamic 18F-FDG scan on the uEXPLORER total-body PET/CT system. Semiquantitative SUV and the SUV ratio relative to blood (SUVR) were calculated for different organs to measure glucose utilization. Tracer kinetic modeling was performed to quantify the microparametric blood-to-tissue 18F-FDG delivery rate [Formula: see text] and the phosphorylation rate k 3, as well as the macroparametric 18F-FDG net influx rate ([Formula: see text]). Statistical tests were performed to examine differences between healthy subjects and recovering COVID-19 subjects. The effect of COVID-19 vaccination was also investigated. Results: We detected no significant difference in lung SUV but significantly higher lung SUVR and [Formula: see text] in COVID-19 recovery, indicating improved sensitivity of kinetic quantification for detecting the difference in glucose metabolism. A significant difference was also observed in the lungs with the phosphorylation rate k 3 but not with [Formula: see text], which suggests that glucose phosphorylation, rather than glucose delivery, drives the observed difference of glucose metabolism. Meanwhile, there was no or little difference in bone marrow 18F-FDG metabolism measured with SUV, SUVR, and [Formula: see text] but a significantly higher bone marrow [Formula: see text] in the COVID-19 group, suggesting a difference in glucose delivery. Vaccinated COVID-19 subjects had a lower lung [Formula: see text] and a higher spleen [Formula: see text] than unvaccinated COVID-19 subjects. Conclusion: Higher lung glucose metabolism and bone marrow glucose delivery were observed with total-body multiparametric 18F-FDG PET in recovering COVID-19 subjects than in healthy subjects, implying continued inflammation during recovery. Vaccination demonstrated potential protection effects. Total-body multiparametric PET of 18F-FDG can provide a more sensitive tool and more insights than conventional whole-body static 18F-FDG imaging to evaluate metabolic changes in systemic diseases such as COVID-19.

To develop a comprehensive PET radiomics model to predict the pathological response after neoadjuvant toripalimab with chemotherapy in resectable stage III non-small-cell lung cancer (NSCLC) patients.
Stage III NSCLC patients who received three cycles of neoadjuvant toripalimab with chemotherapy and underwent 18F-FDG PET/CT were enrolled. Baseline 18F-FDG PET/CT was performed before treatment, and preoperative 18F-FDG PET/CT was performed three weeks after the completion of neoadjuvant treatment. Surgical resection was performed 4-5 weeks after the completion of neoadjuvant treatment. Standardized uptake value (SUV) statistics features and radiomics features were derived from baseline and preoperative PET images. Delta features were derived. The radiologic response and metabolic response were assessed by iRECIST and iPERCIST, respectively. The correlations between PD-L1 expression, driver-gene status, peripheral blood biomarkers, and the pathological responses (complete pathological response [CPR]; major pathological response [MPR]) were assessed. Associations between PET features and pathological responses were evaluated by logistic regression.
Thirty patients underwent surgery and 29 of them performed preoperative PET/CT. Twenty patients achieved MPR and 16 of them achieved CPR. In univariate analysis, five SUV statistics features and two radiomics features were significantly associated with pathological responses. In multi-variate analysis, SUVmax, SUVpeak, SULpeak, and End-PET-GLDM-LargeDependenceHighGrayLevelEmphasis (End-GLDM-LDHGLE) were independently associated with CPR. SUVpeak and SULpeak performed better than SUVmax and SULmax for MPR prediction. No significant correlation, neither between the radiologic response and the pathological response, nor among PD-L1, driver gene status, and baseline PET features was found. Inflammatory response biomarkers by peripheral blood showed no difference in different treatment responses.
The logistic regression model using comprehensive PET features contributed to predicting the pathological response after neoadjuvant toripalimab with chemotherapy in resectable stage III NSCLC patients.

The International Myeloma Working Group recently fully incorporated 18F-FDG PET into multiple myeloma (MM) diagnosis and response evaluation. Moreover, a few studies demonstrated the prognostic value of several biomarkers extracted from this imaging at baseline. Before these 18F-FDG PET biomarkers could be fully endorsed as risk classifiers by the hematologist community, further characterization of underlying molecular aspects was necessary. Methods: Reported prognostic biomarkers (18F-FDG avidity, SUVmax, number of focal lesions, presence of paramedullary disease [PMD] or extramedullary disease) were extracted from 18F-FDG PET imaging at baseline in a group of 139 patients from CASSIOPET, a companion study of the CASSIOPEIA cohort (ClinicalTrials.gov identifier NCT02541383). Transcriptomic analyses using RNA sequencing were realized on sorted bone marrow plasma cells from the same patients. An association with a high-risk gene expression signature (IFM15), molecular classification, progression-free survival, a stringent clinical response, and minimal residual disease negativity were explored. Results:18F-FDG PET results were positive in 79.4% of patients; 14% and 11% of them had PMD and extramedullary disease, respectively. Negative 18F-FDG PET results were associated with lower levels of expression of hexokinase 2 (HK2) (fold change, 2.1; adjusted P = 0.04) and showed enrichment for a subgroup of patients with a low level of bone disease. Positive 18F-FDG PET results displayed 2 distinct signatures: either high levels of expression of proliferation genes or high levels of expression of GLUT5 and lymphocyte antigens. PMD and IFM15 were independently associated with a lower level of progression-free survival, and the presence of both biomarkers defined a group of \"double-positive\" patients at very high risk of progression. PMD and IFM15 were related neither to minimal residual disease assessment nor to a stringent clinical response. Conclusion: Our study confirmed and extended the association between imaging biomarkers and transcriptomic programs in MM. The combined prognostic value of PMD and a high-risk IFM15 signature may help define MM patients with a very high risk of progression.

Alzheimer\'s disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and memory impairment. Amnestic mild cognitive impairment (aMCI) is the intermediate stage between normal cognitive aging and early dementia caused by AD. It can be challenging to differentiate aMCI patients from healthy controls (HC) and mild AD patients.
To validate whether the combination of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) and diffusion tensor imaging (DTI) will improve classification performance compared with that based on a single modality.
A total of thirty patients with AD, sixty patients with aMCI, and fifty healthy controls were included. AD was diagnosed according to the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer\'s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable. aMCI diagnosis was based on Petersen\'s criteria. The 18F-FDG PET and DTI measures were each used separately or in combination to evaluate sensitivity, specificity, and accuracy for differentiating HC, aMCI, and AD using receiver operating characteristic analysis together with binary logistic regression. The rate of accuracy was based on the area under the curve (AUC).
For classifying AD from HC, we achieve an AUC of 0.96 when combining two modalities of biomarkers and 0.93 when using 18F-FDG PET individually. For classifying aMCI from HC, we achieve an AUC of 0.79 and 0.76 using the best individual modality of biomarkers.
Our results show that the combination of two modalities improves classification performance, compared with that using any individual modality.

We report our PET/MRI experience from a pilot study that compared the diagnostic performance of 18F-FDG PET/MRI versus PET/CT in staging of cervical cancer. Methods: Six adults with newly diagnosed cervical cancer underwent a single 18F-FDG injection with a dual-imaging protocol: standard-of-care PET/CT followed by research PET/MRI. The diagnostic interpretation and SUVmax for the 2 modalities were compared. Results: Both modalities detected all primary tumors (median size, 3.9 cm) and all 4 metastases present in 2 of the 6 patients (median size, 0.9 cm). PET/MRI provided greater diagnostic confidence than PET/CT and upstaged the disease in 4 patients. On the basis of the imaging findings alone, the additional information from PET/MRI would have led to a change in clinical management in 3 of 6 patients. The primary lesion showed a median SUV of 12.8 on PET/CT and 18.2 on PET/MRI (P = 0.03). SUVs, however, correlated strongly between the 2 modalities (ρ = 0.96, P < 0.001). Conclusion: Our pilot study supports the notion that PET/MRI has the potential to impact clinical decisions and treatment strategies in women with cervical cancer. Further studies are, however, warranted to define the value that PET/MRI adds to PET/CT.

BACKGROUND: Solitary pulmonary nodules (SPNs) frequently bother oncologists. The differentiation of malignant from benign nodules with non-invasive approach remains a tough challenge. This study was designed to assess the diagnostic accuracy of dynamic computed tomography (CT), dynamic magnetic resonance imaging (MRI), fluorine 18 fluorodeoxyglucose (18F-FDG) positron emission tomography (PET), and technetium 99 m (99mTc) depreotide single photon emission computed tomography (SPECT) for SPNs.
METHODS: Electronic databases of MEDLINE, PubMed, EMBASE, and Cochrane Library were searched to identify relevant trials. The primary evaluation index of diagnostic accuracy was areas under the summary receiver-operating characteristic (SROC) curve. The results were analyzed utilizing Stata 12.0 statistical software.
RESULTS: Seventy-three trials incorporating 7956 individuals were recruited. Sensitivities, specificities, positive likelihood ratios, negative likelihood ratios, diagnostic score, diagnostic odds ratios, and areas under the SROC curve with 95% confidence intervals were, respectively, 0.92 (0.89-0.95), 0.64 (0.54-0.74), 2.60 (1.98-3.42), 0.12 (0.08-0.17), 3.10 (2.62-3.59), 22.24 (13.67-36.17), and 0.91 (0.88-0.93) for CT; 0.92 (0.86-0.95), 0.85 (0.77-0.90), 6.01 (3.90-9.24), 0.10 (0.06-0.17), 4.12 (3.41-4.82), 61.39 (30.41-123.93), and 0.94 (0.92-0.96) for MRI; 0.90 (0.86-0.93), 0.73 (0.65-0.79), 3.28 (2.56-4.20), 0.14 (0.10-0.19), 3.16 (2.69-3.64), 23.68 (14.74-38.05), and 0.90 (0.87-0.92) for 18F-FDG PET; and 0.93 (0.88-0.96), 0.70 (0.56-0.81), 3.12 (2.03-4.81), 0.10 (0.06-0.17), 3.43 (2.63-4.22), 30.74 (13.84-68.27), and 0.93 (0.91-0.95) for 99mTc-depreotide SPECT.
CONCLUSIONS: The dynamic MRI, dynamic CT, 18F-FDG PET, and 99mTc-depreotide SPECT were favorable non-invasive approaches to distinguish malignant SPNs from benign. Moreover, from the viewpoint of cost-effectiveness and avoiding radiation, the dynamic MRI was recommendable for SPNs.

Time-course study of individual dose equivalents of 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography (18F-FDG PET) was conducted in different hospital workers, and the daily work duties were analyzed. For the measurements, a semiconductor dosimeter was used. The values at intervals of 1 min and 1 h, the monthly cumulative and daily cumulative doses, and trend graphs were acquired with dedicated software and displayed on the reader. The following radiation workers with duties involving maximum external exposure work were included: doctors making diagnoses (4.8 μSv/procedure), nurses removing injection needles (3.1 μSv/procedure), pharmacists performing quality control tests (2.9 μSv/procedure), nuclear medicine technologists assisting patient positioning (6.5 μSv/procedure), and cyclotron engineers performing daily checks (13.4 μSv/procedure). The results of analysis of daily work duties revealed the influencing factors of external exposure dose. To reduce the external exposure dose, investigators should shorten the patient\'s contact time with the 18F-FDG source or patient tracer.