18F-FDG PET Imaging Research Articles

Incremental prognostic value of 18F-FDG myocardial ischemic memory imaging for major adverse cardiovascular events in patients with suspected unstable angina

BackgroundThe additional prognostic value of 18F-FDG PET myocardial ischemic memory imaging for patients with suspected unstable angina (UA) is not well established. This study aimed to determine whether 18F-FDG PET imaging provides incremental prognostic information for predicting major adverse cardiac events (MACE) compared to clinical risk factors, GRACE score, and coronary artery calcium score (CACS) in suspected UA patients. MethodsIn this post-hoc analysis of a prospective study, 265 suspected UA patients (62.3% male, mean age 65.0±9.4 years) were enrolled. 18F-FDG positive was defined as focal or focal on diffuse uptake patterns. MACE included cardiovascular death, acute myocardial infarction, heart failure, rehospitalization for UA, and stroke. Multivariable Cox regression was used to identify predictors of MACE, and the incremental prognostic value of 18F-FDG PET imaging was assessed using C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). ResultsOver a median follow-up of 25 months, 51 patients (19.2%) experienced MACE. 18F-FDG positive (HR=3.220, 95% CI: 1.630-6.360, P<0.001) , as well as 18F-FDG standardized uptake ratio (SUR) (HR=1.330, 95%CI: 1.131-1.564, P=0.0006) and Extent (HR=1.045, 95%CI: 1.028-1.062, P<0.0001), were independent predictors of MACE. The addition of 18F-FDG PET imaging significantly improved risk stratification beyond clinical factors, the GRACE score, and CACS, with improved C-index (0.769 vs 0.688, P=0.045), NRI (0.324, P=0.020), and IDI (0.055, P=0.027). Conclusion18F-FDG PET myocardial ischemic memory imaging significantly improves prognostic assessment for suspected UA patients, providing valuable additional risk stratification beyond clinical risk factors, GRACE score, and CACS.

First-in-Human: Simultaneous Hyperpolarized 1- 13 C-Pyruvate Magnetic Resonance Spectroscopy and 18 F-FDG PET (hyperPET) Imaging of a Patient With Lymphoma.

Hyperpolarized 1- 13 C-pyruvate magnetic resonance spectroscopy (MRS) and MRS imaging (MRSI) offer noninvasive and real-time direct assessment of the altered metabolism of cancer cells known as the Warburg effect-a key hallmark of cancer. When combined with simultaneously acquired 18 F-FDG PET in a PET/MR scanner, coined hyperPET by us, this dual-modality may unveil cancer-type specific glucose metabolic phenotypes with potential implications for patient prognostication, treatment-response assessment, and prediction. We here present the first human data of simultaneously acquired hyperpolarized MRS/MRSI and PET performed in a PET/MR scanner-and the first human hyperpolarized MRS/MRSI data from a patient with lymphoma.

Rare Gastric Metastasis of Lobular Type Breast Cancer Detected With 68 Ga-FAPI-46 PET/CT.

Invasive lobular breast cancer (ILBC) is characterized with low tracer uptake on 18 F-FDG PET images. ILBC metastasis to gastrointestinal tract is a rare clinic scenario. We present a case of ILBC who presented with gastric metastasis and diagnosed with 68 Ga-FAPI-46 PET/CT and 18 F-FDG PET/CT images.

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18 F-FDG PET Scans.

We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18 F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence-driven 18 F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets. Two experienced nuclear medicine physicians separately evaluated the quality of 174 18 F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale. The body regisons included the following: (1) the head and neck, including the brain, (2) the chest, (3) the chest-abdomen interval (diaphragmatic region), (4) the abdomen, and (5) the pelvis. Intrareader and interreader reproducibility of the quality scores were calculated using 39 randomly selected scans from the dataset. Utilizing a binarized classification, images were dichotomized into low-quality versus high-quality for physician quality scores ≤3 versus >3, respectively. Inputting the 18 F-FDG PET/CT scans, our proposed fully automated framework applies 2 deep learning (DL) models on CT images to perform region identification and whole-body contour extraction (excluding extremities), then classifies PET regions as low and high quality. For classification, 2 mainstream artificial intelligence-driven approaches, including machine learning (ML) from radiomic features and DL, were investigated. All models were trained and evaluated on scores attributed by each physician, and the average of the scores reported. DL and radiomics-ML models were evaluated on the same test dataset. The performance evaluation was carried out on the same test dataset for radiomics-ML and DL models using the area under the curve, accuracy, sensitivity, and specificity and compared using the Delong test with P values <0.05 regarded as statistically significant. In the head and neck, chest, chest-abdomen interval, abdomen, and pelvis regions, the best models achieved area under the curve, accuracy, sensitivity, and specificity of [0.97, 0.95, 0.96, and 0.95], [0.85, 0.82, 0.87, and 0.76], [0.83, 0.76, 0.68, and 0.80], [0.73, 0.72, 0.64, and 0.77], and [0.72, 0.68, 0.70, and 0.67], respectively. In all regions, models revealed highest performance, when developed on the quality scores with higher intrareader reproducibility. Comparison of DL and radiomics-ML models did not show any statistically significant differences, though DL models showed overall improved trends. We developed a fully automated and human-perceptive equivalent model to conduct region-wise IQA over 18 F-FDG PET images. Our analysis emphasizes the necessity of developing separate models for body regions and performing data annotation based on multiple experts' consensus in IQA studies.

18Fluorodeoxyglucose positron emission tomography (18F-FDG PET)–derived tumoral and peritumoral radiomic parameters can predict pathological subtype and survival in esophageal carcinoma

The aim of this study is to investigate the importance of the quantitative parameters of the tumoral and peritumoral regions in prediction of pathological subtypes and 1-year survival in patients with esophageal carcinoma. A total of 103 patients with esophageal squamous cell carcinoma (SCC) and adenocarcinoma (AC) and in whom 18fluorodeoxyglucose positron emission tomography/computerized tomography (18F-FDG PET/CT) was performed were included in the study. One-year progression-free survival (PFS) and overall survival times of all patients were noted. Primary tumor and peritumoral area were drawn with manual segmentation on the 18F-FDG PET images. Seventy-three quantitative parameters were extracted from tumoral and peritumoral volumes of interest by using software. The differences of parameters of tumoral and peritumoral regions were determined statistically between pathological subtypes and between 1-year survivors and non-survivors. Diagnostic models were created with the statistically significant parameters. The model that consists of a tumoral and a peritumoral parameter could classify the pathological subtypes in 61% of the patients correctly (area under the curve [AUC]: 0.706, 61.2% accuracy, 53.7% sensitivity, and 75% specificity). The model that was created with 2 tumoral parameters could classify the 1-year survival in 66% of the patients correctly (AUC: 0.695, 66% accuracy, 73.6% sensitivity, and 56.1% specificity). The model consisting of a tumoral and a peritumoral parameter detected 1-year PFS in 66% of the patients accurately (AUC: 0.687, 66% accuracy, 72.4% sensitivity, and 55.6% specificity). The quantitative parameters obtained from tumoral and peritumoral regions can provide information about pathological subtypes and 1-year survival in esophageal carcinoma.

The Role of 18F-FDG PET in the Diagnosis of Aortitis and Large Vessel Vasculitis

Background: 18F-FDG PET metabolic imaging provides significant help in the early diagnosis of inflammation of large and medium arteries, even before the appearance of structural vascular alterations. This retrospective study aims to evaluate the role of 18F-FDG PET in the diagnosis and management of large vessel vasculitis (LVV), including aortitis. Methodology: Fifty patients with clinical and/or biological suspicion of large vessel vasculitis but without a definitive diagnosis underwent 18F-FDG PET scanning. A qualitative visual scoring system and an average total vascular score (TVS) were used to compare 18F-FDG uptake in the vascular walls with that in the liver. Patients who had been on corticosteroids for more than 8 days were excluded from the study. Results: The study included 50 patients, 27 women and 23 men, with an average age of 65.8 ± 14.5 years. The analysis of 18F-FDG PET images allowed for the classification of subjects into two distinct groups. Among the 50 subjects, 16 (32%) showed higher 18F-FDG uptake in the vascular walls compared to hepatic uptake (PET-positive group), while 34 (68%) showed lower uptake (PET-negative group). In the positive group, 75% were diagnosed with Horton’s disease, and the remaining 25% with other inflammatory diseases such as Takayasu arteritis or polyarteritis nodosa. None of the subjects in the positive group had an aortic aneurysm. All patients with a positive 18F-FDG PET had elevated levels of C-reactive protein. The TVS obtained for aortitis alone was 5.9 ± 4.7, while the score for more global vasculitis was obviously higher, at 10.4 ± 6.3. Conclusion: 18F-FDG PET has become a valuable tool in the diagnosis of large vessel vasculitis, particularly in the early stages of the disease. It also allows for mapping of arterial involvement and estimation of disease severity using the TVS.

FDG Uptake in Brown Adipose Tissue Activated by a Long-Acting β2-Adrenergic Receptor Agonist Inhaler Prescribed for Bronchial Asthma.

We herein report a case of an elderly woman in whom brown adipose tissue (BAT) was visible on 18 F-FDG PET imaging performed in the summer. In this patient, none of the well-known factors that induce BAT activation in adults, including cold exposure, catecholamine-producing tumors, or β3-adrenergic receptor agonists administration, were present. Daily inhalation of a long-acting β2-adrenergic agonist for treatment of her bronchial asthma was considered the cause of her BAT visualization. This case suggests the need to consider β2-adrenergic receptor agonists as a cause of BAT visualization.

18F-FDG PET/CT biomarkers as predictors of long term outcomes and survival rates in patients with high risk malignant pulmonary masses/nodules treated with stereotactic ablative radiotherapy.

Stereotactic ablative body radiotherapy (SABR) is a standard treatment option for patients with malignant pulmonary masses (including primary and metastatic lesions) who are unfit for surgery or who are medically operable but refuse surgery. Flourine-18 flurodeoxyglucose positron emission tomography (18F-FDG PET) volumetric metabolic parameters, i.e., metabolic tumour volume (MTV) and total lesion glycolysis (TLG) play an important role in assessing the biological characteristics of some tumours and its role as potential prognostic factors has also been introduced. The aim of this retrospective study is to assess the value of baseline metabolic volumetric parameters as prognostic imaging biomarkers in patients with pulmonary masses/nodules treated with SABR. 70 patients were included in this retrospective study (39 male and 31 female, age range 47-91years, mean 76years). Standardized uptake value (SUVmax), SUVmean, MTV and TLG for all the patients were calculated on baseline 18F-FDG PET/CT. Patient outcome was divided into 3 categories free of disease, stable disease and disease progression. There was no significant statistical difference in the SUVmax and SUVmean in all the three categories. Mean SUVmax ranges from 7.13 to 8.08 with its highest value in the stable disease and lowest value in the progressive disease categories. Similarly, the average SUVmean was 4.9 in the free of disease category and 4.68 in the progressive disease category. MTV and TLG were low in the free of disease and the highest in progressive disease. MTV increased from 2.25 cm3 in free of disease category to 3.23 cm3 and 7.29 cm3 in stable disease and progressive disease, respectively. TLG has increased from 11.7 in the disease-free survival category to 18.77 and 40.39 in the stable and progressive disease, respectively. Patients with low MTV had longer overall survival (OS) than patients with high MTV (37months versus 27months, p value = 0. 0018). In addition, OS was longer in patients with low TLG (36months versus 24months, p value = 0.016). TLG and MTV are more useful than SUVmax and SUVmean for predicting outcome, OS and progression-free survival (PFS) in patients receiving SABR. The TLG and MTV measurement on 18F-FDG PET imaging may be routinely recommended in baseline 18F-FDG PET/CT prior to SABR.

Cross-Modality Image Translation From Brain 18 F-FDG PET/CT Images to Fluid-Attenuated Inversion Recovery Images Using the CypixGAN Framework.

PET/CT and MRI can accurately diagnose dementia but are expensive and inconvenient for patients. Therefore, we aimed to generate synthetic fluid-attenuated inversion recovery (FLAIR) images from 18 F-FDG PET and CT images of the human brain using a generative adversarial network (GAN)-based deep learning framework called the CypixGAN, which combined the CycleGAN framework with the L1 loss function of the pix2pix. Data from 143 patients who underwent PET/CT and MRI were used for training (n = 79), validation (n = 20), and testing (n = 44) the deep learning frameworks. Synthetic FLAIR images were generated using the pix2pix, CycleGAN, and CypixGAN, and white matter hyperintensities (WMHs) were then segmented. The performance of CypixGAN was compared with that of the other frameworks. The CypixGAN outperformed the pix2pix and CycleGAN in generating synthetic FLAIR images with superior visual quality. Peak signal-to-noise ratio and structural similarity index (mean ± standard deviation) estimated using the CypixGAN (20.23 ± 1.31 and 0.80 ± 0.02, respectively) were significantly higher than those estimated using the pix2pix (19.35 ± 1.43 and 0.79 ± 0.02, respectively) and CycleGAN (18.74 ± 1.49 and 0.78 ± 0.02, respectively) ( P < 0.001). WMHs in synthetic FLAIR images generated using the CypixGAN closely resembled those in ground-truth images, as indicated by the low absolute percentage volume differences and high dice similarity coefficients. The CypixGAN generated high-quality FLAIR images owing to the preservation of spatial information despite using unpaired images. This framework may help improve diagnostic performance and cost-effectiveness of PET/CT when MRI scan is unavailable.

Metabolic network connectivity disturbances in Parkinson's disease: a novel imaging biomarker.

The diagnosis of Parkinson's Disease (PD) presents ongoing challenges. Advances in imaging techniques like 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have highlighted metabolic alterations in PD, yet the dynamic network interactions within the metabolic connectome remain elusive. To this end, we examined a dataset comprising 49 PD patients and 49 healthy controls. By employing a personalized metabolic connectome approach, we assessed both within- and between-network connectivities using Standard Uptake Value (SUV) and Jensen-Shannon Divergence Similarity Estimation (JSSE). A random forest algorithm was utilized to pinpoint key neuroimaging features differentiating PD from healthy states. Specifically, the results revealed heightened internetwork connectivity in PD, specifically within the somatomotor (SMN) and frontoparietal (FPN) networks, persisting after multiple comparison corrections (P < 0.05, Bonferroni adjusted for 10% and 20% sparsity). This altered connectivity effectively distinguished PD patients from healthy individuals. Notably, this study utilizes 18F-FDG PET imaging to map individual metabolic networks, revealing enhanced connectivity in the SMN and FPN among PD patients. This enhanced connectivity may serve as a promising imaging biomarker, offering a valuable asset for early PD detection.

Improving 18F-FDG PET Quantification Through a Spatial Normalization Method.

Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset's different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning-based brain PET spatial normalization techniques for various clinical and research radiotracers.

Voxel-based dosimetry with integrated Y-90 PET/MRI and prediction of response of primary and metastatic liver tumors to radioembolization with Y-90 glass microspheres.

In this study, we aimed to evaluate the response of the primary and metastatic liver tumors to radioembolization with 90Y glass microspheres and investigate its correlations with dosimetric variables calculated with 90Y PET/MRI. In this ambispective study, 44 patients treated with 90Y glass microspheres and imaged with 90Y PET/MRI were included for analysis. Dosimetric analysis was performed for every perfused lesion using dose-volume histograms. Response was assessed by comparing pre-treatment and follow-up total lesion glycolysis (TLG) values derived from 18F-FDG PET imaging. The relationship between ΔTLG and log-transformed dosimetric variables was analyzed with linear mixed effects regression models. ROC analyses were performed to compare discriminatory power of the variables in predicting response and complete response. Regression and ROC analyses demonstrated that mean tumor dose and almost all D values were statistically significant predictors of treatment response and complete treatment response. Specifically, D60, D70 and D80 values exhibited significantly higher discriminatory power for predicting treatment response compared to the mean dose (Dmean) delivered to tumor. High specificity cut-off values to predict response were determined as 160.75 Gy for Dmean, 95.50 Gy for D60, 89 Gy for D70, and 59.50 Gy for D80. Similarly, high-specificity cut-off values to predict complete response were 262.75 Gy for Dmean, 173 Gy for D70, 140.5 Gy for D80, and 100 Gy for D90. In this study, we demonstrated that voxel-based dosimetry with post-treatment 90Y PET/MRI can predict response to treatment. D60, D70 and D80 variables also did have greater discriminatory power compared to Dmean in prediction of response. In addition, we present high-specificity cut-offs to predict response (CR + PR) and complete response (CR) for both Dmean and several D variables derived from dose-volume histograms.

Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on 18F-FDG PET

The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography ( 18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.

Initial clinical experience using 68Ga-FAPI-46 PET/CT for detecting various cancer types.

Numerous studies have shown that gallium-68-labeled fibroblast activation protein inhibitor (68Ga-FAPI) positron emission tomography/computed tomography (PET/CT) scans would yield high intra-tumoral tracer uptake and low uptake in normal tissues as background, thus allowing for excellent visualization of lesions in the cancer microenvironment. This study set out to compare the suitability of novel 68Ga-FAPI-46 PET versus routine fluorine-18-fluorodeoxyglucose (18F-FDG) PET and other few cases of 68Ga-DOTATATE/68Ga-Pentixafor PET/CT for the assessment of different types of cancer. A retrospective analysis of 11 patients (6 males, 5 females; average age: 53 years, range: 10-58 years) with histopathologically confirmed, well-differentiated adenocarcinoma, medullar thyroid cancer (MTC), papillary thyroid carcinoma (PTC), cervical, gastric, glioblastoma multiform (GBM), colon, Ewing's sarcoma, and breast cancer was performed. These patients underwent PET/CT scans using four different radiotracers (9 18F-FDG, 11 68Ga- FAPI, 3 68Ga-DOTATATE, and 1 68Ga-Pentixafor). The patients' PET/CT images were visually evaluated for cancer detection, and analyzed semi-quantitatively through image- derived metrics, such as target-to-background ratio (TBR) and maximum standardized uptake value (SUVmax), for recurrence and metastasis. The study of 11 patients revealed that 68Ga-FAPI-46 was more effective than other tracers for detecting metastases, with 55 vs. 49 metastases in the lymph nodes, 4 vs. 3 in the liver, and 4 vs. 3 in the bones detected in comparison to 18F-FDG. No significant differences were observed in 68Ga-DOTATATE and 68Ga-Pentixafor PET images. In addition, in five patients, the SUVmax and TBR values in 68Ga-FAPI-46 PET images were significantly higher than those in 18F-FDG PET images for lymph nodes and bone metastases. Although the SUVmax in 68Ga-FAPI-46 and 18F-FDG PET images for liver metastases was comparable, 68Ga-FAPI- 46 had a significantly higher TBR than 18F-FDG. Our findings suggest that FAPI PET/CT is not suitable for evaluating GBM and Ewing sarcoma but generally outperforms 18F-FDG PET/CT in various types of breast cancer, gastrointestinal, gynecological, PTC and MTC. However, larger trials are needed to validate these preliminary findings.

Melatonin attenuates scopolamine-induced cognitive dysfunction through SIRT1/IRE1α/XBP1 pathway.

The prevalence of dementia around the world is increasing, and these patients are more likely to have cognitive impairments, mood and anxiety disorders (depression, anxiety, and panic disorder), and attention deficit disorders over their lifetime. Previous studies have proven that melatonin could improve memory loss, but its specific mechanism is still confused. In this study, we used invivo and invitro models to examine the neuroprotective effect of melatonin on scopolamine (SCOP)-induced cognitive dysfunction. The behavioral tests were performed. 18F-FDG PET imaging was used to assess the metabolism of the brain. Protein expressions were determined through kit detection, Western blot, and immunofluorescence. Nissl staining was conducted to reflect neurodegeneration. MTT assay and RNAi transfection were applied to perform the invitro experiments. We found that melatonin could ameliorate SCOP-induced cognitive dysfunction and relieve anxious-like behaviors or HT22 cell damage. 18F-FDG PET-CT results showed that melatonin could improve cerebral glucose uptake in SCOP-treated mice. Melatonin restored the cholinergic function, increased the expressions of neurotrophic factors, and ameliorated oxidative stress in the brain of SCOP-treated mice. In addition, melatonin upregulated the expression of silent information regulator 1 (SIRT1), which further relieved endoplasmic reticulum (ER) stress by decreasing the expression of phosphorylate inositol-requiring enzyme (p-IRE1α) and its downstream, X-box binding protein 1 (XBP1). These results indicated that melatonin could ameliorate SCOP-induced cognitive dysfunction through the SIRT1/IRE1α/XBP1 pathway. SIRT1 might be the critical target of melatonin in the treatment of dementia.

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

18F-fluorodeoxyglucose positron emission tomography-computed tomography in the localization of the lesions in the osteogenic region of breast cancer bone metastases after therapy.

Accurate efficacy evaluation of bone metastases (BMs) from breast cancer (BC) is an intractable issue in clinical practice, for which solutions are urgently needed. This study aimed to investigate the utility of 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) in the response evaluation of bone metastasis of BC. In total, 22 patients diagnosed with BC and BM were enrolled. These patients underwent repeated 18F-FDG PET/CT evaluations. The patients and each BM site were divided into two groups based on their response to treatment: progressive disease (PD) and nonprogressive disease (non-PD). We analyzed and compared the changes in PET and CT images, as well as the serum concentration of carcinoembryonic antigen (CEA), carbohydrate antigen 153 (CA153), alkaline phosphatase (ALP), and calcium (Ca) over the same time frame. The immunohistochemistry (IHC) of primary lesions between groups and between the primary focus and BM with high 18F-FDG uptake were compared and analyzed. Maximum standard uptake value (SUVmax) after therapy [area under the curve (AUC): 0.932] and Δ-value of SUVmax (AUC: 0.811) on 18F-FDG PET imaging proved significantly valuable for the efficacy of therapy outcomes related to BM lesions (P<0.05). In terms of overall evaluation of BM, age and human epidermal growth factor receptor 2 (HER2) expression were significantly lower in the PD group than in the non-PD group (P<0.05). There were marked differences in CEA after therapy, the changes of CEA, and CA153 (∆-value) between the groups (P<0.05). The SUVmax and Ca concentration after therapy and ∆-value of SUVmax, along with the levels of CA153, CEA, and ALP, were valuable indicators for evaluating the efficacy of individual BMs (P<0.05). IHC of BM in the PD group showed differences compared to primary lesions, with antigen Ki-67 being downregulated in metastatic lesions and HER2 being downregulated in a portion of BMs (2 of 6). Meanwhile, the expression of estrogen receptor (ER) and progesterone receptor (PR) remained relatively unchanged. 18F-FDG PET/CT confers precise assessment of the posttreatment efficacy pertaining to BM in BC. This modality facilitates the identification of poor effect lesions following extant therapies and localization for pathological assessment and may substantially contribute to evaluating therapeutic efficacy, refining treatment strategies, and predicting the disease trajectory of patients with BC and BM.

Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied to 18F-FDG-PET Radiomics.

The histological grade of oral squamous cell carcinoma affects the prognosis. In the present study, we performed a radiomics analysis to extract features from 18F-FDG PET image data, created machine learning models from the features, and verified the accuracy of the prediction of the histological grade of oral squamous cell carcinoma. The subjects were 191 patients in whom an 18F-FDG-PET examination was performed preoperatively and a histopathological grade was confirmed after surgery, and their tumor sizes were sufficient for a radiomics analysis. These patients were split in a 70%/30% ratio for use as training data and testing data, respectively. We extracted 2993 radiomics features from the PET images of each patient. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) machine learning models were created. The areas under the curve obtained from receiver operating characteristic curves for the prediction of the histological grade of oral squamous cell carcinoma were 0.72, 0.71, 0.84, 0.74, and 0.73 for LR, SVM, RF, NB, and KNN, respectively. We confirmed that a PET radiomics analysis is useful for the preoperative prediction of the histological grade of oral squamous cell carcinoma.

Synthesis and Characterization of a Novel PET Tracer for Noninvasive Evaluation of FGL1 Status in Tumors.

Fibrinogen-like protein 1 (FGL1) is a potential novel immune checkpoint target for malignant tumor diagnosis and therapy. Accurate detection of FGL1 levels in tumors via noninvasive PET imaging might be beneficial for managing the disease. To achieve this, multiple FGL1-targeting peptides (FGLP) were designed, and a promising candidate, 68Ga-NOTA-FGLP2, was identified through a high-throughput screening approach using microPET imaging of 68Ga-labeled peptides. Subsequent in vitro cell experiments showed that uptake values of 68Ga-NOTA-FGLP2 in FGL1 positive Huh7 tumor cells were significantly higher than those in FGL1 negative U87 MG tumor cells. Further microPET imaging showed that the Huh7 xenografts were clearly visualized with a favorable contrast. ROI analysis showed that the uptake values of the tracer in Huh7 xenografts were 2.63 ± 0.07% ID/g at 30 min p.i.. After treatment with an excess of unlabeled FGLP2, the tumor uptake significantly decreased to 0.54 ± 0.05% ID/g at 30 min p.i.. Moreover, the uptake in U87 MG xenografts was 0.44 ± 0.06% ID/g at the same time point. The tracer was excreted mainly through the renal system. 18F-FDG PET imaging was also performed in mice bearing Huh7 and U87 MG xenografts, respectively. However, there was no significant difference in the uptake between the tumors with different FGL1 expressions. Preclinical data indicated that 68Ga-NOTA-FGLP2 might be a suitable radiotracer for in vivo noninvasive visualization of tumors with abundant expression of FGL1. Further investigation of 68Ga-NOTA-FGLP2 for tumor diagnosis and therapy is undergoing.

Late-xerostomia prediction model based on 18F-FDG PET image biomarkers of the main salivary glands

Background and purposeRecently, a comprehensive xerostomia prediction model was published, based on baseline xerostomia, mean dose to parotid glands (PG) and submandibular glands (SMG). Previously, PET imaging biomarkers (IBMs) of PG were shown to improve xerostomia prediction. Therefore, this study aimed to explore the potential improvement of the additional PET-IBMs from both PG and SMG to the recent comprehensive xerostomia prediction model (i.e., the reference model). Materials and methodsTotally, 540 head and neck cancer patients were split into training and validation cohorts. PET-IBMs from the PG and SMG, were selected using bootstrapped forward selection based on the reference model. The IBMs from both the PG and SMG with the highest selection frequency were added to the reference model, resulting in a PG-IBM model and a SMG-IBM model which were combined into a composite model. Model performance was assessed using the area under the curve (AUC). Likelihood ratio test compared the predictive performance between the reference model and models including IBMs. ResultsThe final selected PET-IBMs were 90th percentile of the PG SUV and total energy of the SMG SUV. The additional two PET-IBMs in the composite model improved the predictive performance of the reference model significantly. The AUC of the reference model and the composite model were 0.67 and 0.69 in the training cohort, and 0.71 and 0.73 in the validation cohort, respectively. ConclusionThe composite model including two additional PET-IBMs from PG and SMG improved the predictive performance of the reference xerostomia model significantly, facilitating a more personalized prediction approach.