Performance of ChatGPT incorporated chain-of-thought method in bilingual nuclear medicine physician board examinations.

Objective: This research explores the performance of ChatGPT, compared to human doctors, in bilingual, Mandarin Chinese and English, medical specialty exam in Nuclear Medicine in Taiwan. Methods: The study employed generative pre-trained transformer (GPT-4) and integrated chain-of-thoughts (COT) method to enhance performance by triggering and explaining the thinking process to answer the question in a coherent and logical manner. Questions from the Taiwanese Nuclear Medicine Specialty Exam served as the basis for testing. The research analyzed the correctness of AI responses in different sections of the exam and explored the influence of question length and language proportion on accuracy. Results: AI, especially ChatGPT with COT, exhibited exceptional capabilities in theoretical knowledge, clinical medicine, and handling integrated questions, often surpassing, or matching human doctor performance. However, AI struggled with questions related to medical regulations. The analysis of question length showed that questions within the 109-163 words range yielded the highest accuracy. Moreover, an increase in the proportion of English words in questions improved both AI and human accuracy. Conclusions: This research highlights the potential and challenges of AI in the medical field. ChatGPT demonstrates significant competence in various aspects of medical knowledge. However, areas like medical regulations require improvement. The study also suggests that AI may help in evaluating exam question difficulty and maintaining fairness in examinations. These findings shed light on AI role in the medical field, with potential applications in healthcare education, exam preparation, and multilingual environments. Ongoing AI advancements are expected to further enhance AI utility in the medical domain.

Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant metastases in patients with locally advanced uterine cervical cancer. METHODS: This study used baseline [18F]FDG-PET/CT images of newly diagnosed uterine cervical cancer patients. Data from 186 to 25 patients were analyzed for training and validation cohort, respectively. All patients received chemoradiotherapy (CRT) and follow-up. PET and CT images were augmented by using three-dimensional techniques. The proposed model employed DL to predict distant metastases. Receiver operating characteristic (ROC) curve analysis was performed to measure the model's predictive performance. RESULTS: The area under the ROC curves of the training and validation cohorts were 0.818 and 0.830 for predicting distant metastasis, respectively. In the training cohort, the sensitivity, specificity, and accuracy were 80.0%, 78.0%, and 78.5%, whereas, the sensitivity, specificity, and accuracy for distant failure were 73.3%, 75.5%, and 75.2% in the validation cohort, respectively. CONCLUSION: Through the use of baseline [ 18 F]FDG-PET/CT images, the proposed DL model can predict the development of distant metastases for patients with locally advanced uterine cervical cancer treatment by CRT. External validation must be conducted to determine the model's predictive performance.

Bone Metastases Lesion Segmentation on Breast Cancer Bone Scan Images with Negative Sample Training.

The use of deep learning methods for the automatic detection and quantification of bone metastases in bone scan images holds significant clinical value. A fast and accurate automated system for segmenting bone metastatic lesions can assist clinical physicians in diagnosis. In this study, a small internal dataset comprising 100 breast cancer patients (90 cases of bone metastasis and 10 cases of non-metastasis) and 100 prostate cancer patients (50 cases of bone metastasis and 50 cases of non-metastasis) was used for model training. Initially, all image labels were binary. We used the Otsu thresholding method or negative mining to generate a non-metastasis mask, thereby transforming the image labels into three classes. We adopted the Double U-Net as the baseline model and made modifications to its output activation function. We changed the activation function to SoftMax to accommodate multi-class segmentation. Several methods were used to enhance model performance, including background pre-processing to remove background information, adding negative samples to improve model precision, and using transfer learning to leverage shared features between two datasets, which enhances the model's performance. The performance was investigated via 10-fold cross-validation and computed on a pixel-level scale. The best model we achieved had a precision of 69.96%, a sensitivity of 63.55%, and an F1-score of 66.60%. Compared to the baseline model, this represents an 8.40% improvement in precision, a 0.56% improvement in sensitivity, and a 4.33% improvement in the F1-score. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Imaging prediction of KRAS mutation in patients with rectal cancer through deep metric learning using pretreatment [18F]Fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML). METHODS: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep-learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K-fold cross-validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS: Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET and CT images were used, and the proposed model had an area under the ROC curve of 0.836 for its ability to predict a mutation status. The sensitivity, specificity, and accuracy were 75.3%, 79.3%, and 77.5%, respectively. When PET images alone were used, the area under the curve was 0.817, whereas the sensitivity, specificity, and accuracy were 73.2%, 79.6%, and 76.2%, respectively. CONCLUSIONS: The ML model presented herein revealed that baseline 18F-FDG PET/CT images could provide supplemental information to determine KRAS mutation in RC. Additional studies are required to maximize the predictive accuracy. ADVANCES IN KNOWLEDGE: The results of the ML model presented herein indicate that baseline 18F-FDG PET/CT images could provide supplemental information for determining KRAS mutation in RC.The predictive accuracy of the model was 77.5% when both image types were used and 76.2% when PET images alone were used. Additional studies are required to maximize the predictive accuracy.

Bivalent mRNA COVID-19 Vaccine-Related Pericarditis on 18 F-FDG PET/CT.

ABSTRACT: A 13-year-old boy was suspected with pericarditis after a second booster dose of bivalent mRNA COVID-19 vaccine. After specific preparation for cardiac inflammation with carbohydrate-free, high-fat diet, the 18 F-FDG PET/CT successfully demonstrated simultaneous presentation of vaccination-related axillary lymphadenopathy and pericarditis without the interference of physiological myocardial uptake.

Artificial Intelligence of Object Detection in Skeletal Scintigraphy for Automatic Detection and Annotation of Bone Metastases.

BACKGROUND: When cancer has metastasized to bone, doctors must identify the site of the metastases for treatment. In radiation therapy, damage to healthy areas or missing areas requiring treatment should be avoided. Therefore, it is necessary to locate the precise bone metastasis area. The bone scan is a commonly applied diagnostic tool for this purpose. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation. The study evaluated object detection techniques to improve the efficacy of bone metastases detection on bone scans. METHODS: We retrospectively examined the data of 920 patients, aged 23 to 95 years, who underwent bone scans between May 2009 and December 2019. The bone scan images were examined using an object detection algorithm. RESULTS: After reviewing the image reports written by physicians, nursing staff members annotated the bone metastasis sites as ground truths for training. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. The optimal dice similarity coefficient (DSC) in our study was 0.6640, which differs by 0.04 relative to the optimal DSC of different physicians (0.7040). CONCLUSIONS: Object detection can help physicians to efficiently notice bone metastases, decrease physician workload, and improve patient care.

Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification.

Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer's disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy.

Consolidative chemotherapy after definitive concurrent chemoradiotherapy for esophageal squamous cell carcinoma patients: a population based cohort study.

BACKGROUND: The role of consolidative chemotherapy (CCT) for locally advanced esophageal squamous cell carcinoma (LA-ESCC) patients treated with definitive concurrent chemoradiotherapy (dCCRT) is unclear. We aimed to compare the overall survival (OS) of those treated with vs without CCT via a population based approach. METHODS: Eligible LA-ESCC patients diagnosed between 2011 and 2017 were identified via the Taiwan Cancer Registry. We used propensity score (PS) weighting to balance observable potential confounders between groups. The hazard ratio (HR) of death and incidence of esophageal cancer mortality (IECM) were compared between those with vs without CCT. We also evaluated the OS in supplementary analyses via alternative approaches. RESULTS: Our primary analysis consisted of 368 patients in whom covariates were well balanced after PS weighting. The HR of death when CCT was compared to without was 0.67 (95% confidence interval 0.52-0.86, P = 0.002). The HR of IECM was 0.66 (P = 0.04). The HR of OS remained similarly in favor of CCT in supplementary analyses. CONCLUSIONS: We found that CCT was associated with significantly improved OS for LA-ESCC patients treated with dCCRT. Randomized controlled trials were needed to confirm this finding.

Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input.

BACKGROUND: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability. METHODS: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient's survival 5 years after a cardiac event, i.e., two classes, either yes or no. RESULTS: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%. CONCLUSIONS: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients' 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs.

Effectiveness evaluation of adjuvant concurrent chemoradiotherapy for patients with positron emission tomography-staged esophageal squamous cell carcinoma after complete resection: A population-based cohort study.

BACKGROUND: The role of adjuvant concurrent chemoradiotherapy (ACCRT) is unclear for patients with esophageal squamous cell carcinoma (ESCC) who receive esophagectomy with clean margins. We compared the survival of the ACCRT versus observation groups for these patients staged with positron emission tomography (PET) via a population-based approach. METHODS: Eligible patients with locally advanced ESCC diagnosed between 2011 and 2017 were identified via the Taiwan Cancer Registry. We used propensity score (PS) weighting to balance observable potential confounders between groups. The hazard ratios (HR) of death and incidence of esophageal cancer mortality (IECM) were compared between the ACCRT and observation groups. We also evaluated overall survival (OS) in subgroups of either with or without lymph node metastases. RESULTS: Our primary analysis consisted of 105 patients in whom the covariates were well balanced after PS weighting. The HR for death when ACCRT was compared with observation was 0.58 (95% confidence interval 0.28-1.21, p = 0.15). The results were also not significantly different for IECM or in the subgroup analyses. CONCLUSION: We found that for patients with PET-staged ESCC who received esophagectomy with clean margins, the survival was not statistically different between ACCRT and observation. Further studies (randomized or larger sample size) are needed to clarify this issue.

Rare "Burned-Out" Testicular Mixed Germ Cell Tumor on 18 F-FDG PET/CT.

ABSTRACT: Spontaneous regression of testicular mixed germ cell tumor is rare and is also called burned-out testicular tumor. We herein present the case of a 20-year-old man who was initially diagnosed with metastatic embryonal carcinoma. 18 F-FDG PET/CT demonstrated apparent metastases in the lymph node regions and both lungs. A covert right testicular lesion was noted according to the features on the CT component of PET/CT, which was subsequently confirmed as burned-out testicular mixed germ cell tumor.

18F-Fluciclovine Uptake in Intramuscular Injecting Site of Antiandrogen.

ABSTRACT: 18F-fluciclovine, a radiolabeled amino acid analog, has been approved by US Food and Drug Administration for detecting lesions of biochemical recurrence of prostate adenocarcinoma with PET/CT. However, it is not specific for prostate cancer and has been found to be present in variety of malignant and benign etiologies. We herein present an interesting case of the incidental finding of increasing uptake of 18F-fluciclovine related to intramuscular injection of antiandrogen.

Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography.

OBJECTIVES: Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the mainstay of treatment for patients with locally advanced rectal cancer. Based on baseline 18F-fluorodeoxyglucose ([18F]-FDG)-positron emission tomography (PET)/computed tomography (CT), a new artificial intelligence model using metric learning (ML) was introduced to predict responses to NCRT. PATIENTS AND METHODS: This study used the data of 236 patients with newly diagnosed rectal cancer; the data of 202 and 34 patients were for training and validation, respectively. All patients received pretreatment [18F]FDG-PET/CT, NCRT, and surgery. The treatment response was scored by Dworak tumor regression grade (TRG); TRG3 and TRG4 indicated favorable responses. The model employed ML combined with the Uniform Manifold Approximation and Projection for dimensionality reduction. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS: In the training cohort, 115 patients (57%) achieved TRG3 or TRG4 responses. The area under the ROC curve was 0.96 for the prediction of a favorable response. The sensitivity, specificity, and accuracy were 98.3%, 96.5%, and 97.5%, respectively. The sensitivity, specificity, and accuracy for the validation cohort were 95.0%, 100%, and 98.8%, respectively. CONCLUSIONS: The new ML model presented herein was used to determined that baseline 18F[FDG]-PET/CT images could predict a favorable response to NCRT in patients with rectal cancer. External validation is required to verify the model's predictive value.

Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning.

Patients with bone metastases have poor prognoses. A bone scan is a commonly applied diagnostic tool for this condition. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation, which indicates all-cause bone remodeling. The current study evaluated deep learning techniques to improve the efficacy of bone metastasis detection on bone scans, retrospectively examining 19,041 patients aged 22 to 92 years who underwent bone scans between May 2011 and December 2019. We developed several functional imaging binary classification deep learning algorithms suitable for bone scans. The presence or absence of bone metastases as a reference standard was determined through a review of image reports by nuclear medicine physicians. Classification was conducted with convolutional neural network-based (CNN-based), residual neural network (ResNet), and densely connected convolutional networks (DenseNet) models, with and without contrastive learning. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. A total of 37,427 image sets were analyzed. The overall performance of all models improved with contrastive learning. The accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and negative predictive value (NPV) for the optimal model were 0.961, 0.878, 0.599, 0.712, 0.92 and 0.965, respectively. In particular, the high NPV may help physicians safely exclude bone metastases, decreasing physician workload, and improving patient care.

Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning.

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians' final decisions.

Low-Parameter Small Convolutional Neural Network Applied to Functional Medical Imaging of Tc-99m Trodat-1 Brain Single-Photon Emission Computed Tomography for Parkinson's Disease.

Parkinson's disease (PD), a progressive disease that affects movement, is related to dopaminergic neuron degeneration. Tc-99m Trodat-1 brain (TRODAT) single-photon emission computed tomography (SPECT) aids the functional imaging of dopamine transporters and is used for dopaminergic neuron enumeration. Herein, we employed a convolutional neural network to facilitate PD diagnosis through TRODAT SPECT, which is simpler than models such as VGG16 and ResNet50. We retrospectively collected the data of 3188 patients (age range 20-107 years) who underwent TRODAT SPECT between June 2011 and December 2019. We developed a set of functional imaging multiclassification deep learning algorithms suitable for TRODAT SPECT on the basis of the annotations of medical experts. We then applied our self-proposed model and compared its results with those of four other models, including deep and machine learning models. TRODAT SPECT included three images collected from each patient: one presenting the maximum absorption of the metabolic function of the striatum and two adjacent images. An expert physician determined that our model's accuracy, precision, recall, and F1-score were 0.98, 0.98, 0.98, and 0.98, respectively. Our TRODAT SPECT model provides an objective, more standardized classification correlating to the severity of PD-related diseases, thereby facilitating clinical diagnosis and preventing observer bias.

Experience of sorafenib treatment in differentiated thyroid cancer from Taiwan.

BACKGROUND: Sorafenib has been shown to prolong the progression free survival (PFS) of advanced radioiodine (RAI) refractory differentiated thyroid cancer (DTC) and has been approved by the FDA as the result of the phase III DECISION trial. Sorafenib has been reimbursed for the treatment of RAI refractory DTC in Taiwan since Jan 2017. High percentage of adverse events (AE) was noted in DECISION trial. We conducted a study to show the real-world experience of sorafenib in Taiwan. METHODS: We retrospectively collected the clinical data, including dose, AE, and PFS of sorafenib, of the DTC patients who received sorafenib treatment in National Cheng Kung University Hospital and China Medical University Hospital by chart review from 2012 to 2018. RESULTS: Thirty-six advanced DTC patients with progression were included in this study. The starting dose of sorafenib in most patients was 200 mg twice daily and the mean daily maintenance dose was 433 mg. Five patients had partial response (13.9%) and 28 patients had stable disease (77.8%). The median PFS was 17.3 months (95% confidence interval: 11.9-33.6 months). Daily maintenance dose ≥ 600 mg was associated with better PFS (median PFS, not reached). The most common toxicity of sorafenib was hand foot skin reaction (69%), followed by diarrhea (42%), and skin rash (33%). Most of the toxicities were grade I/II. CONCLUSION: Higher maintenance dose of sorafenib is associated with longer PFS while starting from half dose is feasible to minimize the incidence of high grade toxicities in the real-world use of sorafenib.

Predicting pathological complete response in rectal cancer after chemoradiotherapy with a random forest using 18F-fluorodeoxyglucose positron emission tomography and computed tomography radiomics.

BACKGROUND: Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the standard treatment for patients with locally advanced rectal cancer. This study developed a random forest (RF) model to predict pathological complete response (pCR) based on radiomics derived from baseline 18F-fluorodeoxyglucose ([18F]FDG)-positron emission tomography (PET)/computed tomography (CT). METHODS: This study included 169 patients with newly diagnosed rectal cancer. All patients received 18F[FDG]-PET/CT, NCRT, and surgery. In total, 68 radiomic features were extracted from the metabolic tumor volume. The numbers of splits in a decision tree and trees in an RF were determined based on their effects on predictive performance. Receiver operating characteristic curve analysis was performed to evaluate predictive performance and ascertain the optimal threshold for maximizing prediction accuracy. RESULTS: After NCRT, 22 patients (13%) achieved pCR, and 42 features that could differentiate tumors with pCR were used to construct the RF model. Six decision trees and seven splits were suitable. Accordingly, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 81.8%, 97.3%, 81.8%, 97.3%, and 95.3%, respectively. CONCLUSIONS: By using an RF, we determined that radiomics derived from baseline 18F[FDG]-PET/CT could accurately predict pCR in patients with rectal cancer. Highly accurate and predictive values can be achieved but should be externally validated.

Clinical Outcomes and Prognostic Factors of Patients With Esophageal Squamous Cell Carcinoma With Oligo-recurrence Treated With Radical Re-irradiation.

BACKGROUND: For patients with esophageal squamous cell carcinoma (ESCC) with oligo-recurrence (OR) after previous curative radiotherapy and not eligible for radical resection, the role of radical re-irradiation was not clear. Therefore, we aimed to investigate the outcome and prognostic factors of such patients. PATIENTS AND METHODS: We identified patients with OR of ESCC after previous curative radiotherapy and were treated with radical re-irradiation within 2012-2018 via an in-house prospectively established database. The characteristics of patients, disease, treatment, and outcome were retrospectively obtained via chart review. The first day of re-irradiation was defined as the index date. Overall survival was calculated via the Kaplan-Meier method. Log-rank test was used for univariate analysis and Cox regression method was used for multivariable analysis. RESULTS: We identified thirty patients for analyses. After a median follow-up of 9 (range=2-76) months, the 5-year overall survival rate was 21%. Four patients with possible radiotherapy-related complication in need of inpatient care were identified. Gross tumor volume was the only significant prognostic factor in both univariate and multivariable analyses. CONCLUSION: We found that radical definitive re-irradiation may lead to one-fifth long-term survivors of patients with OR after previous curative radiotherapy for ESCC, and the gross tumor volume was the only significant prognostic factor for these patients. Randomized controlled trials should be considered to compare radical re-irradiation with the current standard of care (systemic therapy) for this population.

Development of a novel radioligand for imaging 18-kD translocator protein (TSPO) in a rat model of Parkinson's disease.

PURPOSE: The inflammation reaction in the brain may stimulate damage repair or possibly lead to secondary brain injury. It is often associated with activated microglia, which would overexpress 18-kDa translocator protein (TSPO). In this study, we successfully developed a new TSPO radioligand, [18F]-2-(4-fluoro-2-(p-tolyloxy)phenyl)-1,2-dihydroisoquinolin-3(4H)-one ([18F]FTPQ), and evaluate its potential to noninvasively detect brain changes in a rat model of Parkinson's disease (PD). PROCEDURES: The precursor (8) for [18F]FTPQ preparation was synthesized via six steps. Radiofluorination was carried out in the presence of a copper catalyst, and the crude product was purified by high-performance liquid chromatography (HPLC) to give the desired [18F]FTPQ. The rat model of PD was established by the injection of 6-OHDA into the right hemisphere of male 8-week-old Sprague-Dawley rats. MicroPET/CT imaging and immunohistochemistry (IHC) were performed to characterize the biological properties of [18F]FTPQ. RESULTS: The overall chemical yield for the precursor (8) was around 14% after multi-step synthesis. The radiofluorination efficiency of [18F]FTPQ was 60 ± 5%. After HPLC purification, the radiochemical purity was higher than 98%. The overall radiochemical yield was approximately 19%. The microPET/CT images demonstrated apparent striatum accumulation in the brains of PD rats at the first 30 min after intravenous injection of [18F]FTPQ. Besides, longitudinal imaging found the uptake of [18F]FTPQ in the brain may reflect the severity of PD. The radioactivity accumulated in the ipsilateral hemisphere of PD rats at 1, 2, and 3 weeks after 6-OHDA administration was 1.84 ± 0.26, 3.43 ± 0.45, and 5.58 ± 0.72%ID/mL, respectively. IHC revealed that an accumulation of microglia/macrophages and astrocytes in the 6-OHDA-injected hemisphere. CONCLUSIONS: In this study, we have successfully synthesized [18F]FTPQ with acceptable radiochemical yield and demonstrated the feasibility of [18F]FTPQ as a TSPO radioligand for the noninvasive monitoring the disease progression of PD.