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Abstract: Diffuse involvement of pancreatic neuroendocrine tumor (PNET) is a rare presentation. Here, we report a case of suspected autoimmune pancreatitis with 18 F-FDG and 18 F-FAPI-42 PET/CT showing increased tracer uptake in the entire pancreas, which was eventually confirmed by biopsy pathologic analysis as diffuse PNET. 18 F-AlF-NOTA-octreotide PET/CT imaging showed heterogeneous tracer uptake in the entire pancreas., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Abstract: Gallbladder's adenocarcinoma with enteroblastic differentiation (GAED) is extremely rare. A 43-year-old woman complained of pain in the right upper abdomen, and enhanced CT showed a cystic and solid mixed mass in the hepatic hilar region. Adenocarcinoma with enteroblastic differentiation was pathologically identified. 18 F-FDG PET/CT revealed a lesion in the gallbladder neck with increased FDG uptake, accompanied by a cystic and solid mixed mass in the hepatic hilar region with liver and lymph node metastases. Gallbladder biopsy was also carried out, and GAED was confirmed. 18 F-FDG PET/CT may assist in the evaluation of GAED and guide biopsy., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

The tumor image segmentation is an important basis for doctors to diagnose and formulate treatment planning. PET-CT is an extremely important technology for recognizing the systemic situation of diseases due to the complementary advantages of their respective modal information. However, current PET-CT tumor segmentation methods generally focus on the fusion of PET and CT features. The fusion of features will weaken the characteristics of the modality itself. Therefore, enhancing the modal features of the lesions can obtain optimized feature sets, which is extremely necessary to improve the segmentation results. This paper proposed an attention module that integrates the PET-CT diagnostic visual field and the modality characteristics of the lesion, that is, the multiple receptive-field lesion attention module. This paper made full use of the spatial domain, frequency domain, and channel attention, and proposed a large receptive-field lesion localization module and a small receptive-field lesion enhancement module, which together constitute the multiple receptive-field lesion attention module. In addition, a network embedded with a multiple receptive-field lesion attention module has been proposed for tumor segmentation. This paper conducted experiments on a private liver tumor dataset as well as two publicly available datasets, the soft tissue sarcoma dataset, and the head and neck tumor segmentation dataset. The experimental results showed that the proposed method achieves excellent performance on multiple datasets, and has a significant improvement compared with DenseUNet, and the tumor segmentation results on the above three PET/CT datasets were improved by 7.25%, 6.5%, 5.29% in Dice per case. Compared with the latest PET-CT liver tumor segmentation research, the proposed method improves by 8.32%., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

To further promote the clinical application of 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in infection and inflammation and standardize the diagnostic process, the experts in relevant fields in China carried out discussion and formed the Expert Consensus on the clinical application of FDG PET/CT in infection and inflammation. This consensus is intended to provide a reference for imaging physicians to select a reasonable diagnostic plan. However, it should be noted that it couldn't include or solve all the problems in clinical operation. Imaging physicians and technicians should develop a comprehensive and reasonable diagnostic procedure according to their professional knowledge, clinical experience and currently available medical resources when facing specific patients.

Objective: Lung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone difference matrix (NGTDM) texture features., Methods: Retrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18 F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUV max and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features., Results: In the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUV max were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively., Conclusion: Compared to SUV max or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.

Lung cancer, the most commonly diagnosed cancer worldwide, usually presents as solid pulmonary nodules (SPNs) on early diagnostic images. Classification of malignant disease at this early timepoint is critical for improving the success of surgical resection and increasing 5-year survival rates. 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/CT has demonstrated value for SPNs diagnosis with high sensitivity to detect malignant SPNs, but lower specificity in diagnosing malignant SPNs in populations with endemic infectious lung disease. This study aimed to determine whether quantitative heterogeneity derived from various texture features on dual time FDG PET/CT images (DTPI) can differentiate between malignant and benign SPNs in patients from granuloma-endemic regions. Machine learning methods were employed to find optimal discrimination between malignant and benign nodules. Machine learning models trained by texture features on DTPI images achieved significant improvements over standard clinical metrics and visual interpretation for discriminating benign from malignant SPNs, especially by texture features on delayed FDG PET/CT images.

Purpose: This study is aimed to compare the diagnostic power of using quantitative analysis or visual analysis with single time point imaging (STPI) PET/CT and dual time point imaging (DTPI) PET/CT for the classification of solitary pulmonary nodules (SPN) lesions in granuloma-endemic regions., Methods: SPN patients who received early and delayed (18)F-FDG PET/CT at 60min and 180min post-injection were retrospectively reviewed. Diagnoses are confirmed by pathological results or follow-ups. Three quantitative metrics, early SUVmax, delayed SUVmax and retention index(the percentage changes between the early SUVmax and delayed SUVmax), were measured for each lesion. Three 5-point scale score was given by blinded interpretations performed by physicians based on STPI PET/CT images, DTPI PET/CT images and CT images, respectively. ROC analysis was performed on three quantitative metrics and three visual interpretation scores., Result: One-hundred-forty-nine patients were retrospectively included. The areas under curve (AUC) of the ROC curves of early SUVmax, delayed SUVmax, RI, STPI PET/CT score, DTPI PET/CT score and CT score are 0.73, 0.74, 0.61, 0.77 0.75 and 0.76, respectively. There were no significant differences between the AUCs in visual interpretation of STPI PET/CT images and DTPI PET/CT images, nor in early SUVmax and delayed SUVmax. The differences of sensitivity, specificity and accuracy between STPI PET/CT and DTPI PET/CT were not significantly different in either quantitative analysis or visual interpretation., Conclusion: In granuloma-endemic regions, DTPI PET/CT did not offer significant improvement over STPI PET/CT in differentiating malignant SPNs in both quantitative analysis and visual interpretation., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Purpose: This study aimed to evaluate the role of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) in expression of tumor programmed death ligand-1 (PD-L1) expression and prognostic significance of 18F-FDG PET/CT at different PD-L1 status in patients with lung adenocarcinoma. Patients and Methods: Seventy-three patients with primary lung adenocarcinoma who received 18F-FDG PET/CT before treatment were retrospectively included in this study. Expression of tumor PD-L1, programmed death-1 (PD-1) and glucose metabolic parameters were evaluated. Results: Tumor PD-L1 expression was positively correlated with maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), hexokinase II (HK-II) and glucose transporter 1 (GLUT-1) (P< 0.0001 for all). SUVmax was a unique independent predictor of tumor PD-L1 expression, with an optimal cut-off value of 9.5. For all the patients, tumor stage (P< 0.001) and SUVmax (P=0.009) were independent prognostic indicators of disease-free survival (DFS)/progression-free survival (PFS) while carcino-embryonic antigen (CEA) (P=0.003), Ki67 (P=0.042), PD-L1 (P=0.048) and TLG (P=0.004) were independent prognostic indicators of overall survival (OS). Tumor stage (P=0.004) and SUVmax (P=0.022) were independent prognostic indicators of DFS/PFS while TLG (P=0.012) and CEA (P=0.045) were independent prognostic indicators of OS in the PD-L1-positive group. In the PD-L1-negative group, tumor stage (P=0.002) and CEA (P=0.006) were unique independent prognostic indicators of DFS/PFS and OS, respectively. Conclusion: 18F-FDG PET/CT may potentially predict tumor PD-L1 expression and play a role in predicting prognosis of PD-L1/PD-1 immunotherapy in lung adenocarcinoma. [ABSTRACT FROM AUTHOR]