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Purpose: The radiopharmaceutical [18F]AlF-NOTA-FAPI-04 presents a promising alternative to 68 Ga-FAPI owing to its relatively longer half-life. This study aimed to evaluate the clinical usefulness of [18F]AlF-NOTA-FAPI-04 PET/CT for the diagnosis of primary and metastatic lesions in various types of gastrointestinal system cancers, compared with 18F-FDG PET/CT. Methods: Patients diagnosed with gastrointestinal system malignancies were prospectively enrolled. All patients underwent both 18F-FDG and 18F-FAPI-04 PET/CT scans within one week, with 44 (73.3%) for cancer staging and 16 (26.7%) for tumor restaging. Diagnostic efficacy of the primary tumor, as well as the presence and number of lymph nodes and distant metastases, were assessed. Tumor uptake was quantified by the maximum standard uptake value (SUVmax). Results: For detection of primary tumor, the diagnostic sensitivity of 18F-FDG PET/CT was 72.7%, while it was 97.7% for 18F-FAPI-04 PET/CT. Based on per-lymph node analysis, the sensitivity, specificity, and accuracy of 18F-FAPI-04 PET/CT in diagnosing metastatic lymph nodes were 91.89%, 92.00%, and 91.96%, respectively. These values were notably higher than those 18F-FDG PET/CT (79.72%, 81.33% and 80.80%, respectively). The 18F-FAPI-04 PET/CT surpassed 18F-FDG PET/CT in detecting suspected metastases in the brain (7 vs. 3), liver (39 vs. 20), bone (79 vs. 51), lung (11 vs. 4), and peritoneal carcinoma (48 vs. 22). Based on per-patient analysis, differential diagnostic accuracies (18F-FAPI-04 vs. 18F-FDG PET/CT) were observed in all patients (91.7% vs. 76.7%), the initial staging group (90.9% vs. 79.5%), and the re-staging group (93.8% vs. 68.7%). Additionally, 18F-FAPI-04 PET/CT revised final diagnosis in 31.7% of patients, contrasting with 18F-FDG PET/CT, and prompted changes in clinical management for 21.7% of the patients. Conclusion: 18F-FAPI-04 PET/CT outperforms 18F-FDG PET/CT in delineating the primary gastrointestinal tumors and detecting suspected metastatic lesions due to a higher target-to-background ratio (TBR). Moreover, 18F-FAPI-04 PET/CT could provide valuable guidance for tumor staging, thereby having a potential impact on patient management. [ABSTRACT FROM AUTHOR]

Background: Determining the status of lymph node (LN) metastasis in rectal cancer patients preoperatively is crucial for the treatment option. However, the diagnostic accuracy of current imaging methods is low. Purpose: To develop and test a model for predicting metastatic LNs of rectal cancer patients based on clinical data and MR images to improve the diagnosis of metastatic LNs. Study type: Retrospective. Subjects: In all, 341 patients with histologically confirmed rectal cancer were divided into one training set (120 cases) and three validation sets (69, 103, 49 cases). Field strength/sequence: 3.0T, axial and sagittal T2‐weighted turbo spin echo and diffusion‐weighted imaging (b = 0 s/mm2, 800 s/mm2) Assessment: In the training dataset, univariate logistic regression was used to identify the clinical factors (age, gender, and tumor markers) and MR data that correlated with LN metastasis. Then we developed a prediction model with these factors by multiple logistic regression analysis. The accuracy of the model was verified using three validation sets and compared with the traditional MRI method. Statistical tests: Univariate and multivariate logistic regression. The area under the curve (AUC) value was used to quantify the diagnostic accuracy of the model. Results: Eight factors (CEA, CA199, ADCmean, mriT stage, mriN stage, CRM, EMVI, and differentiation degree) were significantly associated with LN metastasis in rectal cancer patients (P<0.1). In the training set (120) and the three validation sets (69, 103, 49), the AUC values of the model were much higher than the diagnosis by MR alone (training set, 0.902 vs. 0.580; first validation set, 0.789 vs. 0.743; second validation set, 0.774 vs. 0.573; third validation set, 0.761 vs. 0.524). Data Conclusion: For the diagnosis of metastatic LNs in rectal cancer patients, our proposed logistic regression model, combining clinical and MR data, demonstrated higher diagnostic efficiency than MRI alone. Level of Evidence: 4 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Objective: Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a serious class imbalance between main and side branches., Methods and Procedures: Inspired by the application of Transformer in sequence data, we propose a topological Transformer network (TTN), which solves the vessel branch labeling from a novel perspective of sequence labeling learning. TTN detects differences between branches by establishing their overall correlation. A topological encoding that represents the positions of vessel segments in the artery tree, is proposed to assist the model in classifying branches. Also, a segment-depth loss is introduced to solve the class imbalance between main and side branches., Results: On a dataset with 325 CCTA, our method obtains the best overall result on all branches, the best result on side branches, and a competitive result on main branches., Conclusion: TTN solves two limitations in existing methods perfectly, thus achieving the best result in coronary artery branch labeling task. It is the first Transformer based vessel branch labeling method and is notably different from previous methods., Clinical Impact: This Pre-Clinical Research can be integrated into a computer-aided diagnosis system to generate cardiovascular disease diagnosis report, assisting clinicians in locating the atherosclerotic plaques., (© 2023 The Authors.)