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Background And Purpose: To develop and validate a prognostic nomogram based on pretreatment F-fluorodeoxyglucose positron emission tomography/computed tomography (PET-CT)radiomics parameters and peripheral blood markers for risk stratification in patients with de novo metastatic nasopharyngeal carcinoma (dmNPC).
Materials And Methods: A total of 558 patients with dmNPC were retrospectively enrolled between 2011 and 2019. Eligible patients were randomly divided into training and validation cohorts (7:3 ratio). A Cox regression model was used to identify prognostic factors for overall survival (OS). The predictive accuracy and discriminative ability of the prognostic nomogram were determined using the concordance index (C-index) and calibration curve.
Results: Independent factors derived from multivariable analysis of the training cohort to predict death were lactate dehydrogenase levels, pretreatment Epstein-Barr virus DNA, total lesion glycolysis of locoregional lesions, number of metastatic lesions, and age, all of which were assembled into a nomogram with (nomogram B) or without PET-CT parameters (nomogram A). The C-index of nomogram B for predicting death was 0.70, which was significantly higher than the C-index values for nomogram A. Patients were then stratified into low- and high-risk groups based on the scores calculated using nomogram B for OS. The median OS was significantly higher in the low-risk group than in the high-risk group (69.60 months [95 % CI: 58.50-108.66] vs. 21.40 months [95 % CI: 19.20-23.90]; p<0.01). All the results were confirmed in the validation cohort.
Conclusion: The proposed nomogram including PET-CT parameters yielded accurate prognostic predictions for patients with dmNPC, enabling effective risk stratification for these patients.
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http://dx.doi.org/10.1016/j.oraloncology.2024.106928 | DOI Listing |
Front Oncol
August 2025
Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Objective: To develop a deep learning radiomics(DLR)model integrating PET/CT radiomics, deep learning features, and clinical parameters for early prediction of bone oligometastases (≤5 lesions) in breast cancer.
Methods: We retrospectively analyzed 207 breast cancer patients with 312 bone lesions, comprising 107 benign and 205 malignant lesions, including 89 lesions with confirmed bone metastases. Radiomic features were extracted from computed tomography (CT), positron emission tomography (PET), and fused PET/CT images using PyRadiomics embedded in the uAI Research Portal.
Nucl Med Biol
September 2025
Department of Nuclear Medicine, Hannover Medical School, Germany. Electronic address:
Purpose: The liver-brain axis regulates metabolic homeostasis, with glucose metabolism playing a key role. Liver dysfunction, such as fibrosis, may impact brain metabolism and consequently, brain function. Positron emission tomography (PET) imaging provides a non-invasive approach to study glucose metabolism in both organs.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
September 2025
Department of Dermatology and National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
Purpose: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unresectable or metastatic uveal melanoma (mUM). Notably, a survival benefit has been observed even in the absence of radiographic response. This study aims to investigate the feasibility and prognostic value of artificial intelligence (AI)-assisted quantification and metabolic response assessment of [F]FDG long axial field-of-view (LAFOV) PET/CT in mUM patients undergoing tebentafusp therapy.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
September 2025
Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA.
Purpose: Despite the effectiveness of [Lu]Lu-PSMA-617 in metastatic castration-resistant prostate cancer (mCRPC), hematologic toxicity remains a concern, particularly in patients with bone metastases. This study evaluated whether the extent, intensity, and heterogeneity of bone disease on pretreatment PSMA-PET/CT were associated with hematologic toxicity, PSA response, and overall survival (OS) in mCRPC patients treated with [Lu]Lu-PSMA-617.
Methods: This retrospective study included 96 mCRPC patients who underwent pretreatment PSMA-PET/CT and received standard-of-care [Lu]Lu-PSMA-617.
PLoS One
September 2025
School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China.
In recent years, You Only Look Once (YOLO) models have gradually been applied to medical image object detection tasks due to their good scalability and excellent generalization performance, bringing new perspectives and approaches to this field. However, existing models overlook the impact of numerous consecutive convolutions and the sampling blur caused by bilinear interpolation, resulting in excessive computational costs and insufficient precision in object detection. To address these problems, we propose a YOLOv8-based model using Efficient modulation and dynamic upsampling (YOLO-ED) to detect lung cancer in CT images.
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