AIMC Topic: Radiopharmaceuticals

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A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico Feb 24, 2025
PURPOSE: This study evaluates a three-dimensional (3D) deep learning (DL) model based on fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for predicting the preoperative status of spread through air spa...
Female Aged Aged, 80 and over Sensitivity and Specificity Male Humans Retrospective Studies Positron Emission Tomography Computed Tomography Neoplasm Staging Radiopharmaceuticals Fluorodeoxyglucose F18 Adenocarcinoma of Lung Middle Aged Deep Learning Imaging, Three-Dimensional Lung Neoplasms
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Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.

Journal of cancer research and clinical oncology Feb 20, 2025
PURPOSE: The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried to develop a model to predict the FIGO stage of OC through machine learning algorithms with patients...
Radiomics Algorithms Middle Aged Neoplasm Staging Humans Ovarian Neoplasms Retrospective Studies Radiopharmaceuticals Artificial Intelligence Positron Emission Tomography Computed Tomography Female Fluorodeoxyglucose F18 Machine Learning Aged Adult
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Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.

European journal of nuclear medicine and molecular imaging Feb 18, 2025
AIM: To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image quality of non-ToF PET images for different tracers, reconstructed using BSREM algorithm, towards ToF images.
Positron-Emission Tomography Radioactive Tracers Humans Deep Learning Time Factors Image Processing, Computer-Assisted Radiopharmaceuticals
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Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.

Cancer imaging : the official publication of the International Cancer Imaging Society Feb 18, 2025
BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point F-FDG PET/CT to predict the malignant ris...
Solitary Pulmonary Nodule Positron Emission Tomography Computed Tomography Radiopharmaceuticals Fluorodeoxyglucose F18 Male Middle Aged Convolutional Neural Networks Lung Neoplasms Retrospective Studies Female Aged Adult Humans Neural Networks, Computer
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Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.

BMC medical imaging Feb 17, 2025
BACKGROUND: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the management of cancer patients. Deep learning models excel at segmenting highly metabolic tumors but face ch...
Whole Body Imaging Intestinal Neoplasms Deep Learning Positron Emission Tomography Computed Tomography Radiopharmaceuticals Fluorodeoxyglucose F18 Humans Female Aged Male Middle Aged
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Deep learning-based organ-wise dosimetry of Cu-DOTA-rituximab through only one scanning.

Scientific reports Feb 15, 2025
This study aimed to generate a delayed Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep learning to mitigate the inconvenience and cost of estimating absorbed radiopharmaceutical doses. We acq...
Humans Female Aged Positron-Emission Tomography Organometallic Compounds Rituximab Radiometry Copper Radioisotopes Male Middle Aged Deep Learning Radiopharmaceuticals
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Eliminating the second CT scan of dual-tracer total-body PET/CT via deep learning-based image synthesis and registration.

European journal of nuclear medicine and molecular imaging Feb 11, 2025
PURPOSE: This study aims to develop and validate a deep learning framework designed to eliminate the second CT scan of dual-tracer total-body PET/CT imaging.
Image Processing, Computer-Assisted Positron Emission Tomography Computed Tomography Radiopharmaceuticals Whole Body Imaging Humans Retrospective Studies Female Aged Adult Male Middle Aged Deep Learning
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A machine learning-based 18F-FDG PET/CT multi-modality fusion radiomics model to predict Mediastinal-Hilar lymph node metastasis in NSCLC: a multi-centre study.

Clinical radiology Jan 29, 2025
AIM: To develop and validate a machine learning (ML) model based on positron emission tomography/computed tomography (PET/CT) multi-modality fusion radiomics to improve the prediction efficiency of mediastinal-hilar lymph node metastasis (LNM).
Middle Aged Lung Neoplasms Lymph Nodes Carcinoma, Non-Small-Cell Lung Lymphatic Metastasis Aged Humans Positron Emission Tomography Computed Tomography Adult Machine Learning Radiopharmaceuticals Radiomics Retrospective Studies Fluorodeoxyglucose F18 Male Female Mediastinum
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F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

La Radiologia medica Jan 28, 2025
PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
Female Aged Adult Breast Neoplasms Radiomics Predictive Value of Tests Humans Middle Aged Positron Emission Tomography Computed Tomography Artificial Intelligence Treatment Outcome Radiopharmaceuticals Machine Learning Neoadjuvant Therapy Fluorodeoxyglucose F18 Retrospective Studies Lymphatic Metastasis Chemotherapy, Adjuvant
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F-18 FDG PET/CT based Preoperative Machine Learning Prediction Models for Evaluating Regional Lymph Node Metastasis Status of Patients with Colon Cancer.

Asian Pacific journal of cancer prevention : APJCP Jan 1, 2025
OBJECTIVE: This study aimed to develop a simple machine-learning model incorporating lymph node metastasis status with F-18 Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and clinical information for predicting regio...
Male Middle Aged ROC Curve Lymph Nodes Follow-Up Studies Lymphatic Metastasis Positron Emission Tomography Computed Tomography Colonic Neoplasms Radiopharmaceuticals Humans Machine Learning Prognosis Fluorodeoxyglucose F18 Female Retrospective Studies Aged Aged, 80 and over Adult
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