AIMC Topic: Positron Emission Tomography Computed Tomography
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Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.
Clinical radiology
Apr 2, 2025
AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients and estimating progression-free survival...
Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT-based radiomics features.
Medical physics
Apr 1, 2025
BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (NSCLC) diagnoses, where epidermal growth factor receptor (EGFR) mutations play a pivotal role as indicators for therapeutic intervention with targeted...
Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based on F-FDG PET radiomics and clinical factors.
Annals of nuclear medicine
Mar 29, 2025
OBJECTIVE: This study aimed to develop and validate a nomogram combining F-FDG PET radiomics and clinical factors to non-invasively predict bone marrow involvement (BMI) in patients with lymphoma.
Uncertainty-aware deep learning for segmentation of primary tumor and pathologic lymph nodes in oropharyngeal cancer: Insights from a multi-center cohort.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 25, 2025
PURPOSE: Information on deep learning (DL) tumor segmentation accuracy on a voxel and a structure level is essential for clinical introduction. In a previous study, a DL model was developed for oropharyngeal cancer (OPC) primary tumor (PT) segmentati...
Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.
Computers in biology and medicine
Mar 23, 2025
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.
PET and CT based DenseNet outperforms advanced deep learning models for outcome prediction of oropharyngeal cancer.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Mar 19, 2025
BACKGROUND: In the HECKTOR 2022 challenge set [1], several state-of-the-art (SOTA, achieving best performance) deep learning models were introduced for predicting recurrence-free period (RFP) in head and neck cancer patients using PET and CT images.
Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.
European journal of radiology
Mar 19, 2025
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...
Gallium Isotopes
Middle Aged
Heterocyclic Compounds, 1-Ring
Risk Assessment
Positron Emission Tomography Computed Tomography
Aged, 80 and over
Prostate-Specific Antigen
Humans
Sensitivity and Specificity
Prostatic Neoplasms
Radiopharmaceuticals
Machine Learning
Biomarkers, Tumor
Dipeptides
Retrospective Studies
Male
Gallium Radioisotopes
Aged
The Role of AI in Lymphoma: An Update.
Seminars in nuclear medicine
Mar 11, 2025
Malignant lymphomas encompass a range of malignancies with incidence rising globally, particularly with age. In younger populations, Hodgkin and Burkitt lymphomas predominate, while older populations more commonly experience subtypes such as diffuse ...
Impact of [F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role.
Seminars in nuclear medicine
Mar 5, 2025
Lung cancer remains one of the most prevalent cancers globally and the leading cause of cancer-related deaths, accounting for nearly one-fifth of all cancer fatalities. Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography ([F]FDG...
Preoperative Maximum Standardized Uptake Value Emphasized in Explainable Machine Learning Model for Predicting the Risk of Recurrence in Resected Non-Small Cell Lung Cancer.
JCO clinical cancer informatics
Mar 5, 2025
PURPOSE: To comprehensively analyze the association between preoperative maximum standardized uptake value (SUV) on 18F-fluorodeoxyglucose positron emission tomography-computed tomography and postoperative recurrence in resected non-small cell lung c...
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