Nuclear Medicine

Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT-based radiomics features.

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 agents. The emerging field of radiomics, which involves the extraction of numerous quantitative attributes from medical imaging, when coupled with po...

Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.

We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive coronary artery disease (CAD). We aimed to improve the clinical translatability of this artificial intelligence (AI) approach using the results to derive enhanced total perfusion deficit (TPD) and 17-segment summed scores. We used a cohort of patients und...

Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based on F-FDG PET radiomics and clinical factors.

OBJECTIVE: This study aimed to develop and validate a nomogram combining F-FDG PET radiomics and clinical factors to non-invasively predict bone marro...

Artificial intelligence for tumor [F]FDG-PET imaging: Advancement and future trends-part I.

The advent of sophisticated image analysis techniques has facilitated the extraction of increasingly complex data, such as radiomic features, from var...

F-FDG PET radiomics score construction by automatic machine learning for treatment response prediction in elderly patients with diffuse large B-cell lymphoma: a multicenter study.

PURPOSE: To explore the development and validation of automated machine learning (AutoML) models for F-FDG PET imaging-based radiomics signatures to p...

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subt...

Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.

BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emissio...

PET and CT based DenseNet outperforms advanced deep learning models for outcome prediction of oropharyngeal cancer.

BACKGROUND: In the HECKTOR 2022 challenge set [1], several state-of-the-art (SOTA, achieving best performance) deep learning models were introduced fo...

Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model.

PURPOSE: Low-dose CT protocols are widely used for emergency imaging, follow-ups, and attenuation correction in hybrid PET/CT and SPECT/CT imaging. Ho...

Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network.

OBJECTIVE: To provide fully automatic scanner-independent 5-level categorization of the [I]FP-CIT uptake in striatal subregions in dopamine transporte...

The Role of AI in Lymphoma: An Update.

Malignant lymphomas encompass a range of malignancies with incidence rising globally, particularly with age. In younger populations, Hodgkin and Burki...

An ultra-sensitive, intelligent platform for food safety monitoring: Label-free detection of illegal additives using self-assembled SERS substrates and machine learning.

To overcome the limitations of SERS in food safety monitoring, particularly significant interference from citrate ions, this study introduces an intel...

Impact of [F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role.

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 ...

Enhancing Blood-Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database.

Predicting blood-brain barrier (BBB) penetration is crucial for developing central nervous system (CNS) drugs, representing a significant hurdle in su...

Prediction of Lymph Node Metastasis in Lung Cancer Using Deep Learning of Endobronchial Ultrasound Images With Size on CT and PET-CT Findings.

BACKGROUND AND OBJECTIVE: Echo features of lymph nodes (LNs) influence target selection during endobronchial ultrasound-guided transbronchial needle a...

Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion...

Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study.

BACKGROUND: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a well-established modality for noninvasive...

Artificial intelligence and different image modalities in uveal melanoma diagnosis and prognosis: A narrative review.

BACKGROUND: The most widespread primary intraocular tumor in adults is called uveal melanoma (UM), if detected early enough, it can be curable. Variou...

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.

PURPOSE: The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried ...

Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.

PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. Th...