International Metabolic Prognostic Index Is Superior to Other Metabolic Tumor Volume-Based Prognostication Methods in a Real-Life Cohort of Diffuse Large B-Cell Lymphoma.

Baseline metabolic tumor volume (MTV) is a promising prognostic marker in diffuse large B-cell lymphoma (DLBCL). We assessed the prognostic value of 4 novel metabolic risk scores in a real-life DLBCL cohort and compared them with the revised International Prognostic Index (IPI). We included a consecutive series of untreated DLBCL, not otherwise specified cases that were diagnosed in our hospital from 2008 to 2021 with available baseline [F]FDG PET/CT.

Comparison of PSMA immunohistochemistry scoring systems to parametric [F]PSMA-1007 PET/MRI in primary prostate cancer.

Purpose: Quantification of PSMA expression via PSMA PET is well-established, however quantification of PSMA via immunohistochemistry (IHC) is not standardized. Our aim was to determine the most optimal PSMA IHC scoring system to quantify PSMA expression with PSMA PET as reference standard.

Methods: Primary intermediate- and high-risk prostate cancer patients received an [F]PSMA-1007 PET/MRI followed by radical prostatectomy.

Intra-individual qualitative and quantitative comparison of [Ga]Ga-DOTATATE PET/CT and PET/MRI.

Background: Somatostatin receptor (SSTR) positron emission tomography (PET) is a cornerstone of neuroendocrine tumor (NET) management. Hybrid PET/magnetic resonance imaging (MRI) is now available for NET-imaging, next to PET/computed tomography (CT).

Objectives: To determine whether CT or MRI is the best hybrid partner for [Ga]Ga-DOTATATE PET.

Prospective comparison of [F]AlF-NOTA-octreotide PET/MRI to [Ga]Ga-DOTATATE PET/CT in neuroendocrine tumor patients.

Background: Fluorine-18-labeled SSAs have the potential to become the next-generation tracer in SSTR-imaging in neuroendocrine tumor (NET) patients given their logistical advantages over the current gold standard gallium-68-labeled SSAs. In particular, [F]AlF-OC has already shown excellent clinical performance. We demonstrated in our previous report from our prospective multicenter trial that [F]AlF-OC PET/CT outperforms [Ga]Ga-DOTA-SSA, but histological confirmation was lacking due to ethical and practical reasons.

A multi-label CNN model for the automatic detection and segmentation of gliomas using [F]FET PET imaging.

Purpose: The aim of this study was to develop a convolutional neural network (CNN) for the automatic detection and segmentation of gliomas using [F]fluoroethyl-L-tyrosine ([F]FET) PET.

Methods: Ninety-three patients (84 in-house/7 external) who underwent a 20-40-min static [F]FET PET scan were retrospectively included. Lesions and background regions were defined by two nuclear medicine physicians using the MIM software, such that delineations by one expert reader served as ground truth for training and testing the CNN model, while delineations by the second expert reader were used to evaluate inter-reader agreement.

Effect of sleeve gastrectomy and Roux-en-Y gastric bypass on gastrointestinal physiology.

Background: Knowledge regarding the gastrointestinal physiology after sleeve gastrectomy and Roux-en-Y gastric bypass is urgently needed to understand, prevent and treat the nutritional and pharmacological complications of bariatric surgery.

Aim: To investigate the effect of sleeve gastrectomy and Roux-en-Y gastric bypass on gastrointestinal motility (e.g.

Combined brain and spinal FDG PET allows differentiation between ALS and ALS mimics.

Purpose: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with on average a 1-year delay between symptom onset and diagnosis. Studies have demonstrated the value of [F]-FDG PET as a sensitive diagnostic biomarker, but the discriminatory potential to differentiate ALS from patients with symptoms mimicking ALS has not been investigated. We investigated the combination of brain and spine [F]-FDG PET-CT for differential diagnosis between ALS and ALS mimics in a real-life clinical diagnostic setting.

Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

Purpose: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based on a convolutional neural network (CNN) for CT imaging in SIRT, and the ability of CNN to reduce inter-observer variability of the segmentation.

Methods: A multi-scale CNN was modified for liver segmentation for SIRT patients.