Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI.

Objective: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.

Materials And Methods: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results.

Managing Catecholamine Release Syndrome During and Following Lu-177-DOTATATE in High-Risk Pheochromocytoma Patients.

Pheochromocytomas and paragangliomas (PPGLs) are rare catecholamine-producing tumors that express somatostatin receptors (SSTR) that can be treated with lutetium-177 DOTATATE (Lu-177-TRT); however, treatment can be associated with life-threatening cardiovascular events. A patient case with management strategies for high-risk PPGL patients receiving Lu-177-TRT is described. The 78-year-old patient with metastatic paraganglioma was enrolled and treated under NCT03206060.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

Rationale And Objectives: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs.

Deep Learning-Based T2-Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates.

Background: Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.

Purpose: To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.

Study Type: Retrospective.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy.

Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018.

Exploration of Imaging Biomarkers for Metabolically-Targeted Osteosarcoma Therapy in a Murine Xenograft Model.

Osteosarcoma (OS) is an aggressive pediatric cancer with unmet therapeutic needs. Glutaminase 1 (GLS1) inhibition, alone and in combination with metformin, disrupts the bioenergetic demands of tumor progression and metastasis, showing promise for clinical translation. Three positron emission tomography (PET) clinical imaging agents, [F]fluoro-2-deoxy-2-D-glucose ([F]FDG), 3'-[F]fluoro-3'-deoxythymidine ([F]FLT), and (2S, 4R)-4-[F]fluoroglutamine ([F]GLN), were evaluated in the MG63.

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers.

Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.

Predicting Outcomes of Indeterminate Bone Lesions on F-DCFPyL PSMA PET/CT Scans in the Setting of High-Risk Primary or Recurrent Prostate Cancer.

Indeterminate bone lesions (IBLs) on prostate-specific membrane antigen (PSMA) PET/CT are common. This study aimed to define variables that predict whether such lesions are likely malignant or benign using features on PSMA PET/CT. F-DCFPyL PET/CT imaging was performed on 243 consecutive patients with high-risk primary or biochemically recurrent prostate cancer.

The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms.

Objective: To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research.

Materials And Methods: This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist.

Fluorine-18 Labeled Urea-Based Ligands Targeting Prostate-Specific Membrane Antigen (PSMA) with Increased Tumor and Decreased Renal Uptake.

High expression of prostate-specific membrane antigen (PSMA) in prostate cancers prompted the development of the PSMA-targeted PET-imaging agent [F]DCFPyL, which was recently approved by the FDA. Fluorine-18-labeled Lys-Urea-Glu-based oxime derivatives of [F]DCFPyL were prepared for the comparison of their in vitro and in vivo properties to potentially improve kidney clearance and tumor targeting. The oxime radiotracers were produced by condensation of an aminooxy functionalized PSMA-inhibitor Lys-Urea-Glu scaffold with fluorine-18-labeled aldehydes.

Recovery, recycling and re-irradiation of enriched Ru metal targets for cost effective production of Rh.

Rhodium-105 (0.567 MeV β, 319 keV γ, 35.4 h half-life) was produced by neutron irradiation of enriched Ru (>99%) over multiple decades.

and Comparison of 3,2-HOPO Versus Deferoxamine-Based Chelation of Zirconium-89 to the Antimesothelin Antibody Anetumab.

[Th]Th-3,2-HOPO-MSLN-mAb, a mesothelin (MSLN)-targeted thorium-227 therapeutic conjugate, is currently in phase I clinical trial; however, direct PET imaging using this conjugate is technically challenging. Thus, using the same MSLN antibody, we synthesized 3,2-HOPO and deferoxamine (DFO)-based zirconium-89 antibody conjugates, [Zr]Zr-3,2-HOPO-MSLN-mAb and [Zr]Zr-DFO-MSLN-mAb, respectively, and compared them and . [Zr]Zr-3,2-HOPO-MSLN-mAb and [Zr]Zr-DFO-MSLN-mAb were evaluated to determine binding affinity and immunoreactivity in HT29-MSLN and PDX (NCI-Meso16, NCI-Meso21) cells.

Automated Synthesis of Fluorine-18 Labeled CXCR4 Ligand via the Conjugation with Nicotinic Acid -Hydroxysuccinimide Ester (6-[F]SFPy).

The C-X-C motif chemokine receptor 4 (CXCR4) is a seven-transmembrane G protein-coupled receptor that is overexpressed in numerous diseases, particularly in various cancers and is a powerful chemokine, attracting cells to the bone marrow niche. Therefore, CXCR4 is an attractive target for imaging and therapeutic purposes. The goal of this study is to develop an efficient, reproducible, and straightforward method to prepare a fluorine-18 labeled CXCR4 ligand.

Evaluation of Se/As generator and production of Se for supplying As as a potential PET imaging radionuclide.

Positron-emitting As is the PET imaging counterpart for beta-emitting As. Its parent, no carrier added (n.c.

Chemistry and radiochemistry of As, Re and Rh isotopes relevant to radiopharmaceutical applications: high specific activity radionuclides for imaging and treatment.

The chemistry and radiochemistry of high specific activity radioisotopes of arsenic, rhenium and rhodium are reviewed with emphasis on University of Missouri activities over the past several decades, and includes recent results. The nuclear facilities at the University of Missouri (10 MW research reactor and 16.5 MeV GE PETtrace cyclotron) allow research and development into novel theranostic radionuclides.

Bulk production and evaluation of high specific activity Re for cancer therapy using enriched WO targets in a proton beam.

Introduction: Rhenium-186g (t = 3.72 d) is a β emitting isotope suitable for theranostic applications. Current production methods rely on reactor production by way of the reaction Re(n,γ)Re, which results in low specific activities limiting its use for cancer therapy.

Primary amine stabilization of a dicopper(III) bis(μ-oxo) species: modeling the ligation in pMMO.

Here we report the formation of the first examples of dicopper(III) bis(μ-oxo) complexes ligated by the primary amines, propylenediamine, and N,N,-dimethyl propylenediamine. Stabilization of these new compounds is effected at -125 °C by "core capture"- introduction of exogenous ligand to a preformed dicopper(III) bis(μ-oxo) complex supported by the peralkylated tetramethyl propylenediamine. Primary amine ligation in these compounds matches the single primary amine coordination of the putative active site of particulate methane monooxygenase (pMMO) and polysaccharide monooxygenase.