Hyperparathyroidism after radioactive iodine therapy.

Am J Surg

Department of Surgery, University of California San Francisco and UCSF/Mt Zion Medical Center, 1600 Divisadero Street, #C347, San Francisco, CA 94143-1674, USA.

Published: September 2007

Background: Radioactive iodine (RAI) treatment has been suggested to cause primary hyperparathyroidism (HPT). We describe a series of patients with HPT and a history of RAI exposure.

Methods: Patient demographic and clinical information was evaluated, including the latency time to the development of HPT after RAI exposure.

Results: We treated 11 patients with HPT and a history of RAI exposure. RAI treatment was administered for benign thyroid disease in 9 (82%) cases. Thirty-six cases of HPT after RAI exposure in the English literature were compiled for further analysis. In this collective experience, the average latency time to the development of HPT after RAI treatment was 13.5 +/- 9.1 years and was found to be inversely correlated with age at RAI exposure.

Conclusions: Patients who undergo RAI treatment are at risk of developing HPT, and this risk appears to increase in elderly patients. Serum calcium surveillance is recommended for patients who have undergone RAI treatment.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.amjsurg.2007.04.005DOI Listing

Publication Analysis

Top Keywords

rai treatment
20
hpt rai
12
rai
10
radioactive iodine
8
patients hpt
8
hpt history
8
history rai
8
latency time
8
time development
8
development hpt
8

Similar Publications

Background The thyroid gland is the most susceptible organ to radiation during the exposure of teeth because the thyroid area appears to be within the primary beam, and the dose levels are relatively high even after using collimation. This study aims to develop an eco-friendly thyroid shield by reusing lead foils from intra-oral periapical radiographic films and evaluate its effectiveness in intraoral radiography. Methods A total of 16 patients undergoing endodontic procedures who gave written consent to participate in the study were included and divided into four categories: anterior, canine, premolar, and molar.

View Article and Find Full Text PDF

Accuracy of Fully Automated and Human-assisted AI-based CT Quantification of Pleural Effusion Changes after Thoracentesis.

Radiol Artif Intell

January 2025

From the Department of Radiology (E.J.H., S.K., H.K., D. K., S.H.Y.) and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak- ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine (E.J.H., H.K., S.H.Y.), Seoul, Korea; Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine (S-J.Y., Seoul, Korea).

Quantifying pleural effusion change on chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion change on CT images, using the volume of drained fluid as the reference standard. Seventy-nine participants (mean age, 65 ± [SD] 13 years; 47 male) undergoing thoracentesis were prospectively enrolled from October 2021 to September 2023.

View Article and Find Full Text PDF

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High- Resolution Short Echo Time MR Spectroscopy Datasets.

Radiol Artif Intell

January 2025

From the Department of Radiation Oncology (A.S.G., V.H., H.S.) and Department of Radiology and Imaging Sciences (B.D.W.), Emory University School of Medicine, 1701 Uppergate Dr, C5008 Winship Cancer Institute, Atlanta, GA 30322; Department of Radiology, University of Miami {School of Medicine?}, Miami, Fla (S.S., A.A.M.); Department of {Radiology?} Northwestern University {Feinberg School of Medicine?}, Chicago, Ill (L.A.D.C.); Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, Ga (Y.L.); Department of Psychology, Emory University, Atlanta, Ga (M.T.); and Department of Radiology, Duke University Medical Center, Durham, NC (B.J.S.).

Purpose To develop and evaluate the performance of NNFit, a self-supervised deep-learning method for quantification of high-resolution short echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/GRAPPA scans from clinical trials for glioblastoma (Trial 1, May 2014-October 2018) and major-depressive-disorder (Trial 2, 2022- 2023). The training dataset included 685k spectra from 20 participants (60 scans) in Trial 1.

View Article and Find Full Text PDF

A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Radiol Artif Intell

January 2025

From the Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou 510060, P. R. China (J.K., C.F.W., Z.H.C., G.Q.Z., Y.Q.W., L.L., Y.S.); Department of Radiation Therapy, Nanhai People's Hospital, The Sixth Affiliated Hospital, South China University of Technology, Foshan, China (J.Y.P., L.J.L.); and Department of Electronic Engineering, Information School, Yunnan University, Kunming, China (W.B.L.).

Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally- advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This multicenter retrospective study included 1039 LA-NPC patients (779 male, 260 female, mean age 44 [standard deviation: 11]) diagnosed between April 2009 and December 2015. A radiomics- clinical prognostic model (Model RC) was developed using pre-and post-IC MRI and other clinical factors using graph convolutional neural networks (GCN).

View Article and Find Full Text PDF

Purpose: Transurethral resection of bladder tumor (TURBT) is the initial staging procedure for new bladder cancers (BCs). For muscle-invasive bladder cancers (MIBCs), TURBT may delay definitive treatment. We investigated whether definitive treatment can be expedited for MIBC using flexible cystoscopic biopsy and multiparametric magnetic resonance imaging (mpMRI) for initial staging.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!