Automatic prediction of non-iodine-avid status in lung metastases for radioactive I¹³¹ treatment in differentiated thyroid cancer patients.

ABSTRACT:

Objectives

The growing incidence of differentiated thyroid cancer (DTC) have been linked to insulin resistance and metabolic syndrome. The imperative need for developing effective diagnostic imaging tools to predict the non-iodine-avid status of lung metastasis (LMs) in differentiated thyroid cancer (DTC) patients is underscored to prevent unnecessary radioactive iodine treatment (RAI).

Methods

Primary cohort consisted 1962 pretreated LMs of 496 consecutive DTC patients with pretreated initially diagnosed LMs who underwent chest CT and subsequent post-treatment radioiodine SPECT. After automatic lesion segmentation by SE V-Net, SE Net deep learning was trained to predict non-iodine-avid status of LMs. External validation cohort contained 123 pretreated LMs of 24 consecutive

SUBMITTER: Gao X 

PROVIDER: S-EPMC11201526 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature