Imaging prediction of <i>KRAS</i> mutation in patients with rectal cancer through deep metric learning using pretreatment [¹⁸F]Fluorodeoxyglucose positron emission tomography/computed tomography.

ABSTRACT:

Objectives

To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML).

Methods

This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep-learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K-fold cross-validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance.

Results

Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET

SUBMITTER: Wu KC 

PROVIDER: S-EPMC10607399 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature