|
KMID : 0311120230640050320
|
|
Yonsei Medical Journal
2023 Volume.64 No. 5 p.320 ~ p.326
|
|
|
Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics
|
|
Kim So-Young
Lee Jae-Hoon
Park Eun-Jung
Lee Hye-Sun
Baik Seung-Hyuk
Jeon Tae-Joo
Lee Kang-Young
Ryu Young-Hoon
Kang Jeong-Hyun
|
|
|
Abstract
|
|
|
Purpose: We investigated the feasibility of preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics with machine learning to predict microsatellite instability (MSI) status in colorectal can cer (CRC) patients.
Materials and Methods: Altogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating character istic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters.
Results: The incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively; p=0.490). Logistic regression analysis revealed that the rad_score was an independent pre dictor of MSI status in the training set. The rad_score performed better than metabolic tumor volume when assessed using the AUROC (0.867 vs. 0.794, p=0.015).
Conclusion: Our predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters.
|
|
|
KEYWORD
|
|
|
Colorectal cancer, microsatellite instability, positron emission tomography, image analysis, machine learning
|
|
|
FullTexts / Linksout information
|
|
|
|
|
Listed journal information
|
|
   
|