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Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics

 Soyoung Kim  ;  Jae-Hoon Lee  ;  Eun Jung Park  ;  Hye Sun Lee  ;  Seung Hyuk Baik  ;  Tae Joo Jeon  ;  Kang Young Lee  ;  Young Hoon Ryu  ;  Jeonghyun Kang 
 YONSEI MEDICAL JOURNAL, Vol.64(5) : 320-326, 2023-05 
Journal Title
Issue Date
Colorectal Neoplasms* / diagnostic imaging ; Colorectal Neoplasms* / genetics ; Fluorodeoxyglucose F18* ; Humans ; Machine Learning ; Microsatellite Instability ; Positron Emission Tomography Computed Tomography ; Retrospective Studies
Colorectal cancer ; image analysis ; machine learning ; microsatellite instability ; positron emission tomography
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.
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1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kang, Jeonghyun(강정현) ORCID logo https://orcid.org/0000-0001-7311-6053
Kim, Soyoung(김소영) ORCID logo https://orcid.org/0000-0002-6163-1434
Park, Eun Jung(박은정) ORCID logo https://orcid.org/0000-0002-4559-2690
Ryu, Young Hoon(유영훈) ORCID logo https://orcid.org/0000-0002-9000-5563
Lee, Kang Young(이강영)
Lee, Jae Hoon(이재훈) ORCID logo https://orcid.org/0000-0002-9898-9886
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
Jeon, Tae Joo(전태주) ORCID logo https://orcid.org/0000-0002-7574-6734
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