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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 cancer (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 characteristic 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 predictor 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.

Affiliations

Soyoung Kim 1, Jae-Hoon Lee 2, Eun Jung Park 3, Hye Sun Lee 4, Seung Hyuk Baik 3, Tae Joo Jeon 1, Kang Young Lee 5, Young Hoon Ryu 1, Jeonghyun Kang 6
1Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
2Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. docnuke@yuhs.ac.
3Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
4Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea.
5Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
6Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. ravic@naver.com.