Quantitative Prediction of Microsatellite Instability in Colorectal Cancer With Preoperative PET/CT-Based Radiomics

Jiaru Li; Ziyi Yang; Bowen Xin; Yichao Hao; Lisheng Wang; Shaoli Song; Junyan Xu; Xiuying Wang

doi:10.3389/fonc.2021.702055

Quantitative Prediction of Microsatellite Instability in Colorectal Cancer With Preoperative PET/CT-Based Radiomics

Front Oncol. 2021 Jul 22:11:702055. doi: 10.3389/fonc.2021.702055. eCollection 2021.

Authors

Jiaru Li¹, Ziyi Yang^{2

3}, Bowen Xin¹, Yichao Hao¹, Lisheng Wang⁴, Shaoli Song^{2

3}, Junyan Xu^{2

3}, Xiuying Wang¹

Affiliations

¹ School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
² Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.
³ Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
⁴ Department of Automation, Shanghai Jiao Tong University, Shanghai, China.

Abstract

Objectives: Microsatellite instability (MSI) status is an important hallmark for prognosis prediction and treatment recommendation of colorectal cancer (CRC). To address issues due to the invasiveness of clinical preoperative evaluation of microsatellite status, we investigated the value of preoperative ¹⁸F-FDG PET/CT radiomics with machine learning for predicting the microsatellite status of colorectal cancer patients.

Methods: A total of 173 patients that underwent ¹⁸F-FDG PET/CT scans before operations were retrospectively analyzed in this study. The microsatellite status for each patient was identified as microsatellite instability-high (MSI-H) or microsatellite stable (MSS), according to the test for mismatch repair gene proteins with immunohistochemical staining methods. There were 2,492 radiomic features in total extracted from ¹⁸F-FDG PET/CT imaging. Then, radiomic features were selected through multivariate random forest selection and univariate relevancy tests after handling the imbalanced dataset through the random under-sampling method. Based on the selected features, we constructed a BalancedBagging model based on Adaboost classifiers to identify the MSI status in patients with CRC. The model performance was evaluated by the area under the curve (AUC), sensitivity, specificity, and accuracy on the validation dataset.

Results: The ensemble model was constructed based on two radiomic features and achieved an 82.8% AUC for predicting the MSI status of colorectal cancer patients. The sensitivity, specificity, and accuracy were 83.3, 76.3, and 76.8%, respectively. The significant correlation of the selected two radiomic features with multiple effective clinical features was identified (p < 0.05).

Conclusion: ¹⁸F-FDG PET/CT radiomics analysis with the machine learning model provided a quantitative, efficient, and non-invasive mechanism for identifying the microsatellite status of colorectal cancer patients, which optimized the treatment decision support.

Keywords: 18F-FDG PET/CT; colorectal cancer; machine learning; microsatellite status; radiomics.