Qian Pei1, Xiaoping Yi2,3, Chen Chen4, Peipei Pang5, Yan Fu6,7, Guangwu Lei6, Changyong Chen6, Fengbo Tan1, Guanghui Gong8, Qingling Li9, Hongyan Zai1, Bihong T Chen10. 1. Department of General Surgery, Xiangya Hospital, Central South University, Changsha, People's Republic of China. 2. Department of Radiology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, People's Republic of China. yixiaoping@csu.edu.cn. 3. National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, 410008, People's Republic of China. yixiaoping@csu.edu.cn. 4. Department of Radiology, 331 Hospital of Zhuzhou City, Zhuzhou, People's Republic of China. 5. GE Healthcare, Hangzhou, 310000, People's Republic of China. 6. Department of Radiology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, People's Republic of China. 7. National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, 410008, People's Republic of China. 8. Department of Pathology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, People's Republic of China. 9. Department of Pathology, Xiangya Hospital, Central South University, No. 87 Xiangya Road, Changsha, 410008, People's Republic of China. liqinglingw168@163.com. 10. Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA.
Abstract
OBJECTIVES: Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC. METHODS: A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves. RESULTS: The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value. CONCLUSIONS: We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC. KEY POINTS: • There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status. • Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease. • Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.
OBJECTIVES: Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC. METHODS: A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves. RESULTS: The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value. CONCLUSIONS: We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC. KEY POINTS: • There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status. • Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease. • Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.
Authors: Rachel Phelps; Richard Gallon; Christine Hayes; Eli Glover; Philip Gibson; Ibrahim Edidi; Tom Lee; Sarah Mills; Adam Shaw; Rakesh Heer; Angela Ralte; Ciaron McAnulty; Mauro Santibanez-Koref; John Burn; Michael S Jackson Journal: Cancers (Basel) Date: 2022-08-08 Impact factor: 6.575