Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer
机构：[1]Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China,[2]School of Physics, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, China,[3]The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China,广东省中医院[4]Department of Radiology, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan, China,[5]Department of Radiology, Guangzhou Concord Cancer Center, Guangzhou, China,[6]Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China,深圳市康宁医院深圳医学信息中心中国医学科学院阜外医院深圳医院[7]Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, China深圳市康宁医院深圳医学信息中心
In locally advanced rectal cancer (LARC), an improved ability to predict prognosis before and after treatment is needed for individualized treatment. We aimed to utilize pre- and post-treatment clinical predictors and baseline magnetic resonance imaging (MRI) radiomic features for establishing prognostic models to predict progression-free survival (PFS) in patients with LARC. Patients with LARC diagnosed between March 2014 and May 2016 were included in this retrospective study. A radiomic signature based on extracted MRI features and clinical prognostic models based on clinical features were constructed in the training cohort to predict 3-year PFS. C-indices were used to evaluate the predictive accuracies of the radiomic signature, clinical prognostic models, and integrated prognostic model (iPostM). In total, 166 consecutive patients were included (110 vs. 56 for training vs. validation). Eleven radiomic features were filtered out to construct the radiomic signature, which was significantly related to PFS. The MRI feature-derived radiomic signature exhibited better prognostic performance than the clinical prognostic models (P = 0.007 vs. 0.077). Then, we proposed an iPostM that combined the radiomic signature with tumor regression grade. The iPostM achieved the highest C-indices in the training and validation cohorts (0.942 and 0.752, respectively), outperforming other models in predicting PFS (all P < 0.05). Decision curve analysis and survival curves of the validation cohort verified that iPostM demonstrated the best performance and facilitated risk stratification. Therefore, iPostM provided the most reliable prognostic prediction for PFS in patients with LARC.
基金：
This study has received funding by the Key-Area Research
and Development of Guangdong Province (Grant No.
2020B1111190001), Science and Technology Planning Project
of Guangzhou City, China (Grant No. 201907010043) and the
National Natural Science Foundation of China (Grant No.
61975244, 82171906, and 81902638).
第一作者机构：[1]Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, China,
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Xie Fei,Zhao Qin,Li Shuqi,et al.Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer[J].FRONTIERS IN ONCOLOGY.2022,12:doi:10.3389/fonc.2022.901287.
APA：
Xie, Fei,Zhao, Qin,Li, Shuqi,Wu, Shuangshuang,Li, Jinli...&Cai, Peiqiang.(2022).Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer.FRONTIERS IN ONCOLOGY,12,
MLA：
Xie, Fei,et al."Establishment and validation of novel MRI radiomic feature-based prognostic models to predict progression-free survival in locally advanced rectal cancer".FRONTIERS IN ONCOLOGY 12.(2022)
