Yankai Meng1, Yuchen Zhang2,3, Di Dong3,4, Chunming Li2, Xiao Liang1, Chongda Zhang1, Lijuan Wan1, Xinming Zhao1, Kai Xu5, Chunwu Zhou1, Jie Tian3, Hongmei Zhang1. 1. Department of Radiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China. 2. University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China. 3. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China. 4. University of Chinese Academy of Sciences, Beijing, China. 5. Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, P.R. China.
Abstract
BACKGROUND: Locally advanced rectal cancer (LARC) patient stratification by clinicoradiologic factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification of LARC patients, personalized treatment, and prognostication. PURPOSE/HYPOTHESIS: To compare the ability of a radiomic signature to predict disease-free survival (DFS) with that of a clinicoradiologic risk model in individual patients with LARC. STUDY TYPE: Retrospective study. POPULATION: In all, 108 consecutive patients (allocated to a training and validation set with a 1:1 ratio) with LARC treated with neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision (TME). FIELD STRENGTH/SEQUENCE: Axial 3D LAVA multienhanced MR sequence at 3T. ASSESSMENT: ITK-SNAP software was used for manual segmentation of 3D pre-nCRT MR images. All manual tumor segmentations were performed by a gastrointestinal tract radiologist, and validated by a senior radiologist. The clinicoradiologic risk factors with potential prognostic outcomes were identified in univariate analysis based on the Cox regression model for the whole set. The results showed that ypT, ypN, EMVI, and MRF were potential clinicoradiologic risk factors. Interestingly, only ypN and MRF were identified as independent predictors in multivariate analysis based on the Cox regression model. STATISTICAL TESTS: A radiomic signature based on 485 3D features was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association of the radiomic signature with DFS was investigated by Kaplan-Meier survival curves. Survival curves were compared by the log-rank test. Three models were built and assessed for their predictive values, using the Harrell concordance index and integrated time-dependent area under the curve. RESULTS: The novel radiomic signature stratified patients into low- and high-risk groups for DFS in the training set (hazard ratio [HR] = 6.83; P < 0.001), and was successfully validated in the validation set (HR = 2.92; P < 0.001). The model combining the radiomic signature and clinicoradiologic findings had the best performance (C index = 0.788, 95% confidence interval [CI] 0.72-0.86; integrated time-dependent area under the curve of 0.837 at 3 years). DATA CONCLUSION: The novel radiomic signature could be used to predict DFS in patients with LARC. Furthermore, combining this radiomic signature with clinicoradiologic features significantly improved the ability to estimate DFS (P = 0.001, 0.005 in training set and in validation set, respectively), and may help guide individualized treatment in such patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.
BACKGROUND: Locally advanced rectal cancer (LARC) patient stratification by clinicoradiologic factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification of LARC patients, personalized treatment, and prognostication. PURPOSE/HYPOTHESIS: To compare the ability of a radiomic signature to predict disease-free survival (DFS) with that of a clinicoradiologic risk model in individual patients with LARC. STUDY TYPE: Retrospective study. POPULATION: In all, 108 consecutive patients (allocated to a training and validation set with a 1:1 ratio) with LARC treated with neoadjuvant chemoradiotherapy (nCRT) followed by total mesorectal excision (TME). FIELD STRENGTH/SEQUENCE: Axial 3D LAVA multienhanced MR sequence at 3T. ASSESSMENT: ITK-SNAP software was used for manual segmentation of 3D pre-nCRT MR images. All manual tumor segmentations were performed by a gastrointestinal tract radiologist, and validated by a senior radiologist. The clinicoradiologic risk factors with potential prognostic outcomes were identified in univariate analysis based on the Cox regression model for the whole set. The results showed that ypT, ypN, EMVI, and MRF were potential clinicoradiologic risk factors. Interestingly, only ypN and MRF were identified as independent predictors in multivariate analysis based on the Cox regression model. STATISTICAL TESTS: A radiomic signature based on 485 3D features was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The association of the radiomic signature with DFS was investigated by Kaplan-Meier survival curves. Survival curves were compared by the log-rank test. Three models were built and assessed for their predictive values, using the Harrell concordance index and integrated time-dependent area under the curve. RESULTS: The novel radiomic signature stratified patients into low- and high-risk groups for DFS in the training set (hazard ratio [HR] = 6.83; P < 0.001), and was successfully validated in the validation set (HR = 2.92; P < 0.001). The model combining the radiomic signature and clinicoradiologic findings had the best performance (C index = 0.788, 95% confidence interval [CI] 0.72-0.86; integrated time-dependent area under the curve of 0.837 at 3 years). DATA CONCLUSION: The novel radiomic signature could be used to predict DFS in patients with LARC. Furthermore, combining this radiomic signature with clinicoradiologic features significantly improved the ability to estimate DFS (P = 0.001, 0.005 in training set and in validation set, respectively), and may help guide individualized treatment in such patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018.
Authors: Iram Shahzadi; Alex Zwanenburg; Annika Lattermann; Annett Linge; Christian Baldus; Jan C Peeken; Stephanie E Combs; Markus Diefenhardt; Claus Rödel; Simon Kirste; Anca-Ligia Grosu; Michael Baumann; Mechthild Krause; Esther G C Troost; Steffen Löck Journal: Sci Rep Date: 2022-06-17 Impact factor: 4.996
Authors: Joao Miranda; Gary Xia Vern Tan; Maria Clara Fernandes; Onur Yildirim; John A Sims; Jose de Arimateia Batista Araujo-Filho; Felipe Augusto de M Machado; Antonildes N Assuncao-Jr; Cesar Higa Nomura; Natally Horvat Journal: Clin Imaging Date: 2021-11-16 Impact factor: 2.420