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Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer
Author(s) -
Meng Yankai,
Zhang Yuchen,
Dong Di,
Li Chunming,
Liang Xiao,
Zhang Chongda,
Wan Lijuan,
Zhao Xinming,
Xu Kai,
Zhou Chunwu,
Tian Jie,
Zhang Hongmei
Publication year - 2018
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.25968
Subject(s) - medicine , proportional hazards model , colorectal cancer , univariate , receiver operating characteristic , chemoradiotherapy , radiology , oncology , multivariate statistics , radiation therapy , cancer , computer science , machine learning
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;48:605–614.