Zhihui Li1, Fangying Chen2, Shaoting Zhang2, Fu Shen3, Yong Lu4, Xiaolu Ma2, Yuwei Xia5, Chengwei Shao2. 1. Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China. 2. Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, China. 3. Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, China. ssff_53@163.com. 4. Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. 5. Huiying Medical Technology Co., Ltd, Beijing, China.
Abstract
PURPOSE: To build and validate a magnetic resonance imaging-based radiomics model to preoperatively evaluate tumor budding (TB) in locally advanced rectal cancer (LARC). METHODS: Pathologically confirmed LARC cases submitted to preoperative rectal MRI in two distinct hospitals were enrolled in this retrospective study and assigned to cohort 1 (training set, n = 77; test set, n = 51) and cohort 2 (validation set, n = 96). Radiomics features were obtained from multiple sequences, comprising high-resolution T2, contrast-enhanced T1, and diffusion-weighted imaging (T2WI, CE-T1WI, and DWI, respectively). The least absolute shrinkage and selection operator (LASSO) was utilized to select the optimal features from T2WI, CE-T1WI, DWI, and the combination of multi-sequences, respectively. A support vector machine (SVM) classifier was utilized to construct various radiomics models for discriminating the TB grades. Receiver operating characteristic curve analysis and decision curve analysis (DCA) were carried out to determine the diagnostic value. RESULTS: Five optimal features associated with TB grade were determined from combined multi-sequence data. Accordingly, a radiomics model based on combined multi-sequences had an area under the curve of 0.796, with an accuracy of 81.2% in the validation set, showing a better performance in comparison with other models in both cohorts (p < 0.05). DCA exhibited a clinical benefit for this radiomics model. CONCLUSION: The novel MRI-based radiomics model combining multiple sequences is an effective and non-invasive approach for evaluating TB grade preoperatively in patients with LARC.
PURPOSE: To build and validate a magnetic resonance imaging-based radiomics model to preoperatively evaluate tumor budding (TB) in locally advanced rectal cancer (LARC). METHODS: Pathologically confirmed LARC cases submitted to preoperative rectal MRI in two distinct hospitals were enrolled in this retrospective study and assigned to cohort 1 (training set, n = 77; test set, n = 51) and cohort 2 (validation set, n = 96). Radiomics features were obtained from multiple sequences, comprising high-resolution T2, contrast-enhanced T1, and diffusion-weighted imaging (T2WI, CE-T1WI, and DWI, respectively). The least absolute shrinkage and selection operator (LASSO) was utilized to select the optimal features from T2WI, CE-T1WI, DWI, and the combination of multi-sequences, respectively. A support vector machine (SVM) classifier was utilized to construct various radiomics models for discriminating the TB grades. Receiver operating characteristic curve analysis and decision curve analysis (DCA) were carried out to determine the diagnostic value. RESULTS: Five optimal features associated with TB grade were determined from combined multi-sequence data. Accordingly, a radiomics model based on combined multi-sequences had an area under the curve of 0.796, with an accuracy of 81.2% in the validation set, showing a better performance in comparison with other models in both cohorts (p < 0.05). DCA exhibited a clinical benefit for this radiomics model. CONCLUSION: The novel MRI-based radiomics model combining multiple sequences is an effective and non-invasive approach for evaluating TB grade preoperatively in patients with LARC.
Authors: Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies Journal: Magn Reson Imaging Date: 2012-08-13 Impact factor: 2.546
Authors: Iris D Nagtegaal; Robert D Odze; David Klimstra; Valerie Paradis; Massimo Rugge; Peter Schirmacher; Kay M Washington; Fatima Carneiro; Ian A Cree Journal: Histopathology Date: 2019-11-13 Impact factor: 5.087