Kaiming Xue1, Lin Liu1, Yunxia Liu1, Yan Guo2, Yuhang Zhu1, Mengchao Zhang3. 1. Department of Radiology, China-Japan Union Hospital of Jilin University, NO. 126 Xiantai Street, Changchun, 130033, China. 2. GE Healthcare, Beijing, China. 3. Department of Radiology, China-Japan Union Hospital of Jilin University, NO. 126 Xiantai Street, Changchun, 130033, China. zhangmengchao@jlu.edu.cn.
Abstract
PURPOSE: To establish and validate a radiomics model based on multi-sequence magnetic resonance (MR) images for preoperative prediction of immunoscore in rectal cancer. MATERIALS AND METHODS: This retrospective study included 133 patients with pathologically confirmed rectal cancer after surgical resection who underwent MR examination before treatment within two weeks. All patients were randomly divided into training cohort (n = 92) and validation (n = 41) cohort according to a ratio of 7:3. The volumes of interest were manually delineated in the T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) images, from which a total of 804 radiomics features were extracted. Thereafter, we used Spearman correlation analysis and gradient boosting decision tree (GBDT) algorithm to select the strongest features, and the radiomics scores were established using multivariate logistic regression algorithm, including two single-mode models and two dual-mode models. The predictive performance and the clinical usefulness of the model were assessed by the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). RESULTS: Integrated model A based on T2WI and ADC images showed a better predictive performance, which yielded an AUC of 0.770 (95% CI 0.673-0.867) in the training cohort and 0.768 (95% CI 0.619-0.917) in the validation cohort. Calibration curve showed good agreement between predicted results of the model and actual events, and DCA indicated good clinical usefulness. Moreover, stratification analysis proved that the integrated model A had strong robustness. CONCLUSIONS: Integrated model A based on T2WI and ADC images has the potential to be used as a non-invasive tool for preoperative prediction of immunoscore in rectal cancer. It may be useful in evaluating prognosis and guiding individualized immunotherapy of patients.
PURPOSE: To establish and validate a radiomics model based on multi-sequence magnetic resonance (MR) images for preoperative prediction of immunoscore in rectal cancer. MATERIALS AND METHODS: This retrospective study included 133 patients with pathologically confirmed rectal cancer after surgical resection who underwent MR examination before treatment within two weeks. All patients were randomly divided into training cohort (n = 92) and validation (n = 41) cohort according to a ratio of 7:3. The volumes of interest were manually delineated in the T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) images, from which a total of 804 radiomics features were extracted. Thereafter, we used Spearman correlation analysis and gradient boosting decision tree (GBDT) algorithm to select the strongest features, and the radiomics scores were established using multivariate logistic regression algorithm, including two single-mode models and two dual-mode models. The predictive performance and the clinical usefulness of the model were assessed by the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). RESULTS: Integrated model A based on T2WI and ADC images showed a better predictive performance, which yielded an AUC of 0.770 (95% CI 0.673-0.867) in the training cohort and 0.768 (95% CI 0.619-0.917) in the validation cohort. Calibration curve showed good agreement between predicted results of the model and actual events, and DCA indicated good clinical usefulness. Moreover, stratification analysis proved that the integrated model A had strong robustness. CONCLUSIONS: Integrated model A based on T2WI and ADC images has the potential to be used as a non-invasive tool for preoperative prediction of immunoscore in rectal cancer. It may be useful in evaluating prognosis and guiding individualized immunotherapy of patients.
Authors: Jürgen Weitz; Moritz Koch; Jürgen Debus; Thomas Höhler; Peter R Galle; Markus W Büchler Journal: Lancet Date: 2005 Jan 8-14 Impact factor: 79.321
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Authors: Jérôme Galon; Bernhard Mlecnik; Gabriela Bindea; Helen K Angell; Anne Berger; Christine Lagorce; Alessandro Lugli; Inti Zlobec; Arndt Hartmann; Carlo Bifulco; Iris D Nagtegaal; Richard Palmqvist; Giuseppe V Masucci; Gerardo Botti; Fabiana Tatangelo; Paolo Delrio; Michele Maio; Luigi Laghi; Fabio Grizzi; Martin Asslaber; Corrado D'Arrigo; Fernando Vidal-Vanaclocha; Eva Zavadova; Lotfi Chouchane; Pamela S Ohashi; Sara Hafezi-Bakhtiari; Bradly G Wouters; Michael Roehrl; Linh Nguyen; Yutaka Kawakami; Shoichi Hazama; Kiyotaka Okuno; Shuji Ogino; Peter Gibbs; Paul Waring; Noriyuki Sato; Toshihiko Torigoe; Kyogo Itoh; Prabhu S Patel; Shilin N Shukla; Yili Wang; Scott Kopetz; Frank A Sinicrope; Viorel Scripcariu; Paolo A Ascierto; Francesco M Marincola; Bernard A Fox; Franck Pagès Journal: J Pathol Date: 2014-01 Impact factor: 7.996