Silin Chen1, Ning Li1,2, Yuan Tang1, Bo Chen1, Hui Fang1, Shunan Qi1, Ninging Lu1, Yong Yang1, Yongwen Song1, Yueping Liu1, Shulian Wang1, Ye-Xiong Li1, Jing Jin1,2. 1. Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 2. Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
Abstract
PURPOSE: To create a prognostic prediction radiomics model for soft tissue sarcoma (STS) of the extremities and trunk treated with neoadjuvant radiotherapy. METHODS: This study included 62 patients with STS of the extremities and trunk who underwent magnetic resonance imaging (MRI) before neoadjuvant radiotherapy. After tumour segmentation and preprocessing, 851 radiomics features were extracted. The radiomics score was constructed according to the least absolute shrinkage and selection operator (LASSO) method. Survival analysis (disease-free survival; DFS) was performed using the log-rank test and Cox's proportional hazards regression model. The nomogram model was established based on the log-rank test and Cox regression model. Harrell's concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic factors. The clinical utility of the model was assessed by decision curve analysis (DCA). RESULTS: The univariate survival analysis showed that tumour location (p = 0.032), clinical stage (p = 0.022), tumour size (p = 0.005) and the radiomics score were correlated with DFS (p < 0.05). The multivariate analysis showed that tumour location, tumour size, and the radiomics score were independent prognostic factors for DFS (p < 0.05). The combined clinical-radiomics model based on the multivariate analysis showed the best predictive ability for DFS (C-index: 0.781; Area Under Curve: 0.791). DCA revealed that the use of the radiomics score-based nomogram was associated with better benefit gains relative to the prediction of 2-year DFS events than other models in the threshold probability range between 0.12 and 0.38. CONCLUSION: The radiomics score from pretreatment MRI is an independent prognostic factor for DFS in patients with STS of the extremities and trunk. The radiomics score-based nomogram could improve prognostic stratification ability and thus contribute to individualized therapy for STS patients.
PURPOSE: To create a prognostic prediction radiomics model for soft tissue sarcoma (STS) of the extremities and trunk treated with neoadjuvant radiotherapy. METHODS: This study included 62 patients with STS of the extremities and trunk who underwent magnetic resonance imaging (MRI) before neoadjuvant radiotherapy. After tumour segmentation and preprocessing, 851 radiomics features were extracted. The radiomics score was constructed according to the least absolute shrinkage and selection operator (LASSO) method. Survival analysis (disease-free survival; DFS) was performed using the log-rank test and Cox's proportional hazards regression model. The nomogram model was established based on the log-rank test and Cox regression model. Harrell's concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic factors. The clinical utility of the model was assessed by decision curve analysis (DCA). RESULTS: The univariate survival analysis showed that tumour location (p = 0.032), clinical stage (p = 0.022), tumour size (p = 0.005) and the radiomics score were correlated with DFS (p < 0.05). The multivariate analysis showed that tumour location, tumour size, and the radiomics score were independent prognostic factors for DFS (p < 0.05). The combined clinical-radiomics model based on the multivariate analysis showed the best predictive ability for DFS (C-index: 0.781; Area Under Curve: 0.791). DCA revealed that the use of the radiomics score-based nomogram was associated with better benefit gains relative to the prediction of 2-year DFS events than other models in the threshold probability range between 0.12 and 0.38. CONCLUSION: The radiomics score from pretreatment MRI is an independent prognostic factor for DFS in patients with STS of the extremities and trunk. The radiomics score-based nomogram could improve prognostic stratification ability and thus contribute to individualized therapy for STS patients.
Authors: Dian Wang; Qiang Zhang; Burton L Eisenberg; John M Kane; X Allen Li; David Lucas; Ivy A Petersen; Thomas F DeLaney; Carolyn R Freeman; Steven E Finkelstein; Ying J Hitchcock; Manpreet Bedi; Anurag K Singh; George Dundas; David G Kirsch Journal: J Clin Oncol Date: 2015-02-09 Impact factor: 44.544
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Authors: Maria Anna Smolle; Dimosthenis Andreou; Per-Ulf Tunn; Joanna Szkandera; Bernadette Liegl-Atzwanger; Andreas Leithner Journal: EFORT Open Rev Date: 2017-10-17
Authors: Thomas F DeLaney; Yen-Lin Chen; Elizabeth H Baldini; Dian Wang; Judith Adams; Shea B Hickey; Beow Y Yeap; Stephen M Hahn; Karen De Amorim Bernstein; G Petur Nielsen; Edwin Choy; John T Mullen; Sam S Yoon Journal: Adv Radiat Oncol Date: 2017-01-04