Tian-Yu Tang1, Yun Jiao1, Ying Cui1, Deng-Ling Zhao1, Yi Zhang1, Zhi Wang1, Xiang-Pan Meng1, Xin-Dao Yin2, Yun-Jun Yang3, Gao-Jun Teng1, Sheng-Hong Ju4. 1. Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing, 210009, China. 2. Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210006, Jiangsu, China. 3. Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China. 4. Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing, 210009, China. jsh0836@hotmail.com.
Abstract
BACKGROUND AND PURPOSE: This study aimed at developing a radiomics signature (R score) as prognostic biomarkers based on penumbra quantification and to validate the radiomics nomogram to predict the clinical outcomes for thrombolysis for acute ischemic stroke (AIS) patients. METHODS: In total, 168 patients collected from seven centers were retrospectively included. A score of mismatch was defined as MIS. Based on a short-term clinical label, 456 radiomics features were evaluated with feature selection methods. R score was constructed with the selected features. To compare the predictive capabilities of the clinical factors, MIS, and R score, three nomograms were developed and evaluated, according to the short-term clinical assessment on day 7. Finally, the radiomics nomogram was validated by predicting the 3-month clinical outcomes of AIS patients, in an external cohort. RESULTS: R scores were found to be significantly higher in patients with favorable clinical outcomes in both training and validation datasets. The predictive value of the radiomics nomogram estimating favorable clinical outcomes was modest, with a concordance index (C-index) of 0.695 [95% confidence interval (CI) 0.667-0.723) in an external validation dataset. In addition, the area under curve (AUC) of the radiomics nomogram predicting favorable clinical outcome reached 0.886 (95% CI 0.809-0.963) on day 7 and 0.777 (95% CI 0.666-0.888) at 3 months. CONCLUSIONS: The radiomics signature is an independent biomarker for estimating the clinical outcomes in AIS patients. By improving the individualized prediction of the clinical outcome for AIS patients 3 months after onset, the radiomics nomogram adds more value to the current clinical decision-making process.
BACKGROUND AND PURPOSE: This study aimed at developing a radiomics signature (R score) as prognostic biomarkers based on penumbra quantification and to validate the radiomics nomogram to predict the clinical outcomes for thrombolysis for acute ischemic stroke (AIS) patients. METHODS: In total, 168 patients collected from seven centers were retrospectively included. A score of mismatch was defined as MIS. Based on a short-term clinical label, 456 radiomics features were evaluated with feature selection methods. R score was constructed with the selected features. To compare the predictive capabilities of the clinical factors, MIS, and R score, three nomograms were developed and evaluated, according to the short-term clinical assessment on day 7. Finally, the radiomics nomogram was validated by predicting the 3-month clinical outcomes of AISpatients, in an external cohort. RESULTS: R scores were found to be significantly higher in patients with favorable clinical outcomes in both training and validation datasets. The predictive value of the radiomics nomogram estimating favorable clinical outcomes was modest, with a concordance index (C-index) of 0.695 [95% confidence interval (CI) 0.667-0.723) in an external validation dataset. In addition, the area under curve (AUC) of the radiomics nomogram predicting favorable clinical outcome reached 0.886 (95% CI 0.809-0.963) on day 7 and 0.777 (95% CI 0.666-0.888) at 3 months. CONCLUSIONS: The radiomics signature is an independent biomarker for estimating the clinical outcomes in AISpatients. By improving the individualized prediction of the clinical outcome for AISpatients 3 months after onset, the radiomics nomogram adds more value to the current clinical decision-making process.
Authors: Nalini M Singh; Jordan B Harrod; Sandya Subramanian; Mitchell Robinson; Ken Chang; Suheyla Cetin-Karayumak; Adrian Vasile Dalca; Simon Eickhoff; Michael Fox; Loraine Franke; Polina Golland; Daniel Haehn; Juan Eugenio Iglesias; Lauren J O'Donnell; Yangming Ou; Yogesh Rathi; Shan H Siddiqi; Haoqi Sun; M Brandon Westover; Susan Whitfield-Gabrieli; Randy L Gollub Journal: Neuroinformatics Date: 2022-03-28