Wenting Rui1, Nidan Qiao2,3,4,5,6, Yao Zhao7,8,9,10,11, Zhenwei Yao12, Yue Wu1, Yong Zhang13, Ababikere Aili14, Zhaoyun Zhang15, Hongying Ye15, Yongfei Wang2,3,4,5,6. 1. Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China. 2. Department of Neurosurgery, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China. 3. Neurosurgical Institute of Fudan University, Shanghai, People's Republic of China. 4. Shanghai Clinical Medical Center of Neurosurgery, Shanghai, People's Republic of China. 5. Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, People's Republic of China. 6. National Center for Neurological Disorders, Shanghai, People's Republic of China. 7. Department of Neurosurgery, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China. zhaoyaohs@qq.com. 8. Neurosurgical Institute of Fudan University, Shanghai, People's Republic of China. zhaoyaohs@qq.com. 9. Shanghai Clinical Medical Center of Neurosurgery, Shanghai, People's Republic of China. zhaoyaohs@qq.com. 10. Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, People's Republic of China. zhaoyaohs@qq.com. 11. National Center for Neurological Disorders, Shanghai, People's Republic of China. zhaoyaohs@qq.com. 12. Department of Radiology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China. zwyao@fudan.edu.cn. 13. GE Healthcare, MR Research, Huatuo Road, Shanghai, 201203, People's Republic of China. 14. Department of Radiology, Kuqa County People's Hospital, Aksu, 842000, Xinjiang, People's Republic of China. 15. Department of Endocrinology, Huashan Hospital, Fudan University, Mid Wulumuqi Road, Shanghai, 200040, People's Republic of China.
Abstract
OBJECTIVE: To predict silent corticotroph adenomas (SCAs) among non-functioning pituitary adenomas preoperatively using noninvasive radiomics. METHODS: A total of 302 patients including 146 patients diagnosed with SCAs and 156 patients with non-SCAs were enrolled (training set: n = 242; test set: n = 60). Tumor segmentation was manually generated using ITK-SNAP. From T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI, 2550 radiomics features were extracted using Pyradiomics. Pearson's correlation coefficient values were calculated to exclude redundant features. Several machine learning algorithms were developed to predict SCAs incorporating the radiomics and semantic features including clinical, laboratory, and radiology-associated features. The performance of models was evaluated by AUC. RESULTS: Patients in the SCA group were younger (49.5 vs 55.2 years old) and more female (85.6% vs 37.2%) than those in the non-SCA group (p < 0.001). More invasiveness (p = 0.011) and cystic and microcystic change (p < 0.001) were observed in patients with SCAs. The ensemble algorithm presented the largest AUC of 0.927 among all the algorithms trained in the test set, and the accuracy, specificity, and sensitivity of predicting SCAs were all 0.867 (at cut-off 0.5). The overall model performed better than that only using semantic features available in the clinic. Radiomics prediction was the most important feature, with gender ranking second and age ranking third. Radiomics features on T2WI were superior to those on other MR modalities in SCA prediction. CONCLUSION: Our ensemble learning model outperformed current clinical practice in differentiating patients with SCAs and non-SCAs using radiomics, which might help make appropriate treatment strategies. KEY POINTS: • Radiomics might improve the preoperative diagnosis of SCAs by MR images. • T2WI was superior to T1WI and CE-T1WI in the preoperative diagnosis of SCAs. • The ensemble machine learning model outperformed current clinical practice in SCAs diagnosis and treatment decision-making could be more individualised using the nomogram.
OBJECTIVE: To predict silent corticotroph adenomas (SCAs) among non-functioning pituitary adenomas preoperatively using noninvasive radiomics. METHODS: A total of 302 patients including 146 patients diagnosed with SCAs and 156 patients with non-SCAs were enrolled (training set: n = 242; test set: n = 60). Tumor segmentation was manually generated using ITK-SNAP. From T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI, 2550 radiomics features were extracted using Pyradiomics. Pearson's correlation coefficient values were calculated to exclude redundant features. Several machine learning algorithms were developed to predict SCAs incorporating the radiomics and semantic features including clinical, laboratory, and radiology-associated features. The performance of models was evaluated by AUC. RESULTS: Patients in the SCA group were younger (49.5 vs 55.2 years old) and more female (85.6% vs 37.2%) than those in the non-SCA group (p < 0.001). More invasiveness (p = 0.011) and cystic and microcystic change (p < 0.001) were observed in patients with SCAs. The ensemble algorithm presented the largest AUC of 0.927 among all the algorithms trained in the test set, and the accuracy, specificity, and sensitivity of predicting SCAs were all 0.867 (at cut-off 0.5). The overall model performed better than that only using semantic features available in the clinic. Radiomics prediction was the most important feature, with gender ranking second and age ranking third. Radiomics features on T2WI were superior to those on other MR modalities in SCA prediction. CONCLUSION: Our ensemble learning model outperformed current clinical practice in differentiating patients with SCAs and non-SCAs using radiomics, which might help make appropriate treatment strategies. KEY POINTS: • Radiomics might improve the preoperative diagnosis of SCAs by MR images. • T2WI was superior to T1WI and CE-T1WI in the preoperative diagnosis of SCAs. • The ensemble machine learning model outperformed current clinical practice in SCAs diagnosis and treatment decision-making could be more individualised using the nomogram.
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