Nidan Qiao1,2,3,4,5, Ming Shen1,2,4,5, Wenqiang He1,2,4,5, Min He6, Zhaoyun Zhang6, Hongying Ye6, Yiming Li6, Xuefei Shou1,2,4,5, Shiqi Li1,2,4,5, Changzhen Jiang7, Yongfei Wang8,9,10,11, Yao Zhao12,13,14,15,16,17. 1. Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China. 2. Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China. 3. Medical Science in Clinical Investigation, Harvard Medical School, Boston, USA. 4. Neurosurgical Institute of Fudan University, Shanghai, China. 5. Shanghai Pituitary Tumor Center, Shanghai, China. 6. Department of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China. 7. Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fujian Medical University, 20 Chazhong Road, Fujian, China. 893416880@qq.com. 8. Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China. eamns@hotmail.com. 9. Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China. eamns@hotmail.com. 10. Neurosurgical Institute of Fudan University, Shanghai, China. eamns@hotmail.com. 11. Shanghai Pituitary Tumor Center, Shanghai, China. eamns@hotmail.com. 12. Department of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China. zhaoyaohs@qq.com. 13. Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China. zhaoyaohs@qq.com. 14. Neurosurgical Institute of Fudan University, Shanghai, China. zhaoyaohs@qq.com. 15. Shanghai Pituitary Tumor Center, Shanghai, China. zhaoyaohs@qq.com. 16. State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China. zhaoyaohs@qq.com. 17. National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China. zhaoyaohs@qq.com.
Abstract
PURPOSE: Accurate prediction of postoperative remission is beneficial for effective patient-physician communication in acromegalic patients. This study aims to train and validate machine learning prediction models for early endocrine remission of acromegalic patients. METHODS: The training cohort included 833 patients with growth hormone (GH) secreting pituitary adenoma from 2010 to 2018. We trained a partial model (only using pre-operative variables) and a full model (using all variables) to predict off-medication endocrine remission at six-month follow-up after surgery using multiple algorithms. The models were validated in 99 prospectively collected patients from a second campus and 52 patients from a third institution. RESULTS: C-statistic and the accuracy of the best partial model was 0.803 (95% CI 0.757-0.849) and 72.5% (95% CI 67.6-77.5%), respectively. C-statistic and the accuracy of the best full model was 0.888 (95% CI 0.861-0.914) and 80.3% (95% CI 77.5-83.1%), respectively. The c-statistics (and accuracy) of using only Knosp grade, total resection, or postoperative day 1 GH level as the single predictor were lower than our partial model or full model (p < 0.001). C-statistics remained similar in the prospective cohort (partial model 0.798, and full model 0.903) and in the external cohort (partial model 0.771, and full model 0.871). A web-based application integrated with the trained models was published at https://deepvep.shinyapps.io/Acropred/ . CONCLUSION: We developed and validated interpretable and applicable machine learning models to predict early endocrine remission after surgical resection of a GH-secreting pituitary adenoma. Predication accuracy of the trained models were better than those using single variables.
PURPOSE: Accurate prediction of postoperative remission is beneficial for effective patient-physician communication in acromegalicpatients. This study aims to train and validate machine learning prediction models for early endocrine remission of acromegalicpatients. METHODS: The training cohort included 833 patients with growth hormone (GH) secreting pituitary adenoma from 2010 to 2018. We trained a partial model (only using pre-operative variables) and a full model (using all variables) to predict off-medication endocrine remission at six-month follow-up after surgery using multiple algorithms. The models were validated in 99 prospectively collected patients from a second campus and 52 patients from a third institution. RESULTS: C-statistic and the accuracy of the best partial model was 0.803 (95% CI 0.757-0.849) and 72.5% (95% CI 67.6-77.5%), respectively. C-statistic and the accuracy of the best full model was 0.888 (95% CI 0.861-0.914) and 80.3% (95% CI 77.5-83.1%), respectively. The c-statistics (and accuracy) of using only Knosp grade, total resection, or postoperative day 1 GH level as the single predictor were lower than our partial model or full model (p < 0.001). C-statistics remained similar in the prospective cohort (partial model 0.798, and full model 0.903) and in the external cohort (partial model 0.771, and full model 0.871). A web-based application integrated with the trained models was published at https://deepvep.shinyapps.io/Acropred/ . CONCLUSION: We developed and validated interpretable and applicable machine learning models to predict early endocrine remission after surgical resection of a GH-secreting pituitary adenoma. Predication accuracy of the trained models were better than those using single variables.
Authors: Tyler Cardinal; Casey Collet; Michelle Wedemeyer; Peter A Singer; Martin Weiss; Gabriel Zada; John D Carmichael Journal: Front Endocrinol (Lausanne) Date: 2021-11-18 Impact factor: 5.555
Authors: Quinlan D Buchlak; Nazanin Esmaili; Christine Bennett; Yi Yuen Wang; James King; Tony Goldschlager Journal: PLoS One Date: 2022-07-27 Impact factor: 3.752
Authors: V Cambria; G Beccuti; N Prencipe; F Penner; V Gasco; F Gatti; M Romanisio; M Caputo; E Ghigo; F Zenga; S Grottoli Journal: J Endocrinol Invest Date: 2021-04-10 Impact factor: 4.256