Yanghua Fan1, Xulei Huo1, Xiaojie Li1, Liang Wang2, Zhen Wu3. 1. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China. 2. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China. Electronic address: ttyywangliang@163.com. 3. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China. Electronic address: wz_ttyy@163.com.
Abstract
BACKGROUND: Preoperative differential diagnosis of pineal region tumor can greatly assist clinical decision-making and avoid economic costs and complications caused by unnecessary radiotherapy or invasive procedures. The present study was performed to pre-operatively distinguish pineal region germinoma and pinealoblastoma using a clinicoradiomic model by incorporating radiomic and clinical features. METHODS: 134 pineal region tumor patients (germinoma, 69; pinealoblastoma, 65) with complete clinic-radiological and histopathological data from Tiantan hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, then the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to select the clinical features, and a clinicoradiomic model incorporating the fusion radiomic model and selected clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated. RESULTS: Seven significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.920 and 0.880 in the training and validation sets, respectively. A clinicoradiomic model that incorporated the radiomic model and four selected clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.950 in the training set and 0.940 in the validation set. The analysis of the decision curve showed that the radiomic model and clinicoradiomic model were clinically useful for patients with pineal region tumor. CONCLUSIONS: Our clinicoradiomic model showed great performance and high sensitivity in the differential diagnosis of germinoma and pinealoblastoma, and could contribute to non-invasive development of individualized diagnosis and treatment of patients with pineal region tumor.
BACKGROUND: Preoperative differential diagnosis of pineal region tumor can greatly assist clinical decision-making and avoid economic costs and complications caused by unnecessary radiotherapy or invasive procedures. The present study was performed to pre-operatively distinguish pineal region germinoma and pinealoblastoma using a clinicoradiomic model by incorporating radiomic and clinical features. METHODS: 134 pineal region tumor patients (germinoma, 69; pinealoblastoma, 65) with complete clinic-radiological and histopathological data from Tiantan hospital were retrospectively reviewed and randomly assigned to training and validation sets. Radiomic features were extracted from MR images, then the elastic net and recursive feature elimination algorithms were applied to select radiomic features for constructing a fusion radiomic model. Subsequently, multivariable logistic regression analysis was used to select the clinical features, and a clinicoradiomic model incorporating the fusion radiomic model and selected clinical features was constructed for individual predictions. The calibration, discriminating capacity, and clinical usefulness were also evaluated. RESULTS: Seven significant radiomic features were selected to construct a fusion radiomic model that achieved an area under the curve (AUC) value of 0.920 and 0.880 in the training and validation sets, respectively. A clinicoradiomic model that incorporated the radiomic model and four selected clinical features was constructed and showed good discrimination and calibration, with an AUC of 0.950 in the training set and 0.940 in the validation set. The analysis of the decision curve showed that the radiomic model and clinicoradiomic model were clinically useful for patients with pineal region tumor. CONCLUSIONS: Our clinicoradiomic model showed great performance and high sensitivity in the differential diagnosis of germinoma and pinealoblastoma, and could contribute to non-invasive development of individualized diagnosis and treatment of patients with pineal region tumor.