Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI.

Background: The application of deep learning has allowed significant progress in medical imaging. However, few studies have focused on the diagnosis of benign and malignant spinal tumors using medical imaging and age information at the patient level. This study proposes a multi-model weighted fusion framework (WFF) for benign and malignant diagnosis of spinal tumors based on magnetic resonance imaging (MRI) images and age information. Methods: The proposed WFF included a tumor detection model, sequence classification model, and age information statistic module based on sagittal MRI sequences obtained from 585 patients with spinal tumors (270 benign, 315 malignant) between January 2006 and December 2019 from the cooperative hospital. The experimental results of the WFF were compared with those of one radiologist (D1) and two spine surgeons (D2 and D3). Results: In the case of reference age information, the accuracy (ACC) (0.821) of WFF was higher than three doctors' ACC (D1: 0.686; D2: 0.736; D3: 0.636). Without age information, the ACC (0.800) of the WFF was also higher than that of the three doctors (D1: 0.750; D2: 0.664; D3:0.614). Conclusions: The proposed WFF is effective in the diagnosis of benign and malignant spinal tumors with complex histological types on MRI. [ABSTRACT FROM AUTHOR]