Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI

Spinal metastases are a common occurrence, and many patients do not have a clear history of primary tumors when diagnosed with spinal metastases. Patients with cancer of unknown primary often undergo comprehensive and invasive diagnostic work-ups, which can be expensive and time-consuming. To narrow down the search for primary tumor sites in spinal metastases patients with cancer of unknown primary, an artificial intelligence model that can assess tumor origin on MRI may be beneficial. Our work builds upon the considerable interest in investigating the feasibility of using a ResNet-50 convolutional neural network model based on MRI in predicting primary tumor sites in spinal metastases. In this preliminary study, for the 5-class classification of spinal metastases originating from the lung, kidney, prostate, and mammary or thyroid glands, the AUC-ROC and top-1 accuracy of the ResNet-50 model were 0.77 and 52.97%, respectively. Therefore, we believe that the ResNet-50 CNN model for predicting primary tumor sites in spinal metastases using MRI has the potential to help prioritize examinations and treatments in cases of unknown primary for radiologists and oncologists. We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fatsuppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing. A deep learning model using ResNet-50 CNN was trained to classify primary tumor sites. Top-1 accuracy, precision, sensitivity, area under the curve for the receiver-operating characteristic (AUC-ROC), and F1 score were considered as the evaluation metrics. A total of 295 spinal metastases patients (mean age +/- standard deviation, l59.9 years +/- 10.9; 154 men) were evaluated. Included metastases originated from lung cancer (n = 142), kidney cancer (n = 50), mammary cancer (n = 41), thyroid cancer (n = 34), and prostate cancer (n = 28). For 5-class classification, AUC-ROC and top-1 accuracy were 0.77 and 52.97%, respectively. Additionally, AUC-ROC for different sequence subsets ranged between 0.70 (for T2-weighted) and 0.74 (for fat-suppressed T2-weighted). Our developed ResNet-50 CNN model for predicting primary tumor sites in spinal metastases at MRI has the potential to help prioritize the examinations and treatments in case of unknown primary for radiologists and oncologists.