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Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods

医学 无线电技术 鉴定(生物学) 深度学习 放射科 脊柱肿瘤 磁共振成像 人工智能 生物 计算机科学 植物
作者
Shuo Duan,Guanmei Cao,Yichun Hua,Jun‐nan Hu,Yali Zheng,Fangfang Wu,Shuai Xu,Tianhua Rong,Baoge Liu
出处
期刊:World Neurosurgery [Elsevier BV]
卷期号:175: e823-e831 被引量:3
标识
DOI:10.1016/j.wneu.2023.04.029
摘要

To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods.We recruited and retrospectively reviewed 173 patients diagnosed with spinal metastases at two different centers between July 2018 and June 2021. Of these, 68 involved lung cancer and 105 were other types of cancer. They were assigned to an internal cohort of 149 patients, randomly divided into a training set and a validation set, and to an external cohort of 24 patients. All patients underwent CET1-MR imaging before surgery or biopsy. We developed two predictive algorithms: a DL model and a RAD model. We compared performance between models, and against human radiological assessment, via accuracy (ACC) and receiver operating characteristic (ROC) analyses. Furthermore, we analyzed the correlation between RAD and DL features.The DL model outperformed RAD model across the board, with ACC/ area under the receiver operating characteristic curve (AUC) values of 0.93/0.94 (DL) versus 0.84/0.93 (RAD) when applied to the training set from the internal cohort, 0.74/0.76 versus 0.72/0.75 when applied to the validation set, and 0.72/0.76 versus 0.69/0.72 when applied to the external test cohort. For the validation set, it also outperformed expert radiological assessment (ACC: 0.65, AUC: 0.68). We only found weak correlations between DL and RAD features.The DL algorithm successfully identified the origin of spinal metastases from pre-operative CET1-MR images, outperforming both RAD models and expert assessment by trained radiologists.
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