■脊柱转移是恶性肿瘤的常见并发症,可能引起疼痛和神经损伤。识别和转诊脊柱转移患者的自动化系统可以帮助克服及时治疗的障碍。我们描述了训练,优化和验证自然语言处理算法,以从脊柱MRI的放射学报告中识别椎体转移和转移性硬膜外索压迫(MECC)的存在。
■一组放射科医师审查了2008年1月1日至2019年4月14日进行的脊柱MRI研究患者的报告,以评估癌症的存在并生成标记的数据集用于模型训练。使用正则表达式,从报告中提取印象部分,并将其转换为所有小写字母,同时删除所有非字母字符.然后使用doc2vec算法对报告进行标记化和矢量化。然后将它们用于训练神经网络以预测脊柱肿瘤或MECC的可能性。对于每个报告,该模型提供了一个从0到1的数字,对应于它的印象。然后我们从测试集外获得了111份MRI报告,92手动标记为阴性,19使用MECC测试模型的性能。
■审查了约37,579份放射学报告。大约36,676人被标记为阴性,和903与MECC。我们选择0.02的截止值作为阳性结果以优化低假阴性率。在此阈值下,我们发现100%的灵敏度和2.2%的低假阳性率。
■所描述的NLP模型以高精度预测了脊柱MRI报告中脊柱肿瘤和MECC的存在。我们计划在我们的EMR中实施该算法,以便更快地将这些患者转诊给适当的专家,允许降低发病率和增加生存率。
UNASSIGNED: Metastasis to the spinal column is a common complication of malignancy, potentially causing pain and neurologic injury. An automated system to identify and refer patients with spinal metastases can help overcome barriers to timely treatment. We describe the training, optimization and validation of a natural language processing algorithm to identify the presence of vertebral metastasis and metastatic epidural cord compression (MECC) from radiology reports of spinal MRIs.
UNASSIGNED: Reports from patients with spine
MRI studies performed between January 1, 2008 and April 14, 2019 were reviewed by a team of radiologists to assess for the presence of cancer and generate a labeled dataset for model training. Using regular expression, impression sections were extracted from the
reports and converted to all lower-
case letters with all nonalphabetic characters removed. The reports were then tokenized and vectorized using the doc2vec algorithm. These were then used to train a neural network to predict the likelihood of spinal tumor or MECC. For each report, the model provided a number from 0 to 1 corresponding to its impression. We then obtained 111
MRI reports from outside the test set, 92 manually labeled negative and 19 with MECC to test the model\'s performance.
UNASSIGNED: About 37,579 radiology reports were reviewed. About 36,676 were labeled negative, and 903 with MECC. We chose a cutoff of 0.02 as a positive result to optimize for a low false negative rate. At this threshold we found a 100% sensitivity rate with a low false positive rate of 2.2%.
UNASSIGNED: The NLP model described predicts the presence of spinal tumor and MECC in spine
MRI reports with high accuracy. We plan to implement the algorithm into our EMR to allow for faster referral of these patients to appropriate specialists, allowing for reduced morbidity and increased survival.