Radiomics classification of fresh and old vertebral compression fractures: Impact of compression grade and morphology on diagnostic performance

IF 3.2 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Ke Zhang , Yunfei Zhu , Chaoran Liu , Wenjuan Li , Jielin Pan , Ximeng Li , Shaolin Li , Guobin Hong
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引用次数: 0

Abstract

Objectives

To develop a radiomics model for identifying fresh or old vertebral compression fractures (VCFs) from CT images, thereby assisting physicians in making more effective decisions.

Methods

Patients with VCFs who underwent both CT and MRI within one week were retrospectively enrolled from June 2018 to February 2023. VCFs were categorized as subgroups according to compression grades (mild, moderate or severe) or morphology types (wedge-shaped, biconcave or crush). For each subgroup, a radiomics classification model was built based on 1834 radiomics features extracted from the training dataset. And the diagnostic performance was evaluated in the testing dataset using receiver operating characteristic (ROC).

Results

The radiomics model trained on the entire cohort achieved an area under ROC curve (AUC) of 0.824. A nomogram integrating radiomics feature and clinical characteristics reached an AUC of 0.897. We graded the degree of compression as mild, moderate, and severe VCFs. The best performance was observed in the severe subgroup, with an AUC of 0.927, while the AUCs for mild and moderate were 0.633 and 0.774, respectively. In the morphology subgroups, the crush-type VCFs demonstrated the best performance, achieving an AUC of 0.909, while the AUCs for wedge-shaped and biconcave were 0.841 and 0.897, respectively.

Conclusion

The radiomics models effectively distinguished fresh and old VCFs, performing better when combined with clinical features. However, different grades and morphologies of VCFs showed distinct CT imaging patterns that could impact model performance, warranting consideration in future research and clinical applications.
新老椎体压缩性骨折的放射组学分类:压缩等级和形态学对诊断性能的影响
目的建立一种从CT图像中识别新发或旧发椎体压缩性骨折(VCFs)的放射组学模型,从而帮助医生做出更有效的决策。方法回顾性纳入2018年6月至2023年2月期间一周内接受CT和MRI检查的vcf患者。根据压缩等级(轻度、中度或重度)或形态类型(楔形、双凹或挤压)将vcf分为亚组。对于每个子组,基于从训练数据集中提取的1834个放射组学特征构建放射组学分类模型。采用受试者工作特征(receiver operating characteristic, ROC)对测试数据集的诊断性能进行评价。结果对整个队列进行训练的放射组学模型的ROC曲线下面积(AUC)为0.824。结合放射组学特征和临床特征的nomogram AUC为0.897。我们将压迫程度分为轻度、中度和重度vcf。重度亚组的AUC为0.927,轻度和中度亚组的AUC分别为0.633和0.774。在形态亚组中,挤压型vcf表现最佳,AUC为0.909,楔形和双凹型的AUC分别为0.841和0.897。结论放射组学模型能有效区分新旧vcf,结合临床表现效果更好。然而,不同级别和形态的vcf表现出不同的CT成像模式,这可能会影响模型的性能,值得在未来的研究和临床应用中考虑。
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来源期刊
CiteScore
6.70
自引率
3.00%
发文量
398
审稿时长
42 days
期刊介绍: European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.
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