Wang Hongfei, Zhang Teng, Cheung Kenneth Man-Chee, Shea Graham Ka-Hon
Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong.
EClinicalMedicine. 2021 Nov 29;42:101220. doi: 10.1016/j.eclinm.2021.101220. eCollection 2021 Dec.
Prediction of curve progression risk in adolescent idiopathic scoliosis (AIS) remains elusive. Prior studies have revealed the potential for three-dimensional (3D) morphological parameters to prognosticate progression, but these require specialized biplanar imaging equipment and labor-intensive software reconstruction. This study aimed to formulate a deep learning model with standing posteroanterior (PA) X-rays at first clinic visit to differentiate between progressive (P) and non-progressive (NP) curves.
For this retrospective cohort study, we identified patients presenting with AIS between October 2015 to April 2020 at our tertiary referral centre. Patients with mild curvatures (11 - 30) who were skeletally immature (Risser sign of ≤2) were recruited. Patients receiving biplanar X-ray radiographs (EOS™) were divided between a training-cross-validation cohort (328 patients) and independent testing cohort (110 patients). Another 52 patients receiving standard PA spinal X-rays were recruited for cross-platform validation. Following 3D reconstruction, we designated the major curve apex upon PA X-rays as the region of interest (ROI) for machine learning. A self-attentive capsule network was constructed to differentiate between curves manifesting P and NP trajectories. A two-stage transfer learning strategy was introduced to pre-train and fine-tune the model. Model performance (accuracy, sensitivity, specificity) was compared to that of traditional convolutional neural networks (CNNs) and a clinical parameter-based logistic regression model.
3D reconstruction identified that apical rotation of the major curve and torsion were significantly different between P and NP curve trajectories. Our predictive model utilizing an ROI centered on the major curve apex achieved an accuracy of 76.6%, a sensitivity of 75.2% and a specificity of 80.2% upon independent testing. Cross-platform performance upon standard standing PA X-rays yielded an accuracy of 77.1%, a sensitivity of 73.5% and a specificity of 81.0%. Errors in prediction occurred when the degree of apical rotation / torsion was discrepant from that of the subsequent curve trajectory but could be rectified by considering serial X-rays. Performance was superior to that of traditional CNNs as well as clinical parameter-based regression models.
This is the first report of automated prediction of AIS curve progression based on radiomics and deep learning, towards directing treatment strategy at first visit. Patients predicted to be at-risk of progression may be counselled to receive early bracing with enforcement of treatment compliance. Over-treatment may be avoided in curves deemed to be non-progressive. Results need to be consolidated in larger sample populations of different ethnicities.
The Society for the Relief of Disabled Children (SRDC)
青少年特发性脊柱侧凸（AIS）曲线进展风险的预测仍然难以捉摸。先前的研究已经揭示了三维（3D）形态学参数预测进展的潜力，但这些需要专门的双平面成像设备和劳动密集型的软件重建。本研究旨在建立一个基于首次门诊站立后前位（PA）X线片的深度学习模型，以区分进展性（P）和非进展性（NP）曲线。
对于这项回顾性队列研究，我们确定了2015年10月至2020年4月在我们的三级转诊中心就诊的AIS患者。招募骨骼未成熟（Risser征≤2）且侧弯较轻（11-30°）的患者。接受双平面X线片（EOS™）检查的患者被分为训练-交叉验证队列（328例患者）和独立测试队列（110例患者）。另外招募了52例接受标准PA脊柱X线检查的患者进行跨平台验证。在进行3D重建后，我们将PA X线片上的主曲线顶点指定为机器学习的感兴趣区域（ROI）。构建了一个自注意力胶囊网络来区分表现为P和NP轨迹的曲线。引入了两阶段迁移学习策略来预训练和微调模型。将模型性能（准确性、敏感性、特异性）与传统卷积神经网络（CNN）和基于临床参数的逻辑回归模型进行比较。
3D重建显示，主曲线的顶点旋转和扭转在P和NP曲线轨迹之间存在显著差异。我们利用以主曲线顶点为中心的ROI的预测模型在独立测试中的准确率为76.6%，敏感性为75.2%，特异性为80.2%。标准站立PA X线片的跨平台性能准确率为77.1%，敏感性为73.5%，特异性为81.0%。当顶点旋转/扭转程度与后续曲线轨迹不一致时会出现预测错误，但可以通过考虑系列X线片来纠正。性能优于传统的CNN以及基于临床参数的回归模型。
这是第一份基于放射组学和深度学习对AIS曲线进展进行自动预测的报告，旨在指导首次就诊时的治疗策略。对于预计有进展风险的患者，可以建议其早期佩戴支具并加强治疗依从性。对于被认为是非进展性的曲线，可以避免过度治疗。结果需要在不同种族的更大样本群体中进行巩固。
残疾儿童救济协会（SRDC）
