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Purpose <p>&#xa0;While 3D radiographic parameters are increasingly used to assess adolescent idiopathic scoliosis (AIS), barriers to 3D imaging limit their routine use. 2D-to-3D prediction algorithms have been proposed as a substitute. This study sought to determine if a predicted 3D kyphosis, derived from 2D images, is a more robust predictor of FEV1 in patients with AIS than a traditional 2D analysis.</p> Methods <p>&#xa0;A retrospective, cross-sectional review of 259 AIS patients with surgical-range thoracic curves (&gt; 40°) was performed. We built two multivariate linear regression models to predict FEV1. Due to structural multicollinearity, the deformity measures were tested separately. Model A included&#xa0;Main Thoracic Cobb,&#xa0;BMI, and Age. Model B included&#xa0;<Emphasis FontCategory="NonProportional">Predicted 3D T5-T12 Kyphosis</Emphasis>,&#xa0;<Emphasis FontCategory="NonProportional">BMI</Emphasis>, and&#xa0;<Emphasis FontCategory="NonProportional">Age</Emphasis>. The models were compared using the Akaike Information Criterion (AIC) and adjusted.</p> Results <p>The cohort (87% female, mean age 15.7 ± 3.3&#xa0;years) presented with severe deformities (mean Main Thoracic Cobb 68.4° ± 16.5°) and widespread restrictive impairment (71%). Multivariate Analysis revealed that Model A, (2D Main Thoracic Cobb) was statistically superior to Model B (Predicted 3D T5 – T12 Kyphosis) with a lower AIC (−292.5 vs. −265.9) and a higher adjusted <i>R</i><sup><i>2</i></sup>&#xa0;(0.241 vs. 0.159). All factors were significant independent predictors.</p> Conclusion <p>&#xa0;The predicted 3D-based model was not superior, while a parsimonious 2D-based multivariate model including&#xa0;Main Thoracic Cobb,&#xa0;BMI, and Age explained a significantly larger proportion of the variance in FEV1. This specific 2D-to-3D prediction algorithm is an imperfect proxy and is not a valid substitute for true 3D imaging in predicting pulmonary risk.</p>

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Predicting pulmonary function in adolescent idiopathic scoliosis: is a simple 2D radiograph still the winner?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Rafael Garcia de Oliveira,
  • Tarcisio Eloy Pessoa de Barros Filho,
  • Alex de Oliveira Araujo,
  • Raphael Martus Marcon,
  • Arya Varthi,
  • Dominick Tuason,
  • Cicero Ricardo Gomes,
  • Alexandre Fogaça Cristante

摘要

Purpose

 While 3D radiographic parameters are increasingly used to assess adolescent idiopathic scoliosis (AIS), barriers to 3D imaging limit their routine use. 2D-to-3D prediction algorithms have been proposed as a substitute. This study sought to determine if a predicted 3D kyphosis, derived from 2D images, is a more robust predictor of FEV1 in patients with AIS than a traditional 2D analysis.

Methods

 A retrospective, cross-sectional review of 259 AIS patients with surgical-range thoracic curves (> 40°) was performed. We built two multivariate linear regression models to predict FEV1. Due to structural multicollinearity, the deformity measures were tested separately. Model A included Main Thoracic Cobb, BMI, and Age. Model B included Predicted 3D T5-T12 Kyphosis, BMI, and Age. The models were compared using the Akaike Information Criterion (AIC) and adjusted.

Results

The cohort (87% female, mean age 15.7 ± 3.3 years) presented with severe deformities (mean Main Thoracic Cobb 68.4° ± 16.5°) and widespread restrictive impairment (71%). Multivariate Analysis revealed that Model A, (2D Main Thoracic Cobb) was statistically superior to Model B (Predicted 3D T5 – T12 Kyphosis) with a lower AIC (−292.5 vs. −265.9) and a higher adjusted R2 (0.241 vs. 0.159). All factors were significant independent predictors.

Conclusion

 The predicted 3D-based model was not superior, while a parsimonious 2D-based multivariate model including Main Thoracic Cobb, BMI, and Age explained a significantly larger proportion of the variance in FEV1. This specific 2D-to-3D prediction algorithm is an imperfect proxy and is not a valid substitute for true 3D imaging in predicting pulmonary risk.