Radiomics-powered radiographic image analysis for enhanced mechanical complications prediction and surgical planning in adult spine deformity

IF 5.1 1区医学 Q1 CLINICAL NEUROLOGY

Spine Journal Pub Date : 2026-03-01 Epub Date: 2025-10-10 DOI:10.1016/j.spinee.2025.10.011

Ferran Pellisé MD, PhD , Sleiman Haddad MD, PhD, FRCS , Susana Núñez-Pereira MD, PhD , Caglar Yilgor MD , Anika Pupak PhD , Manuel Ramírez-Valencia MD , Javier Pizones MD, PhD , Ahmet Alanay MD , Ibrahim Obeid MD , Frank S. Kleinstueck MD , Fabio Galbusera PhD , Oleguer Sagarra PhD , European Spine Study Group

{"title":"Radiomics-powered radiographic image analysis for enhanced mechanical complications prediction and surgical planning in adult spine deformity","authors":"Ferran Pellisé MD, PhD , Sleiman Haddad MD, PhD, FRCS , Susana Núñez-Pereira MD, PhD , Caglar Yilgor MD , Anika Pupak PhD , Manuel Ramírez-Valencia MD , Javier Pizones MD, PhD , Ahmet Alanay MD , Ibrahim Obeid MD , Frank S. Kleinstueck MD , Fabio Galbusera PhD , Oleguer Sagarra PhD , European Spine Study Group","doi":"10.1016/j.spinee.2025.10.011","DOIUrl":null,"url":null,"abstract":"<div><h3>BACKGROUND CONTEXT</h3><div>Radiomics, a technique employing machine learning (ML) to extract quantitative features from processed radiographic images, holds promise for improving clinical prediction models. It offers the potential to comprehensively characterize spinal shape and alignment. We hypothesized that processed image (PrIm) algorithms outperform traditional radiographic measurements (TRM) and scores in predicting postoperative mechanical complications (MC) in adult spinal deformity (ASD).</div></div><div><h3>PURPOSE</h3><div>The aim was to compare the performance of PrIm algorithms to TRM-GAP score in the prediction of MC in ASD patients.</div></div><div><h3>STUDY DESIGN/SETTING</h3><div>An AI-leveraged retrospective analysis was conducted using data from a prospective international multicenter database dedicated to ASD.</div></div><div><h3>PATIENT SAMPLE</h3><div>The study focused on ASD patients aged 18 or older who were surgically treated and who had a minimum follow-up period of two years, with complete preoperative, 6-week and 2-year follow-up.</div></div><div><h3>OUTCOME MEASURES</h3><div>Major mechanical complications such as rod fractures, pseudarthrosis, or junctional kyphosis or failure.</div></div><div><h3>METHODS</h3><div>Processed full-spine standing radiographic images were analyzed using an automatic vertebral centroid generation algorithm to map posteroanterior (PA) and lateral spinal shape. Distances and angles between each vertebra and the pelvic centroid were automatically obtained. Machine learning models were constructed using Catboost, combining nonradiographic variables (Non-R: demographic, PROMS, surgical), TRM + GAP score, and PrIm features. AUC-ROC, sensitivity, specificity, and Brier score (0= perfectly calibrated / 1=poor) were used to evaluate prediction accuracy. SHapley Additive exPlanations (SHAP) values were employed to assess variable contributions and address overfitting/noise.</div></div><div><h3>RESULTS</h3><div>A total of 690 patients (81% female, 52±19 years, 9.7±3.9 levels, 18.6% 3CO, 43.5% pelvic fixation, 37.5% MC) were analyzed. The Non-R + PrIm model outperformed the present “Gold Standard” model (Non-R + TRM-GAP): AUC-ROC 0.75 vs 0.71 (p=.009), accuracy 0.72 vs 0.62 (p<.001), specificity 0.79 vs 0.60 (p<.001), sensitivity 0.52 vs 0.70 (p<.001), and Brier score 0.17 vs 0.22 (p<.001). Adding TRM and GAP score to Non-R + PrIm model did not improve model estimates. SHAP adjusted models summed 35 variables and revealed PrIm's superior predictive importance, contributing 65.7% to the model compared to Non-R (Surgical factors 16.1%, PROMS 11.3% and demographics 6.9%). Personalized SHAP decision plots identified the most critical vertebral centroids associated with MC risk both globally and individually.</div></div><div><h3>CONCLUSION</h3><div>Radiomics powered by full-spine processed radiographic images enable the most accurate predictive models for MC in ASD. This novel approach offers clinicians a powerful and time-efficient tool for personalized surgical planning, ultimately enhancing ASD surgical outcomes.</div></div>","PeriodicalId":49484,"journal":{"name":"Spine Journal","volume":"26 3","pages":"Pages 578-585"},"PeriodicalIF":5.1000,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spine Journal","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1529943025008198","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/10/10 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

BACKGROUND CONTEXT

Radiomics, a technique employing machine learning (ML) to extract quantitative features from processed radiographic images, holds promise for improving clinical prediction models. It offers the potential to comprehensively characterize spinal shape and alignment. We hypothesized that processed image (PrIm) algorithms outperform traditional radiographic measurements (TRM) and scores in predicting postoperative mechanical complications (MC) in adult spinal deformity (ASD).

PURPOSE

The aim was to compare the performance of PrIm algorithms to TRM-GAP score in the prediction of MC in ASD patients.

STUDY DESIGN/SETTING

An AI-leveraged retrospective analysis was conducted using data from a prospective international multicenter database dedicated to ASD.

PATIENT SAMPLE

The study focused on ASD patients aged 18 or older who were surgically treated and who had a minimum follow-up period of two years, with complete preoperative, 6-week and 2-year follow-up.

OUTCOME MEASURES

Major mechanical complications such as rod fractures, pseudarthrosis, or junctional kyphosis or failure.

METHODS

Processed full-spine standing radiographic images were analyzed using an automatic vertebral centroid generation algorithm to map posteroanterior (PA) and lateral spinal shape. Distances and angles between each vertebra and the pelvic centroid were automatically obtained. Machine learning models were constructed using Catboost, combining nonradiographic variables (Non-R: demographic, PROMS, surgical), TRM + GAP score, and PrIm features. AUC-ROC, sensitivity, specificity, and Brier score (0= perfectly calibrated / 1=poor) were used to evaluate prediction accuracy. SHapley Additive exPlanations (SHAP) values were employed to assess variable contributions and address overfitting/noise.

RESULTS

A total of 690 patients (81% female, 52±19 years, 9.7±3.9 levels, 18.6% 3CO, 43.5% pelvic fixation, 37.5% MC) were analyzed. The Non-R + PrIm model outperformed the present “Gold Standard” model (Non-R + TRM-GAP): AUC-ROC 0.75 vs 0.71 (p=.009), accuracy 0.72 vs 0.62 (p<.001), specificity 0.79 vs 0.60 (p<.001), sensitivity 0.52 vs 0.70 (p<.001), and Brier score 0.17 vs 0.22 (p<.001). Adding TRM and GAP score to Non-R + PrIm model did not improve model estimates. SHAP adjusted models summed 35 variables and revealed PrIm's superior predictive importance, contributing 65.7% to the model compared to Non-R (Surgical factors 16.1%, PROMS 11.3% and demographics 6.9%). Personalized SHAP decision plots identified the most critical vertebral centroids associated with MC risk both globally and individually.

CONCLUSION

Radiomics powered by full-spine processed radiographic images enable the most accurate predictive models for MC in ASD. This novel approach offers clinicians a powerful and time-efficient tool for personalized surgical planning, ultimately enhancing ASD surgical outcomes.

查看原文本刊更多论文

放射组学支持的影像学分析增强成人脊柱畸形机械并发症预测和手术计划。

背景背景：放射组学是一种利用机器学习（ML）从处理过的放射图像中提取定量特征的技术，有望改善临床预测模型。它提供了全面表征脊柱形状和对齐的潜力。我们假设处理图像（PrIm）算法在预测成人脊柱畸形（ASD）术后机械并发症（MC）方面优于传统放射测量（TRM）和评分。目的：比较PrIm算法与TRM-GAP评分在预测ASD患者MC方面的性能。研究设计/设置：使用前瞻性国际多中心ASD数据库的数据进行人工智能回顾性分析。患者样本：该研究集中于18岁及以上接受手术治疗的ASD患者，随访时间至少为2年，包括术前、6周和2年随访。结局指标：主要机械并发症，如杆骨折、假关节或关节后凸或功能衰竭。方法：使用自动椎体质心生成算法对处理后的全脊柱站立x线图像进行分析，以绘制脊柱后前位（PA）和侧位形状。每个椎体和骨盆质心之间的距离和角度自动获得。使用Catboost构建机器学习模型，结合非放射学变量（Non-R：人口统计学、PROMS、外科）、TRM + GAP评分和PrIm特征。采用AUC-ROC、敏感性、特异性和Brier评分（0=完全校准/ 1=差）评价预测准确性。SHapley加性解释（SHAP）值用于评估变量贡献和处理过拟合/噪声。结果：共分析690例患者（女性81%，52±19岁，9.7±3.9级，3CO 18.6%，骨盆固定43.5%,MC 37.5%）。Non-R + PrIm模型优于目前的“金标准”模型（Non-R + TRM-GAP）： AUC-ROC为0.75 vs 0.71 (p=0.009)，准确率为0.72 vs 0.62(结论：由全脊柱处理的放射影像提供支持的放射组学能够最准确地预测ASD中的MC。这种新颖的方法为临床医生提供了一种强大而高效的个性化手术计划工具，最终提高了ASD的手术效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Spine Journal 医学-临床神经学

CiteScore

8.20

自引率

6.70%

发文量

680

审稿时长

13.1 weeks

期刊介绍： The Spine Journal, the official journal of the North American Spine Society, is an international and multidisciplinary journal that publishes original, peer-reviewed articles on research and treatment related to the spine and spine care, including basic science and clinical investigations. It is a condition of publication that manuscripts submitted to The Spine Journal have not been published, and will not be simultaneously submitted or published elsewhere. The Spine Journal also publishes major reviews of specific topics by acknowledged authorities, technical notes, teaching editorials, and other special features, Letters to the Editor-in-Chief are encouraged.