Radiomics-Powered Radiographic Image Analysis for Enhanced Mechanical Complications Prediction and Surgical Planning in Adult Spine Deformity

Yılgör İ. Ç., Alanay A.

Eurospine, Vienna, Avusturya, 2 - 04 Ekim 2024, cilt.4, ss.3, (Özet Bildiri)

  • Yayın Türü: Bildiri / Özet Bildiri
  • Cilt numarası: 4
  • Doi Numarası: 10.1016/j.bas.2024.103228
  • Basıldığı Şehir: Vienna
  • Basıldığı Ülke: Avusturya
  • Sayfa Sayıları: ss.3
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Evet

Özet

Brain and Spine 4 (2024) 103227


Radiomics-Powered Radiographic Image Analysis for Enhanced

Mechanical Complications Prediction and Surgical Planning in Adult

Spine Deformity


F. Pellise 1, S. Haddad 2, S. Nú~ nez-Pereira 2, C. Yilgor 3, M. Barcheni 2, A. Pupak 2

,

M. Ramirez 2, J. Pizones 4, A. Alanay 3, I. Obeid 5, F. Kleinstück 6, F. Galbusera 6

,

O. Sagarra 7, E.S.S.G. Essg 2

.

1 Spine Unit Orthopaedic Department, Vall d'Hebron

University Hospital, Barcelona, Spain2 Spine Research Unit, Vall d'Hebron

University Hospital, Barcelona, Spain3 Spine Unit Orthopaedic Department,

Acibadem Mehmet Ali Aydinlar University,_

Istanbul, Türkiye4 Spine Unit

Orthopaedic Department, La Paz University Hospital, Madrid, Spain5 Spine Unit

Orthopaedic Department, H^ opital Pellegrin Bordeaux, Bordeaux,

France6 Department of Spine Surgery, Schulthess Klinik, Zürich, Switzerland7 Data

Research, Dribia Data Research, Barcelona, Spain


Introduction: Radiomics, a technique employing machine learning 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 Hypothezise that Processed image

(PrIm) algorithms outperform traditional radiographic measurements (TRM) in

predicting postoperative mechanical complications (MC) in adult spinal defor-

mity (ASD).

Materials and Methods: Retrospective study analyzing prospective preop, 6-

week and 2-year follow-up data of surgical ASD patients. Processed full-spine

standing radiographic images were analyzed using an automatic vertebral and

pelvic centroid generation algorithm to map postero-anterior and lateral spinal

shape. Distances and angles between each vertebral centroid and pelvis were

automatically obtained. Machine learning models were constructed (Catboost),

combining non-radiographic variables (Non-R: demographic, PROMS, surgical),

TRM (Cobb, C7SVL, PI, PT, SS, LL, TK, GT, SVA, GAP score) and PrIm features.

AUC-ROC, sensitivity, speci city, and Brier score (0¼ perfectly calibrated /

1¼poor) were used to evaluate prediction accuracy. SHAP values were employed

to assess variable contributions and address over tting/noise.

Results: 690 patients (81% female, 52 19 years, 9.7 3.9 levels, 18.6% 3CO,

43.5% pelvic xation, 24.3% MC) were analyzed. The PrIm + Non-R model

outperformed the present“Gold Standard” model (TRM + Non-R): AUC-ROC

0.75 vs 0.71 (p¼0.009), accuracy 0.72 vs 0.62 (p<0.001), speci city 0.79 vs 0.60

(p<0.001), sensitivity 0.52 vs 0.70 (p<0.001), and Brier score 0.17 vs 0.21

(p<0.001). Adding TRM to PrIm+Non-R model did not improve model estimates

(Fig). 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 identi ed 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 mechanical complications in ASD.

This novel approach offers clinicians a powerful and time-ef cient tool for

personalized surgical planning.

AUC-ROC versus the number of features sorted by importance.