Purpose: This section presents the significance and aims, stating the broad impact and the rationale or motivation of the work, including potentially some background.

Approach: This section briefly describes the materials and methods, including the statistical analyses, used in the research.

Results: This section provides a core summary of the findings from the work, including study numbers, quantitative analyses, or discoveries.

Conclusions: This section summarizes and interprets the approach and findings from the work, stating also the importance or impact of the findings.

Purpose: Data-intensive modeling could provide insight on the broad variability in outcomes in spine surgery. Previous studies were limited to analysis of demographic and clinical characteristics. We report an analytic framework called “SpineCloud” that incorporates quantitative features extracted from perioperative images to predict spine surgery outcome.

Approach: A retrospective study was conducted in which patient demographics, imaging, and outcome data were collected. Image features were automatically computed from perioperative CT. Postoperative 3- and 12-month functional and pain outcomes were analyzed in terms of improvement relative to the preoperative state. A boosted decision tree classifier was trained to predict outcome using demographic and image features as predictor variables. Predictions were computed based on SpineCloud and conventional demographic models, and features associated with poor outcome were identified from weighting terms evident in the boosted tree.

Results: Neither approach was predictive of 3- or 12-month outcomes based on preoperative data alone in the current, preliminary study. However, SpineCloud predictions incorporating image features obtained during and immediately following surgery (i.e., intraoperative and immediate postoperative images) exhibited significant improvement in area under the receiver operating characteristic (AUC): $\mathrm{AUC}=0.72$ (${\mathrm{CI}}_{95}=0.59$ to 0.83) at 3 months and $\mathrm{AUC}=0.69$ (${\mathrm{CI}}_{95}=0.55$ to 0.82) at 12 months.

Conclusions: Predictive modeling of lumbar spine surgery outcomes was improved by incorporation of image-based features compared to analysis based on conventional demographic data. The SpineCloud framework could improve understanding of factors underlying outcome variability and warrants further investigation and validation in a larger patient cohort.