SpineCloud: image analytics for predictive modeling of spine surgery outcomes.

Lumbar spine surgery suffers from high variability in patient outcomes,1–9 heterogeneous treatment patterns, and a high frequency of undesirable outcomes, such as revision surgery,8 failed back surgery,7 and recurrent lumbar disk herniations.10 This heterogeneity could be attributed to variable patient characteristics, surgeon preferences, hospital and surgeon volume, and complexity of the intervention.11–13 However, current understanding of the contributing factors that determine the outcome variability remains ambiguous and underexplored.14–16 In this context, models to predict patient outcomes provide a valuable resource for patient selection, treatment optimization, and rehabilitative pathway design.11,17–19 Previous studies that investigated variability in pain and functional outcomes were limited to analyzing patient demographic and preoperative clinical characteristics as the underlying factors.11,12,19 While such factors [e.g., age, body mass index (BMI), and smoking status] partially contribute to the outcome variability, additional surgery-specific features could further illuminate and improve the utility of prediction models. Some studies have tended toward development of increasingly complex modeling techniques (e.g., deep neural networks14,20) to improve accuracy, particularly for scenarios in which the predictor variables arise from a limited number of available preoperative characteristics (i.e., demographic data). In spite of devising complex machine learning algorithms, model generalizability is challenging to achieve with a limited set of predictor variables that may not fully explain the variability in clinical outcomes. Even though advanced learning methods perform better under conditions of sparse data and/or limited input variables, increasingly complex models also introduce challenges to explainability in the predictions and the ability to identify actionable features. A large number of images are routinely acquired for perioperative diagnostic and therapeutic evaluation purposes before, during, and after spine surgery. These images play a vital role in diagnosing spinal pathology, determining surgery indications, ensuring safe intervention via intraoperative guidance, and assessing the surgical product in postoperative and follow-up visits. Such images capture distinctive features related to anatomy, pathology, and the changes effected during surgery. In fact, qualitative radiological assessment has been an important basis for outcome measurement prior to (or complementary to) patient-reported outcomes (PROs). Thus, incorporating automatically derived image features in addition to previously investigated patient characteristics could improve the accuracy and utility of prediction models. Consequently, several studies have evaluated the relationship between individual measures manually obtained from images and patient outcomes in lumbar, thoracic, and cervical spine surgery. Example measures include Modic changes,21,22 signal intensity-based metrics,23–25 and the lowest instrumented vertebra take-off angle.26,27 In contrast to manually obtained features, recent advances facilitate the automatic extraction of several anatomical features from spine imaging.28–31 Example image-based features include automatic labeling of vertebral levels, global spinal alignment, endplate angles, intervertebral space, number of levels treated, and measurements of surgical instrumentation. To our knowledge, there are no previous studies evaluating statistical models to predict patient outcomes in lumbar spine surgery that use automatically derived quantitative measurements from radiological images. The objective of this study was to develop and validate a data-analytic framework referred to as “SpineCloud,” which includes quantitative features automatically derived from spine imaging in addition to patient demographic and clinical characteristics to predict function and pain improvement after lumbar spine surgery. A learning algorithm was trained and validated to make predictions by combining disparate features based on demographics and image analytics. In this work, we used a boosted decision tree algorithm that inherently handles ordinal and continuous variables in a common predictor variable space. The prediction task was modeled as a binary classification to estimate functional improvement or nonimprovement at 3- and 12-month intervals after surgery.