New Predictive Index for Survival in Symptomatic Spinal Metastases

Machine Learning in Spine Oncology: A Narrative Review

STUDY DESIGN

Narrative Review.

OBJECTIVE

Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is utilized to analyze and interpret medical imaging and classify tumors with incredible accuracy. The authors present a narrative review that specifically addresses the use of machine learning in spine oncology.

METHODS

This study was conducted in accordance with the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) methodology. A systematic review of the literature in the PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases since inception was performed to present all clinical studies with the search terms '[[Machine Learning] OR [Artificial Intelligence]] AND [[Spine Oncology] OR [Spine Cancer]]'. Data included studies that were extracted and included algorithms, training and test size, outcomes reported. Studies were separated based on the type of tumor investigated using the machine learning algorithms into primary, metastatic, both, and intradural. A minimum of 2 independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies.

RESULTS

Forty-five studies met inclusion criteria out of 480 references screened from the initial search results. Studies were grouped by metastatic, primary, and intradural tumors. The majority of ML studies relevant to spine oncology focused on utilizing a mixture of clinical and imaging features to risk stratify mortality and frailty. Overall, these studies showed that ML is a helpful tool in tumor detection, differentiation, segmentation, predicting survival, predicting readmission rates of patients with either primary, metastatic, or intradural spine tumors.

CONCLUSION

Specialized neural networks and deep learning algorithms have shown to be highly effective at predicting malignant probability and aid in diagnosis. ML algorithms can predict the risk of tumor recurrence or progression based on imaging and clinical features. Additionally, ML can optimize treatment planning, such as predicting radiotherapy dose distribution to the tumor and surrounding normal tissue or in surgical resection planning. It has the potential to significantly enhance the accuracy and efficiency of health care delivery, leading to improved patient outcomes.

Machine Learning in Spine Surgery: A Narrative Review

Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

Current Applications of Machine Learning in Spine: From Clinical View

STUDY DESIGN

Narrative review.

OBJECTIVES

This review aims to present current applications of machine learning (ML) in spine domain to clinicians.

METHODS

We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text.

RESULTS

Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost.

CONCLUSIONS

ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.

Is Open Surgery for Metastatic Spinal Cord Compression Secondary to Lung Cancer Really Beneficial? A Systematic Review

OBJECTIVE

Spinal cord compression secondary to metastatic epidural lung cancer generally has a worse prognosis when compared with other metastatic tumors. The purpose of this study was to determine the impact of open surgery on the survival time, ambulation status, and associated prognostic variables of these patients over the past 2 decades.

METHODS

We searched the PubMed, MEDLINE, and CENTRAL databases to identify patients with spinal cord compression secondary to metastatic lung cancer. We assessed the role of the following factors on survival outcome and ambulation recovery: Karnofsky Performance Scale, number of extraspinal bone metastases, number of metastases in the vertebrae, major internal organs involvement, vertebral location of spinal metastases, and neurologic status.

RESULTS

Our initial search yielded 367 articles, from which 11 studies met the eligibility criteria for a total of 395 patients. Significant correlations with the survival period and the recovery in ambulation were found in the prognostic score for the number of metastases in vertebrae (R = 0.74 and 0.88, respectively), and in the prognostic score for metastases to major internal organs (R = 0.82 and 0.81, respectively). A significant correlation with survival period was found in the numbers of extraspinal bone metastases (R = -0.82). The rates of major complications were comparable with those for open surgery for metastatic tumors of other origin.

CONCLUSIONS

Fewer vertebral metastases and less major internal organ involvement were associated with longer survival periods and greater ambulation recovery in patients undergoing open surgery for spinal cord compression secondary to metastatic lung cancer.