Chordomas are rare sarcomas arising from notochordal tissue and occur most commonly in the spine. The standard of care for chordomas without evidence of metastatic disease generally consists of en bloc resection followed by adjuvant radiotherapy. However, long-term (20-year) survival rates are approximately 30%. Chordomas are generally considered as chemo resistant. Therefore, systemic therapies have rarely been employed. Novel immunotherapies, including antibody therapy and tumor vaccines, have shown promise in early trials, leading to extended progression-free survival and symptom relief. However, the outcomes of larger trials using these vectors are heterogeneous. The aim of this review is to summarize novel chordoma treatments in immune-targeted therapies. The current merits, trial outcomes, and toxicities of these novel immune and targeted therapies, including those targeting vascular endothelial growth factor receptor (VEGFR) targets and the epidermal growth factor receptor (EGFR), will be discussed.

Increasing evidence demonstrates disparities among patients with differing insurance statuses in the field of spine surgery. However, no pooled analyses have performed a robust review characterizing differences in postoperative outcomes among patients with varying insurance types. A comprehensive literature search of the PUBMED, MEDLINE(R), ERIC, and EMBASE was performed for studies comparing postoperative outcomes in patients with private insurance versus government insurance. Pooled incidence rates and odds ratios were calculated for each outcome and meta-analyses were conducted for 3 perioperative events and 2 types of complications. In addition to pooled analysis, sub-analyses were performed for each outcome in specific government payer statuses. Thirty-eight studies (5,018,165 total patients) were included. Compared with patients with private insurance, patients with government insurance experienced greater risk of 90-day re-admission (OR 1.84, p<.0001), non-routine discharge (OR 4.40, p<.0001), extended LOS (OR 1.82, p<.0001), any postoperative complication (OR 1.61, p<.0001), and any medical complication (OR 1.93, p<.0001). These differences persisted across outcomes in sub-analyses comparing Medicare or Medicaid to private insurance. Similarly, across all examined outcomes, Medicare patients had a higher risk of experiencing an adverse event compared with non-Medicare patients. Compared with Medicaid patients, Medicare patients were only more likely to experience non-routine discharge (OR 2.68, p=.0007). Patients with government insurance experience greater likelihood of morbidity across several perioperative outcomes. Additionally, Medicare patients fare worse than non-Medicare patients across outcomes, potentially due to age-based discrimination. Based on these results, it is clear that directed measures should be taken to ensure that underinsured patients receive equal access to resources and quality care.

Metastasis to the spinal column is a common complication of malignancy, potentially causing pain and neurologic injury. An automated system to identify and refer patients with spinal metastases can help overcome barriers to timely treatment. We describe the training, optimization and validation of a natural language processing algorithm to identify the presence of vertebral metastasis and metastatic epidural cord compression (MECC) from radiology reports of spinal MRIs. Reports from patients with spine MRI studies performed between January 1, 2008 and April 14, 2019 were reviewed by a team of radiologists to assess for the presence of cancer and generate a labeled dataset for model training. Using regular expression, impression sections were extracted from the reports and converted to all lower-case letters with all nonalphabetic characters removed. The reports were then tokenized and vectorized using the doc2vec algorithm. These were then used to train a neural network to predict the likelihood of spinal tumor or MECC. For each report, the model provided a number from 0 to 1 corresponding to its impression. We then obtained 111 MRI reports from outside the test set, 92 manually labeled negative and 19 with MECC to test the model's performance. About 37,579 radiology reports were reviewed. About 36,676 were labeled negative, and 903 with MECC. We chose a cutoff of 0.02 as a positive result to optimize for a low false negative rate. At this threshold we found a 100% sensitivity rate with a low false positive rate of 2.2%. The NLP model described predicts the presence of spinal tumor and MECC in spine MRI reports with high accuracy. We plan to implement the algorithm into our EMR to allow for faster referral of these patients to appropriate specialists, allowing for reduced morbidity and increased survival.

Patient-specific technologies within the field of adult spinal deformity (ASD) aid surgeons in pre-surgical planning, accurately help identify anatomical landmarks, and can project optimal post-surgical sagittal alignment. This narrative review aims to discuss the current uses of patient-specific technologies in ASD and identify new innovations that may very soon be integrated into patient care. Pre-operatively, machine learning or artificial intelligence helps surgeons to simulate post-operative alignment and provide information for the 3D-printing of pre-contoured rods and patient-specific cages. Intraoperatively, robotic surgery and intraoperative guides allow for more accurate positioning of implants. Implant materials are being developed to allow for better osseointegration and patient outcome monitoring. Despite the significant promise of these technologies, work still needs to be performed to ensure their accuracy, safety, and cost efficacy.