The study of the tumors that develop in the spine and peripheral nerves, including schwannomas, neurofibroma, meningioma, chordoma, sarcoma, ependymoma, and astrocytoma, among others, is a critical endeavor in our lab. Besides following up on the patients with such tumors in Johns Hopkins Medical institutes, we work in a multidisciplinary fashion with multiple national and international institutions better understand these pathologies. These projects aim to enhance our understanding of these tumors, improve diagnostic methods, explore treatment options, and ultimately improve patient outcomes. This project involves studying various imaging techniques, such as MRI and CT scans, and identifying imaging characteristics that aid in accurate tumor detection, localization, and characterization. Additionally, our team explores the utility of molecular and genetic markers that could serve as diagnostic biomarkers for these tumors. Our research also focuses on evaluating and optimizing treatment options for these tumors and identifying the prognostic factors associated with disease progression, recurrence, or favorable responses to specific therapies. This includes investigating surgical techniques, radiation therapy, chemotherapy, targeted therapies, and emerging treatment modalities such as immunotherapy. Such information aids in developing predictive models and personalized treatment plans tailored to individual patients.

Neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2), and Schwannomatosis (SWN) are three related but clinically distinct tumor syndromes with a shared predisposition to develop peripheral and central nervous system tumors. Disease expression and complications of NF1, NF2, and SWN are highly variable, necessitating a multidisciplinary approach to care and optimizing the outcomes. In our lab, we follow the patients with any of these syndromes to evaluate the clinical features, diagnostic workup, management strategies, and prognosis of NF and SWN. In addition, this project includes the use of big data from national medical databases, which contain vast amounts of patient health records, diagnostic information, and treatment outcomes, offering immense potential for research, analysis, and improving healthcare delivery. By understanding the nuances of these tumor syndromes, physicians can tailor treatment plans and interventions for individual patients, resulting in improved outcomes and decreased morbidity. While surgery may be considered for symptomatic or growing tumors, it is not always the first-line treatment due to the potential risk of nerve damage and the high likelihood of tumor recurrence. Thus, we aim to investigate the post-surgical clinical outcomes and their predictors among patients with NF or SWN.

Advances in smartphone technology have enabled us to passively collect movement and activity data to better understand patients’ preoperative and postoperative functions. Creating this digital phenotype, in addition to validated patient-reported outcome measures, lets us better evaluate patient function and quality of life to determine the optimal treatments for different pathologies.

Artificial intelligence (AI) algorithms are rapidly being developed for the automated reading of medical images. The AI algorithms can automatically recognize complex patterns in imaging data and provide quantitative assessments of radiographic characteristics. AI has already been readily incorporated into many medical fields, including identifying nodules in lung cancer screening and evaluating brain tumors. We want to expand AI algorithms to various spine pathologies, including trauma, degenerative diseases, infection, and cancer. We collect the radiologic images to train machine learning (ML) models to recognize spine pathology. This may ultimately obviate certain diagnostic interventions and expedite treatments to improve patient satisfaction and outcomes.

Patients with spine tumors are notably frail, and spine surgery for tumor resection could be associated with many complications, including bleeding, venous thromboembolism, non-routine discharge, prolonged length of stay, wound infections, and reoperation. We are working on identifying the risk factors and predictors associated with these adverse events and thereby developing predictive calculators that can be used as comprehensive tools by surgeons to estimate the potential risk of each of their patients. These specialized tools would facilitate improved patient discussions, lead to more accurate expectations, and potentially identify pathways to help expedite and enhance postoperative care.

Learn more about spine tumor prediction calculators.

Related research papers:

1. A clinical calculator for predicting intraoperative blood loss and transfusion risk in spine tumor patients
2. Influence of tranexamic acid use on venous thromboembolism risk in patients undergoing surgery for spine tumors
3. A web-based calculator for predicting the occurrence of wound complications, wound infection, and unplanned reoperation for wound complications in patients undergoing surgery for spinal metastases
4. Predicting nonroutine discharge in patients undergoing surgery for vertebral column tumors

Optimal patient selection can reduce postoperative complications and mitigate the high costs of spine surgery. Thus, our lab is developing prediction tools to improve the value of surgery, maximize benefits, and minimize costs. These tools are individualized based on the type of surgery, and they can be used at the bedside to estimate patient-specific postoperative outcomes, quality of life, and the possibility of adverse consequences based on the patient’s complex clinical presentation. These prediction models enable spine surgeons to provide their patients with personalized expectations regarding the clinical outcomes, complications, adverse outcomes, and length of hospital stay following the spine surgery.

Learn more about spine prediction calculators.

Related research papers:

1. Preoperative nomograms predict patient-specific cervical spine surgery clinical and quality of life outcomes
2. Patient-specific prediction model for clinical and quality-of-life outcomes after lumbar spine surgery
3. Prediction calculator for nonroutine discharge and length of stay after spine surgery
4. Web-based calculator predicts surgical-site infection after thoracolumbar spine surgery
5. Incidental durotomy: predictive risk model and external validation of natural language process identification algorithm
6. A novel predictive model of intraoperative blood loss in patients undergoing elective lumbar surgery for degenerative pathologies
7. Novel risk calculator for suboccipital decompression for adult chiari malformation
8. Prediction of quality of life improvements in patients with lumbar stenosis following use of membrane stabilizing agents
9. The F2RaD score: A novel prediction score and calculator tool to identify patients at risk of postoperative C5 palsy

Postoperative C5 and C6 nerve root palsy is an adverse outcome after posterior cervical decompression and fusion surgery. It is characterized by paralysis of the deltoid and/or biceps muscles, associated with abnormal sensory symptoms and shoulder pain. Thus, this postoperative complication negatively impacts patient quality of life and expands healthcare costs. In our lab, we are investigating the risk factors for C5 palsy and defining the etiology of this complication. We are also studying the factors affecting the timing and odds of recovery following C5 palsy, as well as treatments. This project aims to provide spine surgeons with landmarks for better patient counseling about postoperative C5 palsy occurrence risk and recovery and potential treatment options.

Learn more about C5 palsy.

Related research papers:

1. The F2RaD score: A novel prediction score and calculator tool to identify patients at risk of postoperative C5 palsy
2. Time to recovery predicted by the severity of postoperative C5 palsy
3. Spinal cord float back is not an independent predictor of postoperative C5 palsy in patients undergoing posterior cervical decompression
4. Preoperative clinical and radiographic variables predict postoperative C5 palsy
5. C5 palsy after cervical spine surgery: A multicenter retrospective review of 59 cases
6. C5 palsy after posterior cervical decompression and fusion: cost and quality-of-life implications
7. Predicting C5 palsy via the use of preoperative anatomic measurements

Good bone quality is critical to avoid fractures and hardware problems after spinal surgery. While DEXA screening is the current method of assessing bone mineral density, many patients do not have DEXA measurements before surgical instrumentation creating a barrier for this evaluation. In this project, we developed and evaluated a simple tool that estimates the Vertebral Bone Quality (VBQ) based on magnetic resonance imaging (MRI). This tool significantly differentiates between healthy versus osteopenic/osteoporotic bones in both patients with degenerative disease as well as in the cancer population.

Learn more about the VBQ score tool.

Related research papers:

1. Vertebral bone quality score predicts fragility fractures independently of bone mineral density
2. Novel MRI-based score for assessment of bone density in operative spine patients
3. Vertebral bone quality score and postoperative lumbar lordosis associated with need for reoperation after lumbar fusion