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Johns Hopkins MedicineJOHNS HOPKINS MEDICINE

The S.P.I.N.E.
Neurosurgery Innovation Lab

Surgical Precision • Intelligence • Neuro-Oncology • Engineering

We unite spinal oncology, artificial intelligence and translational bench science in the Department of Neurosurgery at Johns Hopkins, building prediction models, neural interfaces and molecular insight that make the treatment of spinal tumors measurably more precise.

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C1–C701

Cervical

Spinal Oncology

Primary and metastatic tumors of the spine and spinal cord.

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T1–T1202

Thoracic

Spinal Injury & Regeneration

Optimizing timing, surgery, and recovery after spine and nerve injury.

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L1–L503

Lumbar

Peripheral Nerve

Peripheral nerve injury, reconstruction, and neural decoding.

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S1–S504

Sacral

Basic Science

The biology and mechanics the clinical questions rest on.

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0

Citations

Across 315 indexed publications

0

h-index

i10-index 174

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Publications

2011–2026, 56 journals

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Co-authors

A worldwide collaborative network

Publication figures are computed from the lab's indexed PubMed record. Citation metrics from Google Scholar, retrieved 2026-08-04.

About the laboratory

Four disciplines, one clinical question.

Whether an operation should happen, what it should be, and whether it left the patient better than it found them.

The S.P.I.N.E. Neurosurgery Innovation Lab sits inside the Department of Neurosurgery at Johns Hopkins, where the clinical service treating spinal tumors and the research effort studying them are the same group of people. That proximity is the point: questions arrive from clinic, and answers are tested against the patients who raised them.

Our published record is dominated by three intertwined lines of work. The first is spinal oncology, covering primary tumors such as chordoma and intramedullary spinal cord tumors, and the far more common problem of metastatic disease to the spine. The second is outcomes science, built on the position that patient-reported quality of life, not radiographic success, is the measure of whether an operation worked. The third is the computational layer that connects them: prediction models, machine learning and the large clinical datasets that make individualized estimates possible.

Alongside this, bench and engineering work addresses the biology and mechanics the clinical questions rest on: tumor cell behavior, tissue response, delivery of agents to spinal cord tumors. A neural interface thread extends from brachial plexus reconstruction through to high-density intraoperative recording and decoding.

The laboratory operates on a simple standard: a model is only worth building if it changes a decision, and it is only trustworthy once it has been validated on patients it has never seen.

The acronym

  • S.P.

    Surgical Precision

    Matching the operation to the patient: oncologic strategy, approach selection, and the long-term functional consequences of each.

  • I.

    Intelligence

    Prediction models, machine learning, and big-data analytics that turn thousands of prior cases into patient-specific guidance.

  • N.

    Neuro-Oncology

    The biology and treatment of tumors of the spine, spinal cord, and peripheral nerves, from surgical management to the mechanisms of recurrence and treatment resistance.

  • E.

    Engineering

    Wet-lab and biomechanical work on tumor biology, tissue behavior, and the technologies that reach the operating room.

Portrait of Daniel Lubelski

47

h-index

7.8k

Citations

174

i10-index

Principal Investigator

Daniel Lubelski, MD

Director of Spine Tumor Surgery · Assistant Professor of Neurological Surgery and of Oncology

Daniel Lubelski is Director of Spine Tumor Surgery in the Department of Neurosurgery at Johns Hopkins, where he is also Assistant Director of the Neurosurgery Residency Program and Program Director of the Neurosurgery Spine Fellowship. He is an Assistant Professor of Neurological Surgery and of Oncology. His clinical practice covers complex spine surgery, spine and nerve tumors, brachial plexus injuries and peripheral nerve surgery. His research applies data science, prediction modeling and artificial intelligence to individualize the treatment of spinal disease, with a sustained emphasis on patient-reported quality of life.

Latest publications

Harvested directly from PubMed and categorised by topic. Search, filter and browse the full record.

2026Eur Spine JSenior author

A predictive tool incorporating frailty scores for perioperative risk assessment in patients with spinal metastasis: a national retrospective cohort study

Ghaith AK, Yang X, Alasadi Y, ElNemer W, Ghaith M … Lubelski D

Machine Learning & AISpinal OncologyClinical Outcomes
PubMedDOIFull text1 min abstract
2026Front Hum Neurosci

A prospective, open-label feasibility study protocol of home-based transcranial direct current stimulation for major depressive disorder in elective lumbar spine surgery candidates

Menta AK, Bronckers SP, Goes FS, Witham TF, Cohen DB … Azad TD

Clinical OutcomesSpine Surgery
PubMedDOIFull text1 min abstract
2026J Neurosurg Spine

Age-related differences in surgical outcomes for traumatic central cord syndrome: a multi-institutional causal machine learning analysis

Aude CA, Vattipally VN, Jillala R, Khalifeh J, Hughes LP … Azad TD

Machine Learning & AIDigital Health & InformaticsClinical Outcomes
PubMedDOI2 min abstract
2026Eur Spine JSenior author

Age-related risk factors and treatment outcomes in geriatric patients with spinal low-grade glioma: A nationwide analysis

Rajasekaran J, Khalilullah T, Yang X, Ghaith AK, Bhandarkar S … Lubelski D

Spinal OncologyClinical OutcomesDigital Health & Informatics
PubMedDOIFull text2 min abstract