Linda Liverani

All Publications

• Tumor-specific outcomes in spinal metastases: a systematic review and meta-analysis. Journal of spine surgery (Hong Kong) Sanker, V., Nordin, E. O., Ali, A. S., Gurumurthy, G. D., Thaller, A., Heesen, P., Li, Z., Liverani, L., Cavagnaro, M. J., Ratliff, J., Desai, A. 2026; 12 (6): 91

  Abstract

  Spinal metastases (SMs) are common, debilitating, and often associated with severe pain and neurologic disability. Treatment decisions hinge on anticipated life expectancy, yet survival and recurrence outcomes remain poorly defined across primary tumor types. This study is to systematically review and meta-analyze outcomes of SM stratified by primary cancer, with focus on survival, recurrence, and treatment patterns.PubMed, Scopus, Web of Science Core Collection, and Embase (Ovid) were searched from inception to April 16, 2025. Eligible studies reported outcomes in patients with SM from any primary tumor. Random-effects meta-analyses were performed for survival and recurrence. Study quality was assessed with ROBINS-I.One hundred twenty-three studies (38,780 patients) across nine primary tumors were included; 61 studies (9,222 patients) contributed to meta-analysis. Lung cancer accounted for the largest cohort (n=26,918), followed by gynecologic (n=3,679), breast (n=1,961), and renal (n=1,732). Pooled 1-year survival ranged from 46% in lung SM to 74% in prostate SM. Median survival was shortest for liver (8.2 months) and lung (12.1 months), and longest for thyroid (59.9 months) and melanoma (57.9 months). Recurrence rates were consistently low (<5%), though thyroid SM carried a slightly higher long-term risk.Outcomes in SMs are strongly influenced by primary tumor biology. These data provide benchmarks for clinical decision-making and highlight the need for prospective studies integrating molecular and functional outcomes.

  View details for DOI 10.21037/jss-2025-aw-202

  View details for PubMedID 42434570

  View details for PubMedCentralID PMC13351875

• Inequitable access to neurosurgical care in the United States FRONTIERS IN NEUROLOGY Liverani, L., Dwumfour-Poku, N., Zygourakis, C. C., Vaca, S. D. 2026; 17: 1850876

  Abstract

  Despite a national neurosurgeon density of 1.58 per 100,000 (exceeding the WHO minimum threshold of 1 per 100,000), equitable access to neurosurgical care remains uneven in the United States. Approximately 9.8% of the US population lacks adequate access to neurosurgical services. Rural and socioeconomically disadvantaged communities are most affected. This literature review focuses specifically on the intersection of neurosurgical access and the distribution, composition, and pipeline of the US neurosurgical workforce. We conducted a narrative review of peer-reviewed literature from 2015 to 2025 using PubMed, Scopus, and Web of Science. Search terms included "neurosurgical workforce," "geographic access," "gender equity," "rural neurosurgery," "telemedicine," and "task sharing." Eighty percent of US counties lack neurosurgeons, and only 2.3% of neurosurgeons practice in nonmetropolitan areas. Unemployment, low educational attainment, and poverty independently predict reduced neurosurgeon availability. Geographic access and workforce diversity overlap. States with the fewest training programs also show the lowest representation of women in neurosurgery. Concurrently, graduating medical students interested in neurosurgery who were female, Black/African-American, or Hispanic were significantly more likely to report intention to practice in underserved areas. Addressing geographic, socioeconomic, and gender disparities simultaneously is essential to ensuring timely access to neurosurgical care for all patients. Telemedicine networks, rural training tracks, and intersectional recruitment strategies offer scalable pathways toward a more equitable workforce.

  View details for DOI 10.3389/fneur.2026.1850876

  View details for Web of Science ID 001808662200001

  View details for PubMedID 42394923

  View details for PubMedCentralID PMC13322906

• Atlas-based Multi-Parametric Quantitative Brain MRI Analysis of Children with Neurofibromatosis Type 1. Clinical neuroradiology Duh, A. K., Tam, L. T., Liverani, L., Ng, N. N., McKenna, E. S., Rowe, N. C., Campen, C. J., MacEachern, S. J., Yeom, K. W., Prolo, L. M., Forkert, N. D. 2026

  Abstract

  Neurofibromatosis type 1 (NF1) is a multisystem disorder with wide-ranging clinical presentations. Patients with NF1 may manifest with macrocephaly, strokes, and cognitive deficits, abnormal neural development, and other neurologic symptoms. This study used an atlas-based approach to quantitatively examine structural and physiologic changes of the brain in children with NF 1.Children evaluated for NF1 over a 9-year period at a children's hospital were retrospectively reviewed (n = 34). Children with intracranial tumors or prior strokes were excluded. Patients received diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) perfusion imaging on a 3T MRI scanner. Using an atlas-based approach, quantitative assessment of regional brain volumes, median apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) was performed for the cerebral cortex, thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, nucleus accumbens, brainstem, and cerebral white matter. Differences were tested between NF1 patients and 100 typically developing controls.Compared to controls, children with NF1 demonstrated significantly increased volume measurements in all brain regions (p < 0.001), significantly higher median ADC values in all structures except for the putamen and nucleus accumbens (p < 0.001), and significantly lower median CBF most notable in the cerebral white matter (p < 0.001), globus pallidus (p < 0.001), hippocampus (p < 0.001), amygdala (p < 0.001), and brainstem (p = 0.001).This study measured microstructural and physiologic brain changes in children with NF1 compared to typically developing children. Further studies are needed to elucidate the cellular and molecular basis for these differences. With further refinement, atlas-based quantitative MRI brain signatures may serve as useful biomarkers of neural development, cognitive dysfunction, and risks for vasculopathy-related strokes in children with NF 1.

  View details for DOI 10.1007/s00062-026-01690-0

  View details for PubMedID 42295331

• 3D Deep Learning for Brain Tumor Segmentation and Survival Prediction: A Comprehensive Multi-Modal Analysis Using the BraTS2020 Dataset. Journal of imaging Sanker, V., Mahesh, D., Li, Z., Thaller, A., Heesen, P., Liverani, L., Wang, D., Cavagnaro, M. J., Medikonda, R. T., Prolo, L., Singh, H., Ratliff, J., Desai, A. 2026; 12 (6)

  Abstract

  Three-dimensional deep learning offers promise for automated accurate brain tumor segmentation and survival prediction but requires robust validation across multiple MRI modalities to be effectively implemented in clinical practice.This study presents a comprehensive 3D deep learning framework using 369 cases from the BraTS2020 dataset. A 3D U-Net architecture was developed for tumor segmentation utilizing combined imaging data and optimized for computational efficiency and memory. The final 3D U-Net model segmentations were used to build machine learning 6-month and 12-month survival classifiers. Segmentation models were evaluated using multiple metrics, including the Dice Similarity Coefficient, Hausdorff Distance, and Cohen's d. The classification models were evaluated using AUC-ROC and balanced accuracy.Segmentation achieved a modest, but promising, performance across 30 epochs and with 295 training patients, achieving the best mean validation Dice = 0.8388 and a final-epoch mean Dice of 0.8263. Survival classification with a hybrid clinical and imaging logistic regression showed promising results, with 12-month prediction achieving AUC = 0.746 and 69% accuracy. The top contributing features for the 12-month prediction classifier were extent of resection, T1 contrast-enhanced tumor median, and FLAIR tumor median.This comprehensive framework demonstrates that a multi-modal approach provides meaningful performance gains, while segmentation-derived features show a promising ability to enable survival prediction.

  View details for DOI 10.3390/jimaging12060251

  View details for PubMedID 42346914

• Arrested hydrocephalus and beyond: advances in the pathophysiology, diagnosis, and management of various forms of chronic hydrocephalus: a comprehensive narrative review. Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery Sanker, V., Wahi, R., Jamal, S. M., Elnour, M. A., Venkatesan, A., Liverani, L., Lear, B. P., Cavagnaro, M. J., Desai, A., Prolo, L. 2026; 42 (1)

  Abstract

  Hydrocephalus results from an imbalance between cerebrospinal fluid (CSF) secretion and absorption. Absence or lack of physiological compensatory mechanisms leads to ventricular dilation and associated clinical symptoms. However, when it is compensated, it is termed as arrested hydrocephalus, which ensures a stable balance between the production and clearance of CSF, that results in normalized intraventricular pressure and minimal ventricular dilatation. Although hydrocephalus can be understood using the traditional bulk flow theory of CSF circulation, arrested hydrocephalus poses unique diagnostic difficulties because of its subtle manifestations and lack of radiographic changes. In this thorough review, the pathogenesis, clinical manifestation, diagnostic standards, and available treatments for various types chronic hydrocephalus, including normal pressure hydrocephalus (NPH) and congenital hydrocephalus, are discussed with emphasis on arrested hydrocephalus.

  View details for DOI 10.1007/s00381-026-07263-3

  View details for PubMedID 41986703

  View details for PubMedCentralID 9233635

• From Pixels to Precision: Generative Artificial Intelligence as a Paradigm Shift in Spine Imaging-Technical Foundations, Clinical Applications, and the Path to Safe Clinical Deployment. Neurospine Ashraf, D., Sanker, V., Liverani, L., Park, C., Medikonda, R. T., Cavagnaro, M. J., Jeon, I., Ratliff, J., Desai, A. 2026; 23 (2): 293-313

  Abstract

  Spine imaging represents a complex diagnostic frontier characterized by anatomical variability, motion artifacts, metallic instrumentation interference, and significant inter-reader diagnostic variability (κ=0.20 across institutions). While conventional discriminative artificial intelligence (AI) models achieve >95% accuracy in detecting degenerative changes, they remain limited by data scarcity, heterogeneous protocols, and poor generalizability. In the spine, these limitations are particularly relevant because clinical decisions can often depend on subtle distinctions (such as differentiating levels of canal or foraminal stenosis, characterizing Modic endplate changes, or assessing pedicle and vertebral morphology), where small inconsistencies can meaningfully alter management or surgical planning. Generative AI (GenAI) systems-including generative adversarial networks (GANs), diffusion models, and vision-language models (VLMs)-offer a paradigm shift by learning underlying data structures to generate high-quality synthetic outputs rather than merely classifying existing data. This narrative review, conducted using SANRA (scale for the assessment of narrative review articles) methodology across PubMed, Scopus, Embase, and Cochrane Library, examined GenAI applications in spine imaging. Eligible studies included observational designs through randomized controlled trials exploring image reconstruction, synthetic computed tomography (CT) generation, segmentation, and surgical planning applications. GAN-generated synthetic magnetic resonance imaging sequences reduce scan times by ~40% while maintaining diagnostic confidence; diffusion models enable radiation-free synthetic CT for preoperative planning; and VLMs generate structured radiology reports with hallucination rates <1.12%. However, critical barriers impede clinical translation: external validation gaps reveal AI performance collapse in real-world cohorts (sensitivity drops to 54.9% in cervical fracture detection); hallucinations and anatomical inaccuracies risk misguiding implant sizing; bias amplification magnifies demographic underrepresentation; and fragmented, small datasets lack standardized benchmarks. Technical fragility, computational demands, clinician trust deficits, and unresolved regulatory frameworks for iteratively-updating systems remain unaddressed. Successful integration requires coordinated development across 5 priorities: (1) multi-institutional datasets with cross-vendor harmonization, (2) federated learning frameworks preserving privacy, (3) uncertainty quantification and explainability tools, (4) outcome-linked clinical validation replacing technical metrics, and (5) workflow-integrated systems with DICOM-native interfaces and provenance tracking.

  View details for DOI 10.14245/ns.2551862.931

  View details for PubMedID 42097745

• Sacral chordomas: surgical management, reconstruction, and translational advances: a systematic review. Journal of spine surgery (Hong Kong) Sanker, V., Ghaddar, S., Ali, A. S., Gurumurthy, G. D., Jayan, M., Sheikh, M. Q., Liverani, L., Sunil, G., Kumar, S., Cavagnaro, M. J., Medikonda, R., Ratliff, J., Desai, A. 2026; 12 (3): 36

  Abstract

  Sacral chordomas are rare malignant tumors arising from notochordal remnants that present unique surgical challenges due to their indolent growth, locally aggressive behavior, and high recurrence rates despite treatment. This review synthesizes current evidence on surgical management, reconstruction techniques, adjuvant therapies, and translational advances for sacral chordomas.This systematic review synthesizes current evidence on surgical management, reconstruction techniques, adjuvant therapies, and translational advances through analysis of PubMed, Scopus, and Web of Science databases [2023-2025].En bloc resection with wide negative margins remains the cornerstone of treatment, achieving optimal local control but often at the cost of significant neurological and functional morbidity. Modern reconstruction techniques, including vascularized grafts and three-dimensional (3D)-printed prostheses, have improved spinopelvic stability and postoperative outcomes. Adjuvant particle beam radiotherapy, particularly proton and carbon ion therapy, demonstrates superior 5-year local control rates of 77-89% compared to 10-30% with conventional photon therapy, while stereotactic radiosurgery achieves 81% local control. Systemic therapies remain limited, though targeted inhibitors of platelet-derived growth factor receptor (PDGFR), epidermal growth factor receptor (EGFR), and the PI3K/AKT/mTOR pathway show modest activity with disease stabilization in 60-70% of cases. Novel immunotherapeutic approaches, including Brachyury-targeted vaccines and checkpoint blockade, are under investigation. Molecular insights have identified key drivers, including Brachyury [T-box transcription factor T (TBXT)], receptor tyrosine kinases, and emerging biomarkers that may guide future therapeutic selection.Integrated multimodal care is critical for improving survival and function in patients with sacral chordomas.

  View details for DOI 10.21037/jss-25-180

  View details for PubMedID 41971904

  View details for PubMedCentralID PMC13063029

• Sacral chordomas: surgical management, reconstruction, and translational advances: a systematic review JOURNAL OF SPINE SURGERY Sanker, V., Ghaddar, S., Ali, A., Gurumurthy, G. D., Jayan, M., Sheikh, M., Liverani, L., Sunil, G., Kumar, S., Cavagnaro, M., Medikonda, R., Ratliff, J., Desai, A. 2026

  View details for DOI 10.21037/jss-25-180

  View details for Web of Science ID 001714547500001