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Advances in the Surgical Treatment of Spinal Tumors

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 4373

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Dr. Daniel Lubelski

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Guest Editor

Departments of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Interests: spinal tumor surgery; advanced radiation techniques; minimally invasive spine surgery; spinal metastases; histology-specific treatments; machine learning; deep learning; prognostic modeling; surgical outcomes

Special Issue Information

Dear Colleagues,

Spinal tumors pose significant challenges due to their complex nature and profound impact on patient quality of life as well as survival. This Special Issue focuses on advancing the management of spinal tumors through innovative surgical techniques, cutting-edge radiation therapies, and the integration of artificial intelligence for personalized treatment planning. Topics of interest include minimally invasive and reconstructive spine surgery, novel radiation modalities, the management of spinal metastases, and histology-specific approaches. Contributions addressing multidisciplinary care and quality-of-life outcomes are also encouraged. Original research, databases, or multi-institutional cohorts, in addition to reviews, are welcome, aiming to provide clinically relevant insights that drive advancements in spinal tumor treatment and patient care.

Dr. Daniel Lubelski
Guest Editor

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Keywords

spinal tumors
spinal metastases
minimally invasive spine surgery
radiosurgery
histology-specific treatment
prognostic modeling
machine learning
deep learning
spinal reconstruction
quality of life

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Published Papers (4 papers)

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Research

13 pages, 1606 KB

Open AccessArticle

Evaluating the Real-World Predictive Utility of Karnofsky and ECOG Performance Status for 90-Day Survival After Oncologic Surgery for Metastatic Spinal Tumors

by Rafael De La Garza Ramos, Ali Haider Bangash, Sertac Kirnaz, Rose Fluss, Victoria Cao, Alexander Alexandrov, Liza Belman, Saikiran G. Murthy, Yaroslav Gelfand and Reza Yassari

Cancers 2025, 17(22), 3629; https://doi.org/10.3390/cancers17223629 - 12 Nov 2025

Viewed by 461

Abstract

Background: Performance status is often cited as an independent predictor of survival after metastatic spine tumor surgery (MSTS), but its standalone predictive value for short-term outcomes remains unclear. We aimed to evaluate how well Karnofsky (KPS) and Eastern Cooperative Oncology Group performance status (ECOG-PS) predict 90-day survival, a common surgical candidacy threshold, in patients managed with MSTS. Methods: We conducted a retrospective study of 175 adult patients who underwent MSTS at a single institution (2012–2025). All patients had documented preoperative KPS and ECOG-PS scores. Univariable logistic regression was used to assess associations with 90-day survival. Predictive performance was assessed by discrimination (AUC), diagnostic accuracy, calibration (Brier score), and clinical utility (decision curve analysis). Results: The crude 90-day survival rate was 73%. Both KPS (OR 1.02 [95% CI 1.01 to 1.05]; p = 0.001) and ECOG-PS (OR 0.51 [95% CI 0.36 to 0.73]; p < 0.001) were statistically associated with survival. However, discrimination was modest (AUC 0.65 for KPS, 0.68 for ECOG-PS), with the most balanced diagnostic accuracy achieved at KPS ≥ 70 (sensitivity 0.66, specificity 0.62) and ECOG-PS ≤ 2 (sensitivity 0.76, specificity 0.5). Calibration was fair (Brier scores 0.185 and 0.182, respectively). Decision curve analysis showed minimal net benefit across most threshold probabilities, with ECOG-PS performing slightly better at intermediate thresholds (30–60%), the zone of greatest clinical uncertainty. Conclusions: Despite being widely cited as an independent predictor of postoperative survival in patients with metastatic spine disease, performance status assessed via the KPS and ECOG-PS demonstrated only modest overall discriminatory ability, diagnostic accuracy, calibration, and clinical utility when used alone to predict 90-day survival after MSTS. While both scores retained meaningful value at the extremes (i.e., patients with very poor or very good performance status had more predictable outcomes), caution is warranted in intermediate cases, where performance status alone may be insufficient to guide treatment decisions. These findings highlight the critical difference between statistical association and the real-world clinical utility of a single metric to predict outcome in this patient population. Full article

(This article belongs to the Special Issue Advances in the Surgical Treatment of Spinal Tumors)

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15 pages, 1673 KB

Open AccessArticle

Integration of Next Generation Sequencing Data to Inform Survival Prediction of Patients with Spine Metastasis

by Alexandra Giantini-Larsen, Alexander D. Ramos, Axel Martin, Katherine S. Panageas, Caroline E. Kostrzewa, Zaki Abou-Mrad, Adam Schmitt, Jacqueline F. Bromberg, Anton Safonov, Charles M. Rudin, William Christopher Newman, Mark H. Bilsky and Ori Barzilai

Cancers 2025, 17(13), 2218; https://doi.org/10.3390/cancers17132218 - 2 Jul 2025

Viewed by 950

Abstract

Background/Objectives: Spinal metastatic disease is a life-altering problem for individuals with cancer. Prognostication is key for tailored treatment of spinal metastases. This manuscript provides a comprehensive overview of the genomic profiles of metastatic spine tumors and investigates the potential of mutational data to stratify overall survival (OS) across various histologies. Methods: This is a cohort study of consecutive patients with spine metastatic disease whose tumors were sequenced on a next generation sequencing platform; a machine learning (ML) algorithm was used to stratify OS risk. Results: Targeted sequencing and stratification of OS risk of 282 spine metastases (breast (84), non-small cell lung (56), prostate (49), other (93)) was performed. TP53 (HR 1.80; 95% CI 1.26, 2.56) and KEAP1 (HR 3.95, 95% CI 2.24, 6.98) mutations were associated with poor survival across the entire cohort in univariate Cox proportional hazards models. The ML algorithm categorized breast cancer metastasis into low- and high-risk groups, revealing a median OS of 71 compared to 22 months (HR 3.3, p < 0.001). TP53 mutations and ESR1 mutations conferred poor prognosis. In lung cancer, low- and high-risk groups with median OS of 30 and 6 months (HR 8.3, p < 0.001), respectively, were identified with poor prognosis linked to MET amplification. No significant prognostic associations were identified for spinal prostate metastases. Conclusions: Metastatic spine tumor molecular data allows for the identification of prognostic groups. We present an open-source machine learning algorithm utilizing genomic mutational data that may aid in prognostication and tailored decision making. Full article

(This article belongs to the Special Issue Advances in the Surgical Treatment of Spinal Tumors)

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14 pages, 1028 KB

Open AccessArticle

Exploring the Potential of a Deep Learning Model for Early CT Detection of High-Grade Metastatic Epidural Spinal Cord Compression and Its Impact on Treatment Delays

by James Thomas Patrick Decourcy Hallinan, Junran Wu, Changshuo Liu, Hien Anh Tran, Noah Tian Run Lim, Andrew Makmur, Wilson Ong, Shilin Wang, Ee Chin Teo, Yiong Huak Chan, Hwee Weng Dennis Hey, Leok-Lim Lau, Joseph Thambiah, Hee-Kit Wong, Gabriel Liu, Naresh Kumar, Beng Chin Ooi and Jiong Hao Jonathan Tan

Cancers 2025, 17(13), 2180; https://doi.org/10.3390/cancers17132180 - 28 Jun 2025

Cited by 2 | Viewed by 1010

Abstract

Background: Delay in diagnosing metastatic epidural spinal cord compression (MESCC) adversely impacts clinical outcomes. High-grade MESCC is frequently overlooked on routine staging CT scans. We aim to assess the potential of our deep learning model (DLM) in detecting high-grade MESCC and reducing diagnostic delays. Methods: This retrospective review analyzed 140 patients with surgically treated MESCC between C7 and L2 during 2015–2022. An experienced radiologist (serving as the reference standard), a consultant spine surgeon, and the DLM independently classified staging CT scans into high-grade MESCC or not. The findings were compared to original radiologist (OR) reports; inter-rater agreement was assessed. Diagnostic delay referred to the number of days elapsed from CT to diagnostic MRI scan. Results: Overall, 95/140 (67.8%) patients had preoperative CT scans. High-grade MESCC was identified in 84/95 (88.4%) of the scans by the radiologist (reference standard), but in only 32/95 (33.7%) of the preoperative scans reported by the OR. There was almost perfect agreement between the radiologist and the surgeon (kappa = 0.947, 95% CI = 0.893–1.000) (p < 0.001), and between the radiologist and the DLM (kappa = 0.891, 95% CI = 0.816–0.967) (p < 0.001). In contrast, inter-observer agreement between the OR and all other readers was slight (kappa range = 0.022–0.125). Diagnostic delay was potentially reduced by 20 ± 28 (range = 1–131) days. Conclusions: The original radiologist reports frequently missed high-grade MESCC in staging CT. Our DLM for CT diagnosis of high-grade MESCC showed almost perfect inter-rater agreement with two experienced reviewers. This study is the first to demonstrate that the DLM could help reduce diagnostic delays. Further prospective research is required to understand its precise role in improving the early diagnosis/treatment of MESCC. Full article

(This article belongs to the Special Issue Advances in the Surgical Treatment of Spinal Tumors)

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19 pages, 9960 KB

Open AccessArticle

Histology-Specific Treatment Strategies and Survival Prediction in Lung Cancer Patients with Spinal Metastases: A Nationwide Analysis

by Abdul Karim Ghaith, Xinlan Yang, Taha Khalilullah, Xihang Wang, Melanie Alfonzo Horowitz, Jawad Khalifeh, A. Karim Ahmed, Tej Azad, Joshua Weinberg, Abdel-Hameed Al-Mistarehi, Chase Foster, Meghana Bhimreddy, Arjun K. Menta, Kristin J. Redmond, Nicholas Theodore and Daniel Lubelski

Cancers 2025, 17(8), 1374; https://doi.org/10.3390/cancers17081374 - 21 Apr 2025

Cited by 1 | Viewed by 1478

Abstract

Background/Objectives: Spinal metastases are a common and severe complication of lung cancer, particularly in small cell lung cancer (SCLC), and are associated with poor survival. Despite advancements in treatment, optimal management strategies remain unclear, with significant differences between non-small cell lung cancer (NSCLC) and SCLC. This study evaluates treatment patterns, survival outcomes, and prognostic factors in lung cancer patients with spinal metastases, integrating deep learning survival prediction models. Methods: This retrospective cohort study analyzed the National Cancer Database (NCDB) to identify NSCLC and SCLC patients diagnosed with spinal metastases. Demographics and treatment modalities were analyzed and adjusted for age, sex, and comorbidities. Kaplan–Meier analysis and Cox proportional hazards models assessed overall survival (OS). Five advanced survival prediction models estimated 1-year and 10-year mortality, with feature importance determined via permutation analysis. Results: Among 428,919 lung cancer patients, 5.1% developed spinal metastases, with a significantly higher incidence in SCLC (13.6%) than in NSCLC (5.1%). SCLC patients had poorer OS. Radiation therapy alone was the predominant treatment, and stereotactic body radiation therapy (SBRT) predicted better short- and long-term survival compared to other radiation techniques. High-dose radiation (71–150 Gy BED) improved OS in NSCLC, while reirradiation benefited NSCLC but had a limited impact in SCLC. SurvTrace demonstrated the highest predictive accuracy for 1-year and 10-year mortality, identifying age, radiation dose, reirradiation, and race as key prognostic factors. Conclusions: The management of spinal metastases requires a histology-specific approach. Radiation remains the primary treatment, with SBRT predicting better short- and long-term survival. High-dose radiation and reirradiation should be considered for NSCLC, while the benefits are limited in SCLC. These findings support histology-specific treatment strategies to improve survival of patients with metastatic lung cancer to the spine. Full article

(This article belongs to the Special Issue Advances in the Surgical Treatment of Spinal Tumors)

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