Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.8
4.4
Journals
Cancers
Special Issues
Advances in the Surgical Treatment of Spinal Tumors
share announcement

Advances in the Surgical Treatment of Spinal Tumors

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1519

Special Issue Editor

Dr. Daniel Lubelski

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 1673 KiB  
Article
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 273
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

14 pages, 1028 KiB  
Article
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
Viewed by 288
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

19 pages, 9960 KiB  
Article
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
Viewed by 709
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) [...] Read more.
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop