Evidence map›Paper›PMID 40295452›Full record

ArticleJournal of neuro-oncology2025

Risk calculator for long-term survival prediction of spinal chordoma versus chondrosarcoma: a nationwide analysis.

Abdul Karim Ghaith, Xinlan Yang, Abdel-Hameed Al-Mistarehi, Linda Tang, Nathan Kim, Joshua Weinberg, Jawad Khalifeh, Yuanxuan Xia, Chase H Foster, Kristin Redmond and 7 more

Abstract read
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In one paragraph

Article in Journal of neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Abdul Karim GhaithDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Xinlan YangDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Abdel-Hameed Al-MistarehiDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Linda TangDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Nathan KimUniversity of Hawaii, John A Burns School of Medicine, Honolulu, HI, USA.
Joshua WeinbergDepartment of Neurosurgery, Ohio State University, School of Medicine, Columbus, OH, USA.
Jawad KhalifehDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Yuanxuan XiaDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Chase H FosterDepartment of Neurological Surgery, George Washington University, Washington, DC, USA.
Kristin RedmondDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Sang LeeDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Majid KhanDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
David XuDepartment of Neurosurgery, Ohio State University, School of Medicine, Columbus, OH, USA.
Taha KhalilullahDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Khaled ZaitounUniversity of Hawaii, John A Burns School of Medicine, Honolulu, HI, USA.
Nicholas TheodoreDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Daniel LubelskiDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA. dlubelski@jhmi.edu.ORCID http://orcid.org/0000-0002-9403-9509

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeChordomas and chondrosarcomas are rare, aggressive spinal bone tumors with distinct origins, biological behavior, and treatment challenges, primarily due to their resistance to conventional chemotherapy and radiation. This study aimed to compare clinical characteristics, treatment strategies, and long-term outcomes between spinal chordoma and chondrosarcoma, and to develop a robust machine learning-based model for individualized survival prediction.

methodsWe conducted a retrospective analysis using the National Cancer Database (NCDB) to identify patients diagnosed with spinal chordoma or chondrosarcoma from 2004 to 2017. Demographics, tumor characteristics, comorbidity indices, treatment modalities (surgery, radiation, chemotherapy), and outcomes were extracted. Kaplan-Meier and weighted log-rank analyses assessed overall survival (OS) at predefined intervals (30-day, 90-day, 1-year, 5-year, 10-year). Twelve machine learning and deep learning models were trained to predict 10-year OS. Model performance was evaluated using AUC, Brier Score, and Concordance Index (C-index). A web-based risk calculator was developed using the best-performing ensemble model.

resultsA total of 3175 patients were included (chordoma: n = 1204; chondrosarcoma: n = 1971). Chordoma patients were significantly older, travelled farther for treatment, and had smaller tumors with lower rates of metastatic disease at presentation. Chondrosarcoma patients more frequently underwent gross total resection, while chordoma patients received more radiation therapy, often with higher doses and more frequent use of proton therapy. Kaplan-Meier analysis revealed that chordoma patients had superior 10-year OS compared to chondrosarcoma patients (p < 0.0001). Among those receiving radiation, chondrosarcoma patients treated with radiation alone had the poorest survival. DeepSurv achieved the highest C-index (0.83) and lowest Brier Score (0.14), while ensemble models integrating Gradient Boosting and CatBoost also demonstrated strong performance (AUC > 0.80). Age, tumor type, and radiation therapy were identified as the most influential predictors using SHAP analysis. A publicly accessible, web-based calculator was developed for individualized survival prediction.

conclusionSpinal chordoma and chondrosarcoma differ significantly in clinical features and outcomes, with chordoma showing more favorable long-term survival. The findings highlight the importance of GTR and individualized radiation therapy in optimizing outcomes. The predictive model employing complicated machine learning models provides a valuable tool for estimating long-term survival and guiding personalized treatment strategies, though external validation is needed to strengthen its generalizability and clinical utility.

Indexed as

ChondrosarcomaChordomaSpinal NeoplasmsAdultAgedFemaleFollow-Up StudiesHumansMachine LearningMaleMiddle AgedPrognosisRetrospective StudiesRisk AssessmentSurvival RateYoung AdultDeep learningGTRRadiation therapySpinal chondrosarcomaSpinal chordomaSurvival prediction

Identifiers

PMID40295452

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.