Thieme E-Journals - Journal of Neurological Surgery Part B: Skull Base / Abstract
J Neurol Surg B Skull Base 2026; 87(S 01): S1-S548
DOI: 10.1055/s-0046-1818848
Presentation Abstracts
Oral Presentations

Multi-Institutional Machine-Learning Predictor of Gross-Total Resection in Skull-Base Chondrosarcoma

Authors

  • Juan P. Zuluaga Garcia

    1   The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
  • Franco Rubino

    2   Baptist Medical Center, Little Rock, Arkansas, United States
  • Francisco Call Orellana

    1   The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
  • Esteban Ramirez-Ferrer

    1   The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
  • Geroge Zenonos

    3   University of Pittsburg Medical Center, Pittsburg, Pennsylvania, United States
  • Paul Gardner

    3   University of Pittsburg Medical Center, Pittsburg, Pennsylvania, United States
  • Hanna Algattas

    3   University of Pittsburg Medical Center, Pittsburg, Pennsylvania, United States
  • Juan C. Fernadez-Miranda

    4   Stanford School of Medicine, Stanford, California, United States
  • Vigo Vera

    4   Stanford School of Medicine, Stanford, California, United States
  • Franco DeMonte

    1   The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
  • Shaan M. Raza

    1   The University of Texas MD Anderson Cancer Center, Houston, Texas, United States
 

Objective: Develop and validate an anatomy-driven model that predicts gross-total resection (GTR) for skull-base chondrosarcomas (SCBs).

Methods: We analyzed 179 consecutive SBCs resected at three academic centers. Thirteen preoperative variables were abstracted: tumor location, internal carotid artery (ICA) encasement, compartmental extensions, cranial-nerve involvement, prior radiotherapy, and approach. Data were split 75/25 into training (n = 135) and validation (n = 44). Five algorithms were tuned for validation. For interpretability, multivariable logistic GLM and nomogram were refitted. Performance was summarized with AUC, accuracy, sensitivity/specificity, and Brier score.

Results: Anatomic burden varied by zone, cavernous-sinus invasion clustered in peri-lacerum (66.7%) and petroclival (57.0%); jugular-foramen extension mainly petroclival (37.2%); sinonasal/orbital spread characterized midline lesions. Approach selection mirrored: EEA predominated in midline (80%) and common in petroclival (62%), whereas lateral tumors were mostly open (84.6%). GTR was 56% (101/179) overall. In petroclival disease, corridor choice was decisive (p < 0.001): ETPA 70.3% GTR vs open 25.7% and midline EEA 36.4%.

Operative Approach, EOR, and Residual Disease

Petroclival (N = 121)

Peri-lacerum (N = 30)

Lateral (N = 13)

Midline (N = 15)

p-value

EOR

GTR

61 (50%)

20 (67%)

10 (77%)

10 (67%)

0.116

STR

60 (49%)

10 (35%)

3.0 (23%)

5 (33%)

GTR% x approach

Open

9/35 (26%)

12/19 (63%)

9/11 (82%)

2/3 (67%)

<0.001

EE-Midline

4/11 (36%)

4/4 (100%)

5/8 (63%)

ETPA

45/64 (70%)

2/4 (50%)

0/1 (0%)

3/4 (75%)

Combined/stage

3/11 (27%)

2/3 (67%)

1/1 (100%)

Residual disease

Petrous Apex

21 (17%)

3 (10%)

2 (15%)

<0.001

Meckel’s-cave

11 (8%)

3 (11%)

Cavernous-sinus

9 (7%)

6 (23%)

1 (7%)

Cavernous-ICA

3 (2%)

1 (3%)

Petrous-ICA

4 (3%)

2 (8%)

Jugular-Foramen

7 (6%)

1 (3%)

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Multivariable analysis showed significant lower odds of GTR with graded ICA encasement (90–180° OR=3.12; 181–270° 7.41; 271–359° 9.76; 360° 8.52), prior radiotherapy (OR=4.04), petroclival location (OR=4.72) and infratemporal extension (OR=2.75). ETPA—associated with higher odds of GTR (OR=0.22, p < 0.001)—with a favorable trend for midline-EEA (OR=0.37, p = 0.063). The GLM achieved AUC=0.756, Brier=0.19 (accuracy=0.818).

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Conclusion: Anatomical determinants—particularly petroclival origin, ICA encasement, and lower-cranial-nerve corridors—are the principal barriers to complete resection in SBC. The proposed ML provides a reproducible preoperative tool that aligns corridor choice with individual anatomy, improves likelihood of GTR, and rationalizes use of adjuvant therapy.



Publication History

Article published online:
27 February 2026

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