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Background <p>Stereotactic radiosurgery (SRS) is used in selected patients with craniopharyngioma, yet counseling and follow-up planning often rely on population-level local control rates rather than individualized expectations over time.</p> Objective <p>To develop and internally validate a multicenter survival model to predict imaging-defined time to progression after SRS for craniopharyngioma.</p> Methods <p>We analyzed a multicenter IRRF registry of SRS-treated craniopharyngioma patients. Imaging progression was defined by the overall last imaging response (PD vs. non-PD), with censoring at last imaging follow-up when progression was not observed. A Random Survival Forest (RSF) model was evaluated using 5-fold out-of-fold cross-validation. Performance was assessed using the concordance index, time-dependent AUC at 12, 24, and 60 months with bootstrap 95% confidence intervals, integrated Brier score (IBS) over 0–60 months, and risk-stratified calibration. Benchmarks included a penalized Cox model and a Kaplan–Meier baseline.</p> Results <p>Among 277 patients (event rate 13.0%; median imaging follow-up 57.0 months by reverse Kaplan–Meier), RSF achieved an out-of-fold C-index of 0.905. Time-dependent AUC was 0.895 (95% CI 0.828–0.959) at 12 months, 0.897 (95% CI 0.833–0.952) at 24 months, and 0.934 (95% CI 0.889–0.969) at 60 months. IBS (0–60 months) was 0.050 with favorable calibration.</p> Conclusions <p>A multicenter machine learning survival model can provide individualized, well-calibrated estimates of local control over time after SRS for craniopharyngioma to support non-prescriptive decision support.</p> Clinical trial number <p>Not applicable.</p>

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Personalized prediction of local control after stereotactic radiosurgery for craniopharyngioma: a multicenter machine learning survival model

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Jheremy S. Reyes,
  • Constantinos G. Hadjipanayis,
  • Kenneth Bernstein,
  • Herwin Speckter,
  • Ivan Gonzalez,
  • Tomas Chytka,
  • Roman Liscak,
  • Greg N. Bowden,
  • Takuma Sumi,
  • Kentaro Narita,
  • Hideyuki Kano,
  • Nuria Martínez-Moreno,
  • Roberto Martínez-Álvarez,
  • Piero Picozzi,
  • Andrea Franzini,
  • Manjul Tripathi,
  • Ashutosh Rai,
  • Narendra Kumar,
  • Keiss Douri,
  • David Mathieu,
  • Antonio Dono,
  • Christian Amezquita-Contreras,
  • Angel I. Blanco,
  • Yoshua Esquenazi,
  • Salem M. Tos,
  • Georgios Mantziaris,
  • Selcuk Peker,
  • Yavuz Samanci,
  • Ali Haluk Duzkalir,
  • Ying Meng,
  • Jason P. Sheehan,
  • Douglas Kondziolka,
  • L. Dade Lunsford,
  • Ajay Niranjan

摘要

Background

Stereotactic radiosurgery (SRS) is used in selected patients with craniopharyngioma, yet counseling and follow-up planning often rely on population-level local control rates rather than individualized expectations over time.

Objective

To develop and internally validate a multicenter survival model to predict imaging-defined time to progression after SRS for craniopharyngioma.

Methods

We analyzed a multicenter IRRF registry of SRS-treated craniopharyngioma patients. Imaging progression was defined by the overall last imaging response (PD vs. non-PD), with censoring at last imaging follow-up when progression was not observed. A Random Survival Forest (RSF) model was evaluated using 5-fold out-of-fold cross-validation. Performance was assessed using the concordance index, time-dependent AUC at 12, 24, and 60 months with bootstrap 95% confidence intervals, integrated Brier score (IBS) over 0–60 months, and risk-stratified calibration. Benchmarks included a penalized Cox model and a Kaplan–Meier baseline.

Results

Among 277 patients (event rate 13.0%; median imaging follow-up 57.0 months by reverse Kaplan–Meier), RSF achieved an out-of-fold C-index of 0.905. Time-dependent AUC was 0.895 (95% CI 0.828–0.959) at 12 months, 0.897 (95% CI 0.833–0.952) at 24 months, and 0.934 (95% CI 0.889–0.969) at 60 months. IBS (0–60 months) was 0.050 with favorable calibration.

Conclusions

A multicenter machine learning survival model can provide individualized, well-calibrated estimates of local control over time after SRS for craniopharyngioma to support non-prescriptive decision support.

Clinical trial number

Not applicable.