2005 Proton Versus Photon Adjuvant Radiotherapy: A Multicenter Comparative Evaluation of Recurrence Following Spinal Chordoma Resection

INTRODUCTION: Chordomas are rare tumors of the neural axis arising from the embryonic notochord that often recur following resection and adjuvant radiotherapy. Proton therapy has been introduced as a tissue-sparing option because of the higher level of precision that proton-beam techniques offer compared with traditional photon radiotherapy. METHODS: The clinical records of all patients treated with resection followed by adjuvant proton or photon radiotherapy for chordoma at our institution were reviewed. Patient demographics, type of surgery and radiotherapy, tumor recurrence, and other variables were extracted. Decision tree classifiers were trained and tested to predict long-term recurrence based on unseen data using an 80/20 split. RESULTS: Fifty-three patients with a mean age of 55.2 years receiving surgery and adjuvant proton or photon therapy to treat chordoma were identified; most patients were male. Gross-total resection was achieved in 54.7% of cases. Proton therapy was the most common adjuvant radiotherapy(84.9%), followed by conventional or external-beam radiation therapy(9.4%) and stereotactic radiosurgery(5.7%). Patients receiving proton therapy exhibited a 40% likelihood of having recurrence, significantly lower than the 88% likelihood observed in those treated with nonproton therapy. This was confirmed on logistic regression analysis adjusted for extent of tumor resection and tumor location, which revealed that proton adjuvant radiotherapy was associated with a decreased risk of recurrence(OR: 0.1, 95% CI 0.01-0.71) compared with photon therapy. The decision tree algorithm predicted recurrence with an accuracy of 90%, with the lowest risk of recurrence observed in patients receiving gross-total resection with adjuvant proton therapy (23%). CONCLUSIONS: Adjuvant proton therapy was associated with a lower risk of chordoma recurrence compared with photon therapy. The described machine learning models were able to predict tumor progression based on the extent of tumor resection and adjuvant radiotherapy.