Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic accuracy and outcome prediction are critical for informed decision making and can promote personalized medicine and facilitate optimal patient management. Machine learning has the ability to analyze and combine vast amounts of data, allowing the identification of patterns and the establishment of clinical associations, which can ultimately enhance patient care. Although artificial intelligence techniques have been explored in other areas of spine surgery, such as spinal deformity surgery, precise machine learning models for spinal tumors are lagging behind. Current applications of machine learning in spinal cord tumors include algorithms that improve diagnostic precision by predicting genetic, molecular, and histopathological profiles. Furthermore, artificial intelligence-based systems can assist surgeons with preoperative planning and surgical resection, potentially reducing the risk of recurrence and consequently improving clinical outcomes. Machine learning algorithms promote personalized medicine by enabling prognostication and risk stratification based on accurate predictions of treatment response, survival, and postoperative complications. Despite their promising potential, machine learning models require extensive validation processes and quality assessments to ensure safe and effective translation to clinical practice.

Background: Pineal parenchymal tumor of intermediate differentiation (PPTID) is a rare tumor. This study aims to evaluate patient outcomes and propose a treatment algorithm based on existing literature and our case series. Methods: This prospective observational study includes seven patients diagnosed with PPTID through histopathology. We analyzed their clinical presentation, magnetic resonance imaging findings, surgical approaches, histopathological and immunohistochemical analysis, adjuvant treatments, and outcomes. We conducted univariate and multivariate statistical analyses. Results: The mean patient age was 40 years, with a male predominance. All patients presented with hydrocephalus, four of which required cerebrospinal fluid diversion procedures. The average tumor size was 3.13cm, with 85.7% showing brain invasion. Surgical outcomes included one gross total resection, two near total resections, and four subtotal resections. The supracerebellar infratentorial (Krause) approach was used in 71.4% of cases. About 85.7% were diagnosed with grade 3 PPTID. Five patients received adjuvant radiotherapy. The analysis showed each additional mitosis unit decreased survival by 0.17 units, equating to roughly 2 months (P < 0.016). Follow-up ranged from 6 to 120 months, with a 5-year survival rate of 57.1%. Factors influencing survival included the extent of tumor resection, brain invasion, tumor grade, and adjuvant treatment, although it was not statistically significant due to the small sample size. Conclusion: We propose a treatment algorithm for PPTID and highlight the importance of further research to understand its biological characteristics. Safe maximal resection appears beneficial for higher-grade PPTID, but the role of adjuvant treatment after complete resection of lower-grade tumors remains uncertain.