Bartlett Alyssa M, Shabana Summer, Folz Caroline C, Paturu Mounica, Shaffrey Christoper I, Quist Parastou, Danisa Olumide, Than Khoi D, Passias Peter, Abd-El-Barr Muhammad M
Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA.
Division of Spine Surgery, Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC 27710, USA.
J Clin Med. 2025 Jun 13;14(12):4209. doi: 10.3390/jcm14124209.
Transforaminal lumbar interbody fusion (TLIF) is a commonly employed surgical technique for managing lumbar degenerative disease and spinal instability. While it offers advantages over posterior lumbar interbody fusion (PLIF), traditional TLIF often involves prolonged recovery and morbidity due to muscle retraction. To improve outcomes, several alternative techniques have emerged, including minimally invasive TLIF (MIS-TLIF), trans-Kambin percutaneous TLIF (PE-TLIF), and transfacet TLIF (TF-TLIF). Each approach presents distinct anatomical and technical advantages, yet no standardized framework exists to guide their selection based on individual patient anatomy. In this study, we review the evolution of TLIF techniques and propose a novel algorithm that integrates patient-specific imaging, anatomical variability, and segmentation data to guide surgical decision-making. By analyzing the surgical corridors, indications, and limitations of each approach, and presenting representative clinical cases, we demonstrate how this algorithm can be applied in practice. For instance, TF-TLIF may be optimal in patients requiring direct decompression without major deformity, while PE-TLIF may be appropriate for those with Kambin's triangles measuring ≥ 9 mm, allowing for indirect decompression. This tailored framework aims to optimize outcomes and reduce complications. Further prospective validation and incorporation of AI-driven segmentation tools are needed to support broader clinical implementation.
经椎间孔腰椎椎体间融合术（TLIF）是治疗腰椎退行性疾病和脊柱不稳常用的外科技术。虽然它比后路腰椎椎体间融合术（PLIF）有优势，但传统的TLIF由于肌肉牵拉，往往恢复时间长且发病率高。为了改善治疗效果，出现了几种替代技术，包括微创TLIF（MIS-TLIF）、经坎宾经皮TLIF（PE-TLIF）和经关节突TLIF（TF-TLIF）。每种方法都有独特解剖学和技术优势，但尚无标准化框架根据个体患者解剖结构指导其选择。在本研究中，我们回顾了TLIF技术的发展历程，并提出了一种新算法，该算法整合患者特异性成像、解剖变异和分割数据以指导手术决策。通过分析每种方法的手术通道、适应症和局限性，并展示代表性临床病例，我们证明了该算法在实际应用中的可行性。例如，TF-TLIF对于需要直接减压且无严重畸形的患者可能是最佳选择，而PE-TLIF可能适用于坎宾三角≥9mm的患者，可进行间接减压。这种量身定制的框架旨在优化治疗效果并减少并发症。需要进一步的前瞻性验证并纳入人工智能驱动的分割工具以支持更广泛的临床应用。
