OBJECTIVE Chordomas are rare tumors that often recur regardless of surgery with negative margins and postoperative radiotherapy. The predictive accuracy of widely used immunohistochemical (IHC) markers in addressing the recurrence of skull base chordomas (SBCs) is yet to be determined. This study aimed to investigate IHC markers in the prediction of recurrence after SBC resection with adjuvant radiation therapy. METHODS The authors reviewed the records of patients who had treatment for SBC between January 2017 and June 2021 across the Mayo Clinic in Minnesota, Florida, and Arizona. Exclusion criteria included patients who had no histopathology or recurrence as an outcome. Histopathological markers included cytokeratin A1/A3 only, epithelial membrane antigen (EMA), S100 protein, pan-cytokeratin, IN1, GATA3, CAM5.2, OSCAR, and chondroid. Information from patient records was abstracted, including treatment, clinical and radiological follow-up duration, demographics, and histopathological factors. Decision tree and random forest classifiers were trained and tested to predict the recurrence based on unseen data using an 80/20 split. RESULTS A total of 38 patients with a diagnosis of SBC who underwent resection (gross-total resection: 42.1%; and subtotal resection: 57.9%) and radiation therapy were extracted from the medical records. The mean patient age was 48.2 (SD 19.6) years; most patients were male (n = 23; 60.5%) and White (n = 36; 94.7%). Pan-cytokeratin was associated with an increased risk of postoperative recurrence (OR 14.67, 95% CI 2.44-88.13; p = 0.00517) after resection and adjuvant radiotherapy. The decision tree analysis found pan-cytokeratin-positive tumors to have a 78% chance of being classified as a recurrence, with an accuracy of 75%. The distribution of minimal depth in the prediction of postoperative recurrence indicates that the most important variables were pan-cytokeratin, followed by cytokeratin A1/A3 and EMA. CONCLUSIONS The authors' machine learning algorithm identified pan-cytokeratin as the largest contributor to recurrence among other IHC markers after SBC resection. Machine learning may facilitate the prediction of outcomes in rare tumors, such as chordomas.

中文翻译：

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免疫组织化学标记物预测脊索瘤患者切除和放疗后的复发：来自多中心研究的见解。

目的 脊索瘤是一种罕见的肿瘤，无论切缘阴性的手术和术后放疗如何，都经常复发。广泛使用的免疫组织化学 (IHC) 标记物在解决颅底脊索瘤 (SBC) 复发方面的预测准确性尚未确定。本研究旨在探讨 IHC 标记物在预测 SBC 切除辅助放射治疗后复发的作用。方法 作者回顾了 2017 年 1 月至 2021 年 6 月期间在明尼苏达州、佛罗里达州和亚利桑那州梅奥诊所接受 SBC 治疗的患者记录。排除标准包括没有组织病理学或复发结果的患者。组织病理学标记物仅包括细胞角蛋白 A1/A3、上皮膜抗原 (EMA)、S100 蛋白、泛细胞角蛋白、IN1、GATA3、CAM5.2、OSCAR 和软骨样。从患者记录中提取信息，包括治疗、临床和放射学随访持续时间、人口统计学和组织病理学因素。决策树和随机森林分类器经过训练和测试，可使用 80/20 分割根据未见过的数据来预测复发。结果 从病历中提取出 38 例诊断为 SBC 并接受手术切除（全切除：42.1%；次全切除：57.9%）和放射治疗的患者。患者平均年龄为 48.2 (SD 19.6) 岁；大多数患者是男性（n = 23；60.5%）和白人（n = 36；94.7%）。全细胞角蛋白与切除和辅助放疗后术后复发风险增加相关（OR 14.67，95% CI 2.44-88.13；p = 0.00517）。决策树分析发现，全细胞角蛋白阳性肿瘤有 78% 的机会被归类为复发，准确度为 75%。术后复发预测中最小深度的分布表明最重要的变量是全细胞角蛋白，其次是细胞角蛋白A1/A3和EMA。结论 作者的机器学习算法确定全细胞角蛋白是 SBC 切除后其他 IHC 标记物中导致复发的最大因素。机器学习可能有助于预测罕见肿瘤（例如脊索瘤）的结果。